Agave REVEILLE1 regulates the onset and release of seasonal dormancy in Populus.

Deciduous woody plants like poplar (Populus spp.) have seasonal bud dormancy. It has been challenging to simultaneously delay the onset of bud dormancy in the fall and advance bud break in the spring, as bud dormancy, and bud break were thought to be controlled by different genetic factors. Here, we demonstrate that heterologous expression of the REVEILLE1 gene (named AaRVE1) from Agave (Agave americana) not only delays the onset of bud dormancy but also accelerates bud break in poplar in field trials. AaRVE1 heterologous expression increases poplar biomass yield by 166% in the greenhouse. Furthermore, we reveal that heterologous expression of AaRVE1 increases cytokinin contents, represses multiple dormancy-related genes, and up-regulates bud break-related genes, and that AaRVE1 functions as a transcriptional repressor and regulates the activity of the DORMANCY-ASSOCIATED PROTEIN 1 (DRM1) promoter. Our findings demonstrate that AaRVE1 appears to function as a regulator of bud dormancy and bud break, which has important implications for extending the growing season of deciduous trees in frost-free temperate and subtropical regions to increase crop yield.

A 5-Enolpyruvylshikimate 3-Phosphate Synthase Functions as a Transcriptional Repressor in Populus.

Long-lived perennial plants, with distinctive habits of inter-annual growth, defense, and physiology, are of great economic and ecological importance. However, some biological mechanisms resulting from genome duplication and functional divergence of genes in these systems remain poorly studied. Here, we discovered an association between a poplar (Populus trichocarpa) 5-enolpyruvylshikimate 3-phosphate synthase gene (PtrEPSP) and lignin biosynthesis. Functional characterization of PtrEPSP revealed that this isoform possesses a helix-turn-helix motif in the N terminus and can function as a transcriptional repressor that regulates expression of genes in the phenylpropanoid pathway in addition to performing its canonical biosynthesis function in the shikimate pathway. We demonstrated that this isoform can localize in the nucleus and specifically binds to the promoter and represses the expression of a SLEEPER-like transcriptional regulator, which itself specifically binds to the promoter and represses the expression of PtrMYB021 (known as MYB46 in Arabidopsis thaliana), a master regulator of the phenylpropanoid pathway and lignin biosynthesis. Analyses of overexpression and RNAi lines targeting PtrEPSP confirmed the predicted changes in PtrMYB021 expression patterns. These results demonstrate that PtrEPSP in its regulatory form and PtrhAT form a transcriptional hierarchy regulating phenylpropanoid pathway and lignin biosynthesis in Populus.

Correction to: Diel rewiring and positive selection of ancient plant proteins enabled evolution of CAM photosynthesis in Agave.

Following publication of the original article.

A Suggestion of Converting Protein Intrinsic Disorder to Structural Entropy Using Shannon's Information Theory.

We propose a framework to convert the protein intrinsic disorder content to structural entropy (H) using Shannon's information theory (IT). The structural capacity (C), which is the sum of H and structural information (I), is equal to the amino acid sequence length of the protein. The structural entropy of the residues expands a continuous spectrum, ranging from 0 (fully ordered) to 1 (fully disordered), consistent with Shannon's IT, which scores the fully-determined state 0 and the fully-uncertain state 1. The intrinsically disordered proteins (IDPs) in a living cell may participate in maintaining the high-energy-low-entropy state. In addition, under this framework, the biological functions performed by proteins and associated with the order or disorder of their 3D structures could be explained in terms of information-gains or entropy-losses, or the reverse processes.

Diel rewiring and positive selection of ancient plant proteins enabled evolution of CAM photosynthesis in Agave.

BACKGROUND: Crassulacean acid metabolism (CAM) enhances plant water-use efficiency through an inverse day/night pattern of stomatal closure/opening that facilitates nocturnal CO2 uptake. CAM has evolved independently in over 35 plant lineages, accounting for ~ 6% of all higher plants. Agave species are highly heat- and drought-tolerant, and have been domesticated as model CAM crops for beverage, fiber, and biofuel production in semi-arid and arid regions. However, the genomic basis of evolutionary innovation of CAM in genus Agave is largely unknown. RESULTS: Using an approach that integrated genomics, gene co-expression networks, comparative genomics and protein structure analyses, we investigated the molecular evolution of CAM as exemplified in Agave. Comparative genomics analyses among C3, C4 and CAM species revealed that core metabolic components required for CAM have ancient genomic origins traceable to non-vascular plants while regulatory proteins required for diel re-programming of metabolism have a more recent origin shared among C3, C4 and CAM species. We showed that accelerated evolution of key functional domains in proteins responsible for primary metabolism and signaling, together with a diel re-programming of the transcription of genes involved in carbon fixation, carbohydrate processing, redox homeostasis, and circadian control is required for the evolution of CAM in Agave. Furthermore, we highlighted the potential candidates contributing to the adaptation of CAM functional modules. CONCLUSIONS: This work provides evidence of adaptive evolution of CAM related pathways. We showed that the core metabolic components required for CAM are shared by non-vascular plants, but regulatory proteins involved in re-reprogramming of carbon fixation and metabolite transportation appeared more recently. We propose that the accelerated evolution of key proteins together with a diel re-programming of gene expression were required for CAM evolution from C3 ancestors in Agave.

