Other search tools

About this data

The publication data currently available has been vetted by Vanderbilt faculty, staff, administrators and trainees. The data itself is retrieved directly from NCBI's PubMed and is automatically updated on a weekly basis to ensure accuracy and completeness.

If you have any questions or comments, please contact us.

Results: 1 to 10 of 46

Publication Record

Connections

Ubiquitin turnover and endocytic trafficking in yeast are regulated by Ser57 phosphorylation of ubiquitin.
Lee S, Tumolo JM, Ehlinger AC, Jernigan KK, Qualls-Histed SJ, Hsu PC, McDonald WH, Chazin WJ, MacGurn JA
(2017) Elife 6:
MeSH Terms: Endocytosis, Homeostasis, Phosphoprotein Phosphatases, Phosphorylation, Protein Processing, Post-Translational, Saccharomyces cerevisiae Proteins, Ubiquitin, Yeasts
Show Abstract · Added March 24, 2018
Despite its central role in protein degradation little is known about the molecular mechanisms that sense, maintain, and regulate steady state concentration of ubiquitin in the cell. Here, we describe a novel mechanism for regulation of ubiquitin homeostasis that is mediated by phosphorylation of ubiquitin at the Ser57 position. We find that loss of Ppz phosphatase activity leads to defects in ubiquitin homeostasis that are at least partially attributable to elevated levels of Ser57 phosphorylated ubiquitin. Phosphomimetic mutation at the Ser57 position of ubiquitin conferred increased rates of endocytic trafficking and ubiquitin turnover. These phenotypes are associated with bypass of recognition by endosome-localized deubiquitylases - including Doa4 which is critical for regulation of ubiquitin recycling. Thus, ubiquitin homeostasis is significantly impacted by the rate of ubiquitin flux through the endocytic pathway and by signaling pathways that converge on ubiquitin itself to determine whether it is recycled or degraded in the vacuole.
0 Communities
1 Members
0 Resources
8 MeSH Terms
Isotope-Labeling Studies Support the Electrophilic Compound I Iron Active Species, FeO(3+), for the Carbon-Carbon Bond Cleavage Reaction of the Cholesterol Side-Chain Cleavage Enzyme, Cytochrome P450 11A1.
Yoshimoto FK, Jung IJ, Goyal S, Gonzalez E, Guengerich FP
(2016) J Am Chem Soc 138: 12124-41
MeSH Terms: Alcohol Dehydrogenase, Caproates, Carbon, Cholesterol, Cholesterol Side-Chain Cleavage Enzyme, Ferric Compounds, Isotope Labeling, Oxygen, Yeasts
Show Abstract · Added March 14, 2018
The enzyme cytochrome P450 11A1 cleaves the C20-C22 carbon-carbon bond of cholesterol to form pregnenolone, the first 21-carbon precursor of all steroid hormones. Various reaction mechanisms are possible for the carbon-carbon bond cleavage step of P450 11A1, and most current proposals involve the oxoferryl active species, Compound I (FeO(3+)). Compound I can either (i) abstract an O-H hydrogen atom or (ii) be attacked by a nucleophilic hydroxy group of its substrate, 20R,22R-dihydroxycholesterol. The mechanism of this carbon-carbon bond cleavage step was tested using (18)O-labeled molecular oxygen and purified P450 11A1. P450 11A1 was incubated with 20R,22R-dihydroxycholesterol in the presence of molecular oxygen ((18)O2), and coupled assays were used to trap the labile (18)O atoms in the enzymatic products (i.e., isocaproaldehyde and pregnenolone). The resulting products were derivatized and the (18)O content was analyzed by high-resolution mass spectrometry. P450 11A1 showed no incorporation of an (18)O atom into either of its carbon-carbon bond cleavage products, pregnenolone and isocaproaldehyde . The positive control experiments established retention of the carbonyl oxygens in the enzymatic products during the trapping and derivatization processes. These results reveal a mechanism involving an electrophilic Compound I species that reacts with nucleophilic hydroxy groups in the 20R,22R-dihydroxycholesterol intermediate of the P450 11A1 reaction to produce the key steroid pregnenolone.
0 Communities
1 Members
0 Resources
9 MeSH Terms
Comparative genomics of biotechnologically important yeasts.
