Search

The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.

Cell walls in crops and trees have been engineered for production of biofuels and commodity chemicals, but engineered varieties often fail multi-year field trials and are not commercialized. We engineered reduced expression of a pectin biosynthesis gene (Galacturonosyltransferase 4, GAUT4) in switchgrass and poplar, and find that this improves biomass yields and sugar release from biomass processing. Both traits were maintained in a 3-year field trial of GAUT4-knockdown switchgrass, with up to sevenfold increased saccharification and ethanol production and sixfold increased biomass yield compared with control plants. We show that GAUT4 is an α-1,4-galacturonosyltransferase that synthesizes homogalacturonan (HG). Downregulation of GAUT4 reduces HG and rhamnogalacturonan II (RGII), reduces wall calcium and boron, and increases extractability of cell wall sugars. Decreased recalcitrance in biomass processing and increased growth are likely due to reduced HG and RGII cross-linking in the cell wall.

A critical question for theories of spatial vision concerns the nature of the inputs to perception. The action-specific account asserts that information related to action, specifically a perceiver's ability to perform the intended action, is one of these sources of information. This claim challenges assumptions about the mind in general and perception in particular, and not surprisingly, has been met with much resistance. Alternative explanations include that these effects are due to response bias, rather than genuine differences in perception. Using a paradigm in which ease to block a ball impacts estimated speed of the ball, participants were given explicit feedback about their perceptual judgements to test the response bias alternative. Despite the feedback, the action-specific effect still persisted, thus ruling out a response-bias interpretation. Coupled with other research ruling out additional alternative explanations, the current findings offer an important step towards the claim that a person's ability to act truly influences spatial perception.

The metabolic byproducts secreted by growing cells can be easily measured and provide a window into the state of a cell; they have been essential to the development of microbiology, cancer biology, and biotechnology. Progress in computational modeling of cells has made it possible to predict metabolic byproduct secretion with bottom-up reconstructions of metabolic networks. However, owing to a lack of data, it has not been possible to validate these predictions across a wide range of strains and conditions. Through literature mining, we were able to generate a database of Escherichia coli strains and their experimentally measured byproduct secretions. We simulated these strains in six historical genome-scale models of E. coli, and we report that the predictive power of the models has increased as they have expanded in size and scope. The latest genome-scale model of metabolism correctly predicts byproduct secretion for 35/89 (39%) of designs. The next-generation genome-scale model of metabolism and gene expression (ME-model) correctly predicts byproduct secretion for 40/89 (45%) of designs, and we show that ME-model predictions could be further improved through kinetic parameterization. We analyze the failure modes of these simulations and discuss opportunities to improve prediction of byproduct secretion.

Evidence suggests that novel enzyme functions evolved from low‐level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems‐level adaptations are not well characterized. Furthermore, it remains untested whether knowledge of an organism's promiscuous reaction set, or underground metabolism, can aid in forecasting the genetic basis of metabolic adaptations. Here, we employ a computational model of underground metabolism and laboratory evolution experiments to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non‐native substrates in Escherichia coli K‐12 MG1655. After as few as approximately 20 generations, evolved populations repeatedly acquired the capacity to grow on five predicted non‐native substrates—D‐lyxose, D‐2‐deoxyribose, D‐arabinose, m‐tartrate, and monomethyl succinate. Altered promiscuous activities were shown to be directly involved in establishing high‐efficiency pathways. Structural mutations shifted enzyme substrate turnover rates toward the new substrate while retaining a preference for the primary substrate. Finally, genes underlying the phenotypic innovations were accurately predicted by genome‐scale model simulations of metabolism with enzyme promiscuity.

Background: Race/ethnicity is associated with risk of first-ever spontaneous intracerebral hemorrhage (ICH), but the influence of race/ethnicity on risk of ICH recurrence has not been well studied in a large, diverse population. Methods: We used claims data on all discharges from community hospital inpatient stays in California between January 1, 2005 and December 31, 2011 to identify patients who were hospitalized for ICH. California residents who survived to discharge were included. We used validated diagnosis codes to identify a primary outcome of recurrent ICH. We then used survival curves and log-rank tests for unadjusted analyses of survival across race/ethnic groups and multivariable Cox proportional-hazards regression to determine factors associated with risk of ICH recurrence after adjusting for potential confounders. Results: Between 2005-2011, we identified 31,554 first-ever ICH patients who survived to discharge, of which 15,828 (50%) were white, 6,254 (20%) Hispanic, 4,268 (14%) Asian, and 2,820 (9%) black. There were 1339 recurrences (4.2%) over a median follow-up time of 2.9 years (IQR 3.8). The 1-year recurrence rate was 3.0% (95% CI 2.8%-3.2%). In univariable and multivariable analysis, blacks (HR 1.22, 95% CI 1.01-1.48; p=0.035) and Asians (HR 1.29, 95% CI 1.10-1.50; p=0.001) had a higher risk of ICH recurrence than whites (reference categories). Private insurance was associated with a significant reduction in the risk of ICH recurrence compared to subjects with Medicare (HR 0.60, 95% CI 0.50-0.73, p Conclusions: Black and Asian subjects had a higher risk of ICH recurrence than white or Hispanic subjects. Further research is needed to determine drivers of these disparities.

