Artificial intelligence-powered spatial analysis of tumor infiltrating lymphocytes (TIL) to reflect target gene expressions of novel immuno-oncology agents.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e14534-e14534
Author(s):  
Chan-Young Ock ◽  
Seunghwan Shin ◽  
Wonkyung Jung ◽  
Sangheon Ahn ◽  
Haejoon Kim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Artificial Intelligence ◽  
Spatial Analysis ◽  
Rna Sequencing ◽  
Target Gene ◽  
Target Genes ◽  
Checkpoint Inhibitors ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Gene Expressions

e14534 Background: Novel immuno-oncology (IO) agents are promising but showing their efficacy in early phase clinical trials has been challenging due to limited enrichment strategies using practical biomarker platforms. We hypothesize that an artificial intelligence (AI)-powered spatial analysis of TIL using practically feasible H&E slides, can reflect a specific target gene expression derived from RNA sequencing. This enhances its potential application in early development of novel IO agents. Methods: An AI-powered spatial TIL analyzer, namely Lunit SCOPE IO, was developed with data from 2.8 x 109 micrometer2 H&E-stained tissue regions and 5.9 x 106 TILs from 3,166 whole slide images of multiple cancer types, annotated by board-certified pathologists. Inflamed Score and Immune-Excluded Score was defined as the proportion of all tumor-containing 1 mm2-size tiles within a WSI classified as being of inflamed immune phenotype (high TIL density within cancer epithelium) and immune-excluded phenotype (low TIL density within cancer epithelium, but high TIL density within stroma), respectively. We used RNA sequencing data and H&E images from The Cancer Genome Atlas database, excluding those of mesenchymal origin (n = 7,467). Spearman's rank correlation between each gene expression and IS or IES, respectively, was calculated. Correlation coefficient > 0.2 and false discovery rate (FDR) < 1% was considered as a significant correlation. Results: In a total of 20,304 genes, 871 (4.3%) and 1,155 (5.7%) genes were significantly correlated with Inflamed Score (IS) and Immune-Excluded Score (IES), respectively. The IS was highly related to genes reflecting immune cytolytic activity and targets of approved immune checkpoint inhibitors (Table). Interestingly, it was also significantly correlated with target genes of novel IO such as TIGIT, LAG3, TIM3, IDO, Adenosine receptor A2A, OX40, ICOS, M-CSF, IL2, IL7, and IL12. Moreover, the IES was exclusively correlated with the target genes of CEACAM, TGFB, and IL1. Conclusions: Expression levels of novel I-O target genes are correlated with three scores derived from AI-powered TIL analysis using H&E slides, which can be easily applied to clinical research.[Table: see text]

scholarly journals Genomic data from NSCLC tumors reveals correlation between SHP-2 activity and PD-L1 expression and suggests synergy in combining SHP-2 and PD-1/PD-L1 inhibitors

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256416
Author(s):  
Keller J. Toral ◽  
Mark A. Wuenschel ◽  
Esther P. Black
Keyword(s):  
Gene Expression ◽  
Protein Expression ◽  
Rna Sequencing ◽  
Tumor Regression ◽  
Checkpoint Inhibitors ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Sequencing Data ◽  
Lung Cancers ◽  
Lung Adenocarcinomas

The identification of novel therapies, new strategies for combination of therapies, and repurposing of drugs approved for other indications are all important for continued progress in the fight against lung cancers. Antibodies that target immune checkpoints can unmask an immunologically hot tumor from the immune system of a patient. However, despite accounts of significant tumor regression resulting from these medications, most patients do not respond. In this study, we sought to use protein expression and RNA sequencing data from The Cancer Genome Atlas and two smaller studies deposited onto the Gene Expression Omnibus (GEO) to advance our hypothesis that inhibition of SHP-2, a tyrosine phosphatase, will improve the activity of immune checkpoint inhibitors (ICI) that target PD-1 or PD-L1 in lung cancers. We first collected protein expression data from The Cancer Proteome Atlas (TCPA) to study the association of SHP-2 and PD-L1 expression in lung adenocarcinomas. RNA sequencing data was collected from the same subjects through the NCI Genetic Data Commons and evaluated for expression of the PTPN11 (SHP-2) and CD274 (PD-L1) genes. We then analyzed RNA sequencing data from a series of melanoma patients who were either treatment naïve or resistant to ICI therapy. PTPN11 and CD274 expression was compared between groups. Finally, we analyzed gene expression and drug response data collected from 21 non-small cell lung cancer (NSCLC) patients for PTPN11 and CD274 expression. From the three studies, we hypothesize that the activity of SHP-2, rather than the expression, likely controls the expression of PD-L1 as only a weak relationship between PTPN11 and CD274 expression in either lung adenocarcinomas or melanomas was observed. Lastly, the expression of CD274, not PTPN11, correlates with response to ICI in NSCLC.

