FER and LecRK show haplotype-dependent cold-responsiveness and mediate freezing tolerance in Lotus japonicus.

Many plant species have succeeded in colonizing a wide range of diverse climates through local adaptation, but the underlying molecular genetics remain obscure. We previously found that winter survival was a direct target of selection during colonization of Japan by the perennial legume Lotus japonicus and identified associated candidate genes. Here, we show that two of these, FERONIA-receptor like kinase (LjFER) and a S-receptor-like kinase gene (LjLecRK), are required for non-acclimated freezing tolerance and show haplotype-dependent cold-responsive expression. Our work suggests that recruiting a conserved growth regulator gene, FER, and a receptor-like kinase gene, LecRK, into the set of cold-responsive genes has contributed to freezing tolerance and local climate adaptation in L. japonicus, offering functional genetic insight into perennial herb evolution.

Cellular clarity: a logistic regression approach to identify root epidermal regulators of iron deficiency response.

BACKGROUND: Plants respond to stress through highly tuned regulatory networks. While prior works identified master regulators of iron deficiency responses in A. thaliana from whole-root data, identifying regulators that act at the cellular level is critical to a more comprehensive understanding of iron homeostasis. Within the root epidermis complex molecular mechanisms that facilitate iron reduction and uptake from the rhizosphere are known to be regulated by bHLH transcriptional regulators. However, many questions remain about the regulatory mechanisms that control these responses, and how they may integrate with developmental processes within the epidermis. Here, we use transcriptional profiling to gain insight into root epidermis-specific regulatory processes. RESULTS: Set comparisons of differentially expressed genes (DEGs) between whole root and epidermis transcript measurements identified differences in magnitude and timing of organ-level vs. epidermis-specific responses. Utilizing a unique sampling method combined with a mutual information metric across time-lagged and non-time-lagged windows, we identified relationships between clusters of functionally relevant differentially expressed genes suggesting that developmental regulatory processes may act upstream of well-known Fe-specific responses. By integrating static data (DNA motif information) with time-series transcriptomic data and employing machine learning approaches, specifically logistic regression models with LASSO, we also identified putative motifs that served as crucial features for predicting differentially expressed genes. Twenty-eight transcription factors (TFs) known to bind to these motifs were not differentially expressed, indicating that these TFs may be regulated post-transcriptionally or post-translationally. Notably, many of these TFs also play a role in root development and general stress response. CONCLUSIONS: This work uncovered key differences in -Fe response identified using whole root data vs. cell-specific root epidermal data. Machine learning approaches combined with additional static data identified putative regulators of -Fe response that would not have been identified solely through transcriptomic profiles and reveal how developmental and general stress responses within the epidermis may act upstream of more specialized -Fe responses for Fe uptake.

POPEYE intercellular localization mediates cell-specific iron deficiency responses.

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 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.

Practical spectral photography II: snapshot spectral imaging using linear retarders and microgrid polarization cameras.

Despite recent advances, customized multispectral cameras can be challenging or costly to deploy in some use cases. Complexities span electronic synchronization, multi-camera calibration, parallax and spatial co-registration, and data acquisition from multiple cameras, all of which can hamper their ease of use. This paper discusses a generalized procedure for multispectral sensing using a pixelated polarization camera and anisotropic polymer film retarders to create multivariate optical filters. We then describe the calibration procedure, which leverages neural networks to convert measured data into calibrated spectra (intensity versus wavelength). Experimental results are presented for a multivariate and channeled optical filter. Finally, imaging results taken using a red, green, and blue microgrid polarization camera and the channeled optical filter are presented. Imaging experiments indicated that the calculated spectra's root mean square error is highest in the region where the camera's red, green, and blue filter responses overlap. The average error of the spectral reflectance, measured of our spectralon tiles, was 6.5% for wavelengths spanning 425-675 nm. This technique demonstrates that 12 spectral channels can be obtained with a relatively simple and robust optical setup, and at minimal cost beyond the purchase of the camera.

Modeling cross-regulatory influences on monolignol transcripts and proteins under single and combinatorial gene knockdowns in Populus trichocarpa.

