From milliseconds to lifetimes: tracking the dynamic behavior of transcription factors in gene networks

Time-series transcriptome analysis reveals the cascade mechanism of biological processes following the perturbation of the MVA pathway in Salvia miltiorrhiza

Various biological processes are interconnected in plants. Transcription factors (TFs) often act as regulatory hubs to regulate plant growth and responses to stress by integrating various biological pathways. Despite extensive studies on TFs functions in various plant species, our understanding of the details of TFs regulation remains limited. In this study, clonal seedlings of Salvia miltiorrhiza were exposed to specific inhibitors for 12 h. Time-series transcriptome data, sampled hourly, were used to construct co-expression networks and gene regulatory networks (GRNs). Transcriptome dynamic analysis was utilized to capture the gene expression dynamics of various biological processes and decipher the potential molecular mechanisms that regulate these processes. The perturbation results showed the growth and development processes of S.miltiorrhiza were primarily affected at the early stage, whereas stress response-related biological processes were mainly influenced at the later stage. And there was a correlation between the series of key differentially expressed genes in terpenoid biosynthesis pathways and the topological distribution of these pathways. Furthermore, the GRNs based on TFs indicate that TFs play a crucial role in connecting various biological processes. In the cytoplasmic lysate gene regulatory module, SmWRKY48-SmTCP4-SmWRKY28 constituted a regulation hub regulating S.miltiorrhiza responses to perturbation of the MVA pathway. The regulation hub mediated various pathways, including pyruvate metabolism, glycolysis/gluconeogenesis, amino acid metabolism, and ubiquinone and other terpenoid-quinone biosynthesis.Our findings suggest that perturbation of a key biological pathway in S.miltiorrhiza has time-dependent effects on other biological processes. And SmWRKY48-SmTCP4-SmWRKY28 constitutes the regulatory hub in S.miltiorrhiza responses to perturbation of MVA pathway.

Plant-DTI: Extending the landscape of TF protein and DNA interaction in plants by a machine learning-based approach

As a sessile organism, plants hold elaborate transcriptional regulatory systems that allow them to adapt to variable surrounding environments. Current understanding of plant regulatory mechanisms is greatly constrained by limited knowledge of transcription factor (TF)-DNA interactions. To mitigate this problem, a Plant-DTI predictor (Plant DBD-TFBS Interaction) was developed here as the first machine-learning model that covered the largest experimental datasets of 30 plant TF families, including 7 plant-specific DNA binding domain (DBD) types, and their transcription factor binding sites (TFBSs). Plant-DTI introduced a novel TFBS feature construction, called TFBS base-preference, which enhanced the specificity of TFBS to DBD types. The proposed model showed better predictive performance with the TFBS base-preference than the simple binary representation. Plant-DTI was validated with 22 independent ChIP-seq datasets. It accurately predicted the measured DBD-TFBS pairs along with their TFBS motifs, and effectively predicted interactions of other TFs containing similar DBD types. Comparing to the existing state-of-art methods, Plant-DTI prediction showed a figure of merit in sensitivity and specificity with respect to the position weight matrix (PWM) and TSPTFBS methods. Finally, the proposed Plant-DTI model helped to fill the knowledge gap in the regulatory mechanisms of the cassava sucrose synthase 1 gene (MeSUS1). Plant-DTI predicted MeERF72 as a regulator of MeSUS1 in consistence with the yeast one-hybrid (Y1H) experiment. Taken together, Plant-DTI would help facilitate the prediction of TF-TFBS and TF-target gene (TG) interactions, thereby accelerating the study of transcriptional regulatory systems in plant species.

Understanding of the various aspects of gene regulatory networks related to crop improvement

The hierarchical relationship between transcription factors, associated proteins, and their target genes is defined by a gene regulatory network (GRN). GRNs allow us to understand how the genotype and environment of a plant are incorporated to control the downstream physiological responses. During plant growth or environmental acclimatization, GRNs are diverse and can be differently regulated across tissue types and organs. An overview of recent advances in the development of GRN that speed up basic and applied plant research is given here. Furthermore, the overview of genome and transcriptome involving GRN research along with the exciting advancement and application are discussed. In addition, different approaches to GRN predictions were elucidated. In this review, we also describe the role of GRN in crop improvement, crop plant manipulation, stress responses, speed breeding and identifying genetic variations/locus. Finally, the challenges and prospects of GRN in plant biology are discussed.

