Biology subjects

Matthews, M. L.

Publications and source records attributed to Matthews, M. L..

8 recordsLinked to original sources

bioRxiv2026

Integrating carbon utilization and transport processes into a crop growth model enables the prediction of emergent soybean carbon allocation behavior

Accurately modeling carbon (C) allocation is essential for predicting crop yield and the performance of new cultivars in various environments. Most crop models allocate C empirically, using fixed partitioning tables or harvest indices that prescribe allocation without representing the underlying physiology, limiting their predictive power under novel conditions. A mechanistic alternative, in which C allocation emerges from local utilization and transport, could instead respond dynamically to environmental changes, source-sink perturbations, and organ-level trait modifications. To achieve this design, we integrated a utilization-transport-resistance (UTR) allocation model into the Soybean-BioCro crop growth modeling framework. We calibrated and validated the model using organ biomass data from two soybean cultivars grown at two CO2 levels over eight seasons, achieving accuracy comparable to partitioning-based models while predicting more reasonable carbon allocation fractions. Further, the UTR-BioCro model predicted leaf and stem total nonstructural carbohydrate concentrations with reasonable accuracy compared to experimental measurements across the 2022 growing season. A local sensitivity analysis of the model parameters indicated that the onset of reproductive growth influenced yield more strongly than utilization or transport parameters suggesting the timing of this transition as a potential target for crop improvement. Finally, the UTR-BioCro model reproduced yield responses to source-sink perturbations including shading and pod removal, and captured the qualitative response to defoliation without requiring scenario-specific tuning as most partitioning approaches require. By grounding C allocation in physiological mechanisms, this work provides a foundation for predicting crop responses across diverse environments and engineered traits, supporting crop improvement for a changing environment.

plant biology↗

bioRxiv2026

Kinetic model of a determinate legume root nodule reveals plant metabolic characteristics for more efficient nitrogen fixation symbiosis

While nitrogen fertilizers are widely used in agricultural production, their application incurs significant environmental and energetic costs. In contrast, some crops are less dependent on these fertilizers because they engage in symbioses with rhizobia, nitrogen-fixing bacteria provide ammonium to the plant in exchange for carbon. However, the carbon cost associated with nitrogen fixation can negatively impact crop yields. Improving the efficiency of this metabolic process could alleviate this impact on crop productivity. Mathematical models can help us quantitatively explore metabolic behavior and identify potential targets for metabolic engineering. In this work, we developed a kinetic model of determinate root nodule metabolism, where this symbiotic exchange of carbon from the plant and nitrogen from the bacteria occurs. We used this model to evaluate how the predicted metabolic behavior differs between inefficient and efficient nodules, and to identify potential engineering targets for improving nitrogen fixation efficiency and rate. We show that the enzymes phosphoenolpyruvate carboxylase and pyruvate kinase have significant influence on the predicted rate and efficiency of nitrogen fixation, especially when their expression is varied in combination with oxidative Pentose Phosphate Pathway enzymes like glucose-6-phosphate dehydrogenase and 6-phosphogluconolactonase. The model predicts that pairing a 3-fold decrease in glucose-6-phosphate dehydrogenase activity along with either a 3-fold increase in phosphoenolpyruvate carboxylase activity or decrease in pyruvate kinase activity could increase nitrogen fixation rate by 5.51% while improving nitrogen fixation efficiency by 7.74%.

plant biology↗

bioRxiv2025

Metabolic modeling reveals synergistic growth benefits of engineering maize symbiosis with rhizobia and arbuscular mycorrhizal fungi

