scholarly journals Yeast gene CMR1/YDL156W is consistently co-expressed with genes participating in DNA-metabolic processes in a variety of stringent clustering experiments

2013 ◽  
Vol 10 (81) ◽  
pp. 20120990 ◽  
Author(s):  
Basel Abu-Jamous ◽  
Rui Fa ◽  
David J. Roberts ◽  
Asoke K. Nandi
Keyword(s):  
Protein A ◽  
Supporting Evidence ◽  
Ensemble Clustering ◽  
Clustering Methods ◽  
Yeast Gene ◽  
Functional Relationships ◽  
Uncharacterized Gene ◽  
Consensus Partition ◽  
Yeast Genes ◽  
Microarray Datasets

The binarization of consensus partition matrices (Bi-CoPaM) method has, among its unique features, the ability to perform ensemble clustering over the same set of genes from multiple microarray datasets by using various clustering methods in order to generate tunable tight clusters. Therefore, we have used the Bi-CoPaM method to the most synchronized 500 cell-cycle-regulated yeast genes from different microarray datasets to produce four tight, specific and exclusive clusters of co-expressed genes. We found 19 genes formed the tightest of the four clusters and this included the gene CMR1/YDL156W, which was an uncharacterized gene at the time of our investigations. Two very recent proteomic and biochemical studies have independently revealed many facets of CMR1 protein, although the precise functions of the protein remain to be elucidated. Our computational results complement these biological results and add more evidence to their recent findings of CMR1 as potentially participating in many of the DNA-metabolism processes such as replication, repair and transcription. Interestingly, our results demonstrate the close co-expressions of CMR1 and the replication protein A (RPA), the cohesion complex and the DNA polymerases α , δ and ɛ , as well as suggest functional relationships between CMR1 and the respective proteins. In addition, the analysis provides further substantial evidence that the expression of the CMR1 gene could be regulated by the MBF complex. In summary, the application of a novel analytic technique in large biological datasets has provided supporting evidence for a gene of previously unknown function, further hypotheses to test, and a more general demonstration of the value of sophisticated methods to explore new large datasets now so readily generated in biological experiments.

scholarly journals A simple clustering technique to extract subsets of data for function approximation

2015 ◽  
Vol 17 (5) ◽  
pp. 719-732
Author(s):  
Dulakshi Santhusitha Kumari Karunasingha ◽  
Shie-Yui Liong
Keyword(s):  
Function Approximation ◽  
Prediction Models ◽  
Data Extraction ◽  
Single Parameter ◽  
Subtractive Clustering ◽  
Data Sets ◽  
Clustering Methods ◽  
Clustering Method ◽  
Data Set ◽  
Functional Relationships

A simple clustering method is proposed for extracting representative subsets from lengthy data sets. The main purpose of the extracted subset of data is to use it to build prediction models (of the form of approximating functional relationships) instead of using the entire large data set. Such smaller subsets of data are often required in exploratory analysis stages of studies that involve resource consuming investigations. A few recent studies have used a subtractive clustering method (SCM) for such data extraction, in the absence of clustering methods for function approximation. SCM, however, requires several parameters to be specified. This study proposes a clustering method, which requires only a single parameter to be specified, yet it is shown to be as effective as the SCM. A method to find suitable values for the parameter is also proposed. Due to having only a single parameter, using the proposed clustering method is shown to be orders of magnitudes more efficient than using SCM. The effectiveness of the proposed method is demonstrated on phase space prediction of three univariate time series and prediction of two multivariate data sets. Some drawbacks of SCM when applied for data extraction are identified, and the proposed method is shown to be a solution for them.

scholarly journals Identification of Dephospho-Coenzyme A (Dephospho-CoA) Kinase in Thermococcus kodakarensis and Elucidation of the Entire CoA Biosynthesis Pathway in Archaea

mBio ◽  
2019 ◽  
Vol 10 (4) ◽  
Author(s):  
Takahiro Shimosaka ◽  
Kira S. Makarova ◽  
Eugene V. Koonin ◽  
Haruyuki Atomi
Keyword(s):  
Metabolic Pathways ◽  
Coenzyme A ◽  
Protein A ◽  
Biosynthesis Pathway ◽  
Content Type ◽  
Hyperthermophilic Archaeon ◽  
Thermococcus Kodakarensis ◽  
Uncharacterized Gene ◽  
Wide Range ◽  
Insight Into