Systems seed biology to understand and manipulate rice grain quality and nutrition

Rice is one of the most essential crops since it meets the calorific needs of 3 billion people around the world. Rice seed development initiates upon fertilization, leading to the establishment of two distinct filial tissues, the endosperm and embryo, which accumulate distinct seed storage products, such as starch, storage proteins, and lipids. A range of systems biology tools deployed in dissecting the spatiotemporal dynamics of transcriptome data, methylation, and small RNA based regulation operative during seed development, influencing the accumulation of storage products was reviewed. Studies of other model systems are also considered due to the limited information on the rice transcriptome. This review highlights key genes identified through a holistic view of systems biology targeted to modify biochemical composition and influence rice grain quality and nutritional value with the target of improving rice as a functional food.

Spt20, a structural subunit of the SAGA complex, regulates biofilm formation, asexual development, and virulence of Aspergillus fumigatus

The exopolysaccharide galactosaminogalactan (GAG) plays an important role in mediating adhesion, biofilm formation, and virulence in the pathogenic fungus Aspergillus fumigatus. Previous work showed that in A. fumigatus, the Lim-domain binding protein PtaB can form a complex with the sequence-specific transcription factor SomA for regulating GAG biosynthesis, biofilm formation, and asexual development. However, transcriptional co-activators required for biofilm formation in A. fumigatus remain uncharacterized. In this study, Spt20, an orthologue of the subunit of Saccharomyces cerevisiae transcriptional co-activator Spt-Ada-Gcn5-acetyltransferase (SAGA) complex, was identified as a regulator of biofilm formation and asexual development in A. fumigatus. The loss of spt20 caused severe defects in GAG biosynthesis, biofilm formation, conidiation, and virulence of A. fumigatus. RNA-sequence data demonstrated that Spt20 positively regulates the expression of GAG biosynthesis genes uge3 and agd3, developmental regulator medA, and genes involved in the conidiation pathway. Moreover, more than 10 subunits of the SAGA complex (known from yeast) could be immunoprecipitated with Spt20, suggesting that Spt20 acts as a structural subunit of the SAGA complex. Furthermore, distinct modules of SAGA regulate GAG biosynthesis, biofilm formation, and asexual development in A. fumigatus to varying degrees. In summary, the novel biofilm regulator Spt20 is reported, which plays a crucial role in the regulation of fungal asexual development, GAG biosynthesis, and virulence of A. fumigatus. These findings expand knowledge on the regulatory circuits of the SAGA complex relevant for biofilm formation and asexual development of A. fumigatus. IMPORTANCE Eukaryotic transcription is regulated by a large number of proteins, ranging from sequence-specific DNA binding factors to transcriptional co-activators (chromatin regulators and the general transcription machinery) and their regulators. Previous research indicated that the sequence-specific complex SomA/PtaB regulates biofilm formation and asexual development of Aspergillus fumigatus. However, transcriptional co-activators working with sequence-specific transcription factors to regulate A. fumigatus biofilm formation remain uncharacterized. In this study, Spt20, an orthologue of the subunit of Saccharomyces cerevisiae Spt-Ada-Gcn5-acetyltransferase (SAGA) complex, was identified as a novel regulator of biofilm formation and asexual development of A. fumigatus. Loss of spt20 caused severe defects in galactosaminogalactan (GAG) production, conidiation, and virulence. Moreover, nearly all modules of the SAGA complex were required for biofilm formation and asexual development of A. fumigatus. These results establish the SAGA complex as a transcriptional co-activator required for biofilm formation and asexual development of A. fumigatus.

Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks

All aspects of transcription and its regulation involve dynamic events. However, capturing these dynamic events in gene regulatory networks (GRNs) offers both a promise and a challenge. The promise is that capturing and modeling the dynamic changes in GRNs will allow us to understand how organisms adapt to a changing environment. The ability to mount a rapid transcriptional response to environmental changes is especially important in nonmotile organisms such as plants. The challenge is to capture these dynamic, genome-wide events and model them in GRNs. In this review, we cover recent progress in capturing dynamic interactions of transcription factors with their targets-at both the local and genome-wide levels-and how they are used to learn how GRNs operate as a function of time. We also discuss recent advances that employ time-based machine learning approaches to forecast gene expression at future time points, a key goal of systems biology.

The cis-regulatory codes of response to combined heat and drought stress in Arabidopsis thaliana

Plants respond to their environment by dynamically modulating gene expression. A powerful approach for understanding how these responses are regulated is to integrate information about cis-regulatory elements (CREs) into models called cis-regulatory codes. Transcriptional response to combined stress is typically not the sum of the responses to the individual stresses. However, cis-regulatory codes underlying combined stress response have not been established. Here we modeled transcriptional response to single and combined heat and drought stress in Arabidopsis thaliana. We grouped genes by their pattern of response (independent, antagonistic and synergistic) and trained machine learning models to predict their response using putative CREs (pCREs) as features (median F-measure = 0.64). We then developed a deep learning approach to integrate additional omics information (sequence conservation, chromatin accessibility and histone modification) into our models, improving performance by 6.2%. While pCREs important for predicting independent and antagonistic responses tended to resemble binding motifs of transcription factors associated with heat and/or drought stress, important synergistic pCREs resembled binding motifs of transcription factors not known to be associated with stress. These findings demonstrate how in silico approaches can improve our understanding of the complex codes regulating response to combined stress and help us identify prime targets for future characterization.

Meta Gene Regulatory Networks in Maize Highlight Functionally Relevant Regulatory Interactions

The regulation of gene expression is central to many biological processes. Gene regulatory networks (GRNs) link transcription factors (TFs) to their target genes and represent maps of potential transcriptional regulation. Here, we analyzed a large number of publically available maize (Zea mays) transcriptome data sets including >6000 RNA sequencing samples to generate 45 coexpression-based GRNs that represent potential regulatory relationships between TFs and other genes in different populations of samples (cross-tissue, cross-genotype, and tissue-and-genotype samples). While these networks are all enriched for biologically relevant interactions, different networks capture distinct TF-target associations and biological processes. By examining the power of our coexpression-based GRNs to accurately predict covarying TF-target relationships in natural variation data sets, we found that presence/absence changes rather than quantitative changes in TF gene expression are more likely associated with changes in target gene expression. Integrating information from our TF-target predictions and previous expression quantitative trait loci (eQTL) mapping results provided support for 68 TFs underlying 74 previously identified trans-eQTL hotspots spanning a variety of metabolic pathways. This study highlights the utility of developing multiple GRNs within a species to detect putative regulators of important plant pathways and provides potential targets for breeding or biotechnological applications.

Transient genome-wide interactions of the master transcription factor NLP7 initiate a rapid nitrogen-response cascade

Dynamic reprogramming of gene regulatory networks (GRNs) enables organisms to rapidly respond to environmental perturbation. However, the underlying transient interactions between transcription factors (TFs) and genome-wide targets typically elude biochemical detection. Here, we capture both stable and transient TF-target interactions genome-wide within minutes after controlled TF nuclear import using time-series chromatin immunoprecipitation (ChIP-seq) and/or DNA adenine methyltransferase identification (DamID-seq). The transient TF-target interactions captured uncover the early mode-of-action of NIN-LIKE PROTEIN 7 (NLP7), a master regulator of the nitrogen signaling pathway in plants. These transient NLP7 targets captured in root cells using temporal TF perturbation account for 50% of NLP7-regulated genes not detectably bound by NLP7 in planta. Rapid and transient NLP7 binding activates early nitrogen response TFs, which we validate to amplify the NLP7-initiated transcriptional cascade. Our approaches to capture transient TF-target interactions genome-wide can be applied to validate dynamic GRN models for any pathway or organism of interest.

Conventional methods cannot reveal transient transcription factors (TFs) and targets interactions. Here, Alvarez et al. capture both stable and transient TF-target interactions by time-series ChIP-seq and/or DamID-seq in a cell-based TF perturbation system and show NLP7 as a master TF to initiate a rapid nitrogen-response cascade.

