Revealing the full-length transcriptome of caucasian clover rhizome development

Abstract Background: Caucasian clover (Trifolium ambiguum M.Bieb.) is a strongly rhizomatous, low-crowned perennial leguminous and ground-covering grass. The species may be used as an ornamental plant and is resistant to cold, arid temperatures and grazing due to a well-developed underground rhizome system and a strong clonal reproduction capacity. However, the posttranscriptional mechanism of the development of the rhizome system in caucasian clover has not been comprehensively studied. Additionally, a reference genome for this species has not yet been published, which limits further exploration of many important biological processes in this plant. Result: We adopted PacBio Sequencing and Illumina Sequencing to identify differentially expressed transcripts in five tissues taproot (T1), horizontal rhizome (T2), swelling of taproot (T3), rhizome bud (T4) and rhizome bud tip (T5) of the caucasian clover rhizome. In total, we obtained 19.82 GB clean data and 80,654 nonredundant transcripts were analyzed. Additionally, we identified 78,209 open reading frames (ORFs), 65,227 coding sequences (CDSs), 58,276 simple sequence repeats (SSRs), 6,821 alternative splicing (AS) sites, 24,29 long noncoding RNAs (lncRNAs) and 4,501 putative transcription factors (TFs)from 64 different families. Compared with other tissues, T5 exhibited more differentially expressed genes, and co-upregulated genes in T5 are mainly annotated as involved in phenylpropanoid biosynthesis. We also identified betaine aldehyde dehydrogenase (BADH) as a highly expressed gene specific to T5. WGCNA cluster analysis of transcription factors and physiological indicators were combined to reveal 11 candidate genes (MEgreen-GA3), three of which belong to the HB-KNOX family, that are up-regulated in T3. We analyzed 276 differential transcripts involved in hormone signaling and transduction, and the largest number of transcripts are associated with the IAA signaling pathway, with significant upregulation in T2 and T5. Conclusions: Taken together, this study contributes to our understanding of gene expression across five different tissues and provides preliminary insight into rhizome growth and development in caucasian clover.

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Revealing the full-length transcriptome of caucasian clover rhizome development

10.21203/rs.3.rs-18210/v2 ◽

2020 ◽

Author(s):

Xiujie Yin ◽

Kun Yi ◽

Yihang Zhao ◽

Yao Hu ◽

Xu Li ◽

...

Keyword(s):

Transcription Factors ◽

Open Reading Frames ◽

Differentially Expressed ◽

Betaine Aldehyde Dehydrogenase ◽

Physiological Indicators ◽

Phenylpropanoid Biosynthesis ◽

Rhizome Bud ◽

Horizontal Rhizome ◽

Upregulated Genes ◽

Rhizome System

Abstract Background: Caucasian clover (Trifolium ambiguum M. Bieb.) is a strongly rhizomatous, low-crowned perennial leguminous and ground-covering grass. The species may be used as an ornamental plant and is resistant to cold, arid temperatures and grazing due to a well-developed underground rhizome system and a strong clonal reproduction capacity. However, the posttranscriptional mechanism of the development of the rhizome system in caucasian clover has not been comprehensively studied. Additionally, a reference genome for this species has not yet been published, which limits further exploration of many important biological processes in this plant. Result: We adopted PacBio Sequencing and Illumina Sequencing to identify differentially expressed transcripts in five tissues taproot (T1), horizontal rhizome (T2), swelling of taproot (T3), rhizome bud (T4) and rhizome bud tip (T5) of the caucasian clover rhizome. In total, we obtained 19.82 GB clean data and 80,654 nonredundant transcripts were analysed. Additionally, we identified 78,209 open reading frames (ORFs), 65,227 coding sequences (CDSs), 58,276 simple sequence repeats (SSRs), 6,821 alternative splicing (AS) sites, 24,29 long noncoding RNAs (lncRNAs) and 4,501 putative transcription factors (TFs) from 64 different families. Compared with other tissues, T5 exhibited more differentially expressed genes, and co-upregulated genes in T5 are mainly annotated as involved in phenylpropanoid biosynthesis. We also identified betaine aldehyde dehydrogenase (BADH) as a highly expressed gene-specific to T5. A weighted gene co-expression network analysis (WGCNA) cluster analysis of transcription factors and physiological indicators were combined to reveal 11 candidate genes (MEgreen-GA3), three of which belong to the HB-KNOX family, that are up-regulated in T3. We analysed 276 differential transcripts involved in hormone signaling and transduction, and the largest number of transcripts are associated with the IAA signaling pathway, with significant up-regulation in T2 and T5. Conclusions: Taken together, this study contributes to our understanding of gene expression across five different tissues and provides preliminary insight into rhizome growth and development in caucasian clover.