Introduction: Stroke is a leading cause of morbidity and mortality in the United States, affecting an estimated 15 million people each year. Native Americans (NAs) account for 1.5% of the population. Several recent studies have focused on how stroke affects minority groups. However, existing data on stroke in NAs are extremely limited. We aimed to characterize the demographic and clinical characteristics of this population and identify predictors of in-patient mortality. Methods: We evaluated claims data from acute care hospitals in California between 2005-2011. California has the largest NA population in the United States. Validated ICD-9 codes were used to identify first time primary ischemic (AIS) and hemorrhagic stroke (ICH) admissions and ascertain comorbidities and in-hospital mortality. We used univariate and multivariate logistic regression to compare baseline characteristics across race/ethnic groups and to identify independent predictors of in hospital mortality. Results: We identified 283,107 first time strokes in California between 2005 and 2011, including 234,471 AIS (83%) and 48,636 ICH (17%). Of these, 851 (0.3%) were NAs (Age 66 [SD14], female sex 444 [53%]), including 698 AISs (82%) and 153 (18%) ICHs. For AIS, NAs had intermediate prevalence of relevant vascular risk factors, including history of smoking (15% vs 18% in Blacks and 11% in Whites, p0.2). In both AIS and ICH, predictors of in hospital mortality were similar to other minority groups. Conclusions: Native Americans constitute an important yet understudied minority population in stroke. NA have high prevalence of modifiable risk factors including diabetes and smoking.

Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains—with diverse metabolic deficiencies—were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities., Author summary Many basic characteristics underlying the establishment of cooperative growth in bacterial communities have not been studied in detail. The presented work sought to understand the adaptation of syntrophic communities by first employing a new computational method to generate a comprehensive catalog of E. coli auxotrophic mutants. Many of the knockouts in the catalog had the predicted effect of disabling a major biosynthetic process. As a result, these strains were predicted to be capable of growing when supplemented with many different individual metabolites (i.e., a non-specific auxotroph), but the strains would require a high amount of metabolic cooperation to grow in community. Three such non-specific auxotroph mutants from this catalog were co-cultured with a proven auxotrophic partner in vivo and evolved via adaptive laboratory evolution. In order to successfully grow, each strain in co-culture had to evolve under a pressure to grow cooperatively in its new niche. The non-specific auxotrophs further had to adapt to significant homeostatic changes in cell’s metabolic state caused by knockouts in metabolic genes. The genomes of the successfully growing communities were sequenced, thus providing unique insights into the genetic changes accompanying the formation and optimization of the viable communities. A computational model was further developed to predict how finite protein availability, a fundamental constraint on cell metabolism, could impact the composition of the community (i.e., the relative abundances of each community member).

Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a Biomass Objective Function from experimental data. BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. We used BOFdat to reconstruct the BOF of the Escherichia coli model iML1515, a gold standard in the field. The BOF generated by BOFdat resulted in the most concordant biomass composition, growth rate, and gene essentiality prediction accuracy when compared to other methods. Installation instructions for BOFdat are available in the documentation and the source code is available on GitHub (https://github.com/jclachance/BOFdat)., Author summary The formulation of phenotypic predictions by genome-scale models (GEMs) is dependent on the specified objective. The idea of a biomass objective function (BOF) is to represent all metabolites necessary for cells to double so that optimizing the BOF is equivalent to optimizing growth. Knowledge of the qualitative and quantitative organism’s composition (i.e. which metabolites are necessary for growth and in what proportion) is critical for accurate predictions. We implemented BOFdat with the idea that experimental data should drive the definition of the biomass composition. As omic datasets become more available, the possibility of integrating them to obtain a condition-specific biomass composition is in reach and therefore one of the main features of BOFdat. While major macromolecules, coenzymes, and inorganic ions are ubiquitous components across species, several species-specific components exist in the cell that should be accounted for in the BOF. To identify these, we implemented an approach that minimizes the error between experimental essentiality data and GEM-driven prediction. Hence BOFdat provides an unbiased, data-driven approach to defining BOF that has the potential to improve the quality of new genome-scale models and greatly decrease the time required to generate a new reconstruction.

Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict optimal genetic modifications that improve the rate and yield of chemical production. A new generation of COBRA models and methods is now being developed--encompassing many biological processes and simulation strategies-and next-generation models enable new types of predictions. Here, three key examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering.

Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.

Growth rate and yield are fundamental features of microbial growth. However, we lack a mechanistic and quantitative understanding of the rate-yield relationship. Studies pairing computational predictions with experiments have shown the importance of maintenance energy and proteome allocation in explaining rate-yield tradeoffs and overflow metabolism. Recently, adaptive evolution experiments of Escherichia coli reveal a phenotypic diversity beyond what has been explained using simple models of growth rate versus yield. Here, we identify a two-dimensional rate-yield tradeoff in adapted E. coli strains where the dimensions are (A) a tradeoff between growth rate and yield and (B) a tradeoff between substrate (glucose) uptake rate and growth yield. We employ a multi-scale modeling approach, combining a previously reported coarse-grained small-scale proteome allocation model with a fine-grained genome-scale model of metabolism and gene expression (ME-model), to develop a quantitative description of the full rate-yield relationship for E. coli K-12 MG1655. The multi-scale analysis resolves the complexity of ME-model which hindered its practical use in proteome complexity analysis, and provides a mechanistic explanation of the two-dimensional tradeoff. Further, the analysis identifies modifications to the P/O ratio and the flux allocation between glycolysis and pentose phosphate pathway (PPP) as potential mechanisms that enable the tradeoff between glucose uptake rate and growth yield. Thus, the rate-yield tradeoffs that govern microbial adaptation to new environments are more complex than previously reported, and they can be understood in mechanistic detail using a multi-scale modeling approach., Author summary This study reconciles multiple existing microbial rate-yield tradeoff theories with experimental data. There is great interest in developing quantitative descriptions of the relationship between growth rate and growth yield [1]. However, some reported experiments [2–4] in the literature do not agree with existing theories [5–7]. Specifically, overflow metabolism in E. coli can either be coupled [5, 8] or decoupled [2–4] from growth rate. We found that adaptive laboratory evolution (ALE) experiments of E. coli reveal a two-dimensional rate-yield tradeoff in adapted strains where the dimensions are (i) a tradeoff between growth rate and growth yield, previously reported by [5], and (ii) a tradeoff between substrate uptake rate and growth yield. The appearance of this two-dimensional tradeoff during adaptation suggests that microorganisms adapting to new environments are subject to a more complex set of rate-yield tradeoffs than previously reported [5, 6]. In this study, the two-dimensional rate-yield tradeoff is quantitatively explained through our multi-scale modeling approach, combining a previously reported small-scale proteome allocation model [5] with a genome-scale model of metabolism and gene-expression (ME-model) [9]. The modeling approach is also instrumental to future studies.