Matrix metalloproteinases as target genes for gene regulatory networks driving molecular and cellular pathways related to a multistep pathogenesis of cerebrovascular disease

The present study investigated a joint contribution of matrix metalloproteinases (MMPs) genes to ischemic stroke (IS) development and analyzed interactions between MMP genes and genome-wide associated loci for IS. A total of 1288 unrelated Russians (600 IS patients and 688 healthy individuals) from Central Russia were recruited for the study. Genotyping of seven single nucleotide polymorphisms (SNPs) of MMP genes (rs1799750, rs243865, rs3025058, rs11225395, rs17576, rs486055, and rs2276109) and eight genome-wide associated loci for IS were done using Taq-Man-based assays and MALDI-TOF mass spectrometry iPLEX platform, respectively. Allele - 799T at rs11225395 of the MMP8 gene was significantly associated with a decreased risk of IS after adjustment for sex and age (OR = 0.82; 95%CI, 0.70-0.96; P = 0.016). The model-based multifactor dimensionality reduction method has revealed 21 two-order, 124 three-order, and 474 four-order gene-gene (G×G) interactions models meaningfully (Pperm  < 0.05) associated with the IS risk. The bioinformatic analysis enabled establishing the studied MMP gene polymorphisms possess a clear regulatory potential and may be targeted by gene regulatory networks driving molecular and cellular pathways related to the pathogenesis of IS. In conclusion, the present study was the first to identify an association between polymorphism rs11225395 of the MMP8 gene and IS risk. The study findings also indicate that MMPs deserve special attention as a potential class of genes influencing the multistep mechanisms of cerebrovascular disease including atherosclerosis in cerebral arteries, acute cerebral artery occlusion as well as the ischemic injury of the brain and its recovery.

Keep talking: crosstalk between iron and sulfur networks fine-tunes growth and development to promote survival under iron limitation

Plants are capable of synthesizing all the molecules necessary to complete their life cycle from minerals, water, and light. This plasticity, however, comes at a high energetic cost and therefore plants need to regulate their economy and allocate resources accordingly. Iron-sulfur (Fe-S) clusters are at the center of photosynthesis, respiration, amino acid, and DNA metabolism. Fe-S clusters are extraordinary catalysts, but their main components (Fe2+ and S2-) are highly reactive and potentially toxic. To prevent toxicity, plants have evolved mechanisms to regulate the uptake, storage, and assimilation of Fe and S. Recent advances have been made in understanding the cellular economy of Fe and S metabolism individually, and growing evidence suggests that there is dynamic crosstalk between Fe and S networks. In this review, we summarize and discuss recent literature on Fe sensing, allocation, use efficiency, and, when pertinent, its relationship to S metabolism. Our future perspectives include a discussion about the open questions and challenges ahead and how the plant nutrition field can come together to approach these questions in a cohesive and more efficient way.

TF2Network: predicting transcription factor regulators and gene regulatory networks in Arabidopsis using publicly available binding site information

A gene regulatory network (GRN) is a collection of regulatory interactions between transcription factors (TFs) and their target genes. GRNs control different biological processes and have been instrumental to understand the organization and complexity of gene regulation. Although various experimental methods have been used to map GRNs in Arabidopsis thaliana, their limited throughput combined with the large number of TFs makes that for many genes our knowledge about regulating TFs is incomplete. We introduce TF2Network, a tool that exploits the vast amount of TF binding site information and enables the delineation of GRNs by detecting potential regulators for a set of co-expressed or functionally related genes. Validation using two experimental benchmarks reveals that TF2Network predicts the correct regulator in 75–92% of the test sets. Furthermore, our tool is robust to noise in the input gene sets, has a low false discovery rate, and shows a better performance to recover correct regulators compared to other plant tools. TF2Network is accessible through a web interface where GRNs are interactively visualized and annotated with various types of experimental functional information. TF2Network was used to perform systematic functional and regulatory gene annotations, identifying new TFs involved in circadian rhythm and stress response.

Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism

DNA-binding proteins (DBPs) perform diverse biological functions ranging from transcription to pathogen sensing. Machine learning methods can not only identify DBPs de novo but also provide insights into their DNA-recognition dynamics. However, it remains unclear whether available methods that can accurately predict DNA-binding sites in known DBPs can also identify novel DBPs. Moreover, sequence information is blind to the cellular- and disease-specific contexts of DBP activities, whereas the under-utilized knowledge from public gene expression data offers great promise. To address these issues, we have developed novel methods for predicting DBPs by integrating sequence and gene expression-derived features and applied them to explore human, mouse and Arabidopsis proteomes. While our sequence-based models outperformed the gene expression-based ones, some proteins with weaker DBP-like sequence features were correctly predicted by gene expression-based features, suggesting that these proteins acquire a tangible DBP functionality in a conducive gene expression environment. Analysis of motif enrichment among the co-expressed genes of top 100 candidates DBPs from hitherto unannotated genes provides further avenues to explore their functional associations.

Network Walking charts transcriptional dynamics of nitrogen signaling by integrating validated and predicted genome-wide interactions

Charting a temporal path in gene networks requires linking early transcription factor (TF)-triggered events to downstream effects. We scale-up a cell-based TF-perturbation assay to identify direct regulated targets of 33 nitrogen (N)-early response TFs encompassing 88% of N-responsive Arabidopsis genes. We uncover a duality where each TF is an inducer and repressor, and in vitro cis-motifs are typically specific to regulation directionality. Validated TF-targets (71,836) are used to refine precision of a time-inferred root network, connecting 145 N-responsive TFs and 311 targets. These data are used to chart network paths from direct TF₁-regulated targets identified in cells to indirect targets responding only in planta via Network Walking. We uncover network paths from TGA1 and CRF4 to direct TF₂ targets, which in turn regulate 76% and 87% of TF₁ indirect targets in planta, respectively. These results have implications for N-use and the approach can reveal temporal networks for any biological system.

Temporal control of transcriptional networks enables organisms to adapt to changing environment. Here, the authors use a scaled-up cell-based assay to identify direct targets of nitrogen-early responsive transcription factors and validate a network path mediating dynamic nitrogen signaling in Arabidopsis.

Physical, chemical, and toxicological characteristics of particulate emissions from current technology gasoline direct injection vehicles

We assessed the physical, chemical and toxicological characteristics of particulate emissions from four light-duty gasoline direct injection vehicles when operated over the LA92 driving cycle. Our results showed that particle mass and number emissions increased markedly during accelerations. For three of the four vehicles tested, particulate matter (PM) mass and particle number emissions were markedly higher during cold-start and the first few accelerations following the cold-start period than during the hot running and hot-start segments of the LA92 cycle. For one vehicle (which had the highest emissions overall) the hot-start and cold-start PM emissions were similar. Black carbon emissions were also much higher during the cold-start conditions, indicating severe fuel wetting leading to slow evaporation and pool burning, and subsequent soot formation. Particle number concentrations and black carbon emissions showed large reductions during the urban and hot-start phases of the test cycle. The oxidative potential of PM was quantified with both a chemical and a biological assay, and the gene expression impacts of the PM in a macrophage model with PCR (polymerase chain reaction) and ELISA (enzyme-linked immunosorbent assay) analyses. Inter- and intra-vehicle variability in oxidative potential per milligram of PM emitted was relatively low for both oxidative assays, suggesting that real-world emissions and exposure can be estimated with distance-normalized emission factors. The PCR response from signaling markers for oxidative stress (e.g., NOX1) was greater than from inflammatory, AhR (aryl hydrocarbon receptor), or MAPK (mitogen-activated protein kinase) signaling. Protein production associated with inflammation (tumor necrosis factor alpha-TNFα) and oxidative stress (HMOX-1) were quantified and displayed relatively high inter-vehicle variability, suggesting that these pathways may be activated by different PM components. Correlation of trace metal concentrations and oxidative potential suggests a role for small, insoluble particles in inducing oxidative stress.

