Chemoproteomic strategy identified p120-catenin glutathionylation regulates E-cadherin degradation and cell migration.

Identification of cysteines with high oxidation susceptibility is important for understanding redox-mediated biological processes. In this report, we report a chemical proteomic strategy that finds cysteines with high susceptibility to S-glutathionylation. Our proteomic strategy, named clickable glutathione-based isotope-coded affinity tag (G-ICAT), identified 1,518 glutathionylated cysteines while determining their relative levels of glutathionylated and reduced forms upon adding hydrogen peroxide. Among identified cysteines, we demonstrated that CTNND1 (p120) C692 has high susceptibility to glutathionylation. Also, p120 wild type (WT), compared to C692S, induces its dissociation from E-cadherin under oxidative stress, such as glucose depletion. p120 and E-cadherin dissociation correlated with E-cadherin destabilization via its proteasomal degradation. Lastly, we showed that p120 WT, compared to C692S, increases migration and invasion of MCF7 cells under glucose depletion, supporting a model that p120 C692 glutathionylation increases cell migration and invasion by destabilization of E-cadherin, a core player in cell-cell adhesion.

A genome-scale metabolic reconstruction of soybean and Bradyrhizobium diazoefficiens reveals the cost-benefit of nitrogen fixation.

Nitrogen-fixing symbioses allow legumes to thrive in nitrogen-poor soils at the cost of diverting some photoassimilate to their microsymbionts. Effort is being made to bioengineer nitrogen fixation into nonleguminous crops. This requires a quantitative understanding of its energetic costs and the links between metabolic variations and symbiotic efficiency. A whole-plant metabolic model for soybean (Glycine max) with its associated microsymbiont Bradyrhizobium diazoefficiens was developed and applied to predict the cost-benefit of nitrogen fixation with varying soil nitrogen availability. The model predicted a nitrogen-fixation cost of c. 4.13 g C g-1 N, which when implemented into a crop scale model, translated to a grain yield reduction of 27% compared with a non-nodulating plant receiving its nitrogen from the soil. Considering the lower nitrogen content of cereals, the yield cost to a hypothetical N-fixing cereal is predicted to be less than half that of soybean. Soybean growth was predicted to be c. 5% greater when the nodule nitrogen export products were amides versus ureides. This is the first metabolic reconstruction in a tropical crop species that simulates the entire plant and nodule metabolism. Going forward, this model will serve as a tool to investigate carbon use efficiency and key mechanisms within N-fixing symbiosis in a tropical species forming determinate nodules.

Engineering photosynthesis, nature's carbon capture machine.

Mitigating climate change and sustainably feeding our growing population in the changing climate are 2 significant challenges facing the global community. Engineering photosynthesis, nature's carbon capture machinery, can help us surmount these threats.

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.

Seasonal climate conditions impact the effectiveness of improving photosynthesis to increase soybean yield.

Context: Photosynthetic stimulations have shown promising outcomes in improving crop photosynthesis, including soybean. However, it is still unclear to what extent these changes can impact photosynthetic assimilation and yield under long-term field climate conditions. Objective: In this paper, we present a systematic evaluation of the response of canopy photosynthesis and yield to two critical parameters in leaf photosynthesis: the maximum carboxylation rate of ribulose-1,5-bisphosphate carboxylase/oxygenase (Vcmax) and the maximum electron transport of the ribulose-1,5-bisphosphate regeneration rate (Jmax). Methods: Using the field-scale crop model Soybean-BioCro and ten years of observed climate data in Urbana, Illinois, U.S., we conducted sensitivity experiments to estimate the changes in canopy photosynthesis, leaf area index, and biomass due to the changes in Vcmax and Jmax. Results: The results show that 1) Both the canopy photosynthetic assimilation (An) and pod biomass yields were more sensitive to the changes in Jmax, particularly at high atmospheric carbon-dioxide concentrations ([CO2]); 2) Higher [CO2] undermined the effectiveness of increasing the two parameters to improve An and yield; 3) Under the same [CO2], canopy light interception and canopy respiration were key factors that undermined improvements in An and yield; 4) A canopy with smaller leaf area index tended to have a higher yield improvement, and 5) Increases in assimilations and yields were highly dependent on growing-season climatic conditions. The solar radiation, temperature, and relative humidity were the main climate drivers that impacted the yield improvement, and they had opposite correlations with improved yield during the vegetative phase compared to the reproductive phase. Conclusions: In a world with elevated [CO2], genetic engineering crop photosynthesis should focus more on improving Jmax. Further, long-term climate conditions and seasonal variations must be considered to determine the improvements in soybean canopy photosynthesis and yield at the field scale. Implications: Quantifying the effectiveness of changing Vcmax and Jmax helps understand their individual and combined contributions to potential improvements in assimilation and yield. This work provides a framework for evaluating how altering the photosynthetic rate parameters impacts soybean yield and assimilation under different seasonal climate scenarios at the field scale.