A Comparison of Clinical FISH and Sequencing Based FISH Estimates in Multiple Myeloma: An Mmrf Commpass Analysis

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 374-374 ◽  
Author(s):  
Chase Miller ◽  
Jennifer Yesil ◽  
Mary Derome ◽  
Andrea Donnelly ◽  
Jean Marrian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Rna Sequencing ◽  
Plasma Cell ◽  
Copy Number ◽  
Target Gene ◽  
False Negative ◽  
Single Individual ◽  
Sequencing Data ◽  
Target Gene Expression

Abstract Fluorescent in situ hybridization (FISH) is commonly used in the multiple myeloma field to subtype and risk-stratify patients. There are many benefits to FISH based assays, which are widely used around the world and represent true single cell assays. However, there are significant discrepancies in the specific assays, utilization of reflex testing strategies, and enumeration requirements between clinical centers. By comparison next-generation sequencing tests can be designed to simultaneously detect the copy number abnormalities and translocations detected by clinical FISH along with gene mutations that cannot be detected by FISH. As part of the MMRF CoMMpass Study we have compared the results attained using clinical FISH assays compared to sequencing based FISH (Seq-FISH) results. Clinical FISH reports from a random subset of 339 CoMMpass patients were extraction by a single individual based on the ISCN result lines of each report. To validate the accuracy of the central data extraction, two independent cross validations of 10% of the cohort were performed, after which our data entry error rate is expected to be less than 0.348%. The Seq-FISH results were extracted from the whole genome sequencing data available from each patient using a rapid and fully automated informatics process and the results were cross-validated using the matching exome sequencing data for copy number abnormalities and by RNA sequencing data for dysregulated immunoglobulin translocation target genes. There were 230 patients with clinical FISH and Seq-FISH results. In this cohort, 151 translocations were identified by Seq-FISH. This includes translocations to MYC, CCND2, MAFA, and those involving IgK and IgL, which are not tested by clinical FISH. After filtering non-tested translocations there are 118 translocations identified by Seq-FISH. Only 97 of these translocations had a clinical FISH assay performed with 89 (91.75%) of these being detected by clinical FISH, yet spiked target gene expression was observed in all 89 cases by RNA sequencing. Conversely, 93 translocations were called by clinical FISH, of these 89 were called by Seq-FISH(95.7%). Of the 4 translocations only called by clinical FISH, 3 were t(4;14) and 1 was a t(11;14). In two of these t(4;14) cases we did observe spiked target gene expression by RNA sequencing, suggesting these are false negatives by Seq-FISH. However, the remaining two events appear to be false positive clinical FISH results. The t(4;14) event was only observed in 1/200 cells and a co-occuring t(11;14) was also called, which was confirmed by Seq-FISH and spiked gene expression. Similarly, the one t(11;14) was observed in 3/56 cells but a del13q14 was seen in 47/50 cells, unfortunately RNA sequencing data is not available to cross-validate in this case. Plasma cell enrichment or identification is commonly used to prepare myeloma samples for FISH because even in myeloma, the total plasma cell percentage can be low (median 8.3% in the MMRF CoMMpass Baseline Cohort). Therefore, performing FISH on a sample without performing purification or plasma cell identification will indiscriminately assay non-plasma cells and limit the efficacy of the assay. We looked at the two most common translocations in myeloma, t(4;14) and t(11;14), to test the effect of enrichment on sensitivity. Sensitivity was higher for both sets of translocations in the enriched cohort. There was 1 false negative in the enriched population, yielding sensitivities of 100% (32/32) and 95%(19/20) for CCND1 and WHSC1 respectively. For those reports that did not indicate enrichment was performed the observed sensitivities were 86.36% (19/22) and 92.86% (13/14). Seq-FISH identified almost all of the translocations called by clinical FISH and simultaneously; it identified 30 translocations missed by clinical FISH. The translocations that were not reported by clinical FISH can be attributed to a mixture of the correct assay not being performed and the translocation being missed even though the assay was performed. We believe that Seq-FISH is a viable alternative to clinical FISH, with similar specificity and greater sensitivity. It is important to note that a single Seq-FISH assay is sufficient to investigate all translocations, while each translocation must be investigated separately with clinical FISH. As such, Seq-FISH obviates the concern that a translocation would be missed because the correct assay was not performed. Disclosures McBride: Instat: Employment.