Accurate manipulation of metabolites in monolignol biosynthesis is a key step for controlling lignin content, structure, and other wood properties important to the bioenergy and biomaterial industries. A crucial component of this strategy is predicting how single and combinatorial knockdowns of monolignol specific gene transcripts influence the abundance of monolignol proteins, which are the driving mechanisms of monolignol biosynthesis. Computational models have been developed to estimate protein abundances from transcript perturbations of monolignol specific genes. The accuracy of these models, however, is hindered by their inability to capture indirect regulatory influences on other pathway genes. Here, we examine the manifestation of these indirect influences on transgenic transcript and protein abundances, identifying putative indirect regulatory influences that occur when one or more specific monolignol pathway genes are perturbed. We created a computational model using sparse maximum likelihood to estimate the resulting monolignol transcript and protein abundances in transgenic Populus trichocarpa based on targeted knockdowns of specific monolignol genes. Using in-silico simulations of this model and root mean square error, we showed that our model more accurately estimated transcript and protein abundances, in comparison to previous models, when individual and families of monolignol genes were perturbed. We leveraged insight from the inferred network structure obtained from our model to identify potential genes, including PtrHCT, PtrCAD, and Ptr4CL, involved in post-transcriptional and/or post-translational regulation. Our model provides a useful computational tool for exploring the cascaded impact of single and combinatorial modifications of monolignol specific genes on lignin and other wood properties.

Predicting gene regulatory networks by combining spatial and temporal gene expression data in Arabidopsis root stem cells.

Identifying the transcription factors (TFs) and associated networks involved in stem cell regulation is essential for understanding the initiation and growth of plant tissues and organs. Although many TFs have been shown to have a role in the Arabidopsis root stem cells, a comprehensive view of the transcriptional signature of the stem cells is lacking. In this work, we used spatial and temporal transcriptomic data to predict interactions among the genes involved in stem cell regulation. To accomplish this, we transcriptionally profiled several stem cell populations and developed a gene regulatory network inference algorithm that combines clustering with dynamic Bayesian network inference. We leveraged the topology of our networks to infer potential major regulators. Specifically, through mathematical modeling and experimental validation, we identified PERIANTHIA (PAN) as an important molecular regulator of quiescent center function. The results presented in this work show that our combination of molecular biology, computational biology, and mathematical modeling is an efficient approach to identify candidate factors that function in the stem cells.

Complete proteomic-based enzyme reaction and inhibition kinetics reveal how monolignol biosynthetic enzyme families affect metabolic flux and lignin in Populus trichocarpa.

We established a predictive kinetic metabolic-flux model for the 21 enzymes and 24 metabolites of the monolignol biosynthetic pathway using Populus trichocarpa secondary differentiating xylem. To establish this model, a comprehensive study was performed to obtain the reaction and inhibition kinetic parameters of all 21 enzymes based on functional recombinant proteins. A total of 104 Michaelis-Menten kinetic parameters and 85 inhibition kinetic parameters were derived from these enzymes. Through mass spectrometry, we obtained the absolute quantities of all 21 pathway enzymes in the secondary differentiating xylem. This extensive experimental data set, generated from a single tissue specialized in wood formation, was used to construct the predictive kinetic metabolic-flux model to provide a comprehensive mathematical description of the monolignol biosynthetic pathway. The model was validated using experimental data from transgenic P. trichocarpa plants. The model predicts how pathway enzymes affect lignin content and composition, explains a long-standing paradox regarding the regulation of monolignol subunit ratios in lignin, and reveals novel mechanisms involved in the regulation of lignin biosynthesis. This model provides an explanation of the effects of genetic and transgenic perturbations of the monolignol biosynthetic pathway in flowering plants.

Systems biology of lignin biosynthesis in Populus trichocarpa: heteromeric 4-coumaric acid:coenzyme A ligase protein complex formation, regulation, and numerical modeling.

As a step toward predictive modeling of flux through the pathway of monolignol biosynthesis in stem differentiating xylem of Populus trichocarpa, we discovered that the two 4-coumaric acid:CoA ligase (4CL) isoforms, 4CL3 and 4CL5, interact in vivo and in vitro to form a heterotetrameric protein complex. This conclusion is based on laser microdissection, coimmunoprecipitation, chemical cross-linking, bimolecular fluorescence complementation, and mass spectrometry. The tetramer is composed of three subunits of 4CL3 and one of 4CL5. 4CL5 appears to have a regulatory role. This protein-protein interaction affects the direction and rate of metabolic flux for monolignol biosynthesis in P. trichocarpa. A mathematical model was developed for the behavior of 4CL3 and 4CL5 individually and in mixtures that form the enzyme complex. The model incorporates effects of mixtures of multiple hydroxycinnamic acid substrates, competitive inhibition, uncompetitive inhibition, and self-inhibition, along with characteristic of the substrates, the enzyme isoforms, and the tetrameric complex. Kinetic analysis of different ratios of the enzyme isoforms shows both inhibition and activation components, which are explained by the mathematical model and provide insight into the regulation of metabolic flux for monolignol biosynthesis by protein complex formation.