Understanding the Catalytic Mechanism of Xanthosine Methyltransferase in Caffeine Biosynthesis from QM/MM Molecular Dynamics and Free Energy Simulations.

S-Adenosyl-l-methionine (SAM) dependent xanthosine methyltransferase (XMT) is the key enzyme that catalyzes the first methyl transfer in the caffeine biosynthesis pathway to produce the intermediate 7-methylxanthosine (7mXR). Although XMT has been a subject of extensive discussions, the catalytic mechanism and nature of the substrate involved in the catalysis are still unclear. In this paper, quantum mechanical/molecular mechanical (QM/MM) molecular dynamics (MD) and free energy (potential of mean force or PMF) simulations are undertaken to determine the catalytic mechanism of the XMT-catalyzed reaction. Both xanthosine and its monoanionic form with N3 deprotonated are used as the substrates for the methylation. It is found that while the methyl group can be transferred to the monoanionic form of xanthosine with a reasonable free energy barrier (about 17 kcal/mol), that is not the case for the neutral xanthosine. The results suggest that the substrate for the first methylation step in the caffeine biosynthesis pathway is likely to be the monoanionic form of xanthosine rather than the neutral form as widely adopted. This conclusion is supported by the pKa value on N3 of xanthosine both measured in aqueous phase and calculated in the enzymatic environment. The structural and dynamics information from both the X-ray structure and MD simulations is also consistent with the monoanionic xanthosine scenario. The implications of this conclusion for caffeine biosynthesis are discussed.

A roadmap for research on crassulacean acid metabolism (CAM) to enhance sustainable food and bioenergy production in a hotter, drier world.

Crassulacean acid metabolism (CAM) is a specialized mode of photosynthesis that features nocturnal CO2 uptake, facilitates increased water-use efficiency (WUE), and enables CAM plants to inhabit water-limited environments such as semi-arid deserts or seasonally dry forests. Human population growth and global climate change now present challenges for agricultural production systems to increase food, feed, forage, fiber, and fuel production. One approach to meet these challenges is to increase reliance on CAM crops, such as Agave and Opuntia, for biomass production on semi-arid, abandoned, marginal, or degraded agricultural lands. Major research efforts are now underway to assess the productivity of CAM crop species and to harness the WUE of CAM by engineering this pathway into existing food, feed, and bioenergy crops. An improved understanding of CAM has potential for high returns on research investment. To exploit the potential of CAM crops and CAM bioengineering, it will be necessary to elucidate the evolution, genomic features, and regulatory mechanisms of CAM. Field trials and predictive models will be required to assess the productivity of CAM crops, while new synthetic biology approaches need to be developed for CAM engineering. Infrastructure will be needed for CAM model systems, field trials, mutant collections, and data management.

X-ray structure of a Hg2+ complex of mercuric reductase (MerA) and quantum mechanical/molecular mechanical study of Hg2+ transfer between the C-terminal and buried catalytic site cysteine pairs.

Mercuric reductase, MerA, is a key enzyme in bacterial mercury resistance. This homodimeric enzyme captures and reduces toxic Hg2+ to Hg0, which is relatively unreactive and can exit the cell passively. Prior to reduction, the Hg2+ is transferred from a pair of cysteines (C558' and C559' using Tn501 numbering) at the C-terminus of one monomer to another pair of cysteines (C136 and C141) in the catalytic site of the other monomer. Here, we present the X-ray structure of the C-terminal Hg2+ complex of the C136A/C141A double mutant of the Tn501 MerA catalytic core and explore the molecular mechanism of this Hg transfer with quantum mechanical/molecular mechanical (QM/MM) calculations. The transfer is found to be nearly thermoneutral and to pass through a stable tricoordinated intermediate that is marginally less stable than the two end states. For the overall process, Hg2+ is always paired with at least two thiolates and thus is present at both the C-terminal and catalytic binding sites as a neutral complex. Prior to Hg2+ transfer, C141 is negatively charged. As Hg2+ is transferred into the catalytic site, a proton is transferred from C136 to C559' while C558' becomes negatively charged, resulting in the net transfer of a negative charge over a distance of ∼7.5 Å. Thus, the transport of this soft divalent cation is made energetically feasible by pairing a competition between multiple Cys thiols and/or thiolates for Hg2+ with a competition between the Hg2+ and protons for the thiolates.

