Identification and preliminary characterization of conserved uncharacterized proteins from Chlamydomonas reinhardtii, Arabidopsis thaliana, and Setaria viridis.

The rapid accumulation of sequenced plant genomes in the past decade has outpaced the still difficult problem of genome-wide protein-coding gene annotation. A substantial fraction of protein-coding genes in all plant genomes are poorly annotated or unannotated and remain functionally uncharacterized. We identified unannotated proteins in three model organisms representing distinct branches of the green lineage (Viridiplantae): Arabidopsis thaliana (eudicot), Setaria viridis (monocot), and Chlamydomonas reinhardtii (Chlorophyte alga). Using similarity searching, we identified a subset of unannotated proteins that were conserved between these species and defined them as Deep Green proteins. Bioinformatic, genomic, and structural predictions were performed to begin classifying Deep Green genes and proteins. Compared to whole proteomes for each species, the Deep Green set was enriched for proteins with predicted chloroplast targeting signals predictive of photosynthetic or plastid functions, a result that was consistent with enrichment for daylight phase diurnal expression patterning. Structural predictions using AlphaFold and comparisons to known structures showed that a significant proportion of Deep Green proteins may possess novel folds. Though only available for three organisms, the Deep Green genes and proteins provide a starting resource of high-value targets for further investigation of potentially new protein structures and functions conserved across the green lineage.

Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data.

High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as molecular beam mass spectrometry (py-MBMS) analyses are becoming increasingly popular for the rapid analysis of biomass cell wall composition and typically require the use of different data analysis tools depending on the need and application. Here, the authors report the py-MBMS analysis of several types of lignocellulosic biomass to gain an understanding of spectral patterns and variation with associated biomass composition and use machine learning approaches to classify, differentiate, and predict biomass types on the basis of py-MBMS spectra. Py-MBMS spectra were also corrected for instrumental variance using generalized linear modeling (GLM) based on the use of select ions relative abundances as spike-in controls. Machine learning classification algorithms e.g., random forest, k-nearest neighbor, decision tree, Gaussian Naïve Bayes, gradient boosting, and multilayer perceptron classifiers were used. The k-nearest neighbors (k-NN) classifier generally performed the best for classifications using raw spectral data, and the decision tree classifier performed the worst. After normalization of spectra to account for instrumental variance, all the classifiers had comparable and generally acceptable performance for predicting the biomass types, although the k-NN and decision tree classifiers were not as accurate for prediction of specific sample types. Gaussian Naïve Bayes (GNB) and extreme gradient boosting (XGB) classifiers performed better than the k-NN and the decision tree classifiers for the prediction of biomass mixtures. The data analysis workflow reported here could be applied and extended for comparison of biomass samples of varying types, species, phenotypes, and/or genotypes or subjected to different treatments, environments, etc. to further elucidate the sources of spectral variance, patterns, and to infer compositional information based on spectral analysis, particularly for analysis of data without a priori knowledge of the feedstock composition or identity.

Draft Genome Sequence of the Biofuel-Relevant Microalga Desmodesmus armatus.

A draft genome of 906 scaffolds of 115.8 Mb was assembled for Desmodesmus armatus, a diploid, lipid- and storage carbohydrate-accumulating microalga proven relevant for large-scale, outdoor cultivation, and serves as a model alga platform for improving photosynthetic efficiency and carbon assimilation for next-generation bioenergy production.

Developing Riboswitch-Mediated Gene Regulatory Controls in Thermophilic Bacteria.

Thermophilic bacteria are attractive hosts to produce bio-based chemicals. While various genetic manipulations have been employed in the metabolic engineering of thermophiles, a robust means to regulate gene expression in these bacteria (∼55 °C) is missing. Our bioinformatic search for various riboswitches in thermophilic bacteria revealed that major classes of riboswitches are present, suggesting riboswitches' regulatory roles in these bacteria. By building synthetic constructs incorporating natural and engineered purine riboswitch sequences originated from foreign species, we quantified respective riboswitches activities in repressing and up-regulating gene expression in Geobacillus thermoglucosidasius using a green fluorescence protein. The elicited regulatory response was ligand-concentration-dependent. We further demonstrated that riboswitch-mediated gene expression of adhE (responsible for ethanol production) in Clostridium thermocellum can modulate ethanol production, redirect metabolites, and control cell growth in the adhE knockout mutant. This work has made tunable gene expression feasible across different thermophiles for broad applications including biofuels production and gene-to-trait mapping.

