Functional requirements of histone deacetylases in Tup1-Ssn6 repression

The corepressor complex Tup1-Ssn6 regulates many classes of genes in yeast including cell type specific, glucose repressible, and DNA damage inducible. Tup1 and Ssn6 are recruited to target promoters through their interactions with specific DNA binding proteins such as α2, Mig1, and Crt1. Most promoters that are repressed by this corepressor complex exhibit a high degree of nucleosomal organization. This chromatin domain occludes transcription factor access to the promoter element resulting in gene repression. Previous work indicated that Tup1 interacts with underacetylated isoforms of H3 and H4, and that mutation of these histones synergistically compromises repression. These studies predict that Tup1-hypoacetyalted histone interaction is important to the repression mechanism, and in vivo hyperacetylation might compromise the corepressors ability to repress target genes. ^ One way to alter histone acetylation levels in vivo is to alter the balance between histone acetyltransferases and histone deacetylases. To date five histone deacetylases (HDACs) have been identified in yeast Rpd3, Hos1, Hos2, Hos3 and Hda1. Deletion of single or double HDAC genes had little to no effect on Tup1-Ssn6 repression, but simultaneous deletion of three specific activities Rpd3, Hos1, and Hos2 abolished repression in vivo. Promoter regions of Tup1-Ssn6 target genes in these triple deacetylase mutant cells are dramatically hyperacetylated in both H3 and H4. Examination of bulk histone acetylation levels showed that this specific HDAC triple mutant combination (rpd3 hos1 hos2) caused a dramatic and concomitant hyperacetylation of both H3 and H4. The loss of repression in the rpd3 hos1 hos2 cells, but not in other mutants, is consistent with previous observations, which indicate that histones provide redundant functions in the repression mechanism and that high levels of acetylation are required to prevent Tup1 binding. Investigation into a potential direct interaction between the Tup1-Ssn6 corepressor complex and one or more HDAC activities showed that both Rpd3 and Hos2 interact with the corepressor complex in vivo. These findings indicate that Tup1-Ssn6 repression involves the recruitment of histone deacetylase activities to target promoters, where they locally deacetylate histone residues promoting Tup1-histone tail interaction to initiate and/or maintain the repressed state. ^

Functional analysis of anionic phospholipids in cell metabolism in {\it Escherichia coli\/}

The major goal of this work was to understand the function of anionic phospholipid in E. coli cell metabolism. One important finding from this work is the requirement of anionic phospholipid for the DnaA protein-dependent initiation of DNA replication. An rnhA mutation, which bypasses the need for the DnaA protein through induction of constitutive stable DNA replication, suppressed the growth arrest phenotype of a $pgsA$ mutant in which the synthesis of anionic phospholipid was blocked. The maintenance of plasmids dependent on an $oriC$ site for replication, and therefore DnaA protein, was also compromised under conditions of limiting anionic phospholipid synthesis. These results provide support for the involvement of anionic phospholipids in normal initiation of DNA replication at oriC in vivo by the DnaA protein. In addition, structural and functional requirements of two major anionic phospholipids, phosphatidylglycerol and cardiolipin, were examined. Introduction into cells of the ability to make phosphatidylinositol did not suppress the need for the naturally occurring phosphatidylglycerol. The requirement for phosphatidylglycerol was concluded to be more than maintenance of the proper membrane surface charge. Examination of the role of cardiolipin revealed its ability to replace the zwitterionic phospholipid, phosphatidylethanolamine, in maintaining an optimal membrane lipid organization. This work also reported the DNA sequence of the cls gene, which encodes the CL synthase responsible for the synthesis of cardiolipin. ^

A review of the Malaria Chemoprophylaxis Compliance Program at ExxonMobil Corporation

ExxonMobil, a Fortune 500 oil and gas corporation, has a global workforce with employees assigned to projects in areas at risk for infectious diseases, particularly malaria. As such, the corporation has put in place a program to protect the health of workers and ensure their safety in malaria endemic zones. This program is called the Malaria Control Program (MCP). One component of this program is the more specific Malaria Chemoprophylaxis Compliance Program (MCCP), in which employees enroll following consent to random drug testing for compliance with the company's chemoprophylaxis requirements. Each year, data is gathered on the number of employees working in these locations and are selected randomly and tested for chemoprophylaxis compliance. The selection strives to test each eligible worker once per year. Test results that come back positive for the chemoprophylaxis drug are considered "detects" and tests that are negative for the drug and therefore show the worker is non-compliant at risk for severe malaria infection are considered "non-detect". ^ The current practice report used aggregate data to calculate statistics on test results to reflect compliance among both employees and contractors in various malaria-endemic areas. This aggregate, non-individualized data has been compiled and reflects the effectiveness and reach of ExxonMobil's Malaria Chemoprophylaxis Compliance Program. In order to assess compliance, information on the number of non-detect test results was compared to the number of tests completed per year. The data shows that over time, non-detect results have declined in both employee and contractor populations, and vary somewhat by location due to size and scope of the MCCP implemented in-country. Although the data indicate a positive trend for the corporation, some recommendations have been made for future implementation of the program.^