scholarly journals Longitudinal Analysis of Gene Expression Changes During Cervical Carcinogenesis Reveals Potential Therapeutic Targets

2020 ◽  
Vol 16 ◽  
pp. 117693432092057
Author(s):  
Lijun Yu ◽  
Meiyan Wei ◽  
Fengyan Li
Keyword(s):  
Gene Expression ◽  
Western Blotting ◽  
Protein Interactions ◽  
Target Genes ◽  
Quantitative Polymerase Chain Reaction ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Gene Expressions

Despite advances in the treatment of cervical cancer (CC), the prognosis of patients with CC remains to be improved. This study aimed to explore candidate gene targets for CC. CC datasets were downloaded from the Gene Expression Omnibus database. Genes with similar expression trends in varying steps of CC development were clustered using Short Time-series Expression Miner (STEM) software. Gene functions were then analyzed using the Gene Ontology (GO) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Protein interactions among genes of interest were predicted, followed by drug-target genes and prognosis-associated genes. The expressions of the predicted genes were determined using real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting. Red and green profiles with upward and downward gene expressions, respectively, were screened using STEM software. Genes with increased expression were significantly enriched in DNA replication, cell-cycle-related biological processes, and the p53 signaling pathway. Based on the predicted results of the Drug-Gene Interaction database, 17 drug-gene interaction pairs, including 3 red profile genes (TOP2A, RRM2, and POLA1) and 16 drugs, were obtained. The Cancer Genome Atlas data analysis showed that high POLA1 expression was significantly correlated with prolonged survival, indicating that POLA1 is protective against CC. RT-qPCR and Western blotting showed that the expressions of TOP2A, RRM2, and POLA1 gradually increased in the multistep process of CC. TOP2A, RRM2, and POLA1 may be targets for the treatment of CC. However, many studies are needed to validate our findings.

Discovering new targeted therapies for BRAF mutant-like colorectal cancers.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 3623-3623
Author(s):  
F. Anthony San Lucas ◽  
Scott Kopetz ◽  
Paul A. Scheet ◽  
Eduardo Vilar Sanchez
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Signature ◽  
The Cancer Genome Atlas ◽  
Colorectal Cancers ◽  
Expression Data ◽  
Wild Type ◽  
Sequencing Data

3623 Background: Approximately 10% of colorectal cancers (CRCs) harbor a BRAF mutation (BRAFm). Patients with BRAFm tumors have poor prognosis and are a therapeutic challenge. A BRAFm gene expression signature has been communicated (Popovici et al, JCO 2012), which can identify BRAFm tumors as well as BRAF wild-type tumors that display a similar expression pattern. Collectively, these tumors are termed BRAFm-like. Our goal was to validate this signature using next-generation sequencing and to discover novel therapies for BRAFm-like CRCs using a systems biology approach. Methods: We developed a semi-automated workflow that integrates publicly available tools named the Cancer In-silico Drug Discovery (CIDD). To validate the BRAFm-like signature, we used CIDD to analyze the CRC dataset from the The Cancer Genome Atlas Network (TCGA). Samples were stratified on BRAFm status using exome-sequencing, and expression profiles were inferred from RNA-sequencing. We matched expression profiles with drug-induced signatures inferred from the Connectivity Map (CMap) – a systems biology tool that contains expression data of cell lines treated with 1,500 compounds. CIDD statistically ranks candidate compounds and annotates them to pathways using public databases. Results: When applied to TCGA RNA-sequencing data, a classifier based on the BRAFm-like signature resulted in 93.3% sensitivity and 83.5% specificity for detecting BRAFm samples. When applied to Agilent gene expression data, this resulted in 80% sensitivity and 91.1% specificity. 41% of KRAS-mutated samples and 14% of double wild-type samples were predicted to be BRAFm-like. 100% of MSI-high and 18% of MSS samples were predicted to be BRAFm-like. Compounds near the top of our drug rankings include Gefitinib and MG-262 a proteasome inhibitor. Conclusions: We have validated the BRAFm-like signature using RNA-sequencing and Agilent expression data from the TCGA, and showed a high degree of robustness across technologies. We have identified EGFR and proteasome inhibitors as potential compounds to target BRAFm-like CRCs.