AlphaFold2 modeling and molecular dynamics simulations of an intrinsically disordered protein.

We use AlphaFold2 (AF2) to model the monomer and dimer structures of an intrinsically disordered protein (IDP), Nvjp-1, assisted by molecular dynamics (MD) simulations. We observe relatively rigid dimeric structures of Nvjp-1 when compared with the monomer structures. We suggest that protein conformations from multiple AF2 models and those from MD trajectories exhibit a coherent trend: the conformations of an IDP are deviated from each other and the conformations of a well-folded protein are consistent with each other. We use a residue-residue interaction network (RIN) derived from the contact map which show that the residue-residue interactions in Nvjp-1 are mainly transient; however, those in a well-folded protein are mainly persistent. Despite the variation in 3D shapes, we show that the AF2 models of both disordered and ordered proteins exhibit highly consistent profiles of the pLDDT (predicted local distance difference test) scores. These results indicate a potential protocol to justify the IDPs based on multiple AF2 models and MD simulations.

Paradigm for industrial strain improvement identifies sodium acetate tolerance loci in Zymomonas mobilis and Saccharomyces cerevisiae.

The application of systems biology tools holds promise for rational industrial microbial strain development. Here, we characterize a Zymomonas mobilis mutant (AcR) demonstrating sodium acetate tolerance that has potential importance in biofuel development. The genome changes associated with AcR are determined using microarray comparative genome sequencing (CGS) and 454-pyrosequencing. Sanger sequencing analysis is employed to validate genomic differences and to investigate CGS and 454-pyrosequencing limitations. Transcriptomics, genetic data and growth studies indicate that over-expression of the sodium-proton antiporter gene nhaA confers the elevated AcR sodium acetate tolerance phenotype. nhaA over-expression mostly confers enhanced sodium (Na(+)) tolerance and not acetate (Ac(-)) tolerance, unless both ions are present in sufficient quantities. NaAc is more inhibitory than potassium and ammonium acetate for Z. mobilis and the combination of elevated Na(+) and Ac(-) ions exerts a synergistic inhibitory effect for strain ZM4. A structural model for the NhaA sodium-proton antiporter is constructed to provide mechanistic insights. We demonstrate that Saccharomyces cerevisiae sodium-proton antiporter genes also contribute to sodium acetate, potassium acetate, and ammonium acetate tolerances. The present combination of classical and systems biology tools is a paradigm for accelerated industrial strain improvement and combines benefits of few a priori assumptions with detailed, rapid, mechanistic studies.

Accurate prediction by AlphaFold2 for ligand binding in a reductive dehalogenase and implications for PFAS (per- and polyfluoroalkyl substance) biodegradation.

Despite the success of AlphaFold2 (AF2), it is unclear how AF2 models accommodate for ligand binding. Here, we start with a protein sequence from Acidimicrobiaceae TMED77 (T7RdhA) with potential for catalyzing the degradation of per- and polyfluoroalkyl substances (PFASs). AF2 models and experiments identified T7RdhA as a corrinoid iron-sulfur protein (CoFeSP) which uses a norpseudo-cobalamin (BVQ) cofactor and two Fe4S4 iron-sulfur clusters for catalysis. Docking and molecular dynamics simulations suggest that T7RdhA uses perfluorooctanoic acetate (PFOA) as a substrate, supporting the reported defluorination activity of its homolog, A6RdhA. We showed that AF2 provides processual (dynamic) predictions for the binding pockets of ligands (cofactors and/or substrates). Because the pLDDT scores provided by AF2 reflect the protein native states in complex with ligands as the evolutionary constraints, the Evoformer network of AF2 predicts protein structures and residue flexibility in complex with the ligands, i.e., in their native states. Therefore, an apo-protein predicted by AF2 is actually a holo-protein awaiting ligands.

Time-dependent density functional theory assessment of UV absorption of benzoic acid derivatives.