Monolignol pathway 4-coumaric acid:coenzyme A ligases in Populus trichocarpa: novel specificity, metabolic regulation, and simulation of coenzyme A ligation fluxes.

4-Coumaric acid:coenzyme A ligase (4CL) is involved in monolignol biosynthesis for lignification in plant cell walls. It ligates coenzyme A (CoA) with hydroxycinnamic acids, such as 4-coumaric and caffeic acids, into hydroxycinnamoyl-CoA thioesters. The ligation ensures the activated state of the acid for reduction into monolignols. In Populus spp., it has long been thought that one monolignol-specific 4CL is involved. Here, we present evidence of two monolignol 4CLs, Ptr4CL3 and Ptr4CL5, in Populus trichocarpa. Ptr4CL3 is the ortholog of the monolignol 4CL reported for many other species. Ptr4CL5 is novel. The two Ptr4CLs exhibited distinct Michaelis-Menten kinetic properties. Inhibition kinetics demonstrated that hydroxycinnamic acid substrates are also inhibitors of 4CL and suggested that Ptr4CL5 is an allosteric enzyme. Experimentally validated flux simulation, incorporating reaction/inhibition kinetics, suggested two CoA ligation paths in vivo: one through 4-coumaric acid and the other through caffeic acid. We previously showed that a membrane protein complex mediated the 3-hydroxylation of 4-coumaric acid to caffeic acid. The demonstration here of two ligation paths requiring these acids supports this 3-hydroxylation function. Ptr4CL3 regulates both CoA ligation paths with similar efficiencies, whereas Ptr4CL5 regulates primarily the caffeic acid path. Both paths can be inhibited by caffeic acid. The Ptr4CL5-catalyzed caffeic acid metabolism, therefore, may also act to mitigate the inhibition by caffeic acid to maintain a proper ligation flux. A high level of caffeic acid was detected in stem-differentiating xylem of P. trichocarpa. Our results suggest that Ptr4CL5 and caffeic acid coordinately modulate the CoA ligation flux for monolignol biosynthesis.

Dynamics of BMP signaling and stable gene expression in the early Drosophila embryo.

In developing tissues, morphogen gradients are thought to initialize gene expression patterns. However, the relationship between the dynamics of morphogen-encoded signals and gene expression decisions is largely unknown. Here we examine the dynamics of the Bone Morphogenetic Protein (BMP) pathway in Drosophila blastoderm-stage embryos. In this tissue, the BMP pathway is highly dynamic: it begins as a broad and weak signal on the dorsal half of the embryo, then 20-30 min later refines into a narrow, intense peak centered on the dorsal midline. This dynamical progression of the BMP signal raises questions of how it stably activates target genes. Therefore, we performed live imaging of the BMP signal and found that dorsal-lateral cells experience only a short transient in BMP signaling, after which the signal is lost completely. Moreover, we measured the transcriptional response of the BMP target gene pannier in live embryos and found it to remain activated in dorsal-lateral cells, even after the BMP signal is lost. Our findings may suggest that the BMP pathway activates a memory, or 'ratchet' mechanism that may sustain gene expression.

Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies.

The plant-associated microbiome is a key component of plant systems, contributing to their health, growth, and productivity. The application of machine learning (ML) in this field promises to help untangle the relationships involved. However, measurements of microbial communities by high-throughput sequencing pose challenges for ML. Noise from low sample sizes, soil heterogeneity, and technical factors can impact the performance of ML. Additionally, the compositional and sparse nature of these datasets can impact the predictive accuracy of ML. We review recent literature from plant studies to illustrate that these properties often go unmentioned. We expand our analysis to other fields to quantify the degree to which mitigation approaches improve the performance of ML and describe the mathematical basis for this. With the advent of accessible analytical packages for microbiome data including learning models, researchers must be familiar with the nature of their datasets.