scholarly journals RNA-Sequencing Profiling Analysis of Pericyte-Derived Extracellular Vesicle-Mimetic Nanovesicles-Regulated Genes in Primary Cultured Fibroblasts From Normal and Peyronie’s Disease Penile Tunica Albuginea

2020 ◽  
Author(s):  
Guo Nan Yin ◽  
Shuguang Piao ◽  
Zhiyong Liu ◽  
Lei Wang ◽  
Jiyeon Ock ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Target Genes ◽  
Tunica Albuginea ◽  
Global Gene Expression ◽  
Extracellular Vesicle ◽  
Sequencing Data ◽  
Rt Pcr ◽  
Cultured Fibroblasts ◽  
Primary Fibroblasts

Abstract BackgroundPeyronie’s disease (PD) is a severe fibrotic disease of the tunica albuginea that causes penis curvature and leads to penile pain, deformity, and erectile dysfunction. The role of pericytes in the pathogenesis of fibrosis has recently been determined. Extracellular vesicle (EV)-mimetic nanovesicles (NVs) have attracted attention regarding intercellular communication between cells in the field of fibrosis. However, the global gene expression of pericyte-derived EV-mimetic NVs (PC-NVs) in regulating fibrosis remains unknown. Here, we used RNA-sequencing technology to investigate the potential target genes regulated by PC-NVs in primary fibroblasts derived from human PD plaque. MethodsHuman primary fibroblasts derived from normal and PD patients was cultured and treated with cavernosum pericytes isolated extracellular vesicle (EV)-mimetic nanovesicles (NVs). A global gene expression RNA-sequencing assay was performed on normal fibroblasts, PD fibroblasts, and PD fibroblasts treated with PC-NVs. Reverse transcription polymerase chain reaction (RT-PCR) was used for sequencing data validation. ResultsA total of 4135 genes showed significantly differential expression in the normal fibroblasts, PD fibroblasts, and PD fibroblasts treated with PC-NVs. However, only 91 contra-regulated genes were detected among the three libraries. Furthermore, 20 contra-regulated genes were selected and 11 showed consistent changes in the RNA-sequencing assay, which were validated by RT-PCR. ConclusionThe gene expression profiling results suggested that these validated genes may be good targets for understanding potential mechanisms and conducting molecular studies into PD.

scholarly journals CNVkit-RNA: Copy number inference from RNA-Sequencing data

2018 ◽  
Author(s):  
Eric Talevich ◽  
A. Hunter Shain
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Copy Number ◽  
Point Mutations ◽  
Gc Content ◽  
The Cancer Genome Atlas ◽  
Comparative Genomic ◽  
Sequencing Data ◽  
Number Variation ◽  
User Friendly