Genome-scale models of metabolism and macromolecular expression (ME-models) explicitly compute the optimal proteome composition of a growing cell. ME-models expand upon the well-established genome-scale models of metabolism (M-models), and they enable a new fundamental understanding of cellular growth. ME-models have increased predictive capabilities and accuracy due to their inclusion of the biosynthetic costs for the machinery of life, but they come with a significant increase in model size and complexity. This challenge results in models which are both difficult to compute and challenging to understand conceptually. As a result, ME-models exist for only two organisms (Escherichia coli and Thermotoga maritima) and are still used by relatively few researchers. To address these challenges, we have developed a new software framework called COBRAme for building and simulating ME-models. It is coded in Python and built on COBRApy, a popular platform for using M-models. COBRAme streamlines computation and analysis of ME-models. It provides tools to simplify constructing and editing ME-models to enable ME-model reconstructions for new organisms. We used COBRAme to reconstruct a condensed E. coli ME-model called i JL1678b-ME. This reformulated model gives functionally identical solutions to previous E. coli ME-models while using 1/6 the number of free variables and solving in less than 10 minutes, a marked improvement over the 6 hour solve time of previous ME-model formulations. Errors in previous ME-models were also corrected leading to 52 additional genes that must be expressed in i JL1678b-ME to grow aerobically in glucose minimal in silico media. This manuscript outlines the architecture of COBRAme and demonstrates how ME-models can be created, modified, and shared most efficiently using the new software framework.

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.Here, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the model’s performance parameters, which supports informed model development and facilitates error detection.Memote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

Experimental studies ofEscherichia coliK-12 MG1655 often implicate poorly annotated genes in cellular phenotypes. However, we lack a systematic understanding of these genes. How many are there? What informationisavailable for them? And what features do they share that could explain the gap in our understanding? Efforts to build predictive, whole-cell models ofE. coliinevitably face this knowledge gap. We approached these questions systematically by assembling annotations from the knowledge bases EcoCyc, EcoGene, UniProt, RefSeq, and RegulonDB. We identified the genes that lack direct experimental evidence of function (the “y-ome”) which include 1563 of 4653 unique genes (34%), of which 131 have absolutely no evidence of function. An additional 304 genes (6.6%) are pseudogenes or phantom genes. y-ome genes tend to have lower expression levels and are enriched in the termination region of theE. colichromosome. Where evidence is available for y-ome genes, it most often points to them being membrane proteins and transporters. We resolve the misconception that a gene inE. coliwhose primary name starts with “y” is unannotated, and we discuss the value of the y-ome for systematic improvement ofE. coliknowledge bases and its extension to other organisms.

Genome-scale models (GEMs) rely on a biomass objective function (BOF) to predict phenotype from genotype. Here we present BOFdat, a Python package that offers functions to generate biomass objective function stoichiometric coefficients (BOFsc) from macromolecular cell composition and relative abundances of macromolecules obtained from omic datasets. Growth-associated and non-growth associated maintenance (GAM and NGAM) costs can also be calculated by BOFdat.BOFdat is freely available on the Python Package Index (pip install BOFdat). The source code and an example usage (Jupyter Notebook and example files) are available on GitHub (https://github.com/jclachance/BOFdat). The documentation and API are available through ReadTheDocs (https://bofdat.readthedocs.io).Contactjean-christophe.lachance@usherbrooke.ca, zaking@eng.ucsd.edu, pierre-etienne.jacques@usherbrooke.ca

BackgroundFlux balance analysis (FBA) is a widely-used method for analyzing metabolic networks. However, most existing tools that implement FBA require downloading software and writing code. Furthermore, FBA generates predictions for metabolic networks with thousands of components, so meaningful changes in FBA solutions can be difficult to identify. These challenges make it difficult for beginners to learn how FBA works.ResultsTo meet this need, we present Escher-FBA, a web application for interactive FBA simulations within a pathway visualization. Escher-FBA allows users to set flux bounds, knock out reactions, change objective functions, upload metabolic models, and generate high-quality figures without downloading software or writing code. We provide detailed instructions on how to use Escher-FBA to replicate several FBA simulations that generate real scientific hypotheses.ConclusionsWe designed Escher-FBA to be as intuitive as possible so that users can quickly and easily understand the core concepts of FBA. The web application can be accessed at https://sbrg.github.io/escher-fba.

