Ezekiel Maier

Role of Fat Body Lipogenesis in Protection against the Effects of Caloric Overload in Drosophila

Background: A high sugar diet leads to obesity and insulin resistance in Drosophila. Results: The metabolic fate of dietary glucose is reprogrammed in high sugar-fed and lean animals. Conclusion: Obesity is protective against the deleterious effects of a high sugar diet. Significance: An emerging perspective that obesity is protective against sequelae of human metabolic disease is conserved in the fly. The Drosophila fat body is a liver- and adipose-like tissue that stores fat and serves as a detoxifying and immune responsive organ. We have previously shown that a high sugar diet leads to elevated hemolymph glucose and systemic insulin resistance in developing larvae and adults. Here, we used stable isotope tracer feeding to demonstrate that rearing larvae on high sugar diets impaired the synthesis of esterified fatty acids from dietary glucose. Fat body lipid profiling revealed changes in both carbon chain length and degree of unsaturation of fatty acid substituents, particularly in stored triglycerides. We tested the role of the fat body in larval tolerance of caloric excess. Our experiments demonstrated that lipogenesis was necessary for animals to tolerate high sugar feeding as tissue-specific loss of orthologs of carbohydrate response element-binding protein or stearoyl-CoA desaturase 1 resulted in lethality on high sugar diets. By contrast, increasing the fat content of the fat body by knockdown of king-tubby was associated with reduced hyperglycemia and improved growth and tolerance of high sugar diets. Our work supports a critical role for the fat body and the Drosophila carbohydrate response element-binding protein ortholog in metabolic homeostasis in Drosophila.

Mapping functional transcription factor networks from gene expression data

A critical step in understanding how a genome functions is determining which transcription factors (TFs) regulate each gene. Accordingly, extensive effort has been devoted to mapping TF networks. In Saccharomyces cerevisiae, protein-DNA interactions have been identified for most TFs by ChIP-chip, and expression profiling has been done on strains deleted for most TFs. These studies revealed that there is little overlap between the genes whose promoters are bound by a TF and those whose expression changes when the TF is deleted, leaving us without a definitive TF network for any eukaryote and without an efficient method for mapping functional TF networks. This paper describes NetProphet, a novel algorithm that improves the efficiency of network mapping from gene expression data. NetProphet exploits a fundamental observation about the nature of TF networks: The response to disrupting or overexpressing a TF is strongest on its direct targets and dissipates rapidly as it propagates through the network. Using S. cerevisiae data, we show that NetProphet can predict thousands of direct, functional regulatory interactions, using only gene expression data. The targets that NetProphet predicts for a TF are at least as likely to have sites matching the TFs binding specificity as the targets implicated by ChIP. Unlike most ChIP targets, the NetProphet targets also show evidence of functional regulation. This suggests a surprising conclusion: The best way to begin mapping direct, functional TF-promoter interactions may not be by measuring binding. We also show that NetProphet yields new insights into the functions of several yeast TFs, including a well-studied TF, Cbf1, and a completely unstudied TF, Eds1.

Cryptococcus neoformans Dual GDP-Mannose Transporters and Their Role in Biology and Virulence

ABSTRACT Cryptococcus neoformans is an opportunistic yeast responsible for lethal meningoencephalitis in humans. This pathogen elaborates a polysaccharide capsule, which is its major virulence factor. Mannose constitutes over one-half of the capsule mass and is also extensively utilized in cell wall synthesis and in glycosylation of proteins and lipids. The activated mannose donor for most biosynthetic reactions, GDP-mannose, is made in the cytosol, although it is primarily consumed in secretory organelles. This compartmentalization necessitates specific transmembrane transporters to make the donor available for glycan synthesis. We previously identified two cryptococcal GDP-mannose transporters, Gmt1 and Gmt2. Biochemical studies of each protein expressed in Saccharomyces cerevisiae showed that both are functional, with similar kinetics and substrate specificities in vitro. We have now examined these proteins in vivo and demonstrate that cells lacking Gmt1 show significant phenotypic differences from those lacking Gmt2 in terms of growth, colony morphology, protein glycosylation, and capsule phenotypes. Some of these observations may be explained by differential expression of the two genes, but others suggest that the two proteins play overlapping but nonidentical roles in cryptococcal biology. Furthermore, gmt1 gmt2 double mutant cells, which are unexpectedly viable, exhibit severe defects in capsule synthesis and protein glycosylation and are avirulent in mouse models of cryptococcosis.

