An official website of the United States government
Abstract Bacteria contain conserved mechanisms to control the intracellular levels of metal ions. Metalloregulatory transcription factors bind metal cations and play a central role in regulating gene expression of metal transporters. Often, these transcription factors regulate transcription by binding to a specific DNA sequence in the promoter region of target genes. Understanding the preferred DNA‐binding sequence for transcriptional regulators can help uncover novel gene targets and provide insight into the biological role of the transcription factor in the host organism. Here, we identify consensus DNA‐binding sequences and subsequent transcription regulatory networks for two metalloregulators from the ferric uptake regulator (FUR) and diphtheria toxin repressor (DtxR) superfamilies inThermus thermophilusHB8. By homology search, we classify the DtxR homolog as a manganese‐specific, MntR (TtMntR), and the FUR homolog as a peroxide‐sensing, PerR (TtPerR). Both transcription factors repress separate ZIP transporter genes in vivo, andTtPerR acts as a bifunctional transcription regulator by activating the expression of ferric and hemin transport systems. We showTtPerR andTtMntR bind DNA in the presence of manganese in vitro and in vivo; however,TtPerR is unable to bind DNA in the presence of iron, likely due to iron‐mediated histidine oxidation. Unlike canonical PerR homologs,TtPerR does not appear to contribute to peroxide detoxification. Instead, theTtPerR regulon and DNA binding sequence are more reminiscent of Fur or Mur homologs. Collectively, these results highlight the similarities and differences between two metalloregulatory superfamilies and underscore the interplay of manganese and iron in transcription factor regulation.
more »
« less
Direct regulation of shikimate, early phenylpropanoid, and stilbenoid pathways by Subgroup 2 R2R3‐MYBs in grapevine
SUMMARY The stilbenoid pathway is responsible for the production of resveratrol in grapevine (Vitis viniferaL.). A few transcription factors (TFs) have been identified as regulators of this pathway but the extent of this control has not been deeply studied. Here we show how DNA affinity purification sequencing (DAP‐Seq) allows for the genome‐wide TF‐binding site interrogation in grape. We obtained 5190 and 4443 binding events assigned to 4041 and 3626 genes for MYB14 and MYB15, respectively (approximately 40% of peaks located within −10 kb of transcription start sites). DAP‐Seq of MYB14/MYB15 was combined with aggregate gene co‐expression networks (GCNs) built from more than 1400 transcriptomic datasets from leaves, fruits, and flowers to narrow down bound genes to a set of high confidence targets. The analysis of MYB14, MYB15, and MYB13, a third uncharacterized member of Subgroup 2 (S2), showed that in addition to the few previously known stilbene synthase (STS) targets, these regulators bind to 30 of 47STSfamily genes. Moreover, all three MYBs bind to severalPAL,C4H, and4CLgenes, in addition to shikimate pathway genes, theWRKY03stilbenoid co‐regulator and resveratrol‐modifying gene candidates among which ROMT2‐3 were validated enzymatically. A high proportion of DAP‐Seq bound genes were induced in the activated transcriptomes of transientMYB15‐overexpressing grapevine leaves, validating our methodological approach for delimiting TF targets. Overall, Subgroup 2 R2R3‐MYBs appear to play a key role in binding and directly regulating several primary and secondary metabolic steps leading to an increased flux towards stilbenoid production. The integration of DAP‐Seq and reciprocal GCNs offers a rapid framework for gene function characterization using genome‐wide approaches in the context of non‐model plant species and stands up as a valid first approach for identifying gene regulatory networks of specialized metabolism.