ABSTRACT Dephospho-coenzyme A (dephospho-CoA) kinase (DPCK) catalyzes the ATP-dependent phosphorylation of dephospho-CoA, the final step in coenzyme A (CoA) biosynthesis. DPCK has been identified and characterized in bacteria and eukaryotes but not in archaea. The hyperthermophilic archaeon Thermococcus kodakarensis encodes two homologs of bacterial DPCK and the DPCK domain of eukaryotic CoA synthase, TK1334 and TK2192. We purified the recombinant TK1334 and TK2192 proteins and found that they lacked DPCK activity. Bioinformatic analyses showed that, in several archaea, the uncharacterized gene from arCOG04076 protein is fused with the gene for phosphopantetheine adenylyltransferase (PPAT), which catalyzes the reaction upstream of the DPCK reaction in CoA biosynthesis. This observation suggested that members of arCOG04076, both fused to PPAT and standalone, could be the missing archaeal DPCKs. We purified the recombinant TK1697 protein, a standalone member of arCOG04076 from T. kodakarensis, and demonstrated its GTP-dependent DPCK activity. Disruption of the TK1697 resulted in CoA auxotrophy, indicating that TK1697 encodes a DPCK that contributes to CoA biosynthesis in T. kodakarensis. TK1697 homologs are widely distributed in archaea, suggesting that the arCOG04076 protein represents a novel family of DPCK that is not homologous to bacterial and eukaryotic DPCKs but is distantly related to bacterial and eukaryotic thiamine pyrophosphokinases. We also constructed and characterized gene disruption strains of TK0517 and TK2128, homologs of bifunctional phosphopantothenoylcysteine synthetase-phosphopantothenoylcysteine decarboxylase and PPAT, respectively. Both strains displayed CoA auxotrophy, indicating their contribution to CoA biosynthesis. Taken together with previous studies, the results experimentally validate the entire CoA biosynthesis pathway in T. kodakarensis. IMPORTANCE CoA is utilized in a wide range of metabolic pathways, and its biosynthesis is essential for all life. Pathways for CoA biosynthesis in bacteria and eukaryotes have been established. In archaea, however, the enzyme that catalyzes the final step in CoA biosynthesis, dephospho-CoA kinase (DPCK), had not been identified. In the present study, bioinformatic analyses identified a candidate for the DPCK in archaea, which was biochemically and genetically confirmed in the hyperthermophilic archaeon Thermococcus kodakarensis. Genetic analyses on genes presumed to encode bifunctional phosphopantothenoylcysteine synthetase-phosphopantothenoylcysteine decarboxylase and phosphopantetheine adenylyltransferase confirmed their involvement in CoA biosynthesis. Taken together with previous studies, the results reveal the entire pathway for CoA biosynthesis in a single archaeon and provide insight into the different mechanisms of CoA biosynthesis and their distribution in nature.

GAL11 (SPT13), a transcriptional regulator of diverse yeast genes, affects the phosphorylation state of GAL4, a highly specific transcriptional activator

1991 ◽  
Vol 11 (4) ◽  
pp. 2311-2314
Author(s):  
R M Long ◽  
L M Mylin ◽  
J E Hopper
Keyword(s):  
Gene Transcription ◽  
Protein A ◽  
Transcriptional Activator ◽  
Phosphorylation State ◽  
Phosphorylated Form ◽  
Tightly Coupled ◽  
Gal Genes ◽  
Yeast Genes ◽  
Gal1 Gene

