Concordance of gene expression in human protein complexes reveals tissue specificity and pathology

Disease-causing variants in human genes usually lead to phenotypes specific to only a few tissues. Here, we present a method for predicting tissue specificity based on quantitative deregulation of protein complexes. The underlying assumption is that the degree of coordinated expression among proteins in a complex within a given tissue may pinpoint tissues that will be affected by a mutation in the complex and coordinated expression may reveal the complex to be active in the tissue. We identified known disease genes and their protein complex partners in a high-quality human interactome. Each susceptibility gene''s tissue involvement was ranked based on coordinated expression with its interaction partners in a non-disease global map of human tissue-specific expression. The approach demonstrated high overall area under the curve (0.78) and was very successfully benchmarked against a random model and an approach not using protein complexes. This was illustrated by correct tissue predictions for three case studies on leptin, insulin-like-growth-factor 2 and the inhibitor of NF-κB kinase subunit gamma that show high concordant expression in biologically relevant tissues. Our method identifies novel gene-phenotype associations in human diseases and predicts the tissues where associated phenotypic effects may arise.     

Ablation of mitochondrial DNA results in widespread remodeling of the mitochondrial complexome

So‐called ρ0 cells lack mitochondrial DNA and are therefore incapable of aerobic ATP synthesis. How cells adapt to survive ablation of oxidative phosphorylation remains poorly understood. Complexome profiling analysis of ρ0 cells covered 1,002 mitochondrial proteins and revealed changes in abundance and organization of numerous multiprotein complexes including previously not described assemblies. Beyond multiple subassemblies of complexes that would normally contain components encoded by mitochondrial DNA, we observed widespread reorganization of the complexome. This included distinct changes in the expression pattern of adenine nucleotide carrier isoforms, other mitochondrial transporters, and components of the protein import machinery. Remarkably, ablation of mitochondrial DNA hardly affected the complexes organizing cristae junctions indicating that the altered cristae morphology in ρ0 mitochondria predominantly resulted from the loss of complex V dimers required to impose narrow curvatures to the inner membrane. Our data provide a comprehensive resource for in‐depth analysis of remodeling of the mitochondrial complexome in response to respiratory deficiency.     

The mitochondrial complexome of Arabidopsis thaliana

Mitochondria are central to cellular metabolism and energy conversion. In plants they also enable photosynthesis through additional components and functional flexibility. A majority of those processes relies on the assembly of individual proteins to larger protein complexes, some of which operate as large molecular machines. There has been a strong interest in the makeup and function of mitochondrial protein complexes and protein–protein interactions in plants, but the experimental approaches used typically suffer from selectivity or bias. Here, we present a complexome profiling analysis for leaf mitochondria of the model plant Arabidopsis thaliana for the systematic characterization of protein assemblies. Purified organelle extracts were separated by 1D Blue native (BN) PAGE, a resulting gel lane was dissected into 70 slices (complexome fractions) and proteins in each slice were identified by label free quantitative shot‐gun proteomics. Overall, 1359 unique proteins were identified, which were, on average, present in 17 complexome fractions each. Quantitative profiles of proteins along the BN gel lane were aligned by similarity, allowing us to visualize protein assemblies. The data allow re‐annotating the subunit compositions of OXPHOS complexes, identifying assembly intermediates of OXPHOS complexes and assemblies of alternative respiratory oxidoreductases. Several protein complexes were discovered that have not yet been reported in plants, such as a 530 kDa Tat complex, 460 and 1000 kDa SAM complexes, a calcium ion uniporter complex (150 kDa) and several PPR protein complexes. We have set up a tailored online resource ( https://complexomemap.de/at_mito_leaves ) to deposit the data and to allow straightforward access and custom data analyses.     

