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Author

Sina Ghaemmaghami

Bio: Sina Ghaemmaghami is an academic researcher from University of Rochester. The author has contributed to research in topics: Proteome & Protein turnover. The author has an hindex of 24, co-authored 55 publications receiving 10170 citations. Previous affiliations of Sina Ghaemmaghami include California Institute for Quantitative Biosciences & University of California, San Francisco.


Papers
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Journal ArticleDOI
16 Oct 2003-Nature
TL;DR: A Saccharomyces cerevisiae fusion library is created where each open reading frame is tagged with a high-affinity epitope and expressed from its natural chromosomal location, and it is found that about 80% of the proteome is expressed during normal growth conditions.
Abstract: The availability of complete genomic sequences and technologies that allow comprehensive analysis of global expression profiles of messenger RNA have greatly expanded our ability to monitor the internal state of a cell. Yet biological systems ultimately need to be explained in terms of the activity, regulation and modification of proteins--and the ubiquitous occurrence of post-transcriptional regulation makes mRNA an imperfect proxy for such information. To facilitate global protein analyses, we have created a Saccharomyces cerevisiae fusion library where each open reading frame is tagged with a high-affinity epitope and expressed from its natural chromosomal location. Through immunodetection of the common tag, we obtain a census of proteins expressed during log-phase growth and measurements of their absolute levels. We find that about 80% of the proteome is expressed during normal growth conditions, and, using additional sequence information, we systematically identify misannotated genes. The abundance of proteins ranges from fewer than 50 to more than 10(6) molecules per cell. Many of these molecules, including essential proteins and most transcription factors, are present at levels that are not readily detectable by other proteomic techniques nor predictable by mRNA levels or codon bias measurements.

3,894 citations

Journal ArticleDOI
10 Apr 2009-Science
TL;DR: A ribosomesome-profiling strategy based on the deep sequencing of ribosome-protected mRNA fragments is presented and enables genome-wide investigation of translation with subcodon resolution and is used to monitor translation in budding yeast under both rich and starvation conditions.
Abstract: Techniques for systematically monitoring protein translation have lagged far behind methods for measuring messenger RNA (mRNA) levels. Here, we present a ribosome-profiling strategy that is based on the deep sequencing of ribosome-protected mRNA fragments and enables genome-wide investigation of translation with subcodon resolution. We used this technique to monitor translation in budding yeast under both rich and starvation conditions. These studies defined the protein sequences being translated and found extensive translational control in both determining absolute protein abundance and responding to environmental stress. We also observed distinct phases during translation that involve a large decrease in ribosome density going from early to late peptide elongation as well as widespread regulated initiation at non-adenine-uracil-guanine (AUG) codons. Ribosome profiling is readily adaptable to other organisms, making high-precision investigation of protein translation experimentally accessible.

3,416 citations

Journal ArticleDOI
15 Jun 2006-Nature
TL;DR: A strategy that pairs high-throughput flow cytometry and a library of GFP-tagged yeast strains to monitor rapidly and precisely protein levels at single-cell resolution is presented, revealing a remarkable structure to biological noise.
Abstract: A major goal of biology is to provide a quantitative description of cellular behaviour. This task, however, has been hampered by the difficulty in measuring protein abundances and their variation. Here we present a strategy that pairs high-throughput flow cytometry and a library of GFP-tagged yeast strains to monitor rapidly and precisely protein levels at single-cell resolution. Bulk protein abundance measurements of >2,500 proteins in rich and minimal media provide a detailed view of the cellular response to these conditions, and capture many changes not observed by DNA microarray analyses. Our single-cell data argue that noise in protein expression is dominated by the stochastic production/destruction of messenger RNAs. Beyond this global trend, there are dramatic protein-specific differences in noise that are strongly correlated with a protein's mode of transcription and its function. For example, proteins that respond to environmental changes are noisy whereas those involved in protein synthesis are quiet. Thus, these studies reveal a remarkable structure to biological noise and suggest that protein noise levels have been selected to reflect the costs and potential benefits of this variation.

1,550 citations

Journal ArticleDOI
TL;DR: The developed methodology can be adapted to assess in vivo proteome homeostasis in any model organism that will tolerate a labeled diet and may be particularly useful in the analysis of neurodegenerative diseases in vivo.
Abstract: Advances in systems biology have allowed for global analyses of mRNA and protein expression, but large-scale studies of protein dynamics and turnover have not been conducted in vivo. Protein turnover is an important metabolic and regulatory mechanism in establishing proteome homeostasis, impacting many physiological and pathological processes. Here, we have used organism-wide isotopic labeling to measure the turnover rates of ~2,500 proteins in multiple mouse tissues, spanning four orders of magnitude. Through comparison of the brain with the liver and blood, we show that within the respective tissues, proteins performing similar functions often have similar turnover rates. Proteins in the brain have significantly slower turnover (average lifetime of 9.0 d) compared with those of the liver (3.0 d) and blood (3.5 d). Within some organelles (such as mitochondria), proteins have a narrow range of lifetimes, suggesting a synchronized turnover mechanism. Protein subunits within complexes of variable composition have a wide range of lifetimes, whereas those within well-defined complexes turn over in a coordinated manner. Together, the data represent the most comprehensive in vivo analysis of mammalian proteome turnover to date. The developed methodology can be adapted to assess in vivo proteome homeostasis in any model organism that will tolerate a labeled diet and may be particularly useful in the analysis of neurodegenerative diseases in vivo.

339 citations

Journal ArticleDOI
TL;DR: The results indicate that the thermodynamic stability of monomeric λ repressor within the cell is the same as its stability measured in a simple buffer in vitro, but when the E. coli are placed in a hyperosmotic environment, the in vivo stability is greatly enhanced.
Abstract: The equilibrium between the native and denatured states of a protein can be key to its function and regulation Traditionally, the folding equilibrium constant has been measured in vitro using purified protein and simple buffers However, the biological environment of proteins can differ from these in vitro conditions in ways that could significantly perturb stability Here, we present the first quantitative comparison between the stability of a protein in vitro and in the cytoplasm of Eschericia coli using amide hydrogen exchange detected by MALDI mass spectrometry (SUPREX) The results indicate that the thermodynamic stability of monomeric λ repressor within the cell is the same as its stability measured in a simple buffer in vitro However, when the E coli are placed in a hyperosmotic environment, the in vivo stability is greatly enhanced The in vivo SUPREX method provides a general and quantitative way to measure protein stabilities in the cell and will be useful for applications where intracellular stability information provides important biological insights

177 citations


Cited by
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
TL;DR: MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data, detects peaks, isotope clusters and stable amino acid isotope–labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space and achieves mass accuracy in the p.p.b. range.
Abstract: Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.

12,340 citations

Journal ArticleDOI
TL;DR: This protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results, which takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
Abstract: Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.

10,913 citations

Journal ArticleDOI
19 May 2011-Nature
TL;DR: Using a quantitative model, the first genome-scale prediction of synthesis rates of mRNAs and proteins is obtained and it is found that the cellular abundance of proteins is predominantly controlled at the level of translation.
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.

5,635 citations

Journal ArticleDOI
TL;DR: The Perseus software platform was developed to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data and it is anticipated that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
Abstract: A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

5,165 citations