Benzoic acid (BA) derivatives of environmental relevance exhibit various photophysical and photochemical characteristics. Here, time-dependent density functional theory (TDDFT) is used to calculate photoexcitations of eight selected BAs and the results are compared with UV spectra determined experimentally. High-level gas-phase EOM-CCSD calculations and experimental aqueous-phase spectra were used as the references for the gas-phase and aqueous-phase TDDFT results, respectively. A cluster-continuum model was used in the aqueous-phase calculations. Among the 15 exchange-correlation (XC) functionals assessed, five functionals, including the meta-GGA hybrid M06-2X, double hybrid B2PLYPD, and range-separated functionals CAM-B3LYP, ωB97XD, and LC-ωPBE, were found to be in excellent agreement with the EOM-CCSD gas-phase calculations. These functionals furnished excitation energies consistent with the pH dependence of the experimental spectra with a standard deviation (STDEV) of ∼0.20 eV. A molecular orbital analysis revealed a πσ* feature of the low-lying transitions of the BAs. The CAM-B3LYP functional showed the best overall performance and therefore shows promise for TDDFT calculations of processes involving photoexcitations of benzoic acid derivatives.

TCA and SSRI Antidepressants Exert Selection Pressure for Efflux-Dependent Antibiotic Resistance Mechanisms in Escherichia coli.

Microbial diversity is reduced in the gut microbiota of animals and humans treated with selective serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCAs). The mechanisms driving the changes in microbial composition, while largely unknown, is critical to understand considering that the gut microbiota plays important roles in drug metabolism and brain function. Using Escherichia coli, we show that the SSRI fluoxetine and the TCA amitriptyline exert strong selection pressure for enhanced efflux activity of the AcrAB-TolC pump, a member of the resistance-nodulation-cell division (RND) superfamily of transporters. Sequencing spontaneous fluoxetine- and amitriptyline-resistant mutants revealed mutations in marR and lon, negative regulators of AcrAB-TolC expression. In line with the broad specificity of AcrAB-TolC pumps these mutants conferred resistance to several classes of antibiotics. We show that the converse also occurs, as spontaneous chloramphenicol-resistant mutants displayed cross-resistance to SSRIs and TCAs. Chemical-genomic screens identified deletions in marR and lon, confirming the results observed for the spontaneous resistant mutants. In addition, deletions in 35 genes with no known role in drug resistance were identified that conferred cross-resistance to antibiotics and several displayed enhanced efflux activities. These results indicate that combinations of specific antidepressants and antibiotics may have important effects when both are used simultaneously or successively as they can impose selection for common mechanisms of resistance. Our work suggests that selection for enhanced efflux activities is an important factor to consider in understanding the microbial diversity changes associated with antidepressant treatments. IMPORTANCE Antidepressants are prescribed broadly for psychiatric conditions to alter neuronal levels of synaptic neurotransmitters such as serotonin and norepinephrine. Two categories of antidepressants are selective serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCAs); both are among the most prescribed drugs in the United States. While it is well-established that antidepressants inhibit reuptake of neurotransmitters there is evidence that they also impact microbial diversity in the gastrointestinal tract. However, the mechanisms and therefore biological and clinical effects remain obscure. We demonstrate antidepressants may influence microbial diversity through strong selection for mutant bacteria with increased AcrAB-TolC activity, an efflux pump that removes antibiotics from cells. Furthermore, we identify a new group of genes that contribute to cross-resistance between antidepressants and antibiotics, several act by regulating efflux activity, underscoring overlapping mechanisms. Overall, this work provides new insights into bacterial responses to antidepressants important for understanding antidepressant treatment effects.

AlphaFold2 models indicate that protein sequence determines both structure and dynamics.

AlphaFold 2 (AF2) has placed Molecular Biology in a new era where we can visualize, analyze and interpret the structures and functions of all proteins solely from their primary sequences. We performed AF2 structure predictions for various protein systems, including globular proteins, a multi-domain protein, an intrinsically disordered protein (IDP), a randomized protein, two larger proteins (> 1000 AA), a heterodimer and a homodimer protein complex. Our results show that along with the three dimensional (3D) structures, AF2 also decodes protein sequences into residue flexibilities via both the predicted local distance difference test (pLDDT) scores of the models, and the predicted aligned error (PAE) maps. We show that PAE maps from AF2 are correlated with the distance variation (DV) matrices from molecular dynamics (MD) simulations, which reveals that the PAE maps can predict the dynamical nature of protein residues. Here, we introduce the AF2-scores, which are simply derived from pLDDT scores and are in the range of [0, 1]. We found that for most protein models, including large proteins and protein complexes, the AF2-scores are highly correlated with the root mean square fluctuations (RMSF) calculated from MD simulations. However, for an IDP and a randomized protein, the AF2-scores do not correlate with the RMSF from MD, especially for the IDP. Our results indicate that the protein structures predicted by AF2 also convey information of the residue flexibility, i.e., protein dynamics.