Multiplex CRISPR editing of wood for sustainable fiber production.

The domestication of forest trees for a more sustainable fiber bioeconomy has long been hindered by the complexity and plasticity of lignin, a biopolymer in wood that is recalcitrant to chemical and enzymatic degradation. Here, we show that multiplex CRISPR editing enables precise woody feedstock design for combinatorial improvement of lignin composition and wood properties. By assessing every possible combination of 69,123 multigenic editing strategies for 21 lignin biosynthesis genes, we deduced seven different genome editing strategies targeting the concurrent alteration of up to six genes and produced 174 edited poplar variants. CRISPR editing increased the wood carbohydrate-to-lignin ratio up to 228% that of wild type, leading to more-efficient fiber pulping. The edited wood alleviates a major fiber-production bottleneck regardless of changes in tree growth rate and could bring unprecedented operational efficiencies, bioeconomic opportunities, and environmental benefits.

Functional redundancy of the two 5-hydroxylases in monolignol biosynthesis of Populus trichocarpa: LC-MS/MS based protein quantification and metabolic flux analysis.

Flowering plants have syringyl and guaiacyl subunits in lignin in contrast to the guaiacyl lignin in gymnosperms. The biosynthesis of syringyl subunits is initiated by coniferaldehyde 5-hydroxylase (CAld5H). In Populus trichocarpa there are two closely related CAld5H enzymes (PtrCAld5H1 and PtrCAld5H2) associated with lignin biosynthesis during wood formation. We used yeast recombinant PtrCAld5H1 and PtrCAld5H2 proteins to carry out Michaelis-Menten and inhibition kinetics with LC-MS/MS based absolute protein quantification. CAld5H, a monooxygenase, requires a cytochrome P450 reductase (CPR) as an electron donor. We cloned and expressed three P. trichocarpa CPRs in yeast and show that all are active with both CAld5Hs. The kinetic analysis shows both CAld5Hs have essentially the same biochemical functions. When both CAld5Hs are coexpressed in the same yeast membranes, the resulting enzyme activities are additive, suggesting functional redundancy and independence of these two enzymes. Simulated reaction flux based on Michaelis-Menten kinetics and inhibition kinetics confirmed the redundancy and independence. Subcellular localization of both CAld5Hs as sGFP fusion proteins expressed in P. trichocarpa differentiating xylem protoplasts indicate that they are endoplasmic reticulum resident proteins. These results imply that during wood formation, 5-hydroxylation in monolignol biosynthesis of P. trichocarpa requires the combined metabolic flux of these two CAld5Hs to maintain adequate biosynthesis of syringyl lignin. The combination of genetic analysis, absolute protein quantitation-based enzyme kinetics, homologous CPR specificity, SNP characterization, and ER localization provides a more rigorous basis for a comprehensive systems understanding of 5-hydroxylation in lignin biosynthesis.

Identification of Transcription Factors Regulating SARS-CoV-2 Tropism Factor Expression by Inferring Cell-Type-Specific Transcriptional Regulatory Networks in Human Lungs.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that caused the coronavirus disease 2019 (COVID-19) pandemic. Though previous studies have suggested that SARS-CoV-2 cellular tropism depends on the host-cell-expressed proteins, whether transcriptional regulation controls SARS-CoV-2 tropism factors in human lung cells remains unclear. In this study, we used computational approaches to identify transcription factors (TFs) regulating SARS-CoV-2 tropism for different types of lung cells. We constructed transcriptional regulatory networks (TRNs) controlling SARS-CoV-2 tropism factors for healthy donors and COVID-19 patients using lung single-cell RNA-sequencing (scRNA-seq) data. Through differential network analysis, we found that the altered regulatory role of TFs in the same cell types of healthy and SARS-CoV-2-infected networks may be partially responsible for differential tropism factor expression. In addition, we identified the TFs with high centralities from each cell type and proposed currently available drugs that target these TFs as potential candidates for the treatment of SARS-CoV-2 infection. Altogether, our work provides valuable cell-type-specific TRN models for understanding the transcriptional regulation and gene expression of SARS-CoV-2 tropism factors.

High-throughput image segmentation and machine learning approaches in the plant sciences across multiple scales.

