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1.  Ecology Drives the Distribution of Specialized Tyrosine Metabolism Modules in Fungi 
Genome Biology and Evolution  2014;6(1):121-132.
Gene clusters encoding accessory or environmentally specialized metabolic pathways likely play a significant role in the evolution of fungal genomes. Two such gene clusters encoding enzymes associated with the tyrosine metabolism pathway (KEGG #00350) have been identified in the filamentous fungus Aspergillus fumigatus. The l-tyrosine degradation (TD) gene cluster encodes a functional module that facilitates breakdown of the phenolic amino acid, l-tyrosine through a homogentisate intermediate, but is also involved in the production of pyomelanin, a fungal pathogenicity factor. The gentisate catabolism (GC) gene cluster encodes a functional module likely involved in phenolic compound degradation, which may enable metabolism of biphenolic stilbenes in multiple lineages. Our investigation of the evolution of the TD and GC gene clusters in 214 fungal genomes revealed spotty distributions partially shaped by gene cluster loss and horizontal gene transfer (HGT). Specifically, a TD gene cluster shows evidence of HGT between the extremophilic, melanized fungi Exophiala dermatitidis and Baudoinia compniacensis, and a GC gene cluster shows evidence of HGT between Sordariomycete and Dothideomycete grass pathogens. These results suggest that the distribution of specialized tyrosine metabolism modules is influenced by both the ecology and phylogeny of fungal species.
doi:10.1093/gbe/evt208
PMCID: PMC3914699  PMID: 24391152
pathway evolution; phenolic compound; gene cluster; horizontal gene transfer
2.  The evolutionary imprint of domestication on genome variation and function of the filamentous fungus Aspergillus oryzae 
Current biology : CB  2012;22(15):1403-1409.
Summary
The domestication of animals, plants and microbes fundamentally transformed the lifestyle and demography of the human species [1]. Although the genetic and functional underpinnings of animal and plant domestication are well understood, little is known about microbe domestication [2–6]. We systematically examined genome-wide sequence and functional variation between the domesticated fungus Aspergillus oryzae, whose saccharification abilities humans have harnessed for thousands of years to produce sake, soy sauce and miso from starch-rich grains, and its wild relative A. flavus, a potentially toxigenic plant and animal pathogen [7]. We discovered dramatic changes in the sequence variation and abundance profiles of genes and wholesale primary and secondary metabolic pathways between domesticated and wild relative isolates during growth on rice. Through selection by humans, our data suggest that an atoxigenic lineage of A. flavus gradually evolved into a “cell factory” for enzymes and metabolites involved in the saccharification process. These results suggest that whereas animal and plant domestication was largely driven by Neolithic “genetic tinkering” of developmental pathways, microbe domestication was driven by extensive remodeling of metabolism.
doi:10.1016/j.cub.2012.05.033
PMCID: PMC3416971  PMID: 22795693
primary metabolism; secondary metabolism; saccharification; selective sweep; functional genomics; proteomics
3.  Prediction of gene–phenotype associations in humans, mice, and plants using phenologs 
BMC Bioinformatics  2013;14:203.
Background
Phenotypes and diseases may be related to seemingly dissimilar phenotypes in other species by means of the orthology of underlying genes. Such “orthologous phenotypes,” or “phenologs,” are examples of deep homology, and may be used to predict additional candidate disease genes.
Results
In this work, we develop an unsupervised algorithm for ranking phenolog-based candidate disease genes through the integration of predictions from the k nearest neighbor phenologs, comparing classifiers and weighting functions by cross-validation. We also improve upon the original method by extending the theory to paralogous phenotypes. Our algorithm makes use of additional phenotype data — from chicken, zebrafish, and E. coli, as well as new datasets for C. elegans — establishing that several types of annotations may be treated as phenotypes. We demonstrate the use of our algorithm to predict novel candidate genes for human atrial fibrillation (such as HRH2, ATP4A, ATP4B, and HOPX) and epilepsy (e.g., PAX6 and NKX2-1). We suggest gene candidates for pharmacologically-induced seizures in mouse, solely based on orthologous phenotypes from E. coli. We also explore the prediction of plant gene–phenotype associations, as for the Arabidopsis response to vernalization phenotype.
Conclusions
We are able to rank gene predictions for a significant portion of the diseases in the Online Mendelian Inheritance in Man database. Additionally, our method suggests candidate genes for mammalian seizures based only on bacterial phenotypes and gene orthology. We demonstrate that phenotype information may come from diverse sources, including drug sensitivities, gene ontology biological processes, and in situ hybridization annotations. Finally, we offer testable candidates for a variety of human diseases, plant traits, and other classes of phenotypes across a wide array of species.
doi:10.1186/1471-2105-14-203
PMCID: PMC3704650  PMID: 23800157
4.  Global Transcriptome Changes Underlying Colony Growth in the Opportunistic Human Pathogen Aspergillus fumigatus 
Eukaryotic Cell  2012;11(1):68-78.