more »
« less
- Award ID(s):
- 1916804
- PAR ID:
- 10367759
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- The Plant Journal
- Volume:
- 110
- Issue:
- 2
- ISSN:
- 0960-7412
- Page Range / eLocation ID:
- p. 529-547
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Machine learning approaches have been applied to identify transcription factor (TF)–DNA interaction important for gene regulation and expression. However, due to the enormous search space of the genome, it is challenging to build models capable of surveying entire reference genomes, especially in species where models were not trained. In this study, we surveyed a variety of methods for classification of epigenomics data in an attempt to improve the detection for 12 members of the auxin response factor (ARF)-binding DNAs from maize and soybean as assessed by DNA Affinity Purification and sequencing (DAP-seq). We used the classification for prediction by minimizing the genome search space by only surveying unmethylated regions (UMRs). For identification of DAP-seq-binding events within the UMRs, we achieved 78.72 % accuracy rate across 12 members of ARFs of maize on average by encoding DNA with count vectorization for k-mer with a logistic regression classifier with up-sampling and feature selection. Importantly, feature selection helps to uncover known and potentially novel ARF-binding motifs. This demonstrates an independent method for identification of TF-binding sites. Finally, we tested the model built with maize DAP-seq data and applied it directly to the soybean genome and found high false-negative rates, which accounted for more than 40 % across the ARF TFs tested. The findings in this study suggest the potential use of various methods to predict TF–DNA interactions within and between species with varying degrees of success.more » « less
-
Marshall-Colon, Amy (Ed.)Abstract Gene regulatory networks (GRNs) are defined by a cascade of transcriptional events by which signals, such as hormones or environmental cues, change development. To understand these networks, it is necessary to link specific transcription factors (TFs) to the downstream gene targets whose expression they regulate. Although multiple methods provide information on the targets of a single TF, moving from groups of co-expressed genes to the TF that controls them is more difficult. To facilitate this bottom-up approach, we have developed a web application named TF DEACoN. This application uses a publicly available Arabidopsis thaliana DNA Affinity Purification (DAP-Seq) data set to search for TFs that show enriched binding to groups of co-regulated genes. We used TF DEACoN to examine groups of transcripts regulated by treatment with the ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC), using a transcriptional data set performed with high temporal resolution. We demonstrate the utility of this application when co-regulated genes are divided by timing of response or cell-type-specific information, which provides more information on TF/target relationships than when less defined and larger groups of co-regulated genes are used. This approach predicted TFs that may participate in ethylene-modulated root development including the TF NAM (NO APICAL MERISTEM). We used a genetic approach to show that a mutation in NAM reduces the negative regulation of lateral root development by ACC. The combination of filtering and TF DEACoN used here can be applied to any group of co-regulated genes to predict GRNs that control coordinated transcriptional responses.more » « less
-
Abstract Assessing the dynamics of chromatin features and transcription factor (TF) binding at scale remains a significant challenge in plants. Here, we present PHILO (Plant HIgh-throughput LOw input) ChIP-seq, a high-throughput ChIP-seq platform that enables the cost-effective and extensive capture of TF binding and genome-wide distributions of histone modifications. The PHILO ChIP-seq pipeline is adaptable to many plant species, requires very little starting material (1mg), and provides the option to use MNase (micrococcal nuclease) for chromatin fragmentation. By employing H3K9ac PHILO ChIP-seq on eight Arabidopsis thaliana jasmonic acid (JA) pathway mutants, with the simultaneous processing of over 100 samples, we not only recapitulated but also expanded the current understanding of the intricate interplay between the master TFs MYC2/3/4 and various chromatin regulators. Additionally, our analyses brought to light previously unknown histone acetylation patterns within the regulatory regions of MYC2 target genes in Arabidopsis, which is also conserved in tomato (Solanum lycopersicum). In summary, our PHILO ChIP-seq platform demonstrates its high effectiveness in investigating TF binding and chromatin dynamics on a large scale in plants, paving the way for the cost-efficient realization of complex experimental setups.more » « less
-
Abstract DNA–transcription factor (TF) interactions are essential for gene regulation. Fully characterizing TF recognition specificities and identifying their genomic binding targets are important to understand TF function and regulatory networks. Recently, high-throughput sequencing technology HT-SELEX (high-throughput systematic evolution of ligands by exponential enrichment) has been used to measure hundreds of TFs, providing massive datasets that comprise TF binding preferences. However, there is a need to develop comprehensive computational modeling to fully extract and characterize critical TF binding preferences and fail to distinguish genome-wide binding targets. In this study, we developed a global pairwise model called DCA-Scapes trained with experimental HT-SELEX data. Our approach uncovered high-resolution TF recognition specificity landscapes, enabled the prediction of in vivo binding sequences, and was validated with ChIP-seq (ChIP sequencing) data. In addition, the DCA-Scapes model was utilized to refine the locations of binding regions and accurately identify the binding sites within the ChIP-seq enriched peaks. Moreover, we extended our model to cover the entire human genome, uncovering potential TF target sites that exhibit tissue-specific TF recognition across various cellular environments.more » « less