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®). 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.

Characterizing metabolic drivers of Clostridioides difficile infection with activity-based hydrazine probes.

Many enzymes require post-translational modifications or cofactor machinery for primary function. As these catalytically essential moieties are highly regulated, they act as dual sensors and chemical handles for context-dependent metabolic activity. Clostridioides difficile is a major nosocomial pathogen that infects the colon. Energy generating metabolism, particularly through amino acid Stickland fermentation, is central to colonization and persistence of this pathogen during infection. Here using activity-based protein profiling (ABPP), we revealed Stickland enzyme activity is a biomarker for C. difficile infection (CDI) and annotated two such cofactor-dependent Stickland reductases. We structurally characterized the cysteine-derived pyruvoyl cofactors of D-proline and glycine reductase in C. difficile cultures and showed through cofactor monitoring that their activity is regulated by their respective amino acid substrates. Proline reductase was consistently active in toxigenic C. difficile, confirming the enzyme to be a major metabolic driver of CDI. Further, activity-based hydrazine probes were shown to be active site-directed inhibitors of proline reductase. As such, this enzyme activity, via its druggable cofactor modality, is a promising therapeutic target that could allow for the repopulation of bacteria that compete with C. difficile for proline and therefore restore colonization resistance against C. difficile in the gut.

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 ® ). 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.

Enterococci enhance Clostridioides difficile pathogenesis.

Enteric pathogens are exposed to a dynamic polymicrobial environment in the gastrointestinal tract1. This microbial community has been shown to be important during infection, but there are few examples illustrating how microbial interactions can influence the virulence of invading pathogens2. Here we show that expansion of a group of antibiotic-resistant, opportunistic pathogens in the gut-the enterococci-enhances the fitness and pathogenesis of Clostridioides difficile. Through a parallel process of nutrient restriction and cross-feeding, enterococci shape the metabolic environment in the gut and reprogramme C. difficile metabolism. Enterococci provide fermentable amino acids, including leucine and ornithine, which increase C. difficile fitness in the antibiotic-perturbed gut. Parallel depletion of arginine by enterococci through arginine catabolism provides a metabolic cue for C. difficile that facilitates increased virulence. We find evidence of microbial interaction between these two pathogenic organisms in multiple mouse models of infection and patients infected with C. difficile. These findings provide mechanistic insights into the role of pathogenic microbiota in the susceptibility to and the severity of C. difficile infection.

Wolbachia wAlbB inhibits bluetongue and epizootic hemorrhagic fever viruses in Culicoides midge cells.

Culicoides midges are hematophagous insects that transmit arboviruses of veterinary importance. These viruses include bluetongue virus (BTV) and epizootic hemorrhagic fever virus (EHDV). The endosymbiont Wolbachia pipientis Hertig spreads rapidly through insect host populations and has been demonstrated to inhibit viral pathogen transmission in multiple mosquito vectors. Here, we have demonstrated a replication inhibitory effect on BTV and EHDV in a Wolbachia (wAlbB strain)-infected Culicoides sonorensis Wirth and Jones W8 cell line. Viral replication was significantly reduced by day 5 for BTV and by day 2 for EHDV as detected by real-time polymerase chain reaction (RT-qPCR) of the non-structural NS3 gene of both viruses. Evaluation of innate cellular immune responses as a cause of the inhibitory effect showed responses associated with BTV but not with EHDV infection. Wolbachia density also did not play a role in the observed pathogen inhibitory effects, and an alternative hypothesis is suggested. Applications of Wolbachia-mediated pathogen interference to impact disease transmission by Culicoides midges are discussed.