The GAL4 protein of Saccharomyces cerevisiae is a DNA-binding transcriptional activator that is highly specific for the GAL genes. In vivo levels of GAL gene transcription are closely correlated with the phosphorylation state of GAL4. In vivo levels of GAL gene transcription are also affected by the activity of the GAL11 (SPT13) protein, a protein that has been implicated as a global auxiliary transcriptional factor. Here we examine the influence of GAL11 (SPT13) on the phosphorylation state of GAL4. Cells bearing a gal11 deletion mutation are defective in the production or maintenance of GAL4III, a phosphorylated form of GAL4 that is associated with higher levels of GAL gene transcription. In addition, the gal11 deletion cells are reduced in total GAL4 protein. However, the fivefold-reduced expression of the GAL1 gene observed in gal11 deletion cells cannot be due solely to reduced levels of total GAL4 protein, since gal11 deletion cells amplified for GAL4 production are still markedly reduced in GAL4 protein-dependent transcription. Thus, these data demonstrate that the GAL11 protein augments GAL4 protein-dependent transcription in a manner that is tightly coupled to the formation or maintenance of a phosphorylated form of GAL4.

scholarly journals MOB1, an Essential Yeast Gene Required for Completion of Mitosis and Maintenance of Ploidy

1998 ◽  
Vol 9 (1) ◽  
pp. 29-46 ◽  
Author(s):  
Francis C. Luca ◽  
Mark Winey
Keyword(s):  
Genetic Interaction ◽  
Sequence Similarity ◽  
Spindle Pole Body ◽  
Mitotic Checkpoint ◽  
Spindle Pole ◽  
Restrictive Temperature ◽  
Permissive Temperature ◽  
Yeast Gene ◽  
Pole Body ◽  
Yeast Genes

Mob1p is an essential Saccharomyces cerevisiaeprotein, identified from a two-hybrid screen, that binds Mps1p, a protein kinase essential for spindle pole body duplication and mitotic checkpoint regulation. Mob1p contains no known structural motifs; however MOB1 is a member of a conserved gene family and shares sequence similarity with a nonessential yeast gene,MOB2. Mob1p is a phosphoprotein in vivo and a substrate for the Mps1p kinase in vitro. Conditional alleles ofMOB1 cause a late nuclear division arrest at restrictive temperature. MOB1 exhibits genetic interaction with three other yeast genes required for the completion of mitosis,LTE1, CDC5, and CDC15 (the latter two encode essential protein kinases). Most haploid mutantmob1 strains also display a complete increase in ploidy at permissive temperature. The mechanism for the increase in ploidy may occur through MPS1 function. One mob1strain, which maintains stable haploidy at both permissive and restrictive temperature, diploidizes at permissive temperature when combined with the mps1–1 mutation. Strains containingmob2Δ also display a complete increase in ploidy when combined with the mps1-1 mutation. Perhaps in addition to, or as part of, its essential function in late mitosis, MOB1 is required for a cell cycle reset function necessary for the initiation of the spindle pole body duplication.

Weighted-object ensemble clustering: methods and analysis

2016 ◽  
Vol 51 (2) ◽  
pp. 661-689 ◽  
Author(s):  
Yazhou Ren ◽  
Carlotta Domeniconi ◽  
Guoji Zhang ◽  
Guoxian Yu
Keyword(s):  
Ensemble Clustering ◽  
Clustering Methods

scholarly journals A Novel High-Throughput Screen Reveals Yeast Genes That Increase Secretion of Heterologous Proteins

2006 ◽  
Vol 73 (4) ◽  
pp. 1189-1198 ◽  
Author(s):  
Alane E. Wentz ◽  
Eric V. Shusta
Keyword(s):  
Surface Display ◽  
Yeast Surface Display ◽  
Gene Products ◽  
Heterologous Proteins ◽  
Single Chain ◽  
Yeast Gene ◽  
Genome Wide ◽  
A Genome ◽  
Yeast Surface ◽  
Yeast Genes