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Revealing the full-length transcriptome of caucasian clover rhizome development

BMC Plant Biology ◽

10.1186/s12870-020-02637-4 ◽

2020 ◽

Vol 20 (1) ◽

Cited By ~ 1

Author(s):

Xiujie Yin ◽

Kun Yi ◽

Yihang Zhao ◽

Yao Hu ◽

Xu Li ◽

...

Keyword(s):

Transcription Factors ◽

Open Reading Frames ◽

Betaine Aldehyde Dehydrogenase ◽

Trifolium Ambiguum ◽

Physiological Indicators ◽

Phenylpropanoid Biosynthesis ◽

Rhizome Bud ◽

Horizontal Rhizome ◽

Upregulated Genes ◽

Rhizome System

Abstract Background Caucasian clover (Trifolium ambiguum M. Bieb.) is a strongly rhizomatous, low-crowned perennial leguminous and ground-covering grass. The species may be used as an ornamental plant and is resistant to cold, arid temperatures and grazing due to a well-developed underground rhizome system and a strong clonal reproduction capacity. However, the posttranscriptional mechanism of the development of the rhizome system in caucasian clover has not been comprehensively studied. Additionally, a reference genome for this species has not yet been published, which limits further exploration of many important biological processes in this plant. Result We adopted PacBio sequencing and Illumina sequencing to identify differentially expressed genes (DEGs) in five tissues, including taproot (T1), horizontal rhizome (T2), swelling of taproot (T3), rhizome bud (T4) and rhizome bud tip (T5) tissues, in the caucasian clover rhizome. In total, we obtained 19.82 GB clean data and 80,654 nonredundant transcripts were analysed. Additionally, we identified 78,209 open reading frames (ORFs), 65,227 coding sequences (CDSs), 58,276 simple sequence repeats (SSRs), 6821 alternative splicing (AS) events, 2429 long noncoding RNAs (lncRNAs) and 4501 putative transcription factors (TFs) from 64 different families. Compared with other tissues, T5 exhibited more DEGs, and co-upregulated genes in T5 are mainly annotated as involved in phenylpropanoid biosynthesis. We also identified betaine aldehyde dehydrogenase (BADH) as a highly expressed gene-specific to T5. A weighted gene co-expression network analysis (WGCNA) of transcription factors and physiological indicators were combined to reveal 11 hub genes (MEgreen-GA3), three of which belong to the HB-KNOX family, that are up-regulated in T3. We analysed 276 DEGs involved in hormone signalling and transduction, and the largest number of genes are associated with the auxin (IAA) signalling pathway, with significant up-regulation in T2 and T5. Conclusions This study contributes to our understanding of gene expression across five different tissues and provides preliminary insight into rhizome growth and development in caucasian clover.

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Transcriptomic and Proteomic Profiling Reveal the Key Role of AcMYB16 in the Response of Pseudomonas syringae pv. actinidiae in Kiwifruit

Frontiers in Plant Science ◽

10.3389/fpls.2021.756330 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xiaojie Wang ◽

Yawei Li ◽

Yuanyuan Liu ◽

Dongle Zhang ◽

Min Ni ◽

...