Discovery of Potent and Selective Inhibitors against Protein-Derived Electrophilic Cofactors.

Electrophilic cofactors are widely distributed in nature and play important roles in many physiological and disease processes, yet they have remained blind spots in traditional activity-based protein profiling (ABPP) approaches that target nucleophiles. More recently, reverse-polarity (RP)-ABPP using hydrazine probes identified an electrophilic N-terminal glyoxylyl (Glox) group for the first time in secernin-3 (SCRN3). The biological function(s) of both the protein and Glox as a cofactor has not yet been pharmacologically validated because of the lack of selective inhibitors that could disrupt and therefore identify its activity. Here, we present the first platform for analyzing the reactivity and selectivity of an expanded nucleophilic probe library toward main-chain carbonyl cofactors such as Glox and pyruvoyl (Pyvl) groups. We first applied the library proteome-wide to profile and confirm engagement with various electrophilic protein targets, including secernin-2 (SCRN2), shown here also to possess a Glox group. A broadly reactive indole ethylhydrazine probe was used for a competitive in vitro RP-ABPP assay to screen for selective inhibitors against such cofactors from a set of commercially available nucleophilic fragments. Using Glox-containing SCRN proteins as a case study, naphthyl hydrazine was identified as a potent and selective SCRN3 inhibitor, showing complete inhibition in cell lysates with no significant cross-reactivity detected for other enzymes. Moving forward, this platform provides the fundamental basis for the development of selective Glox inhibitors and represents a starting point to advance small molecules that modulate electrophile-dependent function.

Activity-Based Hydrazine Probes for Protein Profiling of Electrophilic Functionality in Therapeutic Targets.

Most known probes for activity-based protein profiling (ABPP) use electrophilic groups that tag a single type of nucleophilic amino acid to identify cases in which its hyper-reactivity underpins function. Much important biochemistry derives from electrophilic enzyme cofactors, transient intermediates, and labile regulatory modifications, but ABPP probes for such species are underdeveloped. Here, we describe a versatile class of probes for this less charted hemisphere of the proteome. The use of an electron-rich hydrazine as the common chemical modifier enables covalent targeting of multiple, pharmacologically important classes of enzymes bearing diverse organic and inorganic cofactors. Probe attachment occurs by both polar and radicaloid mechanisms, can be blocked by molecules that occupy the active sites, and depends on the proper poise of the active site for turnover. These traits will enable the probes to be used to identify specific inhibitors of individual members of these multiple enzyme classes, making them uniquely versatile among known ABPP probes.

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.

Multiscale plant modeling: from genome to phenome and beyond.

Plants are complex organisms that adapt to changes in their environment using an array of regulatory mechanisms that span across multiple levels of biological organization. Due to this complexity, it is difficult to predict emergent properties using conventional approaches that focus on single levels of biology such as the genome, transcriptome, or metabolome. Mathematical models of biological systems have emerged as useful tools for exploring pathways and identifying gaps in our current knowledge of biological processes. Identification of emergent properties, however, requires their vertical integration across biological scales through multiscale modeling. Multiscale models that capture and predict these emergent properties will allow us to predict how plants will respond to a changing climate and explore strategies for plant engineering. In this review, we (1) summarize the recent developments in plant multiscale modeling; (2) examine multiscale models of microbial systems that offer insight to potential future directions for the modeling of plant systems; (3) discuss computational tools and resources for developing multiscale models; and (4) examine future directions of the field.

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.

Discovery of Electrophiles and Profiling of Enzyme Cofactors.

Reverse-polarity activity-based protein profiling (RP-ABPP) is a chemical proteomics approach that uses nucleophilic probes amenable to "click" chemistry deployed into living cells in culture to capture, immunoprecipitate, and identify protein-bound electrophiles. RP-ABPP is used to characterize the structure and function of reactive electrophilic post-translational modifications (PTMs) and the proteins harboring them, which may uncover unknown or novel functions. RP-ABPP has demonstrated utility as a versatile method to monitor the metabolic regulation of electrophilic cofactors, using a pyruvoyl cofactor in S-adenosyl-L-methionine decarboxylase (AMD1), and to discover novel types of electrophilic modifications on proteins in human cells, such as the glyoxylyl modification on secernin-3 (SCRN3). These cofactors cannot be predicted by sequence, and therefore this area is relatively undeveloped. RP-ABPP is the only global, unbiased approach to discover such electrophiles. Here, we describe the utility of these experiments and provide a detailed protocol for de novo discovery, quantitation, and global profiling of electrophilic functionality of proteins. © 2020 The Authors. Basic Protocol 1: Identification and quantification of probe-reactive proteins Basic Protocol 2: Characterization of the site of probe labeling Basic Protocol 3: Determination and quantitation of electrophile structure.