ABSTRACT The yeast Saccharomyces cerevisiae is an attractive host for the production of heterologous proteins. However, low-yield production of many proteins (from micrograms to milligrams/liter) leaves considerable room for optimization. By engineering the yeast cell via traceable genome-wide libraries, genes that can enhance protein expression level because of their roles in protein transcription, translation, folding, and trafficking processes can be readily identified. This report details a novel approach that combines yeast cDNA overexpression libraries with yeast surface display to allow the rapid flow cytometric screening of engineered yeast for gene products that improve the display of heterologous proteins. After optimization of the screening conditions, a genome-wide scan yielded five yeast gene products that promoted increased display levels of a single-chain T-cell receptor (scTCR). The display-enhancing genes included those coding for cell wall proteins (CCW12, CWP2, and SED1), a ribosomal subunit protein (RPP0), and an endoplasmic reticulum-resident protein (ERO1). Under the premise that yeast surface display levels could be used as a predictor of secretion efficiency, each display-enhancing gene product was tested for its ability to affect secretion levels of multiple scTCR and single-chain antibodies (scFv). All of the selected yeast gene products were shown to promote increased secretion of active protein (1.5-fold to 7.9-fold), with CCW12 and ERO1 being the most generalizable enhancers of scFv/scTCR secretion.

scholarly journals Analysis of a meiosis-specific URS1 site: sequence requirements and involvement of replication protein A.

1997 ◽  
Vol 17 (7) ◽  
pp. 3536-3546 ◽  
Author(s):  
V Gailus-Durner ◽  
C Chintamaneni ◽  
R Wilson ◽  
S J Brill ◽  
A K Vershon
Keyword(s):  
Protein A ◽  
Replication Protein A ◽  
Replication Protein ◽  
Sequence Specificity ◽  
Mobility Shift ◽  
Cell Extracts ◽  
Yeast Genes ◽  
Sequence Requirements

URS1 is a transcriptional repressor site found in the promoters of a wide variety of yeast genes that are induced under stress conditions. In the context of meiotic promoters, URS1 sites act as repressor sequences during mitosis and function as activator sites during meiosis. We have investigated the sequence requirements of the URS1 site of the meiosis-specific HOP1 gene (URS1H) and have found differences compared with a URS1 site from a nonmeiotic gene. We have also observed that the sequence specificity for meiotic activation at this site differs from that for mitotic repression. Base pairs flanking the conserved core sequence enhance meiotic induction but are not required for mitotic repression of HOP1. Electrophoretic mobility shift assays of mitotic and meiotic cell extracts show a complex pattern of DNA-protein complexes, suggesting that several different protein factors bind specifically to the site. We have determined that one of the complexes of URS1H is formed by replication protein A (RPA). Although RPA binds to the double-stranded URS1H site in vitro, it has much higher affinity for single-stranded than for double-stranded URS1H, and one-hybrid assays suggest that RPA does not bind to this site at detectable levels in vivo. In addition, conditional-lethal mutations in RPA were found to have no effect on URS1H-mediated repression. These results suggest that although RPA binds to URS1H in vitro, it does not appear to have a functional role in transcriptional repression through this site in vivo.

scholarly journals Adversarial Graph Embedding for Ensemble Clustering

2019 ◽  
Author(s):  
Zhiqiang Tao ◽  
Hongfu Liu ◽  
Jun Li ◽  
Zhaowen Wang ◽  
Yun Fu
Keyword(s):  
Latent Variables ◽  
Graph Embedding ◽  
Product Layer ◽  
Inner Product ◽  
Ensemble Clustering ◽  
Clustering Methods ◽  
Convolutional Network ◽  
Graph Representations ◽  
Deep Embedding ◽  
Real World Datasets

Ensemble clustering generally integrates basic partitions into a consensus one through a graph partitioning method, which, however, has two limitations: 1) it neglects to reuse original features; 2) obtaining consensus partition with learnable graph representations is still under-explored. In this paper, we propose a novel Adversarial Graph Auto-Encoders (AGAE) model to incorporate ensemble clustering into a deep graph embedding process. Specifically, graph convolutional network is adopted as probabilistic encoder to jointly integrate the information from feature content and consensus graph, and a simple inner product layer is used as decoder to reconstruct graph with the encoded latent variables (i.e., embedding representations). Moreover, we develop an adversarial regularizer to guide the network training with an adaptive partition-dependent prior. Experiments on eight real-world datasets are presented to show the effectiveness of AGAE over several state-of-the-art deep embedding and ensemble clustering methods.

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