SUMMARY 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 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 elucidation of how genome variation contributes to disease1–3. Here, we present BioPlex 2.0 (Biophysical Interactions of ORFEOME-derived complexes), which employs robust affinity purification-mass spectrometry (AP-MS) methodology4 to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein coding genes from the human genome, and constitutes the largest such network to date. With >56,000 candidate interactions, BioPlex 2.0 contains >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 (MCL)5 of interacting proteins identified more than 1300 protein communities representing diverse cellular activities. Genes essential for cell fitness6,7 are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2000 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.

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 due to protein quantification interference is a common effect. We present a multi-proteome model (mixture of human and yeast proteins) in a 6-plex isobaric labeling system to fully document the interference effect, and we report that a multistage MS3-based approach almost completely eliminates interference.

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 ocean's 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.microbial adaptation and ecology ͉ microbial genomics and metagenomics ͉ monitoring environmental health ͉ trophic adaptation T he marine environment is the largest habitat on Earth, accounting for Ͼ90% of the biosphere by volume and harboring microorganisms responsible for Ϸ50% of total global primary production. Within this environment, marine bacteria (and archaea) play a pivotal role in biogeochemical cycles while constantly assimilating, storing, transforming, exporting, and remineralizing the largest pool of organic carbon on the planet (1).Nutrient levels in pelagic waters are not uniform. Large expanses of water are relatively nutrient depleted (e.g., oligotrophic open ocean water), whereas other zones are relatively nutrient rich (e.g., copiotrophic coastal and estuarine waters). Local variations in nutrient content can occur because of physical processes, including upwelling of nutrient rich deep waters or aeolian and riverine deposition, or biological processes such as phytoplankton blooms or aggregation of particulate organic matter. In addition, heterogeneity in ocean waters is not limited to gross differences in nutrient concentrations, but extends to microscale patchiness that occurs throughout the continuum of ocean nutrient concentrations (2).In ecological terms, bacteria are generally defined as rstrategists, having a small body, short generation time, and highly dispersible offspring. Although this strategy is broadly true compared with macroorganisms, bacteria have evolved a wide range of growth and survival strategies to maximize reproductive success. In particular, nutrient type and availability have provided strong selective pressure for defining lifestyle strategies among marine bacteria. However, although a large number of copiotrophic marine organisms (and fewer oligotrophs) have been cultured, the study of trophic strategy has been impaired by a lack of unders...

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 towards 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 15N for a 13C. In an accommodation to the preferred fragmentation pathways of ETD, the TMT-127 and -129 reagents were recently modified such that a 13C was exchanged for a 15N. 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. Based on our ability to resolve and accurately measure the relative intensity of these isobaric reporter ions, we demonstrate that we are able to quantify across 8 samples simultaneously by combining the 13C and 15N 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.

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 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.

burtonii has a high "IQ" (a measure of adaptive potential) compared to many methanogens.Numerous genes in these two over-represented COG categories appear to have been acquired from ε-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 play 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.

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 14 N or 15 N, 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, Student's 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.