Keyword(s):

Transcription Factors ◽

Pseudomonas Syringae ◽

Regulatory Element ◽

Differentially Expressed ◽

Resistant Cultivar ◽

Proteomic Profiling ◽

Phenylpropanoid Biosynthesis ◽

R2r3 Myb ◽

Important Disease

Kiwifruit bacterial canker caused by Pseudomonas syringae pv. actinidiae (Psa), is an important disease of kiwifruit (Actinidia Lind.). Plant hormones may induce various secondary metabolites to resist pathogens via modulation of hormone-responsive transcription factors (TFs), as reported in past studies. In this study, we showed that JA accumulated in the susceptible cultivar Actinidia chinensis ‘Hongyang’ but decreased in the resistant cultivar of A. chinensis var. deliciosa ‘Jinkui’ in response to Psa. Integrated transcriptomic and proteomic analyses were carried out using the resistant cultivar ‘Jinkui’. A total of 5,045 differentially expressed genes (DEGs) and 1,681 differentially expressed proteins (DEPs) were identified after Psa infection. Two pathways, ‘plant hormone signal transduction’ and ‘phenylpropanoid biosynthesis,’ were activated at the protein and transcript levels. In addition, a total of 27 R2R3-MYB transcription factors (TFs) were involved in the response to Psa of ‘Jinkui,’ including the R2R3-MYB TF subgroup 4 gene AcMYB16, which was downregulated in ‘Jinkui’ but upregulated in ‘Hongyang.’ The promoter region of AcMYB16 has a MeJA responsiveness cis-acting regulatory element (CRE). Transient expression of the AcMYB16 gene in the leaves of ‘Jinkui’ induced Psa infection. Together, these data suggest that AcMYB16 acts as a repressor to regulate the response of kiwifruit to Psa infection. Our work will help to unravel the processes of kiwifruit resistance to pathogens and will facilitate the development of varieties with resistance against bacterial pathogens.

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Transcriptome analysis reveals differentially expressed MYB transcription factors associated with silicon response in wheat

Scientific Reports ◽

10.1038/s41598-021-83912-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lidong Hao ◽

Shubing Shi ◽

Haibin Guo ◽

Jinshan Zhang ◽

Peng Li ◽

...

Keyword(s):

Transcription Factors ◽

Molecular Mechanisms ◽

Vital Role ◽

Differentially Expressed ◽

Wheat Seedlings ◽

Go Enrichment ◽

Phenylpropanoid Biosynthesis ◽

Family Gene ◽

R2r3 Myb

AbstractSilicon plays a vital role in plant growth. However, molecular mechanisms in response to silicon have not previously been studied in wheat. In this study, we used RNA-seq technology to identify differentially expressed genes (DEGs) in wheat seedlings treated with silicon. Results showed that many wheat genes responded to silicon treatment, including 3057 DEGs, of which 6.25% (191/3057) were predicted transcription factors (TFs). Approximately 14.67% (28 out of 191) of the differentially expressed TFs belonged to the MYB TF family. Gene ontology (GO) enrichment showed that the highly enriched DEGs were responsible for secondary biosynthetic processes. According to KEGG pathway analysis, the DEGs were related to chaperones and folding catalysts, phenylpropanoid biosynthesis, and protein processing in the endoplasmic reticulum. Moreover, 411 R2R3-MYB TFs were identified in the wheat genome, all of which were classified into 15 groups and accordingly named S1–S15. Among them, 28 were down-regulated under silicon treatment. This study revealed the essential role of MYB TFs in the silicon response mechanism of plants, and provides important genetic resources for breeding silicon-tolerant wheat.

