The peptide GOLVEN10 alters root development and noduletaxis in Medicago truncatula.

The conservation of GOLVEN (GLV)/ROOT MERISTEM GROWTH FACTOR (RGF) peptide encoding genes across plant genomes capable of forming roots or root-like structures underscores their potential significance in the terrestrial adaptation of plants. This study investigates the function and role of GOLVEN peptide-coding genes in Medicago truncatula. Five out of fifteen GLV/RGF genes were notably upregulated during nodule organogenesis and were differentially responsive to nitrogen deficiency and auxin treatment. Specifically, the expression of MtGLV9 and MtGLV10 at nodule initiation sites was contingent upon the NODULE INCEPTION transcription factor. Overexpression of these five nodule-induced GLV genes in hairy roots of M. truncatula and application of their synthetic peptide analogues led to a decrease in nodule count by 25-50%. Uniquely, the GOLVEN10 peptide altered the positioning of the first formed lateral root and nodule on the primary root axis, an observation we term 'noduletaxis'; this decreased the length of the lateral organ formation zone on roots. Histological section of roots treated with synthetic GOLVEN10 peptide revealed an increased cell number within the root cortical cell layers without a corresponding increase in cell length, leading to an elongation of the root likely introducing a spatiotemporal delay in organ formation. At the transcription level, the GOLVEN10 peptide suppressed expression of microtubule-related genes and exerted its effects by changing expression of a large subset of Auxin responsive genes. These findings advance our understanding of the molecular mechanisms by which GOLVEN peptides modulate root morphology, nodule ontogeny, and interactions with key transcriptional pathways.

LegumeIP V3: from models to crops-an integrative gene discovery platform for translational genomics in legumes.

Legumes have contributed to human health, sustainable food and feed production worldwide for centuries. The study of model legumes has played vital roles in deciphering key genes, pathways, and networks regulating biological mechanisms and agronomic traits. Along with emerging breeding technology such as genome editing, translation of the knowledge gained from model plants to crops is in high demand. The updated database (V3) was redesigned for translational genomics targeting the discovery of novel key genes in less-studied non-model legume crops by referring to the knowledge gained in model legumes. The database contains genomic data for all 22 included species, and transcriptomic data covering thousands of RNA-seq samples mostly from model species. The rich biological data and analytic tools for gene expression and pathway analyses can be used to decipher critical genes, pathways, and networks in model legumes. The integrated comparative genomic functions further facilitate the translation of this knowledge to legume crops. Therefore, the database will be a valuable resource to identify important genes regulating specific biological mechanisms or agronomic traits in the non-model yet economically significant legume crops. LegumeIP V3 is available free to the public at https://plantgrn.noble.org/LegumeIP. Access to the database does not require login, registration, or password.

Insertional mutagenesis of Brachypodium distachyon using the Tnt1 retrotransposable element.

Brachypodium distachyon is an annual C3 grass used as a monocot model system in functional genomics research. Insertional mutagenesis is a powerful tool for both forward and reverse genetics studies. In this study, we explored the possibility of using the tobacco retrotransposon Tnt1 to create a transposon-based insertion mutant population in B. distachyon. We developed transgenic B. distachyon plants expressing Tnt1 (R0) and in the subsequent regenerants (R1) we observed that Tnt1 actively transposed during somatic embryogenesis, generating an average of 6.37 insertions per line in a population of 19 independent R1 regenerant plants analyzed. In seed-derived progeny of R1 plants, Tnt1 segregated in a Mendelian ratio of 3:1 and no new Tnt1 transposition was observed. A total of 126 flanking sequence tags (FSTs) were recovered from the analyzed R0 and R1 lines. Analysis of the FSTs showed a uniform pattern of insertion in all the chromosomes (1-5) without any preference for a particular chromosome region. Considering the average length of a gene transcript to be 3.37 kb, we estimated that 29 613 lines are required to achieve a 90% possibility of tagging a given gene in the B. distachyon genome using the Tnt1-based mutagenesis approach. Our results show the possibility of using Tnt1 to achieve near-saturation mutagenesis in B. distachyon, which will aid in functional genomics studies of other C3 grasses.