Prediction of reaction knockouts to maximize succinate production by Actinobacillus succinogenes.

Succinate is a precursor of multiple commodity chemicals and bio-based succinate production is an active area of industrial bioengineering research. One of the most important microbial strains for bio-based production of succinate is the capnophilic gram-negative bacterium Actinobacillus succinogenes, which naturally produces succinate by a mixed-acid fermentative pathway. To engineer A. succinogenes to improve succinate yields during mixed acid fermentation, it is important to have a detailed understanding of the metabolic flux distribution in A. succinogenes when grown in suitable media. To this end, we have developed a detailed stoichiometric model of the A. succinogenes central metabolism that includes the biosynthetic pathways for the main components of biomass-namely glycogen, amino acids, DNA, RNA, lipids and UDP-N-Acetyl-α-D-glucosamine. We have validated our model by comparing model predictions generated via flux balance analysis with experimental results on mixed acid fermentation. Moreover, we have used the model to predict single and double reaction knockouts to maximize succinate production while maintaining growth viability. According to our model, succinate production can be maximized by knocking out either of the reactions catalyzed by the PTA (phosphate acetyltransferase) and ACK (acetyl kinase) enzymes, whereas the double knockouts of PEPCK (phosphoenolpyruvate carboxykinase) and PTA or PEPCK and ACK enzymes are the most effective in increasing succinate production.

Parameter determination and validation for a mechanistic model of the enzymatic saccharification of cellulose-Iß.

Cost-effective production of fuels and chemicals from lignocellulosic biomass often involves enzymatic saccharification, which has been the subject of intense research and development. Recently, a mechanistic model for the enzymatic saccharification of cellulose has been developed that accounts for distribution of cellulose chain lengths, the accessibility of insoluble cellulose to enzymes, and the distinct modes of action of the component cellulases [Griggs et al. (2012) Biotechnol. Bioeng., 109(3):665-675; Griggs et al. (2012) Biotechnol. Bioeng., 109(3):676-685]. However, determining appropriate values for the adsorption, inhibition, and rate parameters required further experimental investigation. In this work, we performed several sets of experiments to aid in parameter estimation and to quantitatively validate the model. Cellulosic materials differing in degrees of polymerization and crystallinity (α-cellulose-Iß and highly crystalline cellulose-Iß ) were digested by component enzymes (EGI/CBHI/ßG) and by mixtures of these enzymes. Based on information from the literature and the results from these experiments, a single set of model parameters was determined, and the model simulation results using this set of parameters were compared with the experimental data of total glucan conversion, chain-length distribution, and crystallinity. Model simulations show significant agreement with the experimentally derived glucan conversion and chain-length distribution curves and provide interesting insights into multiple complex and interacting physico-chemical phenomena involved in enzymatic hydrolysis, including enzyme synergism, substrate accessibility, cellulose chain length distribution and crystallinity, and inhibition of cellulases by soluble sugars.

Proteomic analysis of Chlorella vulgaris: potential targets for enhanced lipid accumulation.

Oleaginous microalgae are capable of producing large quantities of fatty acids and triacylglycerides. As such, they are promising feedstocks for the production of biofuels and bioproducts. Genetic strain-engineering strategies offer a means to accelerate the commercialization of algal biofuels by improving the rate and total accumulation of microalgal lipids. However, the industrial potential of these organisms remains to be met, largely due to the incomplete knowledgebase surrounding the mechanisms governing the induction of algal lipid biosynthesis. Such strategies require further elucidation of genes and gene products controlling algal lipid accumulation. In this study, we have set out to examine these mechanisms and identify novel strain-engineering targets in the oleaginous microalga, Chlorella vulgaris. Comparative shotgun proteomic analyses have identified a number of novel targets, including previously unidentified transcription factors and proteins involved in cell signaling and cell cycle regulation. These results lay the foundation for strain-improvement strategies and demonstrate the power of translational proteomic analysis. BIOLOGICAL SIGNIFICANCE: We have applied label-free, comparative shotgun proteomic analyses, via a transcriptome-to-proteome pipeline, in order to examine the nitrogen deprivation response in the oleaginous microalga, C. vulgaris. Herein, we identify potential targets for strain-engineering strategies targeting enhanced lipid accumulation for algal biofuels applications. Among the identified targets are proteins involved in transcriptional regulation, lipid biosynthesis, cell signaling and cell cycle progression. This article is part of a Special Issue entitled: Translational Plant Proteomics.