As microbes face changing environments, they dynamically allocate macromolecular resources to produce a particular phenotypic state. Broad 'omics' data sets have revealed several interesting phenomena regarding how the proteome is allocated under differing conditions, but the functional consequences of these states and how they are achieved remain open questions. Various types of multi-scale mathematical models have been used to elucidate the genetic basis for systems-level adaptations. In this review, we outline several different strategies by which microbes accomplish resource allocation and detail how mathematical models have aided in our understanding of these processes. Ultimately, such modeling efforts have helped elucidate the principles of proteome allocation and hold promise for further discovery.

Soybean [Glycine max (L.) Merr.] rust is caused by the fungal pathogen Phakopsora pachyrhizi. Six rust resistance loci (Rpp1, 2, 3, 4, 5, and 6) have been reported. Crosses were made between 75 resistant plant introductions (PIs) and a susceptible elite line or cultivar. Bulked segregant analysis (BSA) was used to determine if the PI resistance genes mapped to a previously identified locus or to an unreported locus. Fifty-two PIs had resistance genes that mapped to the Rpp3 region on chromosome 6 of the soybean genome. A set of P. pachyrhizi isolates was used to further characterize the resistance of these PIs. Forty-two of the PIs exhibited the same reaction profiles as either PI 462312 (Rpp3) or ‘Hyuuga’ (Rpp3 and Rpp5) to the panel of isolates. The fine mapping of Rpp1, Rpp3, and Rpp4 and the availability of the SoySNP50K Infinium Chip data on the USDA Soybean Germplasm Collection made it possible to use BSA and isolate data on these PIs to determine in retrospect how effective haplotype analysis would be in narrowing down the PIs to those likely to have a unique source of resistance. Thirty-seven of the 52 PIs (71%) mapping to the Rpp3 region had a haplotype identical to that of PI 462312. A combination of these analyses could prove useful in more rapidly narrowing down resistant PIs to those likely to carry a unique resistance gene.

Asian soybean rust (ASR) resistance gene Rpp2 has been fine mapped into a 188.1 kb interval on Glyma.Wm82.a2, which contains a series of plant resistance ( R ) genes. Asian soybean rust (ASR), caused by the fungus Phakopsora pachyrihizi Syd. & P. Syd., is a serious disease in major soybean [Glycine max (L.) Merr.] production countries worldwide and causes yield losses up to 75 %. Defining the exact chromosomal position of ASR resistance genes is critical for improving the effectiveness of marker-assisted selection (MAS) for resistance and for cloning these genes. The objective of this study was to fine map the ASR resistance gene Rpp2 from the plant introduction (PI) 230970. Rpp2 was previously mapped within a 12.9-cM interval on soybean chromosome 16. The fine mapping was initiated by identifying recombination events in F2 and F3 plants using simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers that flank the gene. Seventeen recombinant plants were identified and then tested with additional genetic markers saturating the gene region to localize the positions of each recombination. The progeny of these selected plants were tested for resistance to ASR and with SSR markers resulting in the mapping of Rpp2 to a 188.1 kb interval on the Williams 82 reference genome (Glyma.Wm82.a2). Twelve genes including ten toll/interleukin-1 receptor (TIR)-nucleotide-binding site (NBS)-leucine-rich repeat (LRR) genes were predicted to exist in this interval on the Glyma.Wm82.a2.v1 gene model map. Eight of these ten genes were homologous to the Arabidopsis TIR-NBS-LRR gene AT5G17680.1. The identified SSR and SNP markers close to Rpp2 and the candidate gene information presented in this study will be significant resources for MAS and gene cloning research.

Phaeodactylum tricornutum is a widely used model organism for studying diatom biology. We created a P. tricornutum-specific literature database, or bibliome, that can be interactively annotated by users to improve the relevance and accuracy of search queries. A bibliome represents the current knowledge about a specific organism or biological process and is the foundation for genome-scale computation models. The bibliome of P. tricornutum was assembled from literature searches of ‘P. tricornutum’ (as topic) in two different existing databases, the Web of Knowledge and PubMed resulting in 2497 independent articles. We manually curated the bibliome for relevance to biochemical, physiological, and molecular biology studies. Literature that described the use of P. tricornutum as feed source and ecotoxicology studies were excluded from the bibliome. This process resulted in a final list of 1124 entries – 20% of which received additional classification in the form of notes or keywords. We present the bibliome in an HTML format that is easily searched and that can be customized according to the interests of individual researchers. This HTML tool can also easily be adapted for other algal species or specific research topics.

Phakopsora pachyrhizi is a fungal pathogen and the cause of Asian soybean rust. P. pachyrhizi was first detected in the continental USA in 2004 and has since been a threat to the soybean industry. There are six described loci that harbor resistance to P. pachyrhizi (Rpp) genes. The resistance of PI 423972 was previously shown to be within 5 cM of the Rpp4 locus of PI 459025B, yet had differential reactions when challenged with P. pachyrhizi isolates India 1973 and Taiwan 1972. In this study, the resistance of PI 423972 was mapped to a 187.5 kb interval between the SNP markers GSM0543 and GSM0387 on chromosome 18 (51,397,064 to 51,584,617 bp, Glyma.Wm82.a2) that overlaps the interval for Rpp4 and is designated as Rpp4-b. A unique haplotype is described for PI 423972 that separates it from PI 459025B, 32 North American soybean ancestors, and all described sources of Rpp gene resistance.