GWASpro: a high-performance genome-wide association analysis server.

SUMMARY: We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. AVAILABILITY AND IMPLEMENTATION: GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The CLE53-SUNN genetic pathway negatively regulates arbuscular mycorrhiza root colonization in Medicago truncatula.

Plants and arbuscular mycorrhizal fungi (AMF) engage in mutually beneficial symbioses based on a reciprocal exchange of nutrients. The beneficial character of the symbiosis is maintained through a mechanism called autoregulation of mycorrhization (AOM). AOM includes root-to-shoot-to-root signaling; however, the molecular details of AOM are poorly understood. AOM shares many features of autoregulation of nodulation (AON) where several genes are known, including the receptor-like kinase SUPER NUMERIC NODULES (SUNN), root-to-shoot mobile CLAVATA3/ENDOSPERM SURROUNDING REGION (ESR)-RELATED (CLE) peptides, and the hydroxyproline O-arabinosyltransferase ROOT DETERMINED NODULATION1 (RDN1) required for post-translational peptide modification. In this work, CLE53 was identified to negatively regulate AMF symbiosis in a SUNN- and RDN1-dependent manner. CLE53 expression was repressed at low phosphorus, while it was induced by AMF colonization and high phosphorus. CLE53 overexpression reduced AMF colonization in a SUNN- and RDN1 dependent manner, while cle53, rdn1, and sunn mutants were more colonized than the wild type. RNA-sequencing identified 700 genes with SUNN-dependent regulation in AMF-colonized plants, providing a resource for future identification of additional AOM genes. Disruption of AOM genes in crops potentially constitutes a novel route for improving AMF-derived phosphorus uptake in agricultural systems with high phosphorus levels.

JRmGRN: joint reconstruction of multiple gene regulatory networks with common hub genes using data from multiple tissues or conditions.

Motivation: Joint reconstruction of multiple gene regulatory networks (GRNs) using gene expression data from multiple tissues/conditions is very important for understanding common and tissue/condition-specific regulation. However, there are currently no computational models and methods available for directly constructing such multiple GRNs that not only share some common hub genes but also possess tissue/condition-specific regulatory edges. Results: In this paper, we proposed a new graphic Gaussian model for joint reconstruction of multiple gene regulatory networks (JRmGRN), which highlighted hub genes, using gene expression data from several tissues/conditions. Under the framework of Gaussian graphical model, JRmGRN method constructs the GRNs through maximizing a penalized log likelihood function. We formulated it as a convex optimization problem, and then solved it with an alternating direction method of multipliers (ADMM) algorithm. The performance of JRmGRN was first evaluated with synthetic data and the results showed that JRmGRN outperformed several other methods for reconstruction of GRNs. We also applied our method to real Arabidopsis thaliana RNA-seq data from two light regime conditions in comparison with other methods, and both common hub genes and some conditions-specific hub genes were identified with higher accuracy and precision. Availability and implementation: JRmGRN is available as a R program from: https://github.com/wenpingd. Supplementary information: Supplementary data are available at Bioinformatics online.

A microRNA biogenesis-like pathway for producing phased small interfering RNA from a long non-coding RNA in rice.

Phased small interfering RNAs (phasiRNAs) are a class of non-coding RNAs that perform essential functions in plants. Unlike microRNA biogenesis from a hairpin structure, the production of phasiRNAs usually requires a phase initiator and an RNA-dependent RNA polymerase (RDR) to form double-strand RNAs. By using full-length rice cDNA (KL-cDNA) to identify phasiRNA loci, we found that a putative non-coding sequence with a long hairpin structure generates the phasiRNAs, which we name Long Hairpin-structure containing non-coding RNA (LHR). The biogenesis of LHR-derived phasiRNAs was dependent on rice DCL4, but not on RDR2/6, DCL1, or DCL3. Since all of the LHR-phasiRNAs (-5p from the forward strand and -3p from the reverse strand of the dsRNAs) are mapped to the forward strand of LHR, LHR-phasiRNAs should be derived from its hairpin structure, similar to a microRNA precursor. A degradome-based validation suggested that several thylakoid-related genes were targeted by LHR-phasiRNAs. In addition, the production of LHR-phasiRNAs was completely abolished in the lhr mutant, which also exhibited decreased plant height, leaf size, and grain weight, probably through the regulation of photosynthesis. Based on our results, we propose a microRNA biogenesis-like pathway for producing phased siRNAs that expands our understanding of the current model of phased siRNA biogenesis in plants.