Analysis of membrane–protein complexes of the marine sulfate reducer Desulfobacula toluolica Tol2 by 1D blue native‐PAGE complexome profiling and 2D blue native‐/SDS‐PAGE

Sulfate‐reducing bacteria (SRB) obtain energy from cytoplasmic reduction of sulfate to sulfide involving APS‐reductase (AprAB) and dissimilatory sulfite reductase (DsrAB). These enzymes are predicted to obtain electrons from membrane redox complexes, i.e. the quinone‐interacting membrane‐bound oxidoreductase (QmoABC) and DsrMKJOP complexes. In addition to these conserved complexes, the genomes of SRB encode a large number of other (predicted) membrane redox complexes, the function and actual formation of which is unknown. This study reports the establishment of 1D Blue Native‐PAGE complexome profiling and 2D BN‐/SDS‐PAGE for analysis of the membrane protein complexome of the marine sulfate reducer Desulfobacula toluolica Tol2. Analysis of normalized score profiles of >800 proteins in combination with hierarchical clustering and identification of 2D BN‐/SDS‐PAGE separated spots demonstrated separation of membrane complexes in their native form, e.g. ATP synthase. In addition to the QmoABC and DsrMKJOP complexes, other complexes were detected that constitute the basic membrane complexome of D. toluolica Tol2, e.g. transport proteins (e.g. sodium/sulfate symporters) or redox complexes involved in Na⁺‐based bioenergetics (RnfABCDEG). Notably, size estimation indicates dimer and quadruple formation of the DsrMKJOP complex in vivo. Furthermore, cluster analysis suggests interaction of this complex with a rhodanese‐like protein (Tol2_C05230) possibly representing a periplasmic electron transfer partner for DsrMKJOP.     

Characterization of multiprotein complexes of the Borrelia burgdorferi outer membrane vesicles

Among bacterial cell envelopes, the Borrelia burgdorferi outer membrane (OM) is structurally unique in that the identities of many protein complexes remain unknown; however, their characterization is the first step toward our understanding of membrane protein interactions and potential functions. Here, we used two-dimensional blue native/SDS-PAGE/mass spectrometric analysis for a global characterization of protein-protein interactions as well as to identify protein complexes in OM vesicles isolated from multiple infectious sensu stricto isolates of B. burgdorferi. Although we uncovered the existence of at least 10 distinct OM complexes harboring several unique subunits, the complexome is dominated by the frequent occurrence of a limited diversity of membrane proteins, most notably P13, outer surface protein (Osp) A, -B, -C, and -D and Lp6.6. The occurrence of these complexes and specificity of subunit interaction were further supported by independent two-dimensional immunoblotting and coimmunoprecipitation assays as well as by mutagenesis studies, where targeted depletion of a subunit member (P66) selectively abolished a specific complex. Although a comparable profile of the OM complexome was detected in two major infectious isolates, such as B31 and 297, certain complexes are likely to occur in an isolate-specific manner. Further assessment of protein complexes in multiple Osp-deficient isolates showed loss of several protein complexes but revealed the existence of additional complex/subunits that are undetectable in wild-type cells. Together, these observations uncovered borrelial antigens involved in membrane protein interactions. The study also suggests that the assembly process of OM complexes is specific and that the core or stabilizing subunits vary between complexes. Further characterization of these protein complexes including elucidation of their biological significance may shed new light on the mechanism of pathogen persistence and the development of preventative measures against the infection.     

From proteomes to complexomes in the era of systems biology

Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex–complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery‐potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.     

Visualization and analysis of the complexome network of Saccharomyces cerevisiae

Most processes in the cell are delivered by protein complexes, rather than individual proteins. While the association of proteins has been studied extensively in protein-protein interaction networks (the interactome), an intuitive and effective representation of complex-complex connections (the complexome) is not yet available. Here, we describe a new representation of the complexome of Saccharomyces cerevisiae. Using the core-module-attachment data of Gavin et al. ( Nature 2006 , 440 , 631 - 6 ), protein complexes in the network are represented as nodes; these are connected by edges that represent shared core and/or module protein subunits. To validate this network, we examined the network topology and its distribution of biological processes. The complexome network showed scale-free characteristics, with a power law-like node degree distribution and clustering coefficient independent of node degree. Connected complexes in the network showed similarities in biological process that were nonrandom. Furthermore, clusters of interacting complexes reflected a higher-level organization of many cellular functions. The strong functional relationships seen in these clusters, along with literature evidence, allowed 44 uncharacterized complexes to be assigned putative functions using guilt-by-association. We demonstrate our network model using the GEOMI visualization platform, on which we have developed capabilities to integrate and visualize complexome data.     