The visual system is influenced by action. Objects that are easier to reach or catch look closer and slower, respectively. Here, we describe evidence for one action-specific effect, and show that none of the six pitfalls can account for the results. Vision is not an isolate module, as shown by this top-down effect of action on perception.

With the rapid adoption of computational tools in the life sciences, scientists are taking on the challenge of developing their own software libraries and releasing them for public use. This trend is being accelerated by popular technologies and platforms, such as GitHub, Jupyter, R/Shiny, that make it easier to develop scientific software and by open-source licenses that make it easier to release software. But how do you build a software library that people will use? And what characteristics do the best libraries have that make them enduringly popular? Here, we provide a reference guide, based on our own experiences, for developing software libraries along with real-world examples to help provide context for scientists who are learning about these concepts for the first time. While we can only scratch the surface of these topics, we hope that this article will act as a guide for scientists who want to write great software that is built to last.

Soybean rust (SBR), caused by Syd. and P. Syd., is a threat to soybean [ (L.) Merr.], production in regions of the world where winters are not cold enough to completely eliminate the many hosts of the fungus, so resistant soybean cultivars would be useful in managing this disease. Resistant germplasm accessions have been identified, and resistance () genes have been mapped to six independent loci, but the high degree of virulence variability among populations and isolates means that the level of resistance to different populations and isolates can vary considerably. Greenhouse assays were conducted from 2008 until 2013 in which seedlings of soybean plant introductions were challenged with bulk isolates collected in the state of Georgia. Accessions were included in the tests either because they carried known genes or because they had appeared resistant to SBR in previous field assays. The resistance was assessed on the basis of infection type, visible urediniospore development, and sometimes lesion density. With a few exceptions, most of the accessions that appeared resistant as adult plants in the field also had resistance reactions as seedlings, and these reactions were generally similar or identical in the different years that the greenhouse assays were conducted. This study demonstrates that greenhouse seedling reactions should usually be reliable indicators of adult plant resistance to related isolates or populations, and confirms the resistance of more than 100 soybean germplasm accessions to fungal isolates and field populations from Georgia.

Maintaining cofactor balance is a critical function in microorganisms, but often the native cofactor balance does not match the needs of an engineered metabolic flux state. Here, an optimization procedure is utilized to identify optimal cofactor-specificity "swaps" for oxidoreductase enzymes utilizing NAD(H) or NADP(H) in the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae. The theoretical yields of all native carbon-containing molecules are considered, as well as theoretical yields of twelve heterologous production pathways in E. coli. Swapping the cofactor specificity of central metabolic enzymes (especially GAPD and ALCD2x) is shown to increase NADPH production and increase theoretical yields for native products in E. coli and yeast--including L-aspartate, L-lysine, L-isoleucine, L-proline, L-serine, and putrescine--and non-native products in E. coli-including 1,3-propanediol, 3-hydroxybutyrate, 3-hydroxypropanoate, 3-hydroxyvalerate, and styrene.

Between 2009 and 2012, 118 soybean [Glycine max (L.) Merr.] accessions from the USDA Soybean Germplasm Collection were screened for resistance to soybean rust (Phakopsora pachyrhizi) at up to five locations in the southeastern United States. In 2009, plant introductions (PIs) from maturity groups III through IX were evaluated for relative disease severity and intensity of sporulation from uredinia compared with 12 susceptible cultivars from the same range of maturity groups. Resistance evaluations were based primarily on disease severity and intensity of sporulation from rust pustules. To assess resistance at several nurseries, a rust index score was calculated from the severity and sporulation ratings. Many of the PIs were moderately to highly resistant at the 2009 locations between Alabama and South Carolina, but the P. pachyrhizi population in Bossier City, LA, was virulent on most of those accessions. The 2011 rating data from Quincy, FL, indicated an increase in the virulence of the pathogen there since 2009, and this trend was observed again in 2012. In contrast, many of the same PIs developed substantially less soybean rust in Attapulgus, GA, in 2012. Despite the comparatively greater disease that many accessions had in Louisiana in 2009 and in Quincy in 2011 and 2012, at least 78 PIs were resistant in Georgia in 2012, and 20 of those were at least moderately resistant in both Florida and Georgia that year. No accessions were immune to rust at all of the nurseries, but PI 200492 (Rpp1), PI 547875 (a backcross line with Rpp1), and PI 567102B (Rpp6) were the most resistant of the accessions with named resistance genes and were among the most resistant accessions overall. Among the most resistant accessions with unknown resistance genes, PI 416826A, PI 417125, PI 567034, and PI 567104B consistently had effective levels of resistance in different locations and years. Information about the most resistant PIs and their reactions to soybean rust infection across years and locations will be useful for the development of rust-resistant soybean cultivars in the United States. D.R. Walker and R.L. Nelson, USDA-ARS, Soybean/Maize Germ plasm, Pathology and Genetics Research Unit and Dep. of Crop Sci

The prediction of cellular function from a genotype is a fundamental goal in biology. For metabolism, constraint-based modelling methods systematize biochemical, genetic and genomic knowledge into a mathematical framework that enables a mechanistic description of metabolic physiology. The use of constraint-based approaches has evolved over ~30 years, and an increasing number of studies have recently combined models with high-throughput data sets for prospective experimentation. These studies have led to validation of increasingly important and relevant biological predictions. As reviewed here, these recent successes have tangible implications in the fields of microbial evolution, interaction networks, genetic engineering and drug discovery.