Draft Genome Sequence Resource of Switchgrass Rust Pathogen, Puccinia novopanici Isolate Ard-01.

Puccinia novopanici is an important biotrophic fungal pathogen that causes rust disease in switchgrass. Lack of genomic resources for P. novopanici has hampered the progress toward developing effective disease resistance against this pathogen. Therefore, we have sequenced the whole genome of P. novopanici and generated a framework to understand pathogenicity mechanisms and identify effectors, repeat element invasion, genome evolution, and comparative genomics among Puccinia spp. in the future. Long- and short-read sequences were generated from P. novopanici genomic DNA by PacBio and Illumina technologies, respectively, and assembled a 99.9-Mb genome. Transcripts of P. novopanici were predicted from assembled genome using MAKER and were further validated by RNAseq data. The genome sequence information of P. novopanici will be a valuable resource for researchers working on monocot rusts and plant disease resistance in general.

Genome-Wide Identification of Medicago Peptides Involved in Macronutrient Responses and Nodulation.

Growing evidence indicates that small, secreted peptides (SSPs) play critical roles in legume growth and development, yet the annotation of SSP-coding genes is far from complete. Systematic reannotation of the Medicago truncatula genome identified 1,970 homologs of established SSP gene families and an additional 2,455 genes that are potentially novel SSPs, previously unreported in the literature. The expression patterns of known and putative SSP genes based on 144 RNA sequencing data sets covering various stages of macronutrient deficiencies and symbiotic interactions with rhizobia and mycorrhiza were investigated. Focusing on those known or suspected to act via receptor-mediated signaling, 240 nutrient-responsive and 365 nodulation-responsive Signaling-SSPs were identified, greatly expanding the number of SSP gene families potentially involved in acclimation to nutrient deficiencies and nodulation. Synthetic peptide applications were shown to alter root growth and nodulation phenotypes, revealing additional regulators of legume nutrient acquisition. Our results constitute a powerful resource enabling further investigations of specific SSP functions via peptide treatment and reverse genetics.

LegumeIP 2.0--a platform for the study of gene function and genome evolution in legumes.

The LegumeIP 2.0 database hosts large-scale genomics and transcriptomics data and provides integrative bioinformatics tools for the study of gene function and evolution in legumes. Our recent updates in LegumeIP 2.0 include gene and protein sequences, gene models and annotations, syntenic regions, protein families and phylogenetic trees for six legume species: Medicago truncatula, Glycine max (soybean), Lotus japonicus, Phaseolus vulgaris (common bean), Cicer arietinum (chickpea) and Cajanus cajan (pigeon pea) and two outgroup reference species: Arabidopsis thaliana and Poplar trichocarpa. Moreover, the LegumeIP 2.0 features the following new data resources and bioinformatics tools: (i) an integrative gene expression atlas for four model legumes that include 550 array hybridizations from M. truncatula, 962 gene expression profiles of G. max, 276 array hybridizations from L. japonicas and 56 RNA-Seq-based gene expression profiles for C. arietinum. These datasets were manually curated and hierarchically organized based on Experimental Ontology and Plant Ontology so that users can browse, search, and retrieve data for their selected experiments. (ii) New functions/analytical tools to query, mine and visualize large-scale gene sequences, annotations and transcriptome profiles. Users may select a subset of expression experiments and visualize and compare expression profiles for multiple genes. The LegumeIP 2.0 database is freely available to the public at http://plantgrn.noble.org/LegumeIP/.