Enhancing a Pathway-Genome Database (PGDB) to capture subcellular localization of metabolites and enzymes: the nucleotide-sugar biosynthetic pathways of Populus trichocarpa.

Understanding how cellular metabolism works and is regulated requires that the underlying biochemical pathways be adequately represented and integrated with large metabolomic data sets to establish a robust network model. Genetically engineering energy crops to be less recalcitrant to saccharification requires detailed knowledge of plant polysaccharide structures and a thorough understanding of the metabolic pathways involved in forming and regulating cell-wall synthesis. Nucleotide-sugars are building blocks for synthesis of cell wall polysaccharides. The biosynthesis of nucleotide-sugars is catalyzed by a multitude of enzymes that reside in different subcellular organelles, and precise representation of these pathways requires accurate capture of this biological compartmentalization. The lack of simple localization cues in genomic sequence data and annotations however leads to missing compartmentalization information for eukaryotes in automatically generated databases, such as the Pathway-Genome Databases (PGDBs) of the SRI Pathway Tools software that drives much biochemical knowledge representation on the internet. In this report, we provide an informal mechanism using the existing Pathway Tools framework to integrate protein and metabolite sub-cellular localization data with the existing representation of the nucleotide-sugar metabolic pathways in a prototype PGDB for Populus trichocarpa. The enhanced pathway representations have been successfully used to map SNP abundance data to individual nucleotide-sugar biosynthetic genes in the PGDB. The manually curated pathway representations are more conducive to the construction of a computational platform that will allow the simulation of natural and engineered nucleotide-sugar precursor fluxes into specific recalcitrant polysaccharide(s). Database URL: The curated Populus PGDB is available in the BESC public portal at http://cricket.ornl.gov/cgi-bin/beocyc_home.cgi and the nucleotide-sugar biosynthetic pathways can be directly accessed at http://cricket.ornl.gov:1555/PTR/new-image?object=SUGAR-NUCLEOTIDES.

Modeling and simulation of aggregation of membrane protein LAT with molecular variability in the number of binding sites for cytosolic Grb2-SOS1-Grb2.

The linker for activation of T cells (LAT), the linker for activation of B cells (LAB), and the linker for activation of X cells (LAX) form a family of transmembrane adaptor proteins widely expressed in lymphocytes. These scaffolding proteins have multiple binding motifs that, when phosphorylated, bind the SH2 domain of the cytosolic adaptor Grb2. Thus, the valence of LAT, LAB and LAX for Grb2 is variable, depending on the strength of receptor activation that initiates phosphorylation. During signaling, the LAT population will exhibit a time-varying distribution of Grb2 valences from zero to three. In the cytosol, Grb2 forms 1:1 and 2:1 complexes with the guanine nucleotide exchange factor SOS1. The 2:1 complex can bridge two LAT molecules when each Grb2, through their SH2 domains, binds to a phosphorylated site on a separate LAT. In T cells and mast cells, after receptor engagement, receptor phosphoyrlation is rapidly followed by LAT phosphorylation and aggregation. In mast cells, aggregates containing more than one hundred LAT molecules have been detected. Previously we considered a homogeneous population of trivalent LAT molecules and showed that for a range of Grb2, SOS1 and LAT concentrations, an equilibrium theory for LAT aggregation predicts the formation of a gel-like phase comprising a very large aggregate (superaggregate). We now extend this theory to investigate the effects of a distribution of Grb2 valence in the LAT population on the formation of LAT aggregates and superaggregate and use stochastic simulations to calculate the fraction of the total LAT population in the superaggregate.