Engineering a novel N2-fixing rhizobia symbiosis in cereal crops is a strategy being pursued to improve agricultural sustainability. However, if such a symbiosis were introduced, it would have to be economically viable in the context of existing nutrient acquisition strategies, including the existing symbiosis with arbuscular mycorrhizal fungi (AMF) that the vast majority of plants already engage in. This raises the question of how the metabolic costs and benefits from these separate symbioses that have partially overlapping functions might impact nutrient status and subsequent plant growth. To address this, we developed metabolic models describing how the relative growth rate of Zea mays is impacted by the AMF Rhizophagus irregularis and a hypothetical N2-fixing symbiosis with Bradyrhizobium diazoefficiens both in isolation and in tandem. To validate the AMF component of our model, we conducted field evaluation of mutant AMF-incompatible maize hybrids and found that the empirically measured AMF-mediated growth benefit agreed well with our models predictions. Our model of the rhizobium symbiosis predicted that the lower N content of cereal crops makes the relative growth rate cost associated with acquiring nitrogen from N2-fixing rhizobia smaller than in legumes. Finally, our model also predicted positive synergies between rhizobia and AMF under nutrient-limited conditions but negative synergies under nutrient, particularly phosphorus, replete conditions. These findings indicate that these bioengineering strategies could improve cereal crop yields and may achieve greater gains in tandem, but soil nutrient status of target sites as well as the nitrogen requirements of specific varieties should be considered.

systems biology↗

bioRxiv2024

Hydralazine inhibits cysteamine dioxygenase to treat preeclampsia and senesce glioblastoma

The vasodilator hydralazine (HYZ) has been used clinically for [~] 70 years and remains on the World Health Organizations List of Essential Medicines as a therapy for preeclampsia. Despite its longstanding use and the concomitant progress toward a general understanding of vasodilation, the target and mechanism of HYZ have remained unknown. We show that HYZ selectively targets 2-aminoethanethiol dioxygenase (ADO) by chelating its metal cofactor and alkylating one of its ligands. This covalent inactivation slows entry of proteins into the Cys/N-degron pathway that ADO initiates. HYZs capacity to stabilize regulators of G-protein signaling (RGS4/5) normally marked for degradation by ADO explains its effect on blood vessel tension and comports with prior associations of insufficient RGS levels with human preeclampsia and analogous symptoms in mice. The established importance of ADO in glioblastoma led us to test HYZ in these cell types. Indeed, a single treatment induced senescence, suggesting a potential new HYZ-based therapy for this deadly brain cancer.

biochemistry↗

bioRxiv2023

Phenelzine-based probes reveal Secernin-3 is involved in thermal nociception

Chemical platforms that facilitate both the identification and elucidation of new areas for therapeutic development are necessary but lacking. Activity-based protein profiling (ABPP) leverages active site-directed chemical probes as target discovery tools that resolve activity from expression and immediately marry the targets identified with lead compounds for drug design. However, this approach has traditionally focused on predictable and intrinsic enzyme functionality. Here, we applied our activity-based proteomics discovery platform to map non-encoded and post-translationally acquired enzyme functionalities (e.g. cofactors) in vivo using chemical probes that exploit the nucleophilic hydrazine pharmacophores found in a classic antidepressant drug (e.g. phenelzine, Nardil (R)). We show the probes are in vivo active and can map proteome-wide tissue-specific target engagement of the drug. In addition to engaging targets (flavoenzymes monoamine oxidase A/B) that are associated with the known therapeutic mechanism as well as several other members of the flavoenzyme family, the probes captured the previously discovered N-terminal glyoxylyl (Glox) group of Secernin-3 (SCRN3) in vivo through a divergent mechanism, indicating this functional feature has biochemical activity in the brain. SCRN3 protein is ubiquitously expressed in the brain, yet gene expression is regulated by inflammatory stimuli. In an inflammatory pain mouse model, behavioral assessment of nociception showed Scrn3 male knockout mice selectively exhibited impaired thermal nociceptive sensitivity. Our study provides a guided workflow to entangle molecular (off)targets and pharmacological mechanisms for therapeutic development.

neuroscience↗

bioRxiv2020

Hydrazines as versatile chemical biology probes and drug-discovery tools for cofactor-dependent enzymes