Riley R, Haridas S, Wolfe KH, Lopes MR, Hittinger CT, Göker M, Salamov AA, Wisecaver JH, Long TM, Calvey CH, Aerts AL, Barry KW, Choi C, Clum A, Coughlan AY, Deshpande S, Douglass AP, Hanson SJ, Klenk HP, LaButti KM, Lapidus A, Lindquist EA, Lipzen AM, Meier-Kolthoff JP, Ohm RA, Otillar RP, Pangilinan JL, Peng Y, Rokas A, Rosa CA, Scheuner C, Sibirny AA, Slot JC, Stielow JB, Sun H, Kurtzman CP, Blackwell M, Grigoriev IV, Jeffries TW
(2016) Proc Natl Acad Sci U S A 113: 9882-7
MeSH Terms: Ascomycota, Biotechnology, Evolution, Molecular, Fungal Proteins, Genetic Code, Genome, Fungal, Genomics, Metabolic Networks and Pathways, Phylogeny, Species Specificity, Yeasts
Show Abstract · Added April 6, 2017
Ascomycete yeasts are metabolically diverse, with great potential for biotechnology. Here, we report the comparative genome analysis of 29 taxonomically and biotechnologically important yeasts, including 16 newly sequenced. We identify a genetic code change, CUG-Ala, in Pachysolen tannophilus in the clade sister to the known CUG-Ser clade. Our well-resolved yeast phylogeny shows that some traits, such as methylotrophy, are restricted to single clades, whereas others, such as l-rhamnose utilization, have patchy phylogenetic distributions. Gene clusters, with variable organization and distribution, encode many pathways of interest. Genomics can predict some biochemical traits precisely, but the genomic basis of others, such as xylose utilization, remains unresolved. Our data also provide insight into early evolution of ascomycetes. We document the loss of H3K9me2/3 heterochromatin, the origin of ascomycete mating-type switching, and panascomycete synteny at the MAT locus. These data and analyses will facilitate the engineering of efficient biosynthetic and degradative pathways and gateways for genomic manipulation.
0 Communities
1 Members
0 Resources
11 MeSH Terms
A Genome-Scale Investigation of How Sequence, Function, and Tree-Based Gene Properties Influence Phylogenetic Inference.
Shen XX, Salichos L, Rokas A
(2016) Genome Biol Evol 8: 2565-80
MeSH Terms: Animals, Base Composition, Genome, Fungal, Mammals, Phylogeny, Sequence Alignment, Yeasts
Show Abstract · Added April 6, 2017
Molecular phylogenetic inference is inherently dependent on choices in both methodology and data. Many insightful studies have shown how choices in methodology, such as the model of sequence evolution or optimality criterion used, can strongly influence inference. In contrast, much less is known about the impact of choices in the properties of the data, typically genes, on phylogenetic inference. We investigated the relationships between 52 gene properties (24 sequence-based, 19 function-based, and 9 tree-based) with each other and with three measures of phylogenetic signal in two assembled data sets of 2,832 yeast and 2,002 mammalian genes. We found that most gene properties, such as evolutionary rate (measured through the percent average of pairwise identity across taxa) and total tree length, were highly correlated with each other. Similarly, several gene properties, such as gene alignment length, Guanine-Cytosine content, and the proportion of tree distance on internal branches divided by relative composition variability (treeness/RCV), were strongly correlated with phylogenetic signal. Analysis of partial correlations between gene properties and phylogenetic signal in which gene evolutionary rate and alignment length were simultaneously controlled, showed similar patterns of correlations, albeit weaker in strength. Examination of the relative importance of each gene property on phylogenetic signal identified gene alignment length, alongside with number of parsimony-informative sites and variable sites, as the most important predictors. Interestingly, the subsets of gene properties that optimally predicted phylogenetic signal differed considerably across our three phylogenetic measures and two data sets; however, gene alignment length and RCV were consistently included as predictors of all three phylogenetic measures in both yeasts and mammals. These results suggest that a handful of sequence-based gene properties are reliable predictors of phylogenetic signal and could be useful in guiding the choice of phylogenetic markers.
