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Phosphorus is essential to plant growth and agricultural crop yields, yet the challenges associated with phosphorus fertilization in agriculture, such as aquatic runoff pollution and poor phosphorus bioavailability, are increasingly difficult to manage. Comprehensively understanding the dynamics of phosphorus uptake and signaling mechanisms will inform the development of strategies to address these issues. This review describes regulatory mechanisms used by specific tissues in the root apical meristem to sense and take up phosphate from the rhizosphere. The major regulatory mechanisms and related hormone crosstalk underpinning phosphate starvation responses, cellular phosphate homeostasis, and plant adaptations to phosphate starvation are also discussed, along with an overview of the major mechanism of plant systemic phosphate starvation responses. Finally, this review discusses recent promising genetic engineering strategies for improving crop phosphorus use and computational approaches that may help further design strategies for improved plant phosphate acquisition. The mechanisms and approaches presented include a wide variety of species including not only Arabidopsis but also crop species such as Oryza sativa (rice), Glycine max (soybean), and Triticum aestivum (wheat) to address both general and species-specific mechanisms and strategies. The aspects of phosphorus deficiency responses and recently employed strategies of improving phosphate acquisition that are detailed in this review may provide insights into the mechanisms or phenotypes that may be targeted in efforts to improve crop phosphorus content and plant growth in low phosphorus soils.

 

Molecular biology aims to understand cellular responses and regulatory dynamics in complex biological systems. However, these studies remain challenging in non-model species due to poor functional annotation of regulatory proteins. To overcome this limitation, we develop a multi-layer neural network that determines protein functionality directly from the protein sequence. We annotate kinases and phosphatases inGlycine max. We use the functional annotations from our neural network, Bayesian inference principles, and high resolution phosphoproteomics to infer phosphorylation signaling cascades in soybean exposed to cold, and identify Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as key temperature regulators. Importantly, the signaling cascade inference does not rely upon known kinase motifs or interaction data, enabling de novo identification of kinase-substrate interactions. Conclusively, our neural network shows generalization and scalability, as such we extend our predictions toOryza sativa,Zea mays,Sorghum bicolor, andTriticum aestivum. Taken together, we develop a signaling inference approach for non-model species leveraging our predicted kinases and phosphatases.

 

Abstract Grafting has been adopted for a wide range of crops to enhance productivity and resilience; for example, grafting of Solanaceous crops couples disease-resistant rootstocks with scions that produce high-quality fruit. However, incompatibility severely limits the application of grafting and graft incompatibility remains poorly understood. In grafts, immediate incompatibility results in rapid death, but delayed incompatibility can take months or even years to manifest, creating a significant economic burden for perennial crop production. To gain insight into the genetic mechanisms underlying this phenomenon, we developed a model system using heterografting of tomato (Solanum lycopersicum) and pepper (Capsicum annuum). These grafted plants express signs of anatomical junction failure within the first week of grafting. By generating a detailed timeline for junction formation, we were able to pinpoint the cellular basis for this delayed incompatibility. Furthermore, we inferred gene regulatory networks for compatible self-grafts and incompatible heterografts based on these key anatomical events, which predict core regulators for grafting. Finally, we examined the role of vascular development in graft formation and uncovered SlWOX4 as a potential regulator of graft compatibility. Following this predicted regulator up with functional analysis, we show that Slwox4 homografts fail to form xylem bridges across the junction, demonstrating that indeed, SlWOX4 is essential for vascular reconnection during grafting, and may function as an early indicator of graft failure. 

The acquisition of quantitative information on plant development across a range of temporal and spatial scales is essential to understand the mechanisms of plant growth. Recent years have shown the emergence of imaging methodologies that enable the capture and analysis of plant growth, from the dynamics of molecules within cells to the measurement of morphometricand physiological traits in field-grown plants. In some instances, these imaging methods can be parallelized across multiple samples to increase throughput. When high throughput is combined with high temporal and spatial resolution, the resulting image-derived data sets could be combined with molecular large-scale data sets to enable unprecedented systems-level computational modeling. Such image-driven functional genomics studies may be expected to appear at an accelerating rate in the near future given the early success of the foundational efforts reviewed here. We present new imaging modalities and review how they have enabled a better understanding of plant growth from the microscopic to the macroscopic scale. 

