@article{muhammad_clark_haque_williams_sozzani_long_2022, title={POPEYE intercellular localization mediates cell-specific iron deficiency responses}, volume={8}, ISSN={["1532-2548"]}, url={https://doi.org/10.1093/plphys/kiac357}, DOI={10.1093/plphys/kiac357}, abstractNote={Plants must tightly regulate iron (Fe) sensing, acquisition, transport, mobilization, and storage to ensure sufficient levels of this essential micronutrient. POPEYE (PYE) is an iron responsive transcription factor that positively regulates the iron deficiency response, while also repressing genes essential for maintaining iron homeostasis. However, little is known about how PYE plays such contradictory roles. Under iron-deficient conditions pPYE:GFP accumulates in the root pericycle while pPYE:PYE-GFP is localized to the nucleus in all Arabidopsis (Arabidopsis thaliana) root cells, suggesting that PYE may have cell-specific dynamics and functions. Using scanning fluorescence correlation spectroscopy (scanning FCS) and cell-specific promoters, we found that PYE-GFP moves between different cells and that the tendency for movement corresponds with transcript abundance. While localization to the cortex, endodermis, and vasculature is required to manage changes in iron availability, vasculature and endodermis localization of PYE-GFP protein exacerbated pye-1 defects and elicited a host of transcriptional changes that are detrimental to iron mobilization. Our findings indicate that PYE acts as a positive regulator of iron deficiency response by regulating iron bioavailability differentially across cells, which may trigger iron uptake from the surrounding rhizosphere and impact root energy metabolism.}, journal={PLANT PHYSIOLOGY}, publisher={Oxford University Press (OUP)}, author={Muhammad, DurreShahwar and Clark, Natalie M. and Haque, Samiul and Williams, Cranos M. and Sozzani, Rosangela and Long, Terri A.}, year={2022}, month={Aug} }
 @misc{muhammad_schmittling_williams_long_2017, title={More than meets the eye: Emergent properties of transcription factors networks in Arabidopsis}, volume={1860}, ISSN={["0006-3002"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84995470767&partnerID=MN8TOARS}, DOI={10.1016/j.bbagrm.2016.07.017}, abstractNote={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.}, number={1}, journal={BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS}, author={Muhammad, Durreshahwar and Schmittling, Selene and Williams, Cranos and Long, Terri A.}, year={2017}, month={Jan}, pages={64–74} }
 @article{koryachko_matthiadis_muhammad_foret_brady_ducoste_tuck_long_williams_2015, title={Clustering and Differential Alignment Algorithm: Identification of Early Stage Regulators in the Arabidopsis thaliana Iron Deficiency Response}, volume={10}, ISSN={["1932-6203"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84943338816&partnerID=MN8TOARS}, DOI={10.1371/journal.pone.0136591}, abstractNote={Time course transcriptome datasets are commonly used to predict key gene regulators associated with stress responses and to explore gene functionality. Techniques developed to extract causal relationships between genes from high throughput time course expression data are limited by low signal levels coupled with noise and sparseness in time points. We deal with these limitations by proposing the Cluster and Differential Alignment Algorithm (CDAA). This algorithm was designed to process transcriptome data by first grouping genes based on stages of activity and then using similarities in gene expression to predict influential connections between individual genes. Regulatory relationships are assigned based on pairwise alignment scores generated using the expression patterns of two genes and some inferred delay between the regulator and the observed activity of the target. We applied the CDAA to an iron deficiency time course microarray dataset to identify regulators that influence 7 target transcription factors known to participate in the Arabidopsis thaliana iron deficiency response. The algorithm predicted that 7 regulators previously unlinked to iron homeostasis influence the expression of these known transcription factors. We validated over half of predicted influential relationships using qRT-PCR expression analysis in mutant backgrounds. One predicted regulator-target relationship was shown to be a direct binding interaction according to yeast one-hybrid (Y1H) analysis. These results serve as a proof of concept emphasizing the utility of the CDAA for identifying unknown or missing nodes in regulatory cascades, providing the fundamental knowledge needed for constructing predictive gene regulatory networks. We propose that this tool can be used successfully for similar time course datasets to extract additional information and infer reliable regulatory connections for individual genes.}, number={8}, journal={PLOS ONE}, author={Koryachko, Alexandr and Matthiadis, Anna and Muhammad, Durreshahwar and Foret, Jessica and Brady, Siobhan M. and Ducoste, Joel J. and Tuck, James and Long, Terri A. and Williams, Cranos}, year={2015}, month={Aug} }