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      Variation and Genetic Control of Protein Abundance in Humans

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          Abstract

          Gene expression differs among both individuals and populations and is thought to be a major determinant of phenotypic variation. Although variation and genetic loci responsible for RNA expression levels have been analyzed extensively in human populations 15 , our knowledge is limited regarding the differences in human protein abundance and their genetic basis. Variation in mRNA expression is not a perfect surrogate for protein expression because the latter is influenced by a battery of post-transcriptional regulatory mechanisms, and, empirically, the correlation between protein and mRNA levels is generally modest 6, 7 . Here we used isobaric tandem mass tag (TMT)-based quantitative mass spectrometry to determine relative protein levels of 5953 genes in lymphoblastoid cell lines (LCLs) from 95 diverse individuals genotyped in the HapMap Project 8, 9 . We found that protein levels are heritable molecular phenotypes that exhibit considerable variation between individuals, populations, and sexes. Levels of specific sets of proteins involved in the same biological process co-vary among individuals, indicating that these processes are tightly regulated at the protein level. We identified cis-pQTLs (protein quantitative trait loci), including variants not detected by previous transcriptome studies. This study demonstrates the feasibility of high throughput human proteome quantification which, when integrated with DNA variation and transcriptome information, adds a new dimension to the characterization of gene expression regulation.

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          Most cited references12

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          Variation in transcription factor binding among humans.

          Differences in gene expression may play a major role in speciation and phenotypic diversity. We examined genome-wide differences in transcription factor (TF) binding in several humans and a single chimpanzee by using chromatin immunoprecipitation followed by sequencing. The binding sites of RNA polymerase II (PolII) and a key regulator of immune responses, nuclear factor kappaB (p65), were mapped in 10 lymphoblastoid cell lines, and 25 and 7.5% of the respective binding regions were found to differ between individuals. Binding differences were frequently associated with single-nucleotide polymorphisms and genomic structural variants, and these differences were often correlated with differences in gene expression, suggesting functional consequences of binding variation. Furthermore, comparing PolII binding between humans and chimpanzee suggests extensive divergence in TF binding. Our results indicate that many differences in individuals and species occur at the level of TF binding, and they provide insight into the genetic events responsible for these differences.
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            The International Protein Index: an integrated database for proteomics experiments.

            Despite the complete determination of the genome sequence of several higher eukaryotes, their proteomes remain relatively poorly defined. Information about proteins identified by different experimental and computational methods is stored in different databases, meaning that no single resource offers full coverage of known and predicted proteins. IPI (the International Protein Index) has been developed to address these issues and offers complete nonredundant data sets representing the human, mouse and rat proteomes, built from the Swiss-Prot, TrEMBL, Ensembl and RefSeq databases.
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              Addressing accuracy and precision issues in iTRAQ quantitation.

              iTRAQ (isobaric tags for relative or absolute quantitation) is a mass spectrometry technology that allows quantitative comparison of protein abundance by measuring peak intensities of reporter ions released from iTRAQ-tagged peptides by fragmentation during MS/MS. However, current data analysis techniques for iTRAQ struggle to report reliable relative protein abundance estimates and suffer with problems of precision and accuracy. The precision of the data is affected by variance heterogeneity: low signal data have higher relative variability; however, low abundance peptides dominate data sets. Accuracy is compromised as ratios are compressed toward 1, leading to underestimation of the ratio. This study investigated both issues and proposed a methodology that combines the peptide measurements to give a robust protein estimate even when the data for the protein are sparse or at low intensity. Our data indicated that ratio compression arises from contamination during precursor ion selection, which occurs at a consistent proportion within an experiment and thus results in a linear relationship between expected and observed ratios. We proposed that a correction factor can be calculated from spiked proteins at known ratios. Then we demonstrated that variance heterogeneity is present in iTRAQ data sets irrespective of the analytical packages, LC-MS/MS instrumentation, and iTRAQ labeling kit (4-plex or 8-plex) used. We proposed using an additive-multiplicative error model for peak intensities in MS/MS quantitation and demonstrated that a variance-stabilizing normalization is able to address the error structure and stabilize the variance across the entire intensity range. The resulting uniform variance structure simplifies the downstream analysis. Heterogeneity of variance consistent with an additive-multiplicative model has been reported in other MS-based quantitation including fields outside of proteomics; consequently the variance-stabilizing normalization methodology has the potential to increase the capabilities of MS in quantitation across diverse areas of biology and chemistry.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                21 September 2013
                15 May 2013
                4 July 2013
                04 January 2014
                : 499
                : 7456
                : 79-82
                Affiliations
                [1 ]Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305
                Author notes
                Correspondence and requests for materials should be addressed to: M.S. ( mpsnyder@ 123456stanford.edu ) or H.T. ( huatang@ 123456stanford.edu )
                [*]

                These authors contributed equally to this work

                Article
                NIHMS473356
                10.1038/nature12223
                3789121
                23676674
                c51f590c-8b38-4cb2-9551-e5b8d6d7c5fa

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Funding
                Funded by: National Institute of General Medical Sciences : NIGMS
                Award ID: R01 GM073059 || GM
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: P50 HG002357 || HG
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