Agriculture has benefited greatly from the rise of big data and high-performance computing. The acquisition and analysis of data across biological scales have resulted in strategies modeling inter- actions between plant genotype and environment, models of root architecture that provide insight into resource utilization, and the elucidation of cell-to-cell communication mechanisms that are instrumental in plant development. Image segmentation and machine learning approaches for interpreting plant image data are among many of the computational methodologies that have evolved to address challenging agricultural and biological problems. These approaches have led to contributions such as the accelerated identification of gene that modulate stress responses in plants and automated high-throughput phenotyping for early detection of plant diseases. The continued acquisition of high throughput imaging across multiple biological scales provides opportunities to further push the boundaries of our understandings quicker than ever before. In this review, we explore the current state of the art methodologies in plant image segmentation and machine learning at the agricultural, organ, and cellular scales in plants. We show how the methodologies for segmentation and classification differ due to the diversity of physical characteristics found at these different scales. We also discuss the hardware technologies most commonly used at these different scales, the types of quantitative metrics that can be extracted from these images, and how the biological mechanisms by which plants respond to abiotic/biotic stresses or genotypic modifications can be extracted from these approaches.

Multiscale Modeling of Cross-Regulatory Transcript and Protein Influences.

With the popularity of high-throughput transcriptomic techniques like RNAseq, models of gene regulatory networks have been important tools for understanding how genes are regulated. These transcriptomic datasets are usually assumed to reflect their associated proteins. This assumption, however, ignores post-transcriptional, translational, and post-translational regulatory mechanisms that regulate protein abundance but not transcript abundance. Here we describe a method to model cross-regulatory influences between the transcripts and proteins of a set of genes using abundance data collected from a series of transgenic experiments. The developed model can capture the effects of regulation that impacts transcription as well as regulatory mechanisms occurring after transcription. This approach uses a sparse maximum likelihood algorithm to determine relationships that influence transcript and protein abundance. An example of how to explore the network topology of this type of model is also presented. This model can be used to predict how the transcript and protein abundances will change in novel transgenic modification strategies.

A multiscale model of lignin biosynthesis for predicting bioenergy traits in Populus trichocarpa.

Understanding the mechanisms behind lignin formation is an important research area with significant implications for the bioenergy and biomaterial industries. Computational models are indispensable tools for understanding this complex process. Models of the monolignol pathway in Populus trichocarpa and other plants have been developed to explore how transgenic modifications affect important bioenergy traits. Many of these models, however, only capture one level of biological organization and are unable to capture regulation across multiple biological scales. This limits their ability to predict how gene modification strategies will impact lignin and other wood properties. While the first multiscale model of lignin biosynthesis in P. trichocarpa spanned the transcript, protein, metabolic, and phenotypic layers, it did not account for cross-regulatory influences that could impact abundances of untargeted monolignol transcripts and proteins. Here, we present a multiscale model incorporating these cross-regulatory influences for predicting lignin and wood traits from transgenic knockdowns of the monolignol genes. The three main components of this multiscale model are (1) a transcript-protein model capturing cross-regulatory influences, (2) a kinetic-based metabolic model, and (3) random forest models relating the steady state metabolic fluxes to 25 physical traits. We demonstrate that including the cross-regulatory behavior results in smaller predictive error for 23 of the 25 traits. We use this multiscale model to explore the predicted impact of novel combinatorial knockdowns on key bioenergy traits, and identify the perturbation of PtrC3H3 and PtrCAld5H1&2 monolignol genes as a candidate strategy for increasing saccharification efficiencies while reducing negative impacts on wood density and height.

Internal defect scanning of sweetpotatoes using interactance spectroscopy.

While standard visible-light imaging offers a fast and inexpensive means of quality analysis of horticultural products, it is generally limited to measuring superficial (surface) defects. Using light at longer (near-infrared) or shorter (X-ray) wavelengths enables the detection of superficial tissue bruising and density defects, respectively; however, it does not enable the optical absorption and scattering properties of sub-dermal tissue to be quantified. This paper applies visible and near-infrared interactance spectroscopy to detect internal necrosis in sweetpotatoes and develops a Zemax scattering simulation that models the measured optical signatures for both healthy and necrotic tissue. This study demonstrates that interactance spectroscopy can detect the unique near-infrared optical signatures of necrotic tissues in sweetpotatoes down to a depth of approximately 5±0.5 mm. We anticipate that light scattering measurement methods will represent a significant improvement over the current destructive analysis methods used to assay for internal defects in sweetpotatoes.