Known chemoproteomic probes generally use warheads that tag a single type of amino acid or modified form thereof to identify cases in which its hyper-reactivity underpins function. Much important biochemistry derives from electron-poor enzyme cofactors, transient intermediates and chemically-labile regulatory modifications, but probes for such species are underdeveloped. Here, we have innovated a versatile class of chemoproteomic probes for this less charted hemisphere of the proteome by using hydrazine as the common chemical warhead. Its electron-rich nature allows it to react by both polar and radicaloid mechanisms and to target multiple, pharmacologically important functional classes of enzymes bearing diverse organic and inorganic cofactors. Probe attachment can be blocked by active-site-directed inhibitors, and elaboration of the warhead supports connection of a target to a lead compound. The capacity of substituted hydrazines to profile, discover and inhibit diverse cofactor-dependent enzymes enables cell and tissue imaging and makes this platform useful for enzyme and drug discovery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/154864v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1e1a54forg.highwire.dtl.DTLVardef@12be680org.highwire.dtl.DTLVardef@129e027org.highwire.dtl.DTLVardef@3b2317_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2020

Structural and molecular determinants of CCS-mediated copper activation of MEK1/2

SummaryNormal physiology relies on the precise coordination of intracellular signal transduction pathways that respond to nutrient availability to balance cell growth and cell death. We recently established a critical mechanistic function for the redox-active micronutrient copper (Cu) in the canonical mitogen activated protein kinase (MAPK) pathway at the level of MEK1 and MEK2. Here we report the X-ray crystal structure of Cu-MEK1 and reveal active site chemical ligands and oxidation state specificity for MEK1 Cu coordination. Mechanistically, the Cu chaperone CCS selectively bound to and facilitated Cu transfer to MEK1. Mutations in MEK1 that disrupt Cu(I) affinity or a CCS small molecule inhibitor reduced Cu-stimulated MEK1 kinase activity. These atomic and molecular level data provide the first mechanistic insights of Cu kinase signaling and could be exploited for the development of novel MEK1/2 inhibitors that either target the Cu structural interface or blunt dedicated Cu delivery mechanisms via CCS.Competing Interest StatementD.C.B holds ownership in Merlon Inc. D.C.B. is an inventor on the patent application 20150017261 entitled Methods of treating and preventing cancer by disrupting the binding of copper in the MAP kinase pathway. No potential conflicts of interest were disclosed by the other authors.View Full Text

biochemistry↗

bioRxiv2019

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

Accurate manipulation of metabolites in the monolignol biosynthetic pathway 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 the inability to capture indirect regulatory influences on other pathway genes. Here, we examine the manifestation of these indirect influences collectively 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 desired single or combinatorial knockdowns of specific monolignol genes. Using in-silico simulations of this model and root mean square error, we show that our model more accurately estimates transcript and protein abundances in differentiating xylem tissue when individual and families of monolignol genes were perturbed. This approach 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. Additionally, these results can be used to guide future experiments to elucidate the mechanisms responsible for the indirect influences.\n\nAuthor summaryEngineering trees to have desirable lignin and wood traits is of significant interest to the bioenergy and biomaterial industries. Genetically modifying the expression of the genes that drive the monolignol biosynthetic pathway is a useful method for obtaining new traits. Modifying the expression of one gene affects not only the abundance of its encoded protein, but can also indirectly impact the amount of other transcripts and proteins. These proteins drive the monolignol biosynthetic pathway. Having an accurate representation of their abundances is key to understanding how lignin and wood traits are altered. We developed a computational model to estimate how the abundance of monolignol transcripts and proteins are changed when one or more monolignol genes are knocked down. Specifying only the abundances of the targeted genes as input, our model estimates how the levels of the other, untargeted, transcripts and proteins are altered. Our model captures indirect regulatory influences at the transcript and protein levels observed in experimental data. The model is an important addition to current models of lignin biosynthesis. By incorporating our approach into the existing models, we expect to improve our ability to explore how new combinations of gene knockdowns impact lignin and many other wood properties.

systems biology↗