Aspergillus fumigatus is the most common and deadly pulmonary fungal infection worldwide. In the lung, the fungus usually forms a dense colony of filaments embedded in a polymeric extracellular matrix. To identify candidate genes involved in this biofilm (BF) growth, we used RNA-Seq to compare the transcriptomes of BF and liquid plankton (PL) growth. Sequencing and mapping of tens of millions sequence reads against the A. fumigatus transcriptome identified 3,728 differentially regulated genes in the two conditions. Although many of these genes, including the ones coding for transcription factors, stress response, the ribosome, and the translation machinery, likely reflect the different growth demands in the two conditions, our experiment also identified hundreds of candidate genes for the observed differences in morphology and pathobiology between BF and PL. We found an overrepresentation of upregulated genes in transport, secondary metabolism, and cell wall and surface functions. Furthermore, upregulated genes showed significant spatial structure across the A. fumigatus genome; they were more likely to occur in subtelomeric regions and colocalized in 27 genomic neighborhoods, many of which overlapped with known or candidate secondary metabolism gene clusters. We also identified 1,164 genes that were downregulated. This gene set was not spatially structured across the genome and was overrepresented in genes participating in primary metabolic functions, including carbon and amino acid metabolism. These results add valuable insight into the genetics of biofilm formation in A. fumigatus and other filamentous fungi and identify many relevant, in the context of biofilm biology, candidate genes for downstream functional experiments.
doi:10.1128/EC.05102-11
PMCID: PMC3255943  PMID: 21724936
5.  Systematic Definition of Protein Constituents along the Major Polarization Axis Reveals an Adaptive Reuse of the Polarization Machinery in Pheromone-Treated Budding Yeast 
Polarizing cells extensively restructure cellular components in a spatially and temporally coupled manner along the major axis of cellular extension. Budding yeast are a useful model of polarized growth, helping to define many molecular components of this conserved process. Besides budding, yeast cells also differentiate upon treatment with pheromone from the opposite mating type, forming a mating projection (the ‘shmoo’) by directional restructuring of the cytoskeleton, localized vesicular transport and overall reorganization of the cytosol. To characterize the proteomic localization changes accompanying polarized growth, we developed and implemented a novel cell microarray-based imaging assay for measuring the spatial redistribution of a large fraction of the yeast proteome, and applied this assay to identify proteins localized along the mating projection following pheromone treatment. We further trained a machine learning algorithm to refine the cell imaging screen, identifying additional shmoo-localized proteins. In all, we identified 74 proteins that specifically localize to the mating projection, including previously uncharacterized proteins (Ycr043c, Ydr348c, Yer071c, Ymr295c, and Yor304c-a) and known polarization complexes such as the exocyst. Functional analysis of these proteins, coupled with quantitative analysis of individual organelle movements during shmoo formation, suggests a model in which the basic machinery for cell polarization is generally conserved between processes forming the bud and the shmoo, with a distinct subset of proteins used only for shmoo formation. The net effect is a defined ordering of major organelles along the polarization axis, with specific proteins implicated at the proximal growth tip.
Upon sensing mating pheromone, budding yeast cells form a mating projection (the ‘shmoo’) that serves as a model for polarized cell growth, involving cytoskeletal/cytosolic restructuring and directed vesicular transport. We developed a cell microarray-based imaging assay for measuring localization of the yeast proteome during polarized growth. We find major organelles ordered along the polarization axis, localize 74 proteins to the growth tip, and observe adaptive reuse of general polarization machinery.
doi:10.1021/pr800524g
PMCID: PMC2651748  PMID: 19053807
Proteomics; polarized growth; subcellular localization; pheromone response; yeast
6.  Broad network-based predictability of Saccharomyces cerevisiae gene loss-of-function phenotypes 
Genome Biology  2007;8(12):R258.
Loss-of-function phenotypes of yeast genes can be predicted from the loss-of-function phenotypes of their neighbours in functional gene networks. This could potentially be applied to the prediction of human disease genes.
We demonstrate that loss-of-function yeast phenotypes are predictable by guilt-by-association in functional gene networks. Testing 1,102 loss-of-function phenotypes from genome-wide assays of yeast reveals predictability of diverse phenotypes, spanning cellular morphology, growth, metabolism, and quantitative cell shape features. We apply the method to extend a genome-wide screen by predicting, then verifying, genes whose disruption elongates yeast cells, and to predict human disease genes. To facilitate network-guided screens, a web server is available .
doi:10.1186/gb-2007-8-12-r258
PMCID: PMC2246260  PMID: 18053250

Results 1-6 (6)