Stem cells divide and differentiate to form all of the specialized cell types in a multicellular organism. In theArabidopsisroot, stem cells are maintained in an undifferentiated state by a less mitotically active population of cells called the quiescent center (QC). Determining how the QC regulates the surrounding stem cell initials, or what makes the QC fundamentally different from the actively dividing initials, is important for understanding how stem cell divisions are maintained. Here we gained insight into the differences between the QC and the cortex endodermis initials (CEI) by studying the mobile transcription factor SHORTROOT (SHR) and its binding partner SCARECROW (SCR). We constructed an ordinary differential equation model of SHR and SCR in the QC and CEI which incorporated the stoichiometry of the SHR-SCR complex as well as upstream transcriptional regulation of SHR and SCR. Our model prediction, coupled with experimental validation, showed that high levels of the SHR-SCR complex are associated with more CEI division but less QC division. Furthermore, our model prediction allowed us to propose the putative upstream SHR regulators SEUSS and WUSCHEL-RELATED HOMEOBOX 5 and to experimentally validate their roles in QC and CEI division. In addition, our model established the timing of QC and CEI division and suggests that SHR repression of QC division depends on formation of the SHR homodimer. Thus, our results support that SHR-SCR protein complex stoichiometry and regulation of SHR transcription modulate the division timing of two different specialized cell types in the root stem cell niche.

 

Predicting gene regulatory networks (GRNs) from expression profiles is a common approach for identifying important biological regulators. Despite the increased use of inference methods, existing computational approaches often do not integrate RNA‐sequencing data analysis, are not automated or are restricted to users with bioinformatics backgrounds. To address these limitations, we developedtuxnet, a user‐friendly platform that can process raw RNA‐sequencing data from any organism with an existing reference genome using a modifiedtuxedopipeline (hisat 2 + cufflinkspackage) and infer GRNs from these processed data.tuxnetis implemented as a graphical user interface and can mine gene regulations, either by applying a dynamic Bayesian network (DBN) inference algorithm,genist, or a regression tree‐based pipeline,rtp‐star. We obtained time‐course expression data of aPERIANTHIA(PAN) inducible line and inferred a GRN usinggenistto illustrate the use oftuxnetwhile gaining insight into the regulations downstream of the Arabidopsis root stem cell regulatorPAN. Usingrtp‐star, we inferred the network ofATHB13, a downstream gene of PAN, for which we obtained wild‐type and mutant expression profiles. Additionally, we generated two networks using temporal data from developmental leaf data and spatial data from root cell‐type data to highlight the use oftuxnetto form new testable hypotheses from previously explored data. Our case studies feature the versatility oftuxnetwhen using different types of gene expression data to infer networks and its accessibility as a pipeline for non‐bioinformaticians to analyze transcriptome data, predict causal regulations, assess network topology and identify key regulators.

 

Common rust, caused byPuccinia sorghi, is a widespread and destructive disease of maize. TheRp1‐Dgene confers resistance to theP. sorghiIN2 isolate, mediating a hypersensitive cell death response (HR). To identify differentially expressed genes (DEGs) and metabolites associated with the compatible (susceptible) interaction and withRp1‐D‐mediated resistance in maize, we performed transcriptomics and targeted metabolome analyses ofP. sorghiIN2‐infected leaves from the near‐isogenic lines H95 and H95:Rp1‐D, which differed for the presence ofRp1‐D. We observed up‐regulation of genes involved in the defence response and secondary metabolism, including the phenylpropanoid, flavonoid, and terpenoid pathways. Metabolome analyses confirmed that intermediates from several transcriptionally up‐regulated pathways accumulated during the defence response. We identified a common response in H95:Rp1‐D and H95 with an additional H95:Rp1‐D‐specific resistance response observed at early time points at both transcriptional and metabolic levels. To better understand the mechanisms underlyingRp1‐D‐mediated resistance, we inferred gene regulatory networks occurring in response toP. sorghiinfection. A number of transcription factors including WRKY53, BHLH124, NKD1, BZIP84, and MYB100 were identified as potentially important signalling hubs in the resistance‐specific response. Overall, this study provides a novel and multifaceted understanding of the maize susceptible and resistance‐specific responses toP. sorghi.