PEPIS: A Pipeline for Estimating Epistatic Effects in Quantitative Trait Locus Mapping and Genome-Wide Association Studies.

The term epistasis refers to interactions between multiple genetic loci. Genetic epistasis is important in regulating biological function and is considered to explain part of the 'missing heritability,' which involves marginal genetic effects that cannot be accounted for in genome-wide association studies. Thus, the study of epistasis is of great interest to geneticists. However, estimating epistatic effects for quantitative traits is challenging due to the large number of interaction effects that must be estimated, thus significantly increasing computing demands. Here, we present a new web server-based tool, the Pipeline for estimating EPIStatic genetic effects (PEPIS), for analyzing polygenic epistatic effects. The PEPIS software package is based on a new linear mixed model that has been used to predict the performance of hybrid rice. The PEPIS includes two main sub-pipelines: the first for kinship matrix calculation, and the second for polygenic component analyses and genome scanning for main and epistatic effects. To accommodate the demand for high-performance computation, the PEPIS utilizes C/C++ for mathematical matrix computing. In addition, the modules for kinship matrix calculations and main and epistatic-effect genome scanning employ parallel computing technology that effectively utilizes multiple computer nodes across our networked cluster, thus significantly improving the computational speed. For example, when analyzing the same immortalized F2 rice population genotypic data examined in a previous study, the PEPIS returned identical results at each analysis step with the original prototype R code, but the computational time was reduced from more than one month to about five minutes. These advances will help overcome the bottleneck frequently encountered in genome wide epistatic genetic effect analysis and enable accommodation of the high computational demand. The PEPIS is publically available at http://bioinfo.noble.org/PolyGenic_QTL/.

The Medicago genome provides insight into the evolution of rhizobial symbioses.

Legumes (Fabaceae or Leguminosae) are unique among cultivated plants for their ability to carry out endosymbiotic nitrogen fixation with rhizobial bacteria, a process that takes place in a specialized structure known as the nodule. Legumes belong to one of the two main groups of eurosids, the Fabidae, which includes most species capable of endosymbiotic nitrogen fixation. Legumes comprise several evolutionary lineages derived from a common ancestor 60 million years ago (Myr ago). Papilionoids are the largest clade, dating nearly to the origin of legumes and containing most cultivated species. Medicago truncatula is a long-established model for the study of legume biology. Here we describe the draft sequence of the M. truncatula euchromatin based on a recently completed BAC assembly supplemented with Illumina shotgun sequence, together capturing ∼94% of all M. truncatula genes. A whole-genome duplication (WGD) approximately 58 Myr ago had a major role in shaping the M. truncatula genome and thereby contributed to the evolution of endosymbiotic nitrogen fixation. Subsequent to the WGD, the M. truncatula genome experienced higher levels of rearrangement than two other sequenced legumes, Glycine max and Lotus japonicus. M. truncatula is a close relative of alfalfa (Medicago sativa), a widely cultivated crop with limited genomics tools and complex autotetraploid genetics. As such, the M. truncatula genome sequence provides significant opportunities to expand alfalfa's genomic toolbox.

Nitrogen remobilization and conservation, and underlying senescence-associated gene expression in the perennial switchgrass Panicum virgatum.

Improving nitrogen (N) remobilization from aboveground to underground organs during yearly shoot senescence is an important goal for sustainable production of switchgrass (Panicum virgatum) as a biofuel crop. Little is known about the genetic control of senescence and N use efficiency in perennial grasses such as switchgrass, which limits our ability to improve the process. Switchgrass aboveground organs (leaves, stems and inflorescences) and underground organs (crowns and roots) were harvested every month over a 3-yr period. Transcriptome analysis was performed to identify genes differentially expressed in various organs during development. Total N content in aboveground organs increased from spring until the end of summer, then decreased concomitant with senescence, while N content in underground organs exhibited an increase roughly matching the decrease in shoot N during fall. Hundreds of senescence-associated genes were identified in leaves and stems. Functional grouping indicated that regulation of transcription and protein degradation play important roles in shoot senescence. Coexpression networks predict important roles for five switchgrass NAC (NAM, ATAF1,2, CUC2) transcription factors (TFs) and other TF family members in orchestrating metabolism of carbohydrates, N and lipids, protein modification/degradation, and transport processes during senescence. This study establishes a molecular basis for understanding and enhancing N remobilization and conservation in switchgrass.