Multiplexed complexome profiling using tandem mass tags

Complexome profiling is a rapidly spreading, powerful technique to gain insight into the nature of protein complexes. It identifies and quantifies protein complexes separated into multiple fractions of increasing molecular mass using mass spectrometry-based, label-free bottom-up proteomics. Complexome profiling enables a sophisticated and thorough characterization of the composition, molecular mass, assembly, and interactions of protein complexes. However, in practice, its application is limited by the large number of samples it generates and the related time of mass spectrometry analyses. Here, we report an improved process workflow that implements tandem mass tags for multiplexing complexome profiling. This workflow substantially reduces the number of samples and measuring time without compromising protein identification or quantification reliability. In profiles from mitochondrial fractions of cells recovering from chloramphenicol treatment, tandem mass tags-multiplexed complexome profiling exhibited migration patterns of mature ATP synthase (complex V) and assembly intermediates that were consistent in composition and abundance with profiles obtained by the label-free approach. Reporter ion quantifications of proteins and complexes unaffected by the chloramphenicol treatment presented less variation in comparison to the label-free method. Incorporation of tandem mass tags enabled an efficient and robust complexome profiling analysis and may foster broader application for protein complex profiling in biomedical research and diagnostics.     

Combinations of protein-chemical complex structures reveal new targets for established drugs

Biological networks are powerful tools for predicting undocumented relationships between molecules. The underlying principle is that existing interactions between molecules can be used to predict new interactions. Here we use this principle to suggest new protein-chemical interactions via the network derived from three-dimensional structures. For pairs of proteins sharing a common ligand, we use protein and chemical superimpositions combined with fast structural compatibility screens to predict whether additional compounds bound by one protein would bind the other. The method reproduces 84% of complexes in a benchmark, and we make many predictions that would not be possible using conventional modeling techniques. Within 19,578 novel predicted interactions are 7,793 involving 718 drugs, including filaminast, coumarin, alitretonin and erlotinib. The growth rate of confident predictions is twice that of experimental complexes, meaning that a complete structural drug-protein repertoire will be available at least ten years earlier than by X-ray and NMR techniques alone.     

Critical Slowing Down Governs the Transition to Neuron Spiking

Many complex systems have been found to exhibit critical transitions, or so-called tipping points, which are sudden changes to a qualitatively different system state. These changes can profoundly impact the functioning of a system ranging from controlled state switching to a catastrophic break-down; signals that predict critical transitions are therefore highly desirable. To this end, research efforts have focused on utilizing qualitative changes in markers related to a system’s tendency to recover more slowly from a perturbation the closer it gets to the transition—a phenomenon called critical slowing down. The recently studied scaling of critical slowing down offers a refined path to understand critical transitions: to identify the transition mechanism and improve transition prediction using scaling laws.Here, we outline and apply this strategy for the first time in a real-world system by studying the transition to spiking in neurons of the mammalian cortex. The dynamical system approach has identified two robust mechanisms for the transition from subthreshold activity to spiking, saddle-node and Hopf bifurcation. Although theory provides precise predictions on signatures of critical slowing down near the bifurcation to spiking, quantitative experimental evidence has been lacking. Using whole-cell patch-clamp recordings from pyramidal neurons and fast-spiking interneurons, we show that 1) the transition to spiking dynamically corresponds to a critical transition exhibiting slowing down, 2) the scaling laws suggest a saddle-node bifurcation governing slowing down, and 3) these precise scaling laws can be used to predict the bifurcation point from a limited window of observation. To our knowledge this is the first report of scaling laws of critical slowing down in an experiment. They present a missing link for a broad class of neuroscience modeling and suggest improved estimation of tipping points by incorporating scaling laws of critical slowing down as a strategy applicable to other complex systems.     