Central oxidoreductase enzymes (eg, dehydrogenases, reductases) in microbial metabolism often have preferential binding specificity for one of the two major currency metabolites NAD(H) and NADP(H). These enzyme specificities result in a division of the metabolic functionality of the currency metabolites: enzymes reducing NAD+ to NADH drive oxidative phosphorylation, and enzymes reducing NADP+ to NADPH drive anabolic reactions. In this work, we introduce the computational method OptSwap, which predicts bioprocessing strain designs by identifying optimal modifications of the cofactor binding specificities of oxidoreductase enzyme and complementary reaction knockouts. Using the Escherichia coli genome-scale metabolic model iJO1366, OptSwap predicted eight growth-coupled production designs with significantly greater product yields or substrate-specific productivities than designs predicted with gene knockouts alone. These designs were identified for the production of L-alanine, succinate, acetate, an...

The metabolic byproducts secreted by growing cells can be easily measured and provide a window into the state of a cell; they have been essential to the development of microbiology1, cancer biology2, and biotechnology3. Progress in computational modeling of cells has made it possible to predict metabolic byproduct secretion with bottom-up reconstructions of metabolic networks. However, owing to a lack of data, it has not been possible to validate these predictions across a wide range of strains and conditions. Through literature mining, we were able to generate a database ofEscherichia colistrains and their experimentally measured byproduct secretions. We simulated these strains in six historical genome-scale models ofE. coli, and we report that the predictive power of the models has increased as they have expanded in size and scope. Next-generation models of metabolism and gene expression are even more capable than previous models, but parameterization poses new challenges.

Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production. The models accurately predict growth phenotypes and known auxotrophies in CHO cells. With the models, we quantify the protein synthesis capacity of CHO cells and demonstrate that common bioprocess treatments, such as histone deacetylase inhibitors, inefficiently increase product yield. However, our simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses.

Summary Switchgrass (Panicum virgatum L.) is a C4 perennial grass and has been identified as a potential bioenergy crop for cellulosic ethanol because of its rapid growth rate, nutrient use efficiency and widespread distribution throughout North America. The improvement of bioenergy feedstocks is needed to make cellulosic ethanol economically feasible, and genetic engineering of switchgrass is a promising approach towards this goal. A crucial component of creating transgenic switchgrass is having the capability of transforming the explants with DNA sequences of interest using vector constructs. However, there are limited options with the monocot plant vectors currently available. With this in mind, a versatile set of Gateway- compatible destination vectors (termed pANIC) was constructed to be used in monocot plants for transgenic crop improvement. The pANIC vectors can be used for transgene overexpres- sion or RNAi-mediated gene suppression. The pANIC vector set includes vectors that can be utilized for particle bombardment or Agrobacterium-mediated transformation. All the vectors contain (i) a Gateway cassette for overexpression or silencing of the target sequence, (ii) a plant selection cassette and (iii) a visual reporter cassette. The pANIC vector set was function- ally validated in switchgrass and rice and allows for high-throughput screening of sequences of interest in other monocot species as well.

Electrochemical deposition of the conjugated polymer poly(3,4-ethylenedioxythiophene) (PEDOT) forms thin, conductive films that are especially suitable for charge transfer at the tissue-electrode interface of neural implants. For this study, the effects of counter-ion choice and annealing parameters on the electrical and structural properties of PEDOT were investigated. Films were polymerized with various organic and inorganic counter-ions. Studies of crystalline order were conducted via X-ray diffraction (XRD). Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) were used to investigate the electrical properties of these films. X-ray photoelectron spectroscopy (XPS) was used to investigate surface chemistry of PEDOT films. The results of XRD experiments showed that films polymerized with certain small counter-ions have a regular structure with strong (100) edge-to-edge correlations of PEDOT chains at ~1.3 nm. After annealing at 170 °C for 1 hour, the XRD peaks attributed to PEDOT disappeared. PEDOT polymerized with LiClO(4) as a counter-ion showed improved impedance and charge storage capacity after annealing at 160 °C.

Poly(3,4-ethylene dioxythiophene) (PEDOT) is a chemically stable, conjugated polymer that is of considerable interest for a variety of applications including coatings for interfacing electronic biomedical devices with living tissue. Here, we describe recent work from our laboratory and elsewhere to investigate the morphology of PEDOT in the solid state. We discuss the importance of oxidative chemical and electrochemical polymerization, as well as the critical role of the counterion used during synthesis and film deposition. We have obtained information about the morphology of PEDOT from a number of different complimentary techniques including X-ray diffraction, optical microscopy, scanning electron microscopy, transmission high-resolution electron microscopy, and low-voltage electron microscopy. We also discuss results from ultraviolet-visible light spectroscopy (UV-Vis), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). PEDOT is a relatively rigid polymer that pa...

