Suzanne Komili

Bio: Suzanne Komili is an academic researcher from Harvard University. The author has contributed to research in topics: Gene & Regulation of gene expression. The author has an hindex of 9, co-authored 9 publications receiving 1610 citations.

Coupling and coordination in gene expression processes: a systems biology view

Abstract: Genome-scale analyses have allowed us to progress beyond studying gene expression at the level of individual components of a given process by providing global information about functional connections between genes, mRNAs and their regulatory proteins. Such analyses have greatly increased our understanding of the interplay between different events in gene regulation and have highlighted previously unappreciated functional connections, including coupling between nuclear and cytoplasmic processes. Genome-wide approaches have also revealed extensive coordination within regulatory levels, such as the organization of transcription factors into regulatory motifs. Overall, these studies enhance our understanding of how the many components of the eukaryotic cell function as a system to allow both coordination and versatility in gene expression.

Arginine methyltransferase affects interactions and recruitment of mRNA processing and export factors

Abstract: Hmt1 is the major type I arginine methyltransferase in the yeast Saccharomyces cerevisiae and facilitates the nucleocytoplasmic transport of mRNA-binding proteins through their methylation. Here we demonstrate that Hmt1 is recruited during the beginning of the transcriptional elongation process. Hmt1 methylates Yra1 and Hrp1, two mRNA-binding proteins important for mRNA processing and export. Moreover, loss of Hmt1 affects interactions between mRNA-binding proteins and Tho2, a component of the TREX (transcription/export) complex that is important for transcriptional elongation and recruitment of mRNA export factors. Furthermore, RNA in situ hybridization analysis demonstrates that loss of Hmt1 results in slowed release of HSP104 mRNA from the sites of transcription. Genome-wide location analysis shows that Hmt1 is bound to specific functional gene classes, many of which are also bound by Tho2 and other mRNA-processing factors. These data suggest a model whereby Hmt1 affects transcriptional elongation and, as a result, influences recruitment of RNA-processing factors.

Global quantification of mammalian gene expression control

Abstract: Gene expression is a multistep process that involves the transcription, translation and turnover of messenger RNAs and proteins. Although it is one of the most fundamental processes of life, the entire cascade has never been quantified on a genome-wide scale. Here we simultaneously measured absolute mRNA and protein abundance and turnover by parallel metabolic pulse labelling for more than 5,000 genes in mammalian cells. Whereas mRNA and protein levels correlated better than previously thought, corresponding half-lives showed no correlation. Using a quantitative model we have obtained the first genome-scale prediction of synthesis rates of mRNAs and proteins. We find that the cellular abundance of proteins is predominantly controlled at the level of translation. Genes with similar combinations of mRNA and protein stability shared functional properties, indicating that half-lives evolved under energetic and dynamic constraints. Quantitative information about all stages of gene expression provides a rich resource and helps to provide a greater understanding of the underlying design principles.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

The Genomic Code for Nucleosome Positioning

Abstract: Eukaryotic genomes are packaged into nucleosome particles that occlude the DNA from interacting with most DNA binding proteins. Nucleosomes have higher affinity for particular DNA sequences, reflecting the ability of the sequence to bend sharply, as required by the nucleosome structure. However, it is not known whether these sequence preferences have a significant influence on nucleosome position in vivo, and thus regulate the access of other proteins to DNA. Here we isolated nucleosome-bound sequences at high resolution from yeast and used these sequences in a new computational approach to construct and validate experimentally a nucleosome–DNA interaction model, and to predict the genome-wide organization of nucleosomes. Our results demonstrate that genomes encode an intrinsic nucleosome organization and that this intrinsic organization can explain ∼50% of the in vivo nucleosome positions. This nucleosome positioning code may facilitate specific chromosome functions including transcription factor binding, transcription initiation, and even remodelling of the nucleosomes themselves.

A genomic code for nucleosome positioning

Abstract: Eukaryotic genomes are packaged into nucleosome particles that occlude the DNA from interacting with most DNA binding proteins. Nucleosomes have higher affinity for particular DNA sequences, reflecting the ability of the sequence to bend sharply, as required by the nucleosome structure. However, it is not known whether these sequence preferences have a significant influence on nucleosome position in vivo, and thus regulate the access of other proteins to DNA. Here we isolated nucleosome-bound sequences at high resolution from yeast and used these sequences in a new computational approach to construct and validate experimentally a nucleosome–DNA interaction model, and to predict the genome-wide organization of nucleosomes. Our results demonstrate that genomes encode an intrinsic nucleosome organization and that this intrinsic organization can explain ∼50% of the in vivo nucleosome positions. This nucleosome positioning code may facilitate specific chromosome functions including transcription factor binding, transcription initiation, and even remodelling of the nucleosomes themselves. Eukaryotic genomes do not exist in vivo as naked DNA, but in complexes known as chromatin. Chromatin contains nucleosomes, short stretches of DNA tightly wrapped around a histone protein core, which exclude most DNA binding proteins and so act as repressors. A combined computational and experimental approach has been used to determine DNA sequence preferences of nucleosomes and to predict genome-wide nucleosome organization. The yeast genome encodes an intrinsic nucleosome organization that explains about half of the in vivo nucleosome positions. Highly conserved across eukaryotes, the code directs transcription factors to their binding sites and facilitates many other specific chromosome functions. An accompanying News and Views piece discusses the role of DNA sequence and other regulators in nucleosome positioning. The cover graphic represents a stretch of chromatin including several nucleosomes. A combined computational and experimental approach is used to determine the DNA sequence preferences of nucleosomes and predict genome-wide nucleosome organization. The yeast genome encodes an intrinsic nucleosome organization, which can explain about 50% of in vivo nucleosome positions.