Examination of triacylglycerol biosynthetic pathways via de novo transcriptomic and proteomic analyses in an unsequenced microalga.

Biofuels derived from algal lipids represent an opportunity to dramatically impact the global energy demand for transportation fuels. Systems biology analyses of oleaginous algae could greatly accelerate the commercialization of algal-derived biofuels by elucidating the key components involved in lipid productivity and leading to the initiation of hypothesis-driven strain-improvement strategies. However, higher-level systems biology analyses, such as transcriptomics and proteomics, are highly dependent upon available genomic sequence data, and the lack of these data has hindered the pursuit of such analyses for many oleaginous microalgae. In order to examine the triacylglycerol biosynthetic pathway in the unsequenced oleaginous microalga, Chlorella vulgaris, we have established a strategy with which to bypass the necessity for genomic sequence information by using the transcriptome as a guide. Our results indicate an upregulation of both fatty acid and triacylglycerol biosynthetic machinery under oil-accumulating conditions, and demonstrate the utility of a de novo assembled transcriptome as a search model for proteomic analysis of an unsequenced microalga.

Kinetic modeling and exploratory numerical simulation of chloroplastic starch degradation.

BACKGROUND: Higher plants and algae are able to fix atmospheric carbon dioxide through photosynthesis and store this fixed carbon in large quantities as starch, which can be hydrolyzed into sugars serving as feedstock for fermentation to biofuels and precursors. Rational engineering of carbon flow in plant cells requires a greater understanding of how starch breakdown fluxes respond to variations in enzyme concentrations, kinetic parameters, and metabolite concentrations. We have therefore developed and simulated a detailed kinetic ordinary differential equation model of the degradation pathways for starch synthesized in plants and green algae, which to our knowledge is the most complete such model reported to date. RESULTS: Simulation with 9 internal metabolites and 8 external metabolites, the concentrations of the latter fixed at reasonable biochemical values, leads to a single reference solution showing ß-amylase activity to be the rate-limiting step in carbon flow from starch degradation. Additionally, the response coefficients for stromal glucose to the glucose transporter k(cat) and KM are substantial, whereas those for cytosolic glucose are not, consistent with a kinetic bottleneck due to transport. Response coefficient norms show stromal maltopentaose and cytosolic glucosylated arabinogalactan to be the most and least globally sensitive metabolites, respectively, and ß-amylase k(cat) and KM for starch to be the kinetic parameters with the largest aggregate effect on metabolite concentrations as a whole. The latter kinetic parameters, together with those for glucose transport, have the greatest effect on stromal glucose, which is a precursor for biofuel synthetic pathways. Exploration of the steady-state solution space with respect to concentrations of 6 external metabolites and 8 dynamic metabolite concentrations show that stromal metabolism is strongly coupled to starch levels, and that transport between compartments serves to lower coupling between metabolic subsystems in different compartments. CONCLUSIONS: We find that in the reference steady state, starch cleavage is the most significant determinant of carbon flux, with turnover of oligosaccharides playing a secondary role. Independence of stationary point with respect to initial dynamic variable values confirms a unique stationary point in the phase space of dynamically varying concentrations of the model network. Stromal maltooligosaccharide metabolism was highly coupled to the available starch concentration. From the most highly converged trajectories, distances between unique fixed points of phase spaces show that cytosolic maltose levels depend on the total concentrations of arabinogalactan and glucose present in the cytosol. In addition, cellular compartmentalization serves to dampen much, but not all, of the effects of one subnetwork on another, such that kinetic modeling of single compartments would likely capture most dynamics that are fast on the timescale of the transport reactions.

Shaping the response: the role of FcεRI and Syk expression levels in mast cell signaling.