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.

Cryptic Wolbachia (Rickettsiales: Rickettsiaceae) Detection and Prevalence in Culicoides (Diptera: Ceratopogonidae) Midge Populations in the United States.

Culicoides midges vector numerous veterinary and human pathogens. Many of these diseases lack effective therapeutic treatments or vaccines to limit transmission. The only effective approach to limit disease transmission is vector control. However, current vector control for Culicoides midges is complicated by the biology of many Culicoides species and is not always effective at reducing midge populations and impacting disease transmission. The endosymbiont Wolbachia pipientis Hertig may offer an alternative control approach to limit disease transmission and affect Culicoides populations. Here the detection of Wolbachia infections in nine species of Culicoides midges is reported. Infections were detected at low densities using qPCR. Wolbachia infections were confirmed with the sequencing of a partial region of the 16S gene. Fluorescence in situ hybridization of Culicoides sonorensis Wirth and Jones adults and dissected ovaries confirm the presence of Wolbachia infections in an important vector of Bluetongue and Epizootic hemorrhagic disease viruses. The presence of Wolbachia in Culicoides populations in the United States suggests the need for further investigation of Wolbachia as a strategy to limit transmission of diseases vectored by Culicoides midges.

Evidence for Modulation of Oxygen Rebound Rate in Control of Outcome by Iron(II)- and 2-Oxoglutarate-Dependent Oxygenases.

Iron(II)- and 2-oxoglutarate-dependent (Fe/2OG) oxygenases generate iron(IV)-oxo (ferryl) intermediates that can abstract hydrogen from aliphatic carbons (R-H). Hydroxylation proceeds by coupling of the resultant substrate radical (R•) and oxygen of the Fe(III)-OH complex ("oxygen rebound"). Nonhydroxylation outcomes result from different fates of the Fe(III)-OH/R• state; for example, halogenation results from R• coupling to a halogen ligand cis to the hydroxide. We previously suggested that halogenases control substrate-cofactor disposition to disfavor oxygen rebound and permit halogen coupling to prevail. Here, we explored the general implication that, when a ferryl intermediate can ambiguously target two substrate carbons for different outcomes, rebound to the site capable of the alternative outcome should be slower than to the adjacent, solely hydroxylated site. We evaluated this prediction for (i) the halogenase SyrB2, which exclusively hydroxylates C5 of norvaline appended to its carrier protein but can either chlorinate or hydroxylate C4 and (ii) two bifunctional enzymes that normally hydroxylate one carbon before coupling that oxygen to a second carbon (producing an oxacycle) but can, upon encountering deuterium at the first site, hydroxylate the second site instead. In all three cases, substrate hydroxylation incorporates a greater fraction of solvent-derived oxygen at the site that can also undergo the alternative outcome than at the other site, most likely reflecting an increased exchange of the initially O2-derived oxygen ligand in the longer-lived Fe(III)-OH/R• states. Suppression of rebound may thus be generally important for nonhydroxylation outcomes by these enzymes.

Flux modeling for monolignol biosynthesis.

The pathway of monolignol biosynthesis involves many components interacting in a metabolic grid to regulate the supply and ratios of monolignols for lignification. The complexity of the pathway challenges any intuitive prediction of the output without mathematical modeling. Several models have been presented to quantify the metabolic flux for monolignol biosynthesis and the regulation of lignin content, composition, and structure in plant cell walls. Constraint-based models using data from transgenic plants were formulated to describe steady-state flux distribution in the pathway. Kinetic-based models using enzyme reaction and inhibition constants were developed to predict flux dynamics for monolignol biosynthesis in wood-forming cells. This review summarizes the recent progress in flux modeling and its application to lignin engineering for improved plant development and utilization.