TMEM70 functions in the assembly of complexes I and V

Protein complexes from the oxidative phosphorylation (OXPHOS) system are assembled with the help of proteins called assembly factors. We here delineate the function of the inner mitochondrial membrane protein TMEM70, in which mutations have been linked to OXPHOS deficiencies, using a combination of BioID, complexome profiling and coevolution analyses. TMEM70 interacts with complex I and V and for both complexes the loss of TMEM70 results in the accumulation of an assembly intermediate followed by a reduction of the next assembly intermediate in the pathway. This indicates that TMEM70 has a role in the stability of membrane-bound subassemblies or in the membrane recruitment of subunits into the forming complex. Independent evidence for a role of TMEM70 in OXPHOS assembly comes from evolutionary analyses. The TMEM70/TMEM186/TMEM223 protein family, of which we show that TMEM186 and TMEM223 are mitochondrial in human as well, only occurs in species with OXPHOS complexes. Our results validate the use of combining complexome profiling with BioID and evolutionary analyses in elucidating congenital defects in protein complex assembly.     

Stress-induced mutagenesis and complex adaptation

Because mutations are mostly deleterious, mutation rates should be reduced by natural selection. However, mutations also provide the raw material for adaptation. Therefore, evolutionary theory suggests that the mutation rate must balance between adaptability—the ability to adapt—and adaptedness—the ability to remain adapted. We model an asexual population crossing a fitness valley and analyse the rate of complex adaptation with and without stress-induced mutagenesis (SIM)—the increase of mutation rates in response to stress or maladaptation. We show that SIM increases the rate of complex adaptation without reducing the population mean fitness, thus breaking the evolutionary trade-off between adaptability and adaptedness. Our theoretical results support the hypothesis that SIM promotes adaptation and provide quantitative predictions of the rate of complex adaptation with different mutational strategies.     

Defining the protein complexome of translation termination factor eRF1: Identification of four novel eRF1‐containing complexes that range from 20S to 57S in size

The eukaryotic eRF1 translation termination factor plays an important role in recognizing stop codons and initiating the end to translation. However, which exact complexes contain eRF1 and at what abundance is not clear. We have used analytical ultracentrifugation with fluorescent detection system to identify the protein complexome of eRF1 in the yeast Saccharomyces cerevisiae. In addition to eRF1 presence in translating polysomes, we found that eRF1 associated with five other macromolecular complexes: 77S, 57S, 39S, 28S, and 20S in size. Generally equal abundances of each of these complexes were found. The 77S complex primarily contained the free 80S ribosome consistent with in vitro studies and did not appear to contain significant levels of the monosomal translating complex that co‐migrates with the free 80S ribosome. The 57S and 39S complexes represented, respectively, free 60S and 40S ribosomal subunits bound to eRF1, associations not previously reported. The novel 28S and 20S complexes (containing minimal masses of 830 KDa and 500 KDa, respectively) lacked significant RNA components and appeared to be oligomeric, as eRF1 has a mass of 49 KDa. The majority of polysomal complexes containing eRF1 were both substantially deadenylated and lacking in closed‐loop factors eIF4E and eIF4G. The thirteen percent of such translating polysomes that contained poly(A) tails had equivalent levels of eIF4E and eIF4G, suggesting these complexes were in a closed‐loop structure. The identification of eRF1 in these unique and previously unrecognized complexes suggests a variety of new roles for eRF1 in the regulation of cellular processes.     