Many receptor systems initiate cell signaling through ligand-induced receptor aggregation. For bivalent ligands binding to mono- or bivalent receptors, a plot of the equilibrium concentration of receptors in aggregates against the log of the free ligand concentration, the cross-linking curve, is symmetric and bell shaped. However, steady state cellular responses initiated through receptor cross-linking may have a different dependence on ligand concentration than the aggregated receptors that initiate and maintain these responses. The authors illustrate by considering the activation of the protein kinase Syk that rapidly occurs after high affinity receptors for IgE, FcεRI, are aggregated on the surface of mast cells and basophils. Using a mathematical model of Syk activation the authors investigate two effects, one straightforward and one less so, that result in Syk activation not qualitatively following the cross-linking curve. Model predictions show that if the mechanism by which Syk is fully activated involves the transphosphorylation of Syk by Syk, then Syk activation curves can be either bell shaped or double humped, depending on the cellular concentrations of Syk and FcεRI. The model also predicts that the Syk activation curve can be non-symmetric with respect to the ligand concentration. The cell can exhibit differential Syk activation at two different ligand concentrations that produce identical distributions of receptor aggregates that form and dissociate at the same rates. The authors discuss how, even though it is only receptor aggregates that trigger responses, differences in total ligand concentration can lead to subtle kinetic effects that yield qualitative differences in the levels of Syk activation.

Transmission of single HIV-1 genomes and dynamics of early immune escape revealed by ultra-deep sequencing.

We used ultra-deep sequencing to obtain tens of thousands of HIV-1 sequences from regions targeted by CD8+ T lymphocytes from longitudinal samples from three acutely infected subjects, and modeled viral evolution during the critical first weeks of infection. Previous studies suggested that a single virus established productive infection, but these conclusions were tempered because of limited sampling; now, we have greatly increased our confidence in this observation through modeling the observed earliest sample diversity based on vastly more extensive sampling. Conventional sequencing of HIV-1 from acute/early infection has shown different patterns of escape at different epitopes; we investigated the earliest escapes in exquisite detail. Over 3-6 weeks, ultradeep sequencing revealed that the virus explored an extraordinary array of potential escape routes in the process of evading the earliest CD8 T-lymphocyte responses--using 454 sequencing, we identified over 50 variant forms of each targeted epitope during early immune escape, while only 2-7 variants were detected in the same samples via conventional sequencing. In contrast to the diversity seen within epitopes, non-epitope regions, including the Envelope V3 region, which was sequenced as a control in each subject, displayed very low levels of variation. In early infection, in the regions sequenced, the consensus forms did not have a fitness advantage large enough to trigger reversion to consensus amino acids in the absence of immune pressure. In one subject, a genetic bottleneck was observed, with extensive diversity at the second time point narrowing to two dominant escape forms by the third time point, all within two months of infection. Traces of immune escape were observed in the earliest samples, suggesting that immune pressure is present and effective earlier than previously reported; quantifying the loss rate of the founder virus suggests a direct role for CD8 T-lymphocyte responses in viral containment after peak viremia. Dramatic shifts in the frequencies of epitope variants during the first weeks of infection revealed a complex interplay between viral fitness and immune escape.

A detailed mathematical model predicts that serial engagement of IgE-Fc epsilon RI complexes can enhance Syk activation in mast cells.

The term serial engagement was introduced to describe the ability of a single peptide, bound to a MHC molecule, to sequentially interact with TCRs within the contact region between a T cell and an APC. In addition to ligands on surfaces, soluble multivalent ligands can serially engage cell surface receptors with sites on the ligand, binding and dissociating from receptors many times before all ligand sites become free and the ligand leaves the surface. To evaluate the role of serial engagement in Syk activation, we use a detailed mathematical model of the initial signaling cascade that is triggered when FcepsilonRI is aggregated on mast cells by multivalent Ags. Although serial engagement is not required for mast cell signaling, it can influence the recruitment of Syk to the receptor and subsequent Syk phosphorylation. Simulating the response of mast cells to ligands that serially engage receptors at different rates shows that increasing the rate of serial engagement by increasing the rate of dissociation of the ligand-receptor bond decreases Syk phosphorylation. Increasing serial engagement by increasing the rate at which receptors are cross-linked (for example by increasing the forward rate constant for cross-linking or increasing the valence of the ligand) increases Syk phosphorylation. When serial engagement enhances Syk phosphorylation, it does so by partially reversing the effects of kinetic proofreading. Serial engagement rapidly returns receptors that have dissociated from aggregates to new aggregates before the receptors have fully returned to their basal state.

