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Background Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures. Results Identifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine. Conclusions The emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism.

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome., Mechanistic insight into the regulation of transcriptional modules remains scarce. Here, the authors identify statistically independent gene sets by applying independent component analysis to a high-quality E. coli RNA-seq data compendium and find that most gene sets represent the effects of specific transcriptional regulators.

Experimental studies of Escherichia coli K-12 MG1655 often implicate poorly annotated genes in cellular phenotypes. However, we lack a systematic understanding of these genes. How many are there? What information is available for them? And what features do they share that could explain the gap in our understanding? Efforts to build predictive, whole-cell models of E. coli inevitably face this knowledge gap. We approached these questions systematically by assembling annotations from the knowledge bases EcoCyc, EcoGene, UniProt and RegulonDB. We identified the genes that lack experimental evidence of function (the ‘y-ome’) which include 1600 of 4623 unique genes (34.6%), of which 111 have absolutely no evidence of function. An additional 220 genes (4.7%) are pseudogenes or phantom genes. y-ome genes tend to have lower expression levels and are enriched in the termination region of the E. coli chromosome. 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 in E. coli whose primary name starts with ‘y’ is unannotated, and we discuss the value of the y-ome for systematic improvement of E. coli knowledge bases and its extension to other organisms.

Mesoplasma florum, a fast‐growing near‐minimal organism, is a compelling model to explore rational genome designs. Using sequence and structural homology, the set of metabolic functions its genome encodes was identified, allowing the reconstruction of a metabolic network representing ˜ 30% of its protein‐coding genes. Growth medium simplification enabled substrate uptake and product secretion rate quantification which, along with experimental biomass composition, were integrated as species‐specific constraints to produce the functional iJL208 genome‐scale model (GEM) of metabolism. Genome‐wide expression and essentiality datasets as well as growth data on various carbohydrates were used to validate and refine iJL208. Discrepancies between model predictions and observations were mechanistically explained using protein structures and network analysis. iJL208 was also used to propose an in silico reduced genome. Comparing this prediction to the minimal cell JCVI‐syn3.0 and its parent JCVI‐syn1.0 revealed key features of a minimal gene set. iJL208 is a stepping‐stone toward model‐driven whole‐genome engineering., The first genome‐scale metabolic model for the near‐minimal bacterium Mesoplasma florum is reported. Comparing the model‐driven prediction of a M. florum genome reduction scenario to a closely related minimal cell reveals key features of a minimal gene set.

A genome contains the information underlying an organism's form and function. Yet, we lack formal framework to represent and study this information. Here, we introduce the Bitome, a matrix composed of binary digits (bits) representing the genomic positions of genomic features. We form a Bitome for the genome of Escherichia coli K-12 MG1655. We find that: (i) genomic features are encoded unevenly, both spatially and categorically; (ii) coding and intergenic features are recapitulated at high resolution; (iii) adaptive mutations are skewed towards genomic positions with fewer features; and (iv) the Bitome enhances prediction of adaptively mutated and essential genes. The Bitome is a formal representation of a genome and may be used to study its fundamental organizational properties.

BackgroundSwitchgrass (Panicum virgatumL.), a North American prairie grassland species, is a potential lignocellulosic biofuel feedstock owing to its wide adaptability and biomass production. Production and genetic manipulation of switchgrass should be useful to improve its biomass composition and production for bioenergy applications. The goal of this project was to develop a high-throughput stable switchgrass transformation method usingAgrobacterium tumefacienswith subsequent plant regeneration.ResultsRegenerable embryogenic cell suspension cultures were established from friable type II callus-derived inflorescences using two genotypes selected from the synthetic switchgrass variety ‘Performer’ tissue culture lines 32 and 605. The cell suspension cultures were composed of a heterogeneous fine mixture culture of single cells and aggregates.Agrobacterium tumefaciensstrain GV3101 was optimum to transfer into cells the pANIC-10A vector with a hygromycin-selectable marker gene and apporRFPorange fluorescent protein marker gene at an 85% transformation efficiency. Liquid cultures gave rise to embryogenic callus and then shoots, of which up to 94% formed roots. The resulting transgenic plants were phenotypically indistinguishable from the non-transgenic parent lines.ConclusionThe new cell suspension-based protocol enables high-throughputAgrobacterium-mediated transformation and regeneration of switchgrass in which plants are recovered within 6–7 months from culture establishment.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.