Effect of diets supplemented with starch and corn oil, marine algae, or hydrogenated palm oil on mammary lipogenic gene expression in cows and goats: A comparative study

A direct comparison of cow and goat performance and milk fatty acid (FA) responses to diets that either induce milk fat depression or increase milk fat content in cows suggests species-specific regulation of lipid metabolism, including mammary lipogenesis. This experiment was conducted to highlight potential mechanisms responsible for the differences in mammary lipogenesis due to diet and ruminant species. Twelve Holstein cows and 12 Alpine goats were fed a basal diet containing no additional lipid (CTL) or a similar diet supplemented with corn oil [5% dry matter intake (DMI)] and wheat starch (COS), marine algae powder (MAP; 1.5% DMI), or hydrogenated palm oil (HPO; 3% DMI), according to a 4 × 4 Latin square design with 28-d experimental periods. Milk yield, milk composition, FA profile, and secretions were measured. On d 27 of each experimental period, the mRNA abundance of 21 genes involved in lipid metabolism or enzyme activities or both were measured in mammary tissue sampled by biopsy. The results showed significant differences in the milk fat response of cows and goats to the dietary treatments. In cows, fat content was lowered by COS (-45%) and MAP (-22%) and increased by HPO (+13%) compared with CTL, and in goats only MAP had an effect compared with CTL, with a decrease of 15%. In both species, COS and MAP lowered the yields (mmol/d per kilogram of body weight) of <C16 and C16 FA. With COS, this decrease was compensated by an increase of >C16 FA in goats but not in cows, and the >C16 FA yield decreased with MAP in both species. Supplementation of HPO increased the yield of milk C16 FA (mmol/d per kilogram of body weight) in cows. These variations in milk fat content and FA secretion were not associated with modifications in the mammary expression of 21 genes involved in major lipid pathways, except for 3 transcription factors: PPARA, INSIG1, and SP1. This absence of large changes might be due to post-transcriptional regulation of these genes and related to the time of sampling of the mammary tissue relative to the previous meal and milking or to differences in the availability of substrate for the corresponding proteins. However, the abundance of 14 mRNA among the 21 encoding for genes studied in the mammary gland was significantly different among species, with 5 more abundant in cows (FADS3, ACSL1, PPARA, LXRA, and PPARG1) and 10 more abundant in goats (FASN, CD36, FABP3, LPL, GPAM, LPIN1, CSN2, MFGE8, and INSIG1). These species specificities of mammary lipid metabolism require further investigation.

PCAF fine-tunes hepatic metabolic syndrome, inflammatory disease, and cancer

The P300/CBP‐associating factor (PCAF), a histone acetyltransferase, is involved in metabolic and pathogenic diseases, particularly of the liver. The effects of PCAF on fine‐tuning liver diseases are extremely complex and vary according to different pathological conditions. This enzyme has dichotomous functions, depending on differently modified sites, which regulate the activities of various enzymes, metabolic functions, and gene expression. Here, we summarize the most recent findings on the functions and targets of PCAF in various metabolic and immunological processes in the liver and review these new discoveries and models of PCAF biology in three areas: hepatic metabolic syndrome, inflammatory disease, and cancer. Finally, we discuss the potential implications of these findings for therapeutic interventions in liver diseases.

Similar bowtie structures and distinct largest strong components are identified in the transcriptional regulatory networks of Arabidopsis thaliana during photomorphogenesis and heat shock

Photomorphogenesis and heat shock are critical biological processes of plants. A recent research constructed the transcriptional regulatory networks (TRNs) of Arabidopsis thaliana during these processes using DNase-seq. In this study, by strong decomposition, we revealed that each of these TRNs can be represented as a similar bowtie structure with only one non-trivial and distinct strong component. We further identified distinct patterns of variation of a few light-related genes in these bowtie structures during photomorphogenesis. These results suggest that bowtie structure may be a common property of TRNs of plants, and distinct variation patterns of genes in bowtie structures of TRNs during biological processes may reflect distinct functions. Overall, our study provides an insight into the molecular mechanisms underlying photomorphogenesis and heat shock, and emphasizes the necessity to investigate the strong connectivity structures while studying TRNs.