AbstractRNA-sequencing is most commonly used to measure gene expression, but it is possible to extract genotypic information from RNA-sequencing data, too. Point mutations and translocations can be detected when they occur in expressed genes, however, there are few software solutions to infer copy number information from RNA-sequencing data. This is because a gene’s expression is dictated by a number of variables, including, but not limited to, copy number variation. Here, we report new functionalities within the software package CNVkit that enable copy number inference from RNA-sequencing data. First, CNVkit removes technical variation in gene expression associated with GC-content and transcript length. Next, CNVkit assigns a weight, dictated by several variables, to each transcript with the net effect of preferentially inferring copy number from highly and stably expressed genes. We benchmarked our approach on 105 melanomas from The Cancer Genome Atlas project and observed a high degree of concordance (R = 0.739) between our estimates and those from array comparative genomic hybridization (aCGH) on the same samples. After initial configuration, the software requires few inputs, is able to process a batch of up to 100 samples in less than ten minutes, and can be used in conjunction with pre-existing features of CNVkit, including visualization tools. Overall, we present a rapid, user-friendly software solution to infer copy number information from gene expression data.

scholarly journals DDX17 promotes hepatocellular carcinoma progression via inhibiting Klf4 transcriptional activity

2019 ◽  
Vol 10 (11) ◽  
Author(s):  
Ying Xue ◽  
Xuebing Jia ◽  
Changcan Li ◽  
Ke Zhang ◽  
Lei Li ◽  
...  
Keyword(s):  
Transcriptional Activity ◽  
Target Gene ◽  
Target Genes ◽  
The Cancer Genome Atlas ◽  
Gene Expressions ◽  
Pathological Characteristics ◽  
Dead Box ◽  
Cancer Genome Atlas ◽  
First Time

Abstract DEAD box RNA helicase 17 (DDX17) is a transcriptional regulator of several transcription factors, which is more appreciated than its role in RNA metabolism. However, prognostic value and biofunction of DDX17 in HCC remain unclear. Illuminating the mechanism underlying the regulating HCC progression by DDX17 may contribute to therapeutic strategies. In our study, we report for the first time that DDX17 was overexpressed in HCC specimens by using The Cancer Genome Atlas (TCGA) and immunohistochemistry (IHC) and correlated to clinical pathological characteristics and patients’ survival. In vitro, DDX17 was ascertained to alter HCC migratory and invasive capacities after overexpression and knockdown in HCC cell lines. Moreover, by performing co-immunoprecipitation (Co-IP) and GST-pull down assay, the physical association between DDX17 and Klf4 was discovered and validated. Additionally, DDX17 could modulate expressions of Klf4 target genes including E-cadherin, MMP2 by inhibiting the promoter activity. The potent correlation between DDX17 and Klf4 target gene expressions was further appraised by a same set of 30 HCC tissues. Besides, we discovered that DDX17 could not deploy its function in regulating Klf4 target gene expressions and HCC progression in Klf4-depletion condition. Intriguingly, DDX17 failed to interact with Klf4 once the zinc-finger domain was deleted and inhibited the binding of Klf4 on MMP-2 promoter. Collectively, our study enucleates novel mechanism of DDX17-mediated oncogenesis by suppressing the transcriptional activity of Klf4 thus is likely to be a therapeutic target in HCC.

scholarly journals HEDGEHOG/GLI Modulates the PRR11-SKA2 Bidirectional Transcription Unit in Lung Squamous Cell Carcinomas

Genes ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 120
Author(s):  
Yiyun Sun ◽  
Dandan Xu ◽  
Chundong Zhang ◽  
Yitao Wang ◽  
Lian Zhang ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Squamous Cell ◽  
Gene Pair ◽  
Gene List ◽  
Ectopic Expression ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Gene Set

We previously demonstrated that proline-rich protein 11 (PRR11) and spindle and kinetochore associated 2 (SKA2) constituted a head-to-head gene pair driven by a prototypical bidirectional promoter. This gene pair synergistically promoted the development of non-small cell lung cancer. However, the signaling pathways leading to the ectopic expression of this gene pair remains obscure. In the present study, we first analyzed the lung squamous cell carcinoma (LSCC) relevant RNA sequencing data from The Cancer Genome Atlas (TCGA) database using the correlation analysis of gene expression and gene set enrichment analysis (GSEA), which revealed that the PRR11-SKA2 correlated gene list highly resembled the Hedgehog (Hh) pathway activation-related gene set. Subsequently, GLI1/2 inhibitor GANT-61 or GLI1/2-siRNA inhibited the Hh pathway of LSCC cells, concomitantly decreasing the expression levels of PRR11 and SKA2. Furthermore, the mRNA expression profile of LSCC cells treated with GANT-61 was detected using RNA sequencing, displaying 397 differentially expressed genes (203 upregulated genes and 194 downregulated genes). Out of them, one gene set, including BIRC5, NCAPG, CCNB2, and BUB1, was involved in cell division and interacted with both PRR11 and SKA2. These genes were verified as the downregulated genes via RT-PCR and their high expression significantly correlated with the shorter overall survival of LSCC patients. Taken together, our results indicate that GLI1/2 mediates the expression of the PRR11-SKA2-centric gene set that serves as an unfavorable prognostic indicator for LSCC patients, potentializing new combinatorial diagnostic and therapeutic strategies in LSCC.