© The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
0 Communities
1 Members
0 Resources
7 MeSH Terms
Novel information theory-based measures for quantifying incongruence among phylogenetic trees.
Salichos L, Stamatakis A, Rokas A
(2014) Mol Biol Evol 31: 1261-71
MeSH Terms: Evolution, Molecular, Information Theory, Models, Genetic, Phylogeny, Yeasts
Show Abstract · Added May 30, 2014
Phylogenies inferred from different data matrices often conflict with each other necessitating the development of measures that quantify this incongruence. Here, we introduce novel measures that use information theory to quantify the degree of conflict or incongruence among all nontrivial bipartitions present in a set of trees. The first measure, internode certainty (IC), calculates the degree of certainty for a given internode by considering the frequency of the bipartition defined by the internode (internal branch) in a given set of trees jointly with that of the most prevalent conflicting bipartition in the same tree set. The second measure, IC All (ICA), calculates the degree of certainty for a given internode by considering the frequency of the bipartition defined by the internode in a given set of trees in conjunction with that of all conflicting bipartitions in the same underlying tree set. Finally, the tree certainty (TC) and TC All (TCA) measures are the sum of IC and ICA values across all internodes of a phylogeny, respectively. IC, ICA, TC, and TCA can be calculated from different types of data that contain nontrivial bipartitions, including from bootstrap replicate trees to gene trees or individual characters. Given a set of phylogenetic trees, the IC and ICA values of a given internode reflect its specific degree of incongruence, and the TC and TCA values describe the global degree of incongruence between trees in the set. All four measures are implemented and freely available in version 8.0.0 and subsequent versions of the widely used program RAxML.
0 Communities
1 Members
0 Resources
5 MeSH Terms
A microfluidic-enabled mechanical microcompressor for the immobilization of live single- and multi-cellular specimens.
Yan Y, Jiang L, Aufderheide KJ, Wright GA, Terekhov A, Costa L, Qin K, McCleery WT, Fellenstein JJ, Ustione A, Robertson JB, Johnson CH, Piston DW, Hutson MS, Wikswo JP, Hofmeister W, Janetopoulos C
(2014) Microsc Microanal 20: 141-51
MeSH Terms: Animals, Cytological Techniques, Drosophila melanogaster, Equipment Design, Microfluidic Analytical Techniques, Paramecium tetraurelia, Single-Cell Analysis, Tetrahymena thermophila, Yeasts
Show Abstract · Added March 10, 2014
A microcompressor is a precision mechanical device that flattens and immobilizes living cells and small organisms for optical microscopy, allowing enhanced visualization of sub-cellular structures and organelles. We have developed an easily fabricated device, which can be equipped with microfluidics, permitting the addition of media or chemicals during observation. This device can be used on both upright and inverted microscopes. The apparatus permits micrometer precision flattening for nondestructive immobilization of specimens as small as a bacterium, while also accommodating larger specimens, such as Caenorhabditis elegans, for long-term observations. The compressor mount is removable and allows easy specimen addition and recovery for later observation. Several customized specimen beds can be incorporated into the base. To demonstrate the capabilities of the device, we have imaged numerous cellular events in several protozoan species, in yeast cells, and in Drosophila melanogaster embryos. We have been able to document previously unreported events, and also perform photobleaching experiments, in conjugating Tetrahymena thermophila.
1 Communities
6 Members
0 Resources
9 MeSH Terms
Microtubule-regulating kinesins.
Sturgill EG, Ohi R
(2013) Curr Biol 23: R946-8
MeSH Terms: Eukaryota, Humans, Kinesin, Microtubules, Tubulin, Yeasts
Added March 7, 2014
0 Communities
1 Members
0 Resources
6 MeSH Terms
IDPQuantify: combining precursor intensity with spectral counts for protein and peptide quantification.