The Rpp6 locus of PI 567102B was mapped from 5,953,237 to 5,998,461 bp (chromosome 18); and a novel allele at the Rpp6 locus or tightly linked gene Rpp[PI567068A] of PI 567068A was mapped from 5,998,461 to 6,160,481 bp. Soybean rust (SBR), caused by the obligate, fungal pathogen Phakopsora pachyrhizi is an economic threat to soybean production, especially in the Americas. Host plant resistance is an important management strategy for SBR. The most recently described resistance to P. pachyrhizi (Rpp) gene is Rpp6 contributed by PI 567102B. Rpp6 was previously mapped to an interval of over four million base pairs on chromosome 18. PI 567068A was recently demonstrated to possess a resistance gene near the Rpp6 locus, yet PI 567068A gave a differential isolate reaction to several international isolates of P. pachyrhizi. The goals of this research were to fine map the Rpp6 locus of PI 567102B and PI 567068A and determine whether or not PI 567068A harbors a novel Rpp6 allele or another allele at a tightly linked resistance locus. Linkage mapping in this study mapped Rpp6 from 5,953,237 to 5,998,461 bp (LOD score of 58.3) and the resistance from PI 567068A from 5,998,461 to 6,160,481 bp (LOD score of 4.4) (Wm82.a1 genome sequence). QTL peaks were 139,033 bp apart from one another as determined by the most significant SNPs in QTL mapping. The results of haplotype analysis demonstrated that PI 567102B and PI 567068A share the same haplotype in the resistance locus containing both Rpp alleles, which was designated as the Rpp6/Rpp[PI567068A] haplotype. The Rpp6/Rpp[PI567068A] haplotype identified in this study can be used as a tool to rapidly screen other genotypes that possess a Rpp gene(s) and detect resistance at the Rpp6 locus in diverse germplasm.

With a novel elite genotype, SA37, and an improved transformation protocol, it is now possible to routinely and efficiently engineer switchgrass using biolistic transformation. Transformation of elite switchgrass (Panicum virgatum L.) genotypes would facilitate the characterization of genes related to cell wall recalcitrance to saccharification. However, transformation of explants from switchgrass plants has remained difficult. Therefore, the objective of this study was to develop a biolistic transformation protocol for elite genotypes. Three switchgrass genotypes (ST1, ST2, and AL2) were previously selected for tissue culture responsiveness. One genotype, SA37, was selected for further use due to its improved formation of callus amenable to transformation. Various medium sets were compared and a previously published medium set provided cultures with >96 % embryogenic callus, and data on transient and stable gene expression of RFP were used to optimize biolistic parameters, and further validate the switchgrass (PvUbi1) promoter. SA37 proved to be the most transformable, whereas eight transgenic calli on average were recovered per bombardment of 20 calli (40 % efficiency) when using a three-day day preculture step, 0.6 M osmotic adjustment medium, 4,482 kPa rupture disks and 0.4 μm gold particles which traveled 9 cm before hitting the target callus tissue. Regenerability was high, especially for ST2, for which it is possible to recover on average over 400 plants per half-gram callus tissue. It is now possible to routinely and efficiently engineer elite switchgrass genotypes using biolistic transformation.

The advent of model-enabled workflows in systems biology allows for the integration of experimental data types with genome-scale models to discover new features of biology. This work demonstrates such a workflow, aimed at establishing a metabolomics platform applied to study the differences in metabolomes between anaerobic and aerobic growth of Escherichia coli. Constraint-based modeling was utilized to deduce a target list of compounds for downstream method development. An analytical and experimental methodology was developed and tailored to the compound chemistry and growth conditions of interest. This included the construction of a rapid sampling apparatus for use with anaerobic cultures. The resulting genome-scale data sets for anaerobic and aerobic growth were validated by comparison to previous small-scale studies comparing growth of E. coli under the same conditions. The metabolomics data were then integrated with the E. coli genome-scale metabolic model (GEM) via a sensitivity analysis that utilized reaction thermodynamics to reconcile simulated growth rates and reaction directionalities. This analysis highlighted several optimal network usage inconsistencies, including the incorrect use of the beta-oxidation pathway for synthesis of fatty acids. This analysis also identified enzyme promiscuity for the pykA gene, that is critical for anaerobic growth, and which has not been previously incorporated into metabolic models of E coli.

Switchgrass (Panicum virgatum L.) is a C4 perennial grass and has been identified as a potential bioenergy crop for cellulosic ethanol because of its rapid growth rate, nutrient use efficiency and widespread distribution throughout North America. The improvement of bioenergy feedstocks is needed to make cellulosic ethanol economically feasible, and genetic engineering of switchgrass is a promising approach towards this goal. A crucial component of creating transgenic switchgrass is having the capability of transforming the explants with DNA sequences of interest using vector constructs. However, there are limited options with the monocot plant vectors currently available. With this in mind, a versatile set of Gateway-compatible destination vectors (termed pANIC) was constructed to be used in monocot plants for transgenic crop improvement. The pANIC vectors can be used for transgene overexpression or RNAi-mediated gene suppression. The pANIC vector set includes vectors that can be utilized for particle bombardment or Agrobacterium-mediated transformation. All the vectors contain (i) a Gateway cassette for overexpression or silencing of the target sequence, (ii) a plant selection cassette and (iii) a visual reporter cassette. The pANIC vector set was functionally validated in switchgrass and rice and allows for high-throughput screening of sequences of interest in other monocot species as well.