Aggregation of membrane proteins by cytosolic cross-linkers: theory and simulation of the LAT-Grb2-SOS1 system.

Ligand-induced receptor aggregation is a well-known mechanism for initiating intracellular signals but oligomerization of distal signaling molecules may also be required for signal propagation. Formation of complexes containing oligomers of the transmembrane adaptor protein, linker for the activation of T cells (LAT), has been identified as critical in mast cell and T cell activation mediated by immune response receptors. Cross-linking of LAT arises from the formation of a 2:1 complex between the adaptor Grb2 and the nucleotide exchange factor SOS1, which bridges two LAT molecules through the interaction of the Grb2 SH2 domain with a phosphotyrosine on LAT. We model this oligomerization and find that the valence of LAT for Grb2, which ranges from zero to three, is critical in determining the nature and extent of aggregation. A dramatic rise in oligomerization can occur when the valence switches from two to three. For valence three, an equilibrium theory predicts the possibility of forming a gel-like phase. This prediction is confirmed by stochastic simulations, which make additional predictions about the size of the gel and the kinetics of LAT oligomerization. We discuss the model predictions in light of recent experiments on RBL-2H3 and Jurkat E6.1 cells and suggest that the gel phase has been observed in activated mast cells.

Thermodynamics and kinetics of competing aggregation processes in a simple model system.

A simple model system has been used to develop thermodynamics and kinetics for bulk and surface aggregation processes capable of competing with each other. The processes are the stepwise aggregation of monomers in a fluid medium and on an impenetrable solid surface bounding the fluid medium, besides the adsorption and desorption of the same species at the solid-fluid interface. Emphasis is on aggregation processes in the high friction limit. The theoretical model is used to compare the kinetics and thermodynamics of the processes and to infer the conditions in which one process dominates another, in the high friction limit, such as in a liquid. The motivation of this study is obtaining insight into competition between aggregation in solution and on an adjoining surface, such as a cell membrane.

Effects of nonproductive binding on the kinetics of enzymatic reactions with patterned substrates.

Existing models of ligand-receptor binding kinetics suggest that clustering surface-associated molecules tends to decrease the rates with which solution phase molecules associate and dissociate. Here, the authors use kinetic Monte Carlo simulations to study the case of an enzyme catalyzing the turnover of substrate molecules immobilized on a surface. The simulations reveal a crossover in the overall reaction rates for randomly distributed and clustered substrate molecules as the enzyme unbinding rate is varied. Approximate expressions for the effective kinetic parameters are introduced, and they show that the observed behavior derives from sequestration of the enzyme in the strong-sticking limit.

Dynamic coupling between coordinates in a model for biomolecular isomerization.

To understand a complex reaction, it is necessary to project the dynamics of the system onto a low-dimensional subspace of physically meaningful coordinates. We recently introduced an automatic method for identifying coordinates that relate closely to stable-state commitment probabilities and successfully applied it to a model for biomolecular isomerization, the C(7eq)-->alpha(R) transition of the alanine dipeptide [A. Ma and A. R. Dinner, J. Phys. Chem. B 109, 6769 (2005)]. Here, we explore approximate means for estimating diffusion tensors for systems subject to restraints in one and two dimensions and then use the results together with an extension of Kramers theory for unimolecular reaction rates [A. Berezhkovskii and A. Szabo, J. Chem. Phys. 122, 014503 (2005)] to show explicitly that both the potential of mean force and the diffusion tensor are essential for describing the dynamics of the alanine dipeptide quantitatively. In particular, the signficance of off-diagonal elements of the diffusion tensor suggests that the coordinates of interest are coupled by the hydrodynamic-like response of the bath of remaining degrees of freedom.

Enhancement of diffusion-controlled reaction rates by surface-induced orientational restriction.

We explore the means by which immobilization of a substrate on a surface can increase the rate of a diffusion-controlled enzymatic reaction. A quasichemical approach is developed and compared with Brownian dynamics simulations. We use these methods to show that restricting only the orientation of the enzyme by long-range interactions with the surface is sufficient for enhancing catalysis.