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Identification of Catabolite Repression as a Physiological Regulator of Biofilm Formation by Bacillus subtilis by Use of DNA Microarrays

Journal of Bacteriology ◽

10.1128/jb.185.6.1951-1957.2003 ◽

2003 ◽

Vol 185 (6) ◽

pp. 1951-1957 ◽

Cited By ~ 168

Author(s):

Nicola R. Stanley ◽

Robert A. Britton ◽

Alan D. Grossman ◽

Beth A. Lazazzera

Keyword(s):

Gene Expression ◽

Bacillus Subtilis ◽

Transcription Factors ◽

Biofilm Formation ◽

Dna Microarrays ◽

Transcriptional Profiling ◽

Open Reading Frames ◽

Differentially Expressed ◽

Confocal Scanning Laser Microscopy ◽

Glucose Depletion

ABSTRACT Biofilms are structured communities of cells that are encased in a self-produced polymeric matrix and are adherent to a surface. Many biofilms have a significant impact in medical and industrial settings. The model gram-positive bacterium Bacillus subtilis has recently been shown to form biofilms. To gain insight into the genes involved in biofilm formation by this bacterium, we used DNA microarrays representing >99% of the annotated B. subtilis open reading frames to follow the temporal changes in gene expression that occurred as cells transitioned from a planktonic to a biofilm state. We identified 519 genes that were differentially expressed at one or more time points as cells transitioned to a biofilm. Approximately 6% of the genes of B. subtilis were differentially expressed at a time when 98% of the cells in the population were in a biofilm. These genes were involved in motility, phage-related functions, and metabolism. By comparing the genes differentially expressed during biofilm formation with those identified in other genomewide transcriptional-profiling studies, we were able to identify several transcription factors whose activities appeared to be altered during the transition from a planktonic state to a biofilm. Two of these transcription factors were Spo0A and sigma-H, which had previously been shown to affect biofilm formation by B. subtilis. A third signal that appeared to be affecting gene expression during biofilm formation was glucose depletion. Through quantitative biofilm assays and confocal scanning laser microscopy, we observed that glucose inhibited biofilm formation through the catabolite control protein CcpA.

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Suppressed Immune-Related Profile Rescues Abortion-Prone Fetuses: A Novel Insight Into the CBA/J × DBA/2J Mouse Model

Reproductive Sciences ◽

10.1177/1933719119828042 ◽

2019 ◽

Vol 26 (11) ◽

pp. 1485-1492

Author(s):

Xiaochun Yi ◽

Jie Zhang ◽

Huixiang Liu ◽

Tianxia Yi ◽

Yuhua Ou ◽

...

Keyword(s):

Immune System ◽

Differentially Expressed ◽

Rna Seq ◽

Poor Treatment ◽

Polymerase Chain ◽

Poor Treatment Outcome ◽

Immune Related Genes ◽

And Control ◽

Upregulated Genes ◽

Tgf Β1

The adverse clinical result and poor treatment outcome in recurrent spontaneous abortion (RSA) make it necessary to understand the pathogenic mechanism. The mating combination CBA/J × DBA/2 has been widely used as an abortion-prone model compared to DBA/2-mated CBA/J mice. Here, we used RNA-seq to get a comprehensive catalogue of genes differentially expressed between survival placenta in abortion-prone model and control. Five hundred twenty-four differentially expressed genes were obtained followed by clustering analysis, Gene Ontology analysis, and pathway analysis. We paid more attention to immune-related genes namely “immune response” and “immune system process” including 33 downregulated genes and 28 upregulated genes. Twenty-one genes contribute to suppressing immune system and 7 are against it. Six genes were validated by reverse transcription-polymerase chain reaction, namely Ccr1l1, Tlr4, Tgf-β1, Tyro3, Gzmb, and Il-1β. Furthermore, Tlr4, Tgf-β1, and Il-1β were analyzed by Western blot. Such immune profile gives us a better understanding of the complicated immune processing in RSA and immunosuppression can rescue pregnancy loss.

