Lily Ting

MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics

Quantitative mass spectrometry–based proteomics is highly versatile but not easily multiplexed. Isobaric labeling strategies allow mass spectrometry–based multiplexed proteome quantification; however, ratio distortion owing to protein quantification interference is a common effect. We present a two-proteome model (mixture of human and yeast proteins) in a sixplex isobaric labeling system to fully document the interference effect, and we report that applying triple-stage mass spectrometry (MS3) almost completely eliminates interference.

The BioPlex Network: A Systematic Exploration of the Human Interactome

Protein interactions form a network whose structure drives cellular function and whose organization informs biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7,668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80%-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. Network structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally related proteins. Finally, BioPlex, in combination with other approaches, can be used to reveal interactions of biological or clinical significance. For example, mutations in the membrane protein VAPB implicated in familial amyotrophic lateral sclerosis perturb a defined community of interactors.

The genomic basis of trophic strategy in marine bacteria

Many marine bacteria have evolved to grow optimally at either high (copiotrophic) or low (oligotrophic) nutrient concentrations, enabling different species to colonize distinct trophic habitats in the oceans. Here, we compare the genome sequences of two bacteria, Photobacterium angustum S14 and Sphingopyxis alaskensis RB2256, that serve as useful model organisms for copiotrophic and oligotrophic modes of life and specifically relate the genomic features to trophic strategy for these organisms and define their molecular mechanisms of adaptation. We developed a model for predicting trophic lifestyle from genome sequence data and tested >400,000 proteins representing >500 million nucleotides of sequence data from 126 genome sequences with metagenome data of whole environmental samples. When applied to available oceanic metagenome data (e.g., the Global Ocean Survey data) the model demonstrated that oligotrophs, and not the more readily isolatable copiotrophs, dominate the oceans free-living microbial populations. Using our model, it is now possible to define the types of bacteria that specific ocean niches are capable of sustaining.

Increasing the Multiplexing Capacity of TMTs Using Reporter Ion Isotopologues with Isobaric Masses

Quantitative mass spectrometry methods offer near-comprehensive proteome coverage; however, these methods still suffer with regards to sample throughput. Multiplex quantitation via isobaric chemical tags (e.g., TMT and iTRAQ) provides an avenue for mass spectrometry-based proteome quantitation experiments to move away from simple binary comparisons and toward greater parallelization. Herein, we demonstrate a straightforward method for immediately expanding the throughput of the TMT isobaric reagents from 6-plex to 8-plex. This method is based upon our ability to resolve the isotopic shift that results from substituting a (15)N for a (13)C. In an accommodation to the preferred fragmentation pathways of ETD, the TMT-127 and -129 reagents were recently modified such that a (13)C was exchanged for a (15)N. As a result of this substitution, the new TMT reporter ions are 6.32 mDa lighter. Even though the mass difference between these reporter ion isotopologues is incredibly small, modern high-resolution and mass accuracy analyzers can resolve these ions. On the basis of our ability to resolve and accurately measure the relative intensity of these isobaric reporter ions, we demonstrate that we are able to quantify across eight samples simultaneously by combining the (13)C- and (15)N-containing reporter ions. Considering the structure of the TMT reporter ion, we believe this work serves as a blueprint for expanding the multiplexing capacity of the TMT reagents to at least 10-plex and possibly up to 18-plex.

Why do cellular proteins linked to K63-polyubiquitin chains not associate with proteasomes?