scholarly journals Mining The Cancer Genome Atlas gene expression data for lineage markers in distinguishing bladder urothelial carcinoma and prostate adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ewe Seng Ch’ng
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
The Cancer Genome Atlas ◽  
Relative Importance ◽  
Expression Data ◽  
Gene Expressions ◽  
Urothelial Carcinomas ◽  
Cancer Genome Atlas ◽  
Lineage Markers ◽  
Genome Atlas

AbstractDistinguishing bladder urothelial carcinomas from prostate adenocarcinomas for poorly differentiated carcinomas derived from the bladder neck entails the use of a panel of lineage markers to help make this distinction. Publicly available The Cancer Genome Atlas (TCGA) gene expression data provides an avenue to examine utilities of these markers. This study aimed to verify expressions of urothelial and prostate lineage markers in the respective carcinomas and to seek the relative importance of these markers in making this distinction. Gene expressions of these markers were downloaded from TCGA Pan-Cancer database for bladder and prostate carcinomas. Differential gene expressions of these markers were analyzed. Standard linear discriminant analyses were applied to establish the relative importance of these markers in lineage determination and to construct the model best in making the distinction. This study shows that all urothelial lineage genes except for the gene for uroplakin III were significantly expressed in bladder urothelial carcinomas (p < 0.001). In descending order of importance to distinguish from prostate adenocarcinomas, genes for uroplakin II, S100P, GATA3 and thrombomodulin had high discriminant loadings (> 0.3). All prostate lineage genes were significantly expressed in prostate adenocarcinomas(p < 0.001). In descending order of importance to distinguish from bladder urothelial carcinomas, genes for NKX3.1, prostate specific antigen (PSA), prostate-specific acid phosphatase, prostein, and prostate-specific membrane antigen had high discriminant loadings (> 0.3). Combination of gene expressions for uroplakin II, S100P, NKX3.1 and PSA approached 100% accuracy in tumor classification both in the training and validation sets. Mining gene expression data, a combination of four lineage markers helps distinguish between bladder urothelial carcinomas and prostate adenocarcinomas.

scholarly journals The EAR Motif in the Arabidopsis MADS Transcription Factor AGAMOUS-Like 15 Is Not Necessary to Promote Somatic Embryogenesis

Plants ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 758
Author(s):  
Sanjay Joshi ◽  
Christian Keller ◽  
Sharyn E. Perry
Keyword(s):  
Gene Expression ◽  
Somatic Embryogenesis ◽  
Target Gene ◽  
Target Genes ◽  
Domain Family ◽  
Binding Factor ◽  
Ear Motif ◽  
Carboxyl Terminal ◽  
Responsive Element ◽  
Carboxy Terminal

AGAMOUS-like 15 (AGL15) is a member of the MADS domain family of transcription factors (TFs) that can directly induce and repress target gene expression, and for which promotion of somatic embryogenesis (SE) is positively correlated with accumulation. An ethylene-responsive element binding factor-associated amphiphilic repression (EAR) motif of form LxLxL within the carboxyl-terminal domain of AGL15 was shown to be involved in repression of gene expression. Here, we examine whether AGL15′s ability to repress gene expression is needed to promote SE. While a form of AGL15 where the LxLxL is changed to AxAxA can still promote SE, another form with a strong transcriptional activator at the carboxy-terminal end, does not promote SE and, in fact, is detrimental to SE development. Select target genes were examined for response to the different forms of AGL15.

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