Background The ubiquitin protein is present in all eukaryotic cells and promoters from ubiquitin genes are good candidates to regulate the constitutive expression of transgenes in plants. Therefore, two switchgrass (Panicum virgatum L.) ubiquitin genes (PvUbi1 and PvUbi2) were cloned and characterized. Reporter constructs were produced containing the isolated 5' upstream regulatory regions of the coding sequences (i.e. PvUbi1 and PvUbi2 promoters) fused to the uidA coding region (GUS) and tested for transient and stable expression in a variety of plant species and tissues. Results PvUbi1 consists of 607 bp containing cis-acting regulatory elements, a 5' untranslated region (UTR) containing a 93 bp non-coding exon and a 1291 bp intron, and a 918 bp open reading frame (ORF) that encodes four tandem, head -to-tail ubiquitin monomer repeats followed by a 191 bp 3' UTR. PvUbi2 consists of 692 bp containing cis-acting regulatory elements, a 5' UTR containing a 97 bp non-coding exon and a 1072 bp intron, a 1146 bp ORF that encodes five tandem ubiquitin monomer repeats and a 183 bp 3' UTR. PvUbi1 and PvUbi2 were expressed in all examined switchgrass tissues as measured by qRT-PCR. Using biolistic bombardment, PvUbi1 and PvUbi2 promoters showed strong expression in switchgrass and rice callus, equaling or surpassing the expression levels of the CaMV 35S, 2x35S, ZmUbi1, and OsAct1 promoters. GUS staining following stable transformation in rice demonstrated that the PvUbi1 and PvUbi2 promoters drove expression in all examined tissues. When stably transformed into tobacco (Nicotiana tabacum), the PvUbi2+3 and PvUbi2+9 promoter fusion variants showed expression in vascular and reproductive tissues. Conclusions The PvUbi1 and PvUbi2 promoters drive expression in switchgrass, rice and tobacco and are strong constitutive promoter candidates that will be useful in genetic transformation of monocots and dicots.

Cortical neural prostheses require chronically implanted small-area microelectrode arrays that simultaneously record and stimulate neural activity. It is necessary to develop new materials with low interface impedance and large charge transfer capacity for this application and we explore the use of conducting polymer poly(3,4-ethylenedioxythiophene) (PEDOT) for the same. We subjected PEDOT coated electrodes to voltage cycling between -0.6 and 0.8 V, 24 h continuous biphasic stimulation at 3 mC/cm2 and accelerated aging for four weeks. Characterization was performed using cyclic voltammetry, electrochemical impedance spectroscopy, and voltage transient measurements. We found that PEDOT coated electrodes showed a charge injection limit 15 times higher than Platinum Iridium (Ptlr) electrodes and electroplated Iridium Oxide (IrOx) electrodes when using constant current stimulation at zero voltage bias. In vivo chronic testing of microelectrode arrays implanted in rat cortex revealed that PEDOT coated electrodes show higher signal-to-noise recordings and superior charge injection compared to Ptlr electrodes.

We have previously reported the design of a new PCR primer pair that allows amplification of a broad range of eubacterial 16S rDNA sequences from ectomycorrhizae (ECM) without co-amplification of plastid or mitochondrial sequences. Here, we report using a similar primer combination to generate three small 16S rDNA libraries from tuberculate ECM of Rhizopogon spp., two from R. vinicolor ECM (libraries Rvi18 and Rvi24) and one from R. vesiculosus ECM (library Rve13). At the class level, libraries were dominated by sequences from the Alphaproteobacteria, Gammaproteobacteria, and Acidobacteria, with some Sphingobacteria, Actinobacteria, Planctomycetacia, and Verrucomicrobiae present as well. Based on the parsimony test implemented in TreeClimber, libraries Rvi18 and Rvi24 were significantly different from Rve13 at the alpha = 0.05 level, while they were only borderline significantly different from each other (p = 0.07). Differences between Rvi and Rve libraries were primarily due to differences in the number of Alphaproteobacteria sequences and specifically sequences from the Rhizobiales, which were more common in the Rve13 library. It is currently unknown what drives these differences between eubacterial communities. Amplification success for eubacterial 16S rDNA sequences was generally low in this study indicating low abundance of bacteria on tuberculate ECM. Attempts to amplify nitrogenase reductase (nifH) sequences were unsuccessful.

The posterodorsal aspect of the medial amygdala (MePD) is sexually dimorphic in regional volume, rostrocaudal extent, and neuronal soma size in rats. These dimorphisms are maintained by circulating gonadal hormones, as castration of adult male rats reduces MePD measures, while testosterone treatment of females increases them. We now report that the MePD is also sexually dimorphic in volume, rostrocaudal extent, and somal area in BALB/c mice. Four weeks after castration of adult male mice, MePD regional volume and soma size are reduced, but rostrocaudal extent is not, compared to sham-castrated males. Treatment of adult ovariectomized females with an aromatized metabolite of testosterone, estradiol, for eight weeks increased MePD volume and soma size, but not rostrocaudal extent. To probe the possible role of afferents in the steroid-induced plasticity of the MePD, we examined the effect of removing the olfactory bulbs in gonadally intact males and in estrogen-treated females. Bulbectomy had no effect on MePD morphology with one exception: among gonadally intact males, neuronal soma size was slightly smaller in the right MePD of bulbectomized males compared to males with intact bulbs. These results indicate that the sexual dimorphism and hormone responsiveness of the MePD that has been extensively studied in rats is also present in mice, which offer genetic tools for future research. We detected little or no evidence that olfactory bulb afferents play a role in maintaining MePD morphology in adult mice.

Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)—in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics). Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools., Author Summary We are now in the age of big data. More than ever before, biological discoveries require powerful and flexible tools for managing large datasets, including both visual and statistical tools. Pathway-based visualization is particularly powerful since it enables one to analyze complex datasets within the context of actual biological processes and to elucidate how each change in a cell effects related processes. To facilitate such approaches, we present Escher, a web application that can be used to rapidly build pathway maps. On Escher maps, diverse datasets related to genes, reactions, and metabolites can be quickly contextualized within metabolism and, increasingly, beyond metabolism. Escher is available now for free use (under the MIT license) at https://escher.github.io.