The Medicago sativa gene index 1.2: a web-accessible gene expression atlas for investigating expression differences between Medicago sativa subspecies.

BACKGROUND: Alfalfa (Medicago sativa L.) is the primary forage legume crop species in the United States and plays essential economic and ecological roles in agricultural systems across the country. Modern alfalfa is the result of hybridization between tetraploid M. sativa ssp. sativa and M. sativa ssp. falcata. Due to its large and complex genome, there are few genomic resources available for alfalfa improvement. RESULTS: A de novo transcriptome assembly from two alfalfa subspecies, M. sativa ssp. sativa (B47) and M. sativa ssp. falcata (F56) was developed using Illumina RNA-seq technology. Transcripts from roots, nitrogen-fixing root nodules, leaves, flowers, elongating stem internodes, and post-elongation stem internodes were assembled into the Medicago sativa Gene Index 1.2 (MSGI 1.2) representing 112,626 unique transcript sequences. Nodule-specific and transcripts involved in cell wall biosynthesis were identified. Statistical analyses identified 20,447 transcripts differentially expressed between the two subspecies. Pair-wise comparisons of each tissue combination identified 58,932 sequences differentially expressed in B47 and 69,143 sequences differentially expressed in F56. Comparing transcript abundance in floral tissues of B47 and F56 identified expression differences in sequences involved in anthocyanin and carotenoid synthesis, which determine flower pigmentation. Single nucleotide polymorphisms (SNPs) unique to each M. sativa subspecies (110,241) were identified. CONCLUSIONS: The Medicago sativa Gene Index 1.2 increases the expressed sequence data available for alfalfa by ninefold and can be expanded as additional experiments are performed. The MSGI 1.2 transcriptome sequences, annotations, expression profiles, and SNPs were assembled into the Alfalfa Gene Index and Expression Database (AGED) at http://plantgrn.noble.org/AGED/ , a publicly available genomic resource for alfalfa improvement and legume research.

MET-XAlign: a metabolite cross-alignment tool for LC/MS-based comparative metabolomics.

Liquid chromatography/mass spectrometry (LC/MS) metabolite profiling has been widely used in comparative metabolomics studies; however, LC/MS-based comparative metabolomics currently faces several critical challenges. One of the greatest challenges is how to effectively align metabolites across different LC/MS profiles; a single metabolite can give rise to multiple peak features, and the grouped peak features that can be used to construct a spectrum pattern of single metabolite can vary greatly between biochemical experiments and even between instrument runs. Another major challenge is that the observed retention time for a single metabolite can also be significantly affected by experimental conditions. To overcome these two key challenges, we present a novel metabolite-based alignment approach entitled MET-XAlign to align metabolites across LC/MS metabolomics profiles. MET-XAlign takes the deduced molecular mass and estimated compound retention time information that can be extracted by our previously published tool, MET-COFEA, and aligns metabolites based on this information. We demonstrate that MET-XAlign is able to cross-align metabolite compounds, either known or unknown, in LC/MS profiles not only across different samples but also across different biological experiments and different electrospray ionization modes. Therefore, our proposed metabolite-based cross-alignment approach is a great step forward and its implementation, MET-XAlign, is a very useful tool in LC/MS-based comparative metabolomics. MET-XAlign has been successfully implemented with core algorithm coding in C++, making it very efficient, and visualization interface coding in the Microsoft.NET Framework. The MET-XAlign software along with demonstrative data is freely available at http://bioinfo.noble.org/manuscript-support/met-xalign/ .

Quality evaluation of extracted ion chromatograms and chromatographic peaks in liquid chromatography/mass spectrometry-based metabolomics data.