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Transcriptome analyses identify five transcription factors differentially expressed in the hypothalamus of post- versus prepubertal Brahman heifers1

Journal of Animal Science ◽

10.2527/jas.2016-0471 ◽

2016 ◽

Vol 94 (9) ◽

pp. 3693-3702 ◽

Cited By ~ 11

Author(s):

M. R. S. Fortes ◽

L. T. Nguyen ◽

M. M. D. C. A. Weller ◽

A. Cánovas ◽

A. Islas-Trejo ◽

...

Keyword(s):

Transcription Factors ◽

Differentially Expressed ◽

Transcriptome Analyses

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Transcriptome analyses and virus induced gene silencing identify genes in the Rpp4-mediated Asian soybean rust resistance pathway

Functional Plant Biology ◽

10.1071/fp12296 ◽

2013 ◽

Vol 40 (10) ◽

pp. 1029 ◽

Cited By ~ 30

Author(s):

Aguida M. A. P. Morales ◽

Jamie A. O'Rourke ◽

Martijn van de Mortel ◽

Katherine T. Scheider ◽

Timothy J. Bancroft ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Gene Silencing ◽

Differentially Expressed ◽

Soybean Rust ◽

Phakopsora Pachyrhizi ◽

R Gene ◽

Virus Induced Gene Silencing ◽

Asian Soybean Rust ◽

Defence Responses

Rpp4 (Resistance to Phakopsora pachyrhizi 4) confers resistance to Phakopsora pachyrhizi Sydow, the causal agent of Asian soybean rust (ASR). By combining expression profiling and virus induced gene silencing (VIGS), we are developing a genetic framework for Rpp4-mediated resistance. We measured gene expression in mock-inoculated and P. pachyrhizi-infected leaves of resistant soybean accession PI459025B (Rpp4) and the susceptible cultivar (Williams 82) across a 12-day time course. Unexpectedly, two biphasic responses were identified. In the incompatible reaction, genes induced at 12 h after infection (hai) were not differentially expressed at 24 hai, but were induced at 72 hai. In contrast, genes repressed at 12 hai were not differentially expressed from 24 to 144 hai, but were repressed 216 hai and later. To differentiate between basal and resistance-gene (R-gene) mediated defence responses, we compared gene expression in Rpp4-silenced and empty vector-treated PI459025B plants 14 days after infection (dai) with P. pachyrhizi. This identified genes, including transcription factors, whose differential expression is dependent upon Rpp4. To identify differentially expressed genes conserved across multiple P. pachyrhizi resistance pathways, Rpp4 expression datasets were compared with microarray data previously generated for Rpp2 and Rpp3-mediated defence responses. Fourteen transcription factors common to all resistant and susceptible responses were identified, as well as fourteen transcription factors unique to R-gene-mediated resistance responses. These genes are targets for future P. pachyrhizi resistance research.

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Genes Encoding Transcription Factors TaDREB5 and TaNFYC-A7 Are Differentially Expressed in Leaves of Bread Wheat in Response to Drought, Dehydration and ABA

Frontiers in Plant Science ◽

10.3389/fpls.2018.01441 ◽

2018 ◽

Vol 9 ◽

Cited By ~ 6

Author(s):

Lyudmila Zotova ◽

Akhylbek Kurishbayev ◽

Satyvaldy Jatayev ◽

Gulmira Khassanova ◽

Askar Zhubatkanov ◽

...

Keyword(s):

Transcription Factors ◽

Bread Wheat ◽

Differentially Expressed ◽

Genes Encoding

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Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System

Frontiers in Medicine ◽

10.3389/fmed.2021.587496 ◽

2021 ◽

Vol 8 ◽

Author(s):

Tingshan He ◽

Liwen Huang ◽

Jing Li ◽

Peng Wang ◽

Zhiqiao Zhang

Keyword(s):

Artificial Intelligence ◽

Ovarian Cancer ◽

Overall Survival ◽

Transcription Factors ◽

Cancer Patients ◽

Prognostic Model ◽

Cox Regression ◽

Differentially Expressed ◽

Immune Genes ◽

Immune Biomarkers

Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms.Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system.Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer.Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.

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