Computational Analysis Reveals a Key Regulator of Cryptococcal Virulence and Determinant of Host Response

ABSTRACT Cryptococcus neoformans is a ubiquitous, opportunistic fungal pathogen that kills over 600,000 people annually. Here, we report integrated computational and experimental investigations of the role and mechanisms of transcriptional regulation in cryptococcal infection. Major cryptococcal virulence traits include melanin production and the development of a large polysaccharide capsule upon host entry; shed capsule polysaccharides also impair host defenses. We found that both transcription and translation are required for capsule growth and that Usv101 is a master regulator of pathogenesis, regulating melanin production, capsule growth, and capsule shedding. It does this by directly regulating genes encoding glycoactive enzymes and genes encoding three other transcription factors that are essential for capsule growth: GAT201, RIM101, and SP1. Murine infection with cryptococci lacking Usv101 significantly alters the kinetics and pathogenesis of disease, with extended survival and, unexpectedly, death by pneumonia rather than meningitis. Our approaches and findings will inform studies of other pathogenic microbes. IMPORTANCE Cryptococcus neoformans causes fatal meningitis in immunocompromised individuals, mainly HIV positive, killing over 600,000 each year. A unique feature of this yeast, which makes it particularly virulent, is its polysaccharide capsule; this structure impedes host efforts to combat infection. Capsule size and structure respond to environmental conditions, such as those encountered in an infected host. We have combined computational and experimental tools to elucidate capsule regulation, which we show primarily occurs at the transcriptional level. We also demonstrate that loss of a novel transcription factor alters virulence factor expression and host cell interactions, changing the lethal condition from meningitis to pneumonia with an exacerbated host response. We further demonstrate the relevant targets of regulation and kinetically map key regulatory and host interactions. Our work elucidates mechanisms of capsule regulation, provides methods and resources to the research community, and demonstrates an altered pathogenic outcome that resembles some human conditions. Cryptococcus neoformans causes fatal meningitis in immunocompromised individuals, mainly HIV positive, killing over 600,000 each year. A unique feature of this yeast, which makes it particularly virulent, is its polysaccharide capsule; this structure impedes host efforts to combat infection. Capsule size and structure respond to environmental conditions, such as those encountered in an infected host. We have combined computational and experimental tools to elucidate capsule regulation, which we show primarily occurs at the transcriptional level. We also demonstrate that loss of a novel transcription factor alters virulence factor expression and host cell interactions, changing the lethal condition from meningitis to pneumonia with an exacerbated host response. We further demonstrate the relevant targets of regulation and kinetically map key regulatory and host interactions. Our work elucidates mechanisms of capsule regulation, provides methods and resources to the research community, and demonstrates an altered pathogenic outcome that resembles some human conditions.

Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast

Significance The ability to engineer specific behaviors into cells would have a significant impact on biomedicine and biotechnology, including applications to regenerative medicine and biofuels production. One way to coax cells to behave in a desired way is to globally modify their gene expression state, making it more like the state of cells with the desired behavior. This paper introduces a broadly applicable algorithm for transcriptome engineering—designing transcription factor deletions or overexpressions to move cells to a gene expression state that is associated with a desired phenotype. This paper also presents an approach to benchmarking and validating such algorithms. The availability of systematic, objective benchmarks for a computational task often stimulates increased effort and rapid progress on that task. The ability to rationally manipulate the transcriptional states of cells would be of great use in medicine and bioengineering. We have developed an algorithm, NetSurgeon, which uses genome-wide gene-regulatory networks to identify interventions that force a cell toward a desired expression state. We first validated NetSurgeon extensively on existing datasets. Next, we used NetSurgeon to select transcription factor deletions aimed at improving ethanol production in Saccharomyces cerevisiae cultures that are catabolizing xylose. We reasoned that interventions that move the transcriptional state of cells using xylose toward that of cells producing large amounts of ethanol from glucose might improve xylose fermentation. Some of the interventions selected by NetSurgeon successfully promoted a fermentative transcriptional state in the absence of glucose, resulting in strains with a 2.7-fold increase in xylose import rates, a 4-fold improvement in xylose integration into central carbon metabolism, or a 1.3-fold increase in ethanol production rate. We conclude by presenting an integrated model of transcriptional regulation and metabolic flux that will enable future efforts aimed at improving xylose fermentation to prioritize functional regulators of central carbon metabolism.

Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach

The Gene Promoter Expression Prediction challenge consisted of predicting gene expression from promoter sequences in a previously unknown experimentally generated data set. The challenge was presented to the community in the framework of the sixth Dialogue for Reverse Engineering Assessments and Methods (DREAM6), a community effort to evaluate the status of systems biology modeling methodologies. Nucleotide-specific promoter activity was obtained by measuring fluorescence from promoter sequences fused upstream of a gene for yellow fluorescence protein and inserted in the same genomic site of yeast Saccharomyces cerevisiae. Twenty-one teams submitted results predicting the expression levels of 53 different promoters from yeast ribosomal protein genes. Analysis of participant predictions shows that accurate values for low-expressed and mutated promoters were difficult to obtain, although in the latter case, only when the mutation induced a large change in promoter activity compared to the wild-type sequence. As in previous DREAM challenges, we found that aggregation of participant predictions provided robust results, but did not fare better than the three best algorithms. Finally, this study not only provides a benchmark for the assessment of methods predicting activity of a specific set of promoters from their sequence, but it also shows that the top performing algorithm, which used machine-learning approaches, can be improved by the addition of biological features such as transcription factor binding sites.

Seven-Up Is a Novel Regulator of Insulin Signaling

Musselman et al. address the overarching question: “What’s so bad about a high-calorie diet?” Using computational biology to analyze mRNA expression profiles, the authors built a Drosophila fat body gene regulatory network that predicted... Insulin resistance is associated with obesity, cardiovascular disease, non-alcoholic fatty liver disease, and type 2 diabetes. These complications are exacerbated by a high-calorie diet, which we used to model type 2 diabetes in Drosophila melanogaster. Our studies focused on the fat body, an adipose- and liver-like tissue that stores fat and maintains circulating glucose. A gene regulatory network was constructed to predict potential regulators of insulin signaling in this tissue. Genomic characterization of fat bodies suggested a central role for the transcription factor Seven-up (Svp). Here, we describe a new role for Svp as a positive regulator of insulin signaling. Tissue-specific loss-of-function showed that Svp is required in the fat body to promote glucose clearance, lipid turnover, and insulin signaling. Svp appears to promote insulin signaling, at least in part, by inhibiting ecdysone signaling. Svp also impairs the immune response possibly via inhibition of antimicrobial peptide expression in the fat body. Taken together, these studies show that gene regulatory networks can help identify positive regulators of insulin signaling and metabolic homeostasis using the Drosophila fat body.

Optimization of Gene Prediction via More Accurate Phylogenetic Substitution Models