NETWORK TOPOLOGY IN HUMAN PROTEIN INTERACTION DATA PREDICTS FUNCTIONAL ASSOCIATION

High-throughput assays, such as yeast two-hybrid system, have generated a huge amount of protein-protein interaction (PPI) data in the past decade. This tremendously increases the need for developing reliable methods to systematically and automatically suggest protein functions and relationships between them. With the available PPI data, it is now possible to study the functions and relationships in the context of a large-scale network. To data, several network-based schemes have been provided to effectively annotate protein functions on a large scale. However, due to those inherent noises in high-throughput data generation, new methods and algorithms should be developed to increase the reliability of functional annotations. Previous work in a yeast PPI network (Samanta and Liang, 2003) has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional associations between proteins, and hence suggest their functions. One advantage of the work is that their algorithm is not sensitive to noises (false positives) in high-throughput PPI data. In this study, we improved their prediction scheme by developing a new algorithm and new methods which we applied on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting functionally associated proteins. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as independent and unbiased benchmarks to evaluate our algorithms and methods within the human PPI network. We showed that, compared with the previous work from Samanta and Liang, our algorithm and methods developed in this study improved the overall quality of functional inferences for human proteins. By applying the algorithms to the human PPI network, we obtained 4,233 significant functional associations among 1,754 proteins. Further comparisons of their KEGG and GO annotations allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made pathway analysis to identify several subclusters that are highly enriched in certain signaling pathways. Particularly, we performed a detailed analysis on a subcluster enriched in the transforming growth factor β signaling pathway (P<10-50) which is important in cell proliferation and tumorigenesis. Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotations in this post-genomic era.

Identifying the roles of the Escherichia coli FtsZ-associated proteins by minimizing the requirements for division

Formation of the FtsZ ring (Z ring) in Escherichia coli is the first step in assembly of the divisome, a molecular machine composed of 14 known proteins which are all required for cell division. Although the biochemical functions of most divisome proteins are unknown, several of these have overlapping roles in ensuring that the Z ring assembles at the cytoplasmic membrane and is active. ^ We identified a single amino acid change in FtsA, R286W, renamed FtsA*, that completely bypasses the requirement for ZipA in cell division. This and other data suggest that FtsA* is a hyperactive form of FtsA that can replace the multiple functions normally assumed by ZipA, which include stabilization of Z rings, recruitment of downstream cell division proteins, and anchoring the Z ring to the membrane. This is the first example of complete functional replacement of an essential prokaryotic cell division protein by another. ^ Cells expressing ftsA* with a complete deletion of ftsK are viable and divide, although many of these ftsK null cells formed multiseptate chains, suggesting a role in cell separation for FtsK. In addition, strains expressing extra ftsAZ, ftsQ, ftsB, zipA or ftsN, were also able to survive and divide in the absence of ftsK. The cytoplasmic and transmembrane domains of FtsQ were sufficient to allow viability and septum formation to ftsK deleted strains. These findings suggest that FtsK is normally involved in stabilizing the divisome and shares functional overlap with other cell division proteins. ^ As well as permitting the removal of other divisome components, the presence of FtsA* in otherwise wild-type cells accelerated Z-ring assembly, which resulted in a significant decrease in the average length of cells. In support of its role in Z-ring stability, FtsA* suppressed the cell division inhibition caused by overexpressing FtsZ. FtsA* did not affect FtsZ turnover within the Z ring as measured by fluorescence recovery after photobleaching. Turnover of FtsA* in the ring was somewhat faster than wild-type FtsA. Yeast two-hybrid data suggest that FtsA* has an increased affinity for FtsZ relative to wild-type FtsA. These results indicate that FtsA* interacts with FtsZ more strongly, and its enhancement of Z ring assembly may explain why FtsA* can permit survival of cells lacking ZipA or FtsK.^

Functional data analysis approaches for genotype-phenotype association studies from next-generation sequencing

Next-generation DNA sequencing platforms can effectively detect the entire spectrum of genomic variation and is emerging to be a major tool for systematic exploration of the universe of variants and interactions in the entire genome. However, the data produced by next-generation sequencing technologies will suffer from three basic problems: sequence errors, assembly errors, and missing data. Current statistical methods for genetic analysis are well suited for detecting the association of common variants, but are less suitable to rare variants. This raises great challenge for sequence-based genetic studies of complex diseases.^ This research dissertation utilized genome continuum model as a general principle, and stochastic calculus and functional data analysis as tools for developing novel and powerful statistical methods for next generation of association studies of both qualitative and quantitative traits in the context of sequencing data, which finally lead to shifting the paradigm of association analysis from the current locus-by-locus analysis to collectively analyzing genome regions.^ In this project, the functional principal component (FPC) methods coupled with high-dimensional data reduction techniques will be used to develop novel and powerful methods for testing the associations of the entire spectrum of genetic variation within a segment of genome or a gene regardless of whether the variants are common or rare.^ The classical quantitative genetics suffer from high type I error rates and low power for rare variants. To overcome these limitations for resequencing data, this project used functional linear models with scalar response to develop statistics for identifying quantitative trait loci (QTLs) for both common and rare variants. To illustrate their applications, the functional linear models were applied to five quantitative traits in Framingham heart studies. ^ This project proposed a novel concept of gene-gene co-association in which a gene or a genomic region is taken as a unit of association analysis and used stochastic calculus to develop a unified framework for testing the association of multiple genes or genomic regions for both common and rare alleles. The proposed methods were applied to gene-gene co-association analysis of psoriasis in two independent GWAS datasets which led to discovery of networks significantly associated with psoriasis.^