Chen YY, Chambers MC, Li M, Ham AJ, Turner JL, Zhang B, Tabb DL
(2013) J Proteome Res 12: 4111-21
MeSH Terms: Fungal Proteins, Humans, Peptide Mapping, Principal Component Analysis, Proteome, Proteomics, Reference Standards, Sensitivity and Specificity, Software, Tandem Mass Spectrometry, Yeasts
Show Abstract · Added March 7, 2014
Differentiating and quantifying protein differences in complex samples produces significant challenges in sensitivity and specificity. Label-free quantification can draw from two different information sources: precursor intensities and spectral counts. Intensities are accurate for calculating protein relative abundance, but values are often missing due to peptides that are identified sporadically. Spectral counting can reliably reproduce difference lists, but differentiating peptides or quantifying all but the most concentrated protein changes is usually beyond its abilities. Here we developed new software, IDPQuantify, to align multiple replicates using principal component analysis, extract accurate precursor intensities from MS data, and combine intensities with spectral counts for significant gains in differentiation and quantification. We have applied IDPQuantify to three comparative proteomic data sets featuring gold standard protein differences spiked in complicated backgrounds. The software is able to associate peptides with peaks that are otherwise left unidentified to increase the efficiency of protein quantification, especially for low-abundance proteins. By combing intensities with spectral counts from IDPicker, it gains an average of 30% more true positive differences among top differential proteins. IDPQuantify quantifies protein relative abundance accurately in these test data sets to produce good correlations between known and measured concentrations.
0 Communities
3 Members
0 Resources
11 MeSH Terms
Automated refinement and inference of analytical models for metabolic networks.
Schmidt MD, Vallabhajosyula RR, Jenkins JW, Hood JE, Soni AS, Wikswo JP, Lipson H
(2011) Phys Biol 8: 055011
MeSH Terms: Algorithms, Computational Biology, Glycolysis, Kinetics, Metabolic Networks and Pathways, Models, Biological, Nonlinear Dynamics, Yeasts
Show Abstract · Added March 27, 2014
The reverse engineering of metabolic networks from experimental data is traditionally a labor-intensive task requiring a priori systems knowledge. Using a proven model as a test system, we demonstrate an automated method to simplify this process by modifying an existing or related model--suggesting nonlinear terms and structural modifications--or even constructing a new model that agrees with the system's time series observations. In certain cases, this method can identify the full dynamical model from scratch without prior knowledge or structural assumptions. The algorithm selects between multiple candidate models by designing experiments to make their predictions disagree. We performed computational experiments to analyze a nonlinear seven-dimensional model of yeast glycolytic oscillations. This approach corrected mistakes reliably in both approximated and overspecified models. The method performed well to high levels of noise for most states, could identify the correct model de novo, and make better predictions than ordinary parametric regression and neural network models. We identified an invariant quantity in the model, which accurately derived kinetics and the numerical sensitivity coefficients of the system. Finally, we compared the system to dynamic flux estimation and discussed the scaling and application of this methodology to automated experiment design and control in biological systems in real time.
1 Communities
2 Members
0 Resources
8 MeSH Terms
Affinity capture of TAP-tagged protein complexes: affinity purification step 2.
Link AJ, Weaver C, Farley A
(2011) Cold Spring Harb Protoc 2011: pdb.prot5607
MeSH Terms: Antibodies, Fungal, Chromatography, Affinity, Complex Mixtures, Fungal Proteins, Immunoglobulin G, Protein Interaction Mapping, Recombinant Fusion Proteins, Yeasts
Added February 20, 2015
0 Communities
1 Members
0 Resources
8 MeSH Terms