Although cellular proteins conjugated to K48‐linked Ub chains are targeted to proteasomes, proteins conjugated to K63‐ubiquitin chains are directed to lysosomes. However, pure 26S proteasomes bind and degrade K48‐ and K63‐ubiquitinated substrates similarly. Therefore, we investigated why K63‐ubiquitinated proteins are not degraded by proteasomes. We show that mammalian cells contain soluble factors that selectively bind to K63 chains and inhibit or prevent their association with proteasomes. Using ubiquitinated proteins as affinity ligands, we found that the main cellular proteins that associate selectively with K63 chains and block their binding to proteasomes are ESCRT0 (Endosomal Sorting Complex Required for Transport) and its components, STAM and Hrs. In vivo, knockdown of ESCRT0 confirmed that it is required to block binding of K63‐ubiquitinated molecules to the proteasome. In addition, the Rad23 proteins, especially hHR23B, were found to bind specifically to K48‐ubiquitinated proteins and to stimulate proteasome binding. The specificities of these proteins for K48‐ or K63‐ubiquitin chains determine whether a ubiquitinated protein is targeted for proteasomal degradation or delivered instead to the endosomal‐lysosomal pathway.

Architecture of the human interactome defines protein communities and disease networks

The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein–protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidating how genome variation contributes to disease. Here we present BioPlex 2.0 (Biophysical Interactions of ORFeome-derived complexes), which uses robust affinity purification–mass spectrometry methodology to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein-coding genes from the human genome, and constitutes, to our knowledge, the largest such network so far. With more than 56,000 candidate interactions, BioPlex 2.0 contains more than 29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering of interacting proteins identified more than 1,300 protein communities representing diverse cellular activities. Genes essential for cell fitness are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2,000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.

The genome sequence of the psychrophilic archaeon, Methanococcoides burtonii: the role of genome evolution in cold adaptation

Psychrophilic archaea are abundant and perform critical roles throughout the Earths expansive cold biosphere. Here we report the first complete genome sequence for a psychrophilic methanogenic archaeon, Methanococcoides burtonii. The genome sequence was manually annotated including the use of a five-tiered evidence rating (ER) system that ranked annotations from ER1 (gene product experimentally characterized from the parent organism) to ER5 (hypothetical gene product) to provide a rapid means of assessing the certainty of gene function predictions. The genome is characterized by a higher level of aberrant sequence composition (51%) than any other archaeon. In comparison to hyper/thermophilic archaea, which are subject to selection of synonymous codon usage, M. burtonii has evolved cold adaptation through a genomic capacity to accommodate highly skewed amino-acid content, while retaining codon usage in common with its mesophilic Methanosarcina cousins. Polysaccharide biosynthesis genes comprise at least 3.3% of protein coding genes in the genome, and Cell wall, membrane, envelope biogenesis COG genes are overrepresented. Likewise, signal transduction (COG category T) genes are overrepresented and M. burtonii has a high ‘IQ’ (a measure of adaptive potential) compared to many methanogens. Numerous genes in these two overrepresented COG categories appear to have been acquired from ɛ- and δ-Proteobacteria, as do specific genes involved in central metabolism such as a novel B form of aconitase. Transposases also distinguish M. burtonii from other archaea, and their genomic characteristics indicate they have an important role in evolving the M. burtonii genome. Our study reveals a capacity for this model psychrophile to evolve through genome plasticity (including nucleotide skew, horizontal gene transfer and transposase activity) that enables adaptation to the cold, and to the biological and physical changes that have occurred over the last several thousand years as it adapted from a marine to an Antarctic lake environment.

Normalization and Statistical Analysis of Quantitative Proteomics Data Generated by Metabolic Labeling