BACKGROUND: Extracted ion chromatogram (EIC) extraction and chromatographic peak detection are two important processing procedures in liquid chromatography/mass spectrometry (LC/MS)-based metabolomics data analysis. Most commonly, the LC/MS technique employs electrospray ionization as the ionization method. The EICs from LC/MS data are often noisy and contain high background signals. Furthermore, the chromatographic peak quality varies with respect to its location in the chromatogram and most peaks have zigzag shapes. Therefore, there is a critical need to develop effective metrics for quality evaluation of EICs and chromatographic peaks in LC/MS based metabolomics data analysis. RESULTS: We investigated a comprehensive set of potential quality evaluation metrics for extracted EICs and detected chromatographic peaks. Specifically, for EIC quality evaluation, we analyzed the mass chromatographic quality index (MCQ index) and propose a novel quality evaluation metric, the EIC-related global zigzag index, which is based on an EIC's first order derivatives. For chromatographic peak quality evaluation, we analyzed and compared six metrics: sharpness, Gaussian similarity, signal-to-noise ratio, peak significance level, triangle peak area similarity ratio and the local peak-related local zigzag index. CONCLUSIONS: Although the MCQ index is suited for selecting and aligning analyte components, it cannot fairly evaluate EICs with high background signals or those containing only a single peak. Our proposed EIC related global zigzag index is robust enough to evaluate EIC qualities in both scenarios. Of the six peak quality evaluation metrics, the sharpness, peak significance level, and zigzag index outperform the others due to the zigzag nature of LC/MS chromatographic peaks. Furthermore, using several peak quality metrics in combination is more efficient than individual metrics in peak quality evaluation.

An RNA-Seq based gene expression atlas of the common bean.

BACKGROUND: Common bean (Phaseolus vulgaris) is grown throughout the world and comprises roughly 50% of the grain legumes consumed worldwide. Despite this, genetic resources for common beans have been lacking. Next generation sequencing, has facilitated our investigation of the gene expression profiles associated with biologically important traits in common bean. An increased understanding of gene expression in common bean will improve our understanding of gene expression patterns in other legume species. RESULTS: Combining recently developed genomic resources for Phaseolus vulgaris, including predicted gene calls, with RNA-Seq technology, we measured the gene expression patterns from 24 samples collected from seven tissues at developmentally important stages and from three nitrogen treatments. Gene expression patterns throughout the plant were analyzed to better understand changes due to nodulation, seed development, and nitrogen utilization. We have identified 11,010 genes differentially expressed with a fold change ≥ 2 and a P-value < 0.05 between different tissues at the same time point, 15,752 genes differentially expressed within a tissue due to changes in development, and 2,315 genes expressed only in a single tissue. These analyses identified 2,970 genes with expression patterns that appear to be directly dependent on the source of available nitrogen. Finally, we have assembled this data in a publicly available database, The Phaseolus vulgaris Gene Expression Atlas (Pv GEA), http://plantgrn.noble.org/PvGEA/ . Using the website, researchers can query gene expression profiles of their gene of interest, search for genes expressed in different tissues, or download the dataset in a tabular form. CONCLUSIONS: These data provide the basis for a gene expression atlas, which will facilitate functional genomic studies in common bean. Analysis of this dataset has identified genes important in regulating seed composition and has increased our understanding of nodulation and impact of the nitrogen source on assimilation and distribution throughout the plant.

MET-COFEA: a liquid chromatography/mass spectrometry data processing platform for metabolite compound feature extraction and annotation.