A quantitative yeast aging proteomics analysis reveals novel aging regulators.

Calorie restriction (CR) is the most robust longevity intervention, extending lifespan from yeast to mammals. Numerous conserved pathways regulating aging and mediating CR have been identified; however, the overall proteomic changes during these conditions remain largely unexplored. We compared proteomes between young and replicatively aged yeast cells under normal and CR conditions using the Stable-Isotope Labeling by Amino acids in Cell culture (SILAC) quantitative proteomics and discovered distinct signatures in the aging proteome. We found remarkable proteomic similarities between aged and CR cells, including induction of stress response pathways, providing evidence that CR pathways are engaged in aged cells. These observations also uncovered aberrant changes in mitochondria membrane proteins as well as a proteolytic cellular state in old cells. These proteomics analyses help identify potential genes and pathways that have causal effects on longevity.

Protein interaction potential landscapes for yeast replicative aging.

We proposed a novel interaction potential landscape approach to map the systems-level profile changes of gene networks during replicative aging in Saccharomyces cerevisiae. This approach enabled us to apply quasi-potentials, the negative logarithm of the probabilities, to calibrate the elevation of the interaction landscapes with young cells as a reference state. Our approach detected opposite landscape changes based on protein abundances from transcript levels, especially for intra-essential gene interactions. We showed that essential proteins play different roles from hub proteins on the age-dependent interaction potential landscapes. We verified that hub proteins tend to avoid other hub proteins, but essential proteins prefer to interact with other essential proteins. Overall, we showed that the interaction potential landscape is promising for inferring network profile change during aging and that the essential hub proteins may play an important role in the uncoupling between protein and transcript levels during replicative aging.

Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells.

Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures to classify microfluidic time-lapse images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. The capsule networks had the most robust performance in detecting one specific category of cell images. An ensemble of three best-fitted single-architecture models achieves the highest overall accuracy, precision, and recall due to complementary performances. In addition, extending classification classes and data augmentation of the training dataset can improve the predictions of the biological categories in our study. This work lays a useful framework for sophisticated deep-learning processing of microfluidic-based assays of yeast replicative aging.

Overexpression of an Agave Phosphoenolpyruvate Carboxylase Improves Plant Growth and Stress Tolerance.

It has been challenging to simultaneously improve photosynthesis and stress tolerance in plants. Crassulacean acid metabolism (CAM) is a CO2-concentrating mechanism that facilitates plant adaptation to water-limited environments. We hypothesized that the ectopic expression of a CAM-specific phosphoenolpyruvate carboxylase (PEPC), an enzyme that catalyzes primary CO2 fixation in CAM plants, would enhance both photosynthesis and abiotic stress tolerance. To test this hypothesis, we engineered a CAM-specific PEPC gene (named AaPEPC1) from Agave americana into tobacco. In comparison with wild-type and empty vector controls, transgenic tobacco plants constitutively expressing AaPEPC1 showed a higher photosynthetic rate and biomass production under normal conditions, along with significant carbon metabolism changes in malate accumulation, the carbon isotope ratio δ13C, and the expression of multiple orthologs of CAM-related genes. Furthermore, AaPEPC1 overexpression enhanced proline biosynthesis, and improved salt and drought tolerance in the transgenic plants. Under salt and drought stress conditions, the dry weight of transgenic tobacco plants overexpressing AaPEPC1 was increased by up to 81.8% and 37.2%, respectively, in comparison with wild-type plants. Our findings open a new door to the simultaneous improvement of photosynthesis and stress tolerance in plants.

Association study based on topological constraints of protein-protein interaction networks.

The non-random interaction pattern of a protein-protein interaction network (PIN) is biologically informative, but its potentials have not been fully utilized in omics studies. Here, we propose a network-permutation-based association study (NetPAS) method that gauges the observed interactions between two sets of genes based on the comparison between permutation null models and the empirical networks. This enables NetPAS to evaluate relationships, constrained by network topology, between gene sets related to different phenotypes. We demonstrated the utility of NetPAS in 50 well-curated gene sets and comparison of association studies using Z-scores, modified Z'-scores, p-values and Jaccard indices. Using NetPAS, a weighted human disease network was generated from the association scores of 19 gene sets from OMIM. We also applied NetPAS in gene sets derived from gene ontology and pathway annotations and showed that NetPAS uncovered functional terms missed by DAVID and WebGestalt. Overall, we show that NetPAS can take topological constraints of molecular networks into account and offer new perspectives than existing methods.