Determining the beginning and end positions of each exon in each protein coding gene within a genome can be difficult because the DNA patterns that signal a gene’s presence have multiple weakly related alternate forms and the DNA fragments that comprise a gene are generally small in comparison to the size of the genome. In response to this challenge, automated gene predictors were created to generate putative gene structures. N SCAN identifies gene structures in a target DNA sequence and can use conservation patterns learned from alignments between a target and one or more informant DNA sequences. N SCAN uses a Bayesian network, generated from a phylogenetic tree, to probabilistically relate the target sequence to the aligned sequence(s). Phylogenetic substitution models are used to estimate substitution likelihood along the branches of the tree. Although N SCAN’s predictive accuracy is already a benchmark for de novo HMM based gene predictors, optimizing its use of substitution models will allow for improved conservation pattern estimates leading to even better accuracy. Selecting optimal substitution models requires avoiding overfitting as more detailed models require more free parameters; unfortunately, the number of parameters is limited by the number of known genes available for parameter estimation (training). In order to optimize substitution model selection, we tested eight Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 1 Optimization of Gene Prediction via More Accurate Phylogenetic Substitution Models Ezekiel Maier, Randall H Brown, and Michael R Brent Department of Computer Science and Engineering, Washington University, Saint Louis, MO, 63130 Abstract: Determining the beginning and end positions of each exon in each protein coding gene within a genome can be difficult because the DNA patterns that signal a gene’s presence have multiple weakly related alternate forms and the DNA fragments that comprise a gene are generally small in comparison to the size of the genome. In response to this challenge, automated gene predictors were created to generate putative gene structures. N-SCAN identifies gene structures in a target DNA sequence and can use conservation patterns learned from alignments between a target and one or more informant DNA sequences. N-SCAN uses a Bayesian network, generated from a phylogenetic tree, to probabilistically relate the target sequence to the aligned sequence(s). Phylogenetic substitution models are used to estimate substitution likelihood along the branches of the tree. Although N-SCAN’s predictive accuracy is already a benchmark for de novo HMM based gene predictors, optimizing its use of substitution models will allow for improved conservation pattern estimates leading to even better accuracy. Selecting optimal substitution models requires avoiding overfitting as more detailed models require more free parameters; unfortunately, the number of parameters is limited by the number of known genes available for parameter estimation (training). In order to optimize substitution model selection, we tested eight models on the entire genome including General, Reversible, HKY, Jukes-Cantor, and Kimura. In addition to testing models on the entire genome, genome feature based model selection strategies were investigated by assessing the ability of each model to accurately reflex the unique conservation patterns present in each genome region. Context dependency was examined using Determining the beginning and end positions of each exon in each protein coding gene within a genome can be difficult because the DNA patterns that signal a gene’s presence have multiple weakly related alternate forms and the DNA fragments that comprise a gene are generally small in comparison to the size of the genome. In response to this challenge, automated gene predictors were created to generate putative gene structures. N-SCAN identifies gene structures in a target DNA sequence and can use conservation patterns learned from alignments between a target and one or more informant DNA sequences. N-SCAN uses a Bayesian network, generated from a phylogenetic tree, to probabilistically relate the target sequence to the aligned sequence(s). Phylogenetic substitution models are used to estimate substitution likelihood along the branches of the tree. Although N-SCAN’s predictive accuracy is already a benchmark for de novo HMM based gene predictors, optimizing its use of substitution models will allow for improved conservation pattern estimates leading to even better accuracy. Selecting optimal substitution models requires avoiding overfitting as more detailed models require more free parameters; unfortunately, the number of parameters is limited by the number of known genes available for parameter estimation (training). In order to optimize substitution model selection, we tested eight models on the entire genome including General, Reversible, HKY, Jukes-Cantor, and Kimura. In addition to testing models on the entire genome, genome feature based model selection strategies were investigated by assessing the ability of each model to accurately reflex the unique conservation patterns present in each genome region. Context dependency was examined using zeroth, first, and second order models. All models were tested on the human and D. melanogaster genomes. Analysis of the data suggests that the nucleotide equilibrium frequency assumption (denoted as i) is the strongest predictor of a model’s accuracy, followed by reversibility and transition/transversion inequality. Furthermore, second order models are shown to give an average of 0.6% improvement over first order models, which give an 18% improvement over zeroth order models. Finally, by limiting parameter usage by the number of training examples available for each feature, genome feature based model selection better estimates substitution likelihood leading to a significant improvement in N-SCAN’s gene annotation accuracy.