A matter of time - How transient transcription factor interactions create dynamic gene regulatory networks

Dynamic reprogramming of transcriptional networks enables cells to adapt to a changing environment. Thus, it is crucial not only to understand what gene targets are regulated by a transcription factor (TF) but also when. This review explores the way TFs function with respect to time, paying particular attention to discoveries made in plants - where coordinated, genome-wide responses to environmental change is crucial to the survival of these sessile organisms. We investigate the molecular mechanisms that mediate transient TF-DNA binding, and assess how these rapid and dynamic interactions translate to long-term temporal regulation of genomes. We also discuss how current molecular techniques can catch, and sometimes miss, transient TF-target interactions that underlie dynamic cellular responses. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

More than meets the eye: Emergent properties of transcription factors networks in Arabidopsis

Uncovering and mathematically modeling Transcription Factor Networks (TFNs) are the first steps in engineering plants with traits that are better equipped to respond to changing environments. Although several plant TFNs are well known, the framework for systematically modeling complex characteristics such as switch-like behavior, oscillations, and homeostasis that emerge from them remain elusive. This review highlights literature that provides, in part, experimental and computational techniques for characterizing TFNs. This review also outlines methodologies that have been used to mathematically model the dynamic characteristics of TFNs. We present several examples of TFNs in plants that are involved in developmental and stress response. In several cases, advanced algorithms capture or quantify emergent properties that serve as the basis for robustness and adaptability in plant responses. Increasing the use of mathematical approaches will shed new light on these regulatory properties that control plant growth and development, leading to mathematical models that predict plant behavior. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

"Hit-and-Run" transcription: de novo transcription initiated by a transient bZIP1 "hit" persists after the "run"

BACKGROUND

Dynamic transcriptional regulation is critical for an organism's response to environmental signals and yet remains elusive to capture. Such transcriptional regulation is mediated by master transcription factors (TF) that control large gene regulatory networks. Recently, we described a dynamic mode of TF regulation named "hit-and-run". This model proposes that master TF can interact transiently with a set of targets, but the transcription of these transient targets continues after the TF dissociation from the target promoter. However, experimental evidence validating active transcription of the transient TF-targets is still lacking.

RESULTS

Here, we show that active transcription continues after transient TF-target interactions by tracking de novo synthesis of RNAs made in response to TF nuclear import. To do this, we introduced an affinity-labeled 4-thiouracil (4tU) nucleobase to specifically isolate newly synthesized transcripts following conditional TF nuclear import. Thus, we extended the TARGET system (Transient Assay Reporting Genome-wide Effects of Transcription factors) to include 4tU-labeling and named this new technology TARGET-tU. Our proof-of-principle example is the master TF Basic Leucine Zipper 1 (bZIP1), a central integrator of metabolic signaling in plants. Using TARGET-tU, we captured newly synthesized mRNAs made in response to bZIP1 nuclear import at a time when bZIP1 is no longer detectably bound to its target. Thus, the analysis of de novo transcripomics demonstrates that bZIP1 may act as a catalyst TF to initiate a transcriptional complex ("hit"), after which active transcription by RNA polymerase continues without the TF being bound to the gene promoter ("run").

CONCLUSION

Our findings provide experimental proof for active transcription of transient TF-targets supporting a "hit-and-run" mode of action. This dynamic regulatory model allows a master TF to catalytically propagate rapid and broad transcriptional responses to changes in environment. Thus, the functional read-out of de novo transcripts produced by transient TF-target interactions allowed us to capture new models for genome-wide transcriptional control.

Role of transcription factor acetylation in the regulation of metabolic homeostasis

Post-translational modifications (PTMs) of transcription factors play a crucial role in regulating metabolic homeostasis. These modifications include phosphorylation, methylation, acetylation, ubiquitination, SUMOylation, and O-GlcNAcylation. Recent studies have shed light on the importance of lysine acetylation at nonhistone proteins including transcription factors. Acetylation of transcription factors affects subcellular distribution, DNA affinity, stability, transcriptional activity, and current investigations are aiming to further expand our understanding of the role of lysine acetylation of transcription factors. In this review, we summarize recent studies that provide new insights into the role of protein lysine-acetylation in the transcriptional regulation of metabolic homeostasis.