In this paper, we present a novel liquid chromatography/mass spectrometry (LC/MS) data processing and analysis platform, MET-COFEA (METabolite COmpound Feature Extraction and Annotation). MET-COFEA detects and clusters chromatographic peak features for each metabolite compound by first comprehensively evaluating retention time and peak shape criteria and then annotating the associations between each peak's observed m/z value with the corresponding metabolite compound's molecular mass. MET-COFEA integrates a series of innovative approaches, including novel mass trace based extracted-ion chromatogram (EIC) extraction, continuous wavelet transform (CWT)-based peak detection, and compound-associated peak clustering and peak annotation algorithms. On the basis of the deduced neutral molecular mass and retention time, we have also developed a new alignment algorithm that uses compound-associated peak groups instead of individual peaks to align the same metabolite compound across samples from different electrospray ionization (ESI) modes, different instruments, even different experimental conditions. MET-COFEA has been systematically tested on a series of LC/MS profiles of mixed standards at different concentrations as well as real untargeted LC/MS plant metabolomics data. We compared the performances of MET-COFEA with the existing publicly available tools at LC/MS peak analysis level and demonstrated its excellent performance in this arena. MET-COFEA is freely available at http://bioinfo.noble.org/manuscript-support/met-cofea/.

ETS1, a Target Gene of the EWSR1::FLI1 Fusion Oncoprotein, Regulates the Expression of the Focal Adhesion Protein TENSIN3.

The mechanistic basis for the metastasis of Ewing sarcomas remains poorly understood, as these tumors harbor few mutations beyond the chromosomal translocation that initiates the disease. Instead, the epigenome of Ewing sarcoma cells reflects the regulatory state of genes associated with the DNA-binding activity of the fusion oncoproteins EWSR1::FLI1 or EWSR1::ERG. In this study, we examined the EWSR1::FLI1/ERG's repression of transcription factor genes, concentrating on those that exhibit a broader range of expression in tumors than in Ewing sarcoma cell lines. Focusing on one of these target genes, ETS1, we detected EWSR1::FLI1 binding and an H3K27me3-repressive mark at this locus. Depletion of EWSR1::FLI1 results in ETS1's binding of promoter regions, substantially altering the transcriptome of Ewing sarcoma cells, including the upregulation of the gene encoding TENSIN3 (TNS3), a focal adhesion protein. Ewing sarcoma cell lines expressing ETS1 (CRISPRa) exhibited increased TNS3 expression and enhanced movement compared with control cells. Visualization of control Ewing sarcoma cells showed a distributed vinculin signal and a network-like organization of F-actin; in contrast, ETS1-activated Ewing sarcoma cells showed an accumulation of vinculin and F-actin toward the plasma membrane. Interestingly, the phenotype of ETS1-activated Ewing sarcoma cell lines depleted of TNS3 resembled the phenotype of the control cells. Critically, these findings have clinical relevance as TNS3 expression in Ewing sarcoma tumors positively correlates with that of ETS1. Implications: ETS1's transcriptional regulation of the gene encoding the focal adhesion protein TENSIN3 in Ewing sarcoma cells promotes cell movement, a critical step in the evolution of metastasis.

Staufen1 Represses the FOXA1-Regulated Transcriptome by Destabilizing FOXA1 mRNA in Colorectal Cancer Cells.

Transcription factors play key roles in development and disease by controlling gene expression. Forkhead box A1 (FOXA1), is a pioneer transcription factor essential for mouse development and functions as an oncogene in prostate and breast cancer. In colorectal cancer (CRC), FOXA1 is significantly downregulated and high FOXA1 expression is associated with better prognosis, suggesting potential tumor suppressive functions. We therefore investigated the regulation of FOXA1 expression in CRC, focusing on well-differentiated CRC cells, where FOXA1 is robustly expressed. Genome-wide RNA stability assays identified FOXA1 as an unstable mRNA in CRC cells. We validated FOXA1 mRNA instability in multiple CRC cell lines and in patient-derived CRC organoids, and found that the FOXA1 3'UTR confers instability to the FOXA1 transcript. RNA pulldowns and mass spectrometry identified Staufen1 (STAU1) as a potential regulator of FOXA1 mRNA. Indeed, STAU1 knockdown resulted in increased FOXA1 mRNA and protein expression due to increased FOXA1 mRNA stability. Consistent with these data, RNA-seq following STAU1 knockdown in CRC cells revealed that FOXA1 targets were upregulated upon STAU1 knockdown. Collectively, this study uncovers a molecular mechanism by which FOXA1 is regulated in CRC cells and provides insights into our understanding of the complex mechanisms of gene regulation in cancer.