Comparative proteomics is a powerful analytical method for learning about the responses of biological systems to changes in growth parameters. To make confident inferences about biological responses, proteomics approaches must incorporate appropriate statistical measures of quantitative data. In the present work we applied microarray-based normalization and statistical analysis (significance testing) methods to analyze quantitative proteomics data generated from the metabolic labeling of a marine bacterium (Sphingopyxis alaskensis). Quantitative data were generated for 1,172 proteins, representing 1,736 high confidence protein identifications (54% genome coverage). To test approaches for normalization, cells were grown at a single temperature, metabolically labeled with 14N or 15N, and combined in different ratios to give an artificially skewed data set. Inspection of ratio versus average (MA) plots determined that a fixed value median normalization was most suitable for the data. To determine an appropriate statistical method for assessing differential abundance, a -fold change approach, Students t test, unmoderated t test, and empirical Bayes moderated t test were applied to proteomics data from cells grown at two temperatures. Inverse metabolic labeling was used with multiple technical and biological replicates, and proteomics was performed on cells that were combined based on equal optical density of cultures (providing skewed data) or on cell extracts that were combined to give equal amounts of protein (no skew). To account for arbitrarily complex experiment-specific parameters, a linear modeling approach was used to analyze the data using the limma package in R/Bioconductor. A high quality list of statistically significant differentially abundant proteins was obtained by using lowess normalization (after inspection of MA plots) and applying the empirical Bayes moderated t test. The approach also effectively controlled for the number of false discoveries and corrected for the multiple testing problem using the Storey-Tibshirani false discovery rate (Storey, J. D., and Tibshirani, R. (2003) Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U.S.A. 100, 9440–9445). The approach we have developed is generally applicable to quantitative proteomics analyses of diverse biological systems.

Cold adaptation in the marine bacterium, Sphingopyxis alaskensis, assessed using quantitative proteomics

The cold marine environment constitutes a large proportion of the Earths biosphere. Sphingopyxis alaskensis was isolated as a numerically abundant bacterium from several cold marine locations, and has been extensively studied as a model marine bacterium. Recently, a metabolic labelling platform was developed to comprehensively identify and quantify proteins from S. alaskensis. The approach incorporated data normalization and statistical validation for the purpose of generating highly confident quantitative proteomics data. Using this approach, we determined quantitative differences between cells grown at 10°C (low temperature) and 30°C (high temperature). Cold adaptation was linked to specific aspects of gene expression: a dedicated protein-folding system using GroESL, DnaK, DnaJ, GrpE, SecB, ClpB and PPIase; polyhydroxyalkanoate-associated storage materials; a link between enzymes in fatty acid metabolism and energy generation; de novo synthesis of polyunsaturated fatty acids in the membrane and cell wall; inorganic phosphate ion transport by a phosphate import PstB homologue; TonB-dependent receptor and bacterioferritin in iron homeostasis; histidine, tryptophan and proline amino acid metabolism; and a large number of proteins without annotated functions. This study provides a new level of understanding on how important marine bacteria can adapt to compete effectively in cold marine environments. This study is also a benchmark for comparative proteomic analyses with other important marine bacteria and other cold-adapted organisms.

Carbon and nitrogen substrate utilization in the marine bacterium Sphingopyxis alaskensis strain RB2256.

Sphingopyxis alaskensis is a marine member of the Alphaproteobacteria that is adapted to heterotrophic growth under nutrient-depleted (oligotrophic) conditions. S. alaskensis strain RB2256 is an ultramicrobacterium (cell volume <0.1 μm3), and has a genome size larger than that of the ultramicrobacterium ‘Candidatus Pelagibacter ubique’ HTCC1062 (SAR11 clade of Alphaproteobacteria): 3.35 versus 1.31 Mbp. In this study, we investigate the carbon and nitrogen metabolism of strain RB2256 using an integrated approach that combines growth and enzyme assays, proteomics and genome analysis. S. alaskensis is able to use specific amino acids and putrescine as a sole carbon and nitrogen source, and higher energy-yielding substrates such as glucose and trehalose as carbon sources. Alanine, in particular, emerges as a very important substrate in S. alaskensis metabolism. In an oligotrophic environment where competition for nutrients is intense, our data support a simplified metabolism for S. alaskensis in which the fate of certain substrates is constrained, especially at the intersections of central carbon and nitrogen metabolism, in order to ensure optimal disposition of scarce resources. This is the first investigation of central metabolism for an oligotrophic ultramicrobacterium that possesses a relatively large genome size. In contrast to the behavior so far observed for SAR11 oligotrophic bacteria, S. alaskensis shows a physiological capacity to exploit increases in ambient nutrient availability and thereby achieve high-population densities.