Complex Permittivity Differences Between Normal and Pathological Tissues: Mechanisms and Medical Significance

The complex permittivity dispersions of normal and pathological biological tissues are surveyed from audio to microwave frequencies. The mechanisms responsible for producing changes to biodlelectric properties are examined, and qualitative and quantitative predictions are made about these changes following the transformation of normal to malignant tissue. For the transformation of rat liver to hepatoma tissue, we predict a peak increase of 73% at 2 MHz for the permittivity, with elevations in conductivity relatively constant at 40-46% between 3 MHz-lGHz. Several features of the predictions are in accord with experimental measurements previously published. Transformed biodielectrlc properties are of potential significance for tissue characterization, medical imaging, diagnosis and therapy.     

Thermodynamic extremization principles and their relevance to ecology

Theories based on simple principles have provided much insight into the common processes that underpin complex ecological systems. Although such theories (e.g. neutral theory, metabolic theories) often neglect specific ecological details, they compensate for this with their generality and broad applicability. We review several simple principles based on ‘thermodynamic extremization’ (the minimization or maximization of a thermodynamic quantity) and explore their application and relevance to ecology. Thermodynamic extremization principles predict that certain energetic quantities (e.g. entropy production) will tend towards maxima or minima within ecological systems, subject to local constraints (e.g. resource availability). These principles have a long history in ecology, but existing applications have had a theoretical focus and have made few quantitative predictions. We show that the majority of existing theories can be unified conceptually and mathematically, a result that should facilitate ecological applications of thermodynamic extremization principles. Recent developments in broader ecological research (e.g. metabolic theories) have allowed quantitative predictions of ecological patterns from thermodynamic extremization principles, and initial predictions have been supported by empirical data. We discuss how the application of extremization principles could be extended and demonstrate one possible extension, using an extremization principle to predict individual size distributions. A key focus in the application of thermodynamic extremization principles to mainstream ecological questions should be the generation of quantitative predictions and subsequent empirical validation.     

Rapid quantitative profiling of complex microbial populations

Diverse and complex microbial ecosystems are found in virtually every environment on earth, yet we know very little about their composition and ecology. Comprehensive identification and quantification of the constituents of these microbial communities—a ‘census’—is an essential foundation for understanding their biology. To address this problem, we developed, tested and optimized a DNA oligonucleotide microarray composed of 10 462 small subunit (SSU) ribosomal DNA (rDNA) probes (7167 unique sequences) selected to provide quantitative information on the taxonomic composition of diverse microbial populations. Using our optimized experimental approach, this microarray enabled detection and quantification of individual bacterial species present at fractional abundances of <0.1% in complex synthetic mixtures. The estimates of bacterial species abundance obtained using this microarray are similar to those obtained by phylogenetic analysis of SSU rDNA sequences from the same samples—the current ‘gold standard’ method for profiling microbial communities. Furthermore, probes designed to represent higher order taxonomic groups of bacterial species reliably detected microbes for which there were no species-specific probes. This simple, rapid microarray procedure can be used to explore and systematically characterize complex microbial communities, such as those found within the human body.     

LEON: multiple aLignment Evaluation Of Neighbours

Sequence alignments are fundamental to a wide range of applications, including database searching, functional residue identification and structure prediction techniques. These applications predict or propagate structural/functional/evolutionary information based on a presumed homology between the aligned sequences. If the initial hypothesis of homology is wrong, no subsequent application, however sophisticated, can be expected to yield accurate results. Here we present a novel method, LEON, to predict homology between proteins based on a multiple alignment of complete sequences (MACS). In MACS, weak signals from distantly related proteins can be considered in the overall context of the family. Intermediate sequences and the combination of individual weak matches are used to increase the significance of low-scoring regions. Residue composition is also taken into account by incorporation of several existing methods for the detection of compositionally biased sequence segments. The accuracy and reliability of the predictions is demonstrated in large-scale comparisons with structural and sequence family databases, where the specificity was shown to be >99% and the sensitivity was estimated to be ~76%. LEON can thus be used to reliably identify the complex relationships between large multidomain proteins and should be useful for automatic high-throughput genome annotations, 2D/3D structure predictions, protein–protein interaction predictions etc.