Although chromosome partitioning during mitosis is well studied, the molecular mechanisms that allow proper segregation of cytoplasmic organelles in human cells are poorly understood. Here we show that mitochondria interact with growing microtubule tips and are transported towards the daughter cell periphery at the end of mitosis. This phenomenon is promoted by the direct and cell cycle-dependent interaction of the mitochondrial protein Miro and the cytoskeletal-associated protein Cenp-F. Cenp-F is recruited to mitochondria by Miro at the time of cytokinesis and associates with microtubule growing tips. Cells devoid of Cenp-F or Miro show decreased spreading of the mitochondrial network as well as cytokinesis-specific defects in mitochondrial transport towards the cell periphery. Thus, Miro and Cenp-F promote anterograde mitochondrial movement and proper mitochondrial distribution in daughter cells.

During interphase, the nuclear envelope (NE) serves as a selective barrier between cytosol and nucleoplasm. When vertebrate cells enter mitosis, the NE is dismantled in the process of nuclear envelope breakdown (NEBD). Disassembly of nuclear pore complexes (NPCs) is a key aspect of NEBD, required for NE permeabilization and formation of a cytoplasmic mitotic spindle. Here, we show that both CDK1 and polo-like kinase 1 (PLK1) support mitotic NPC disintegration by hyperphosphorylation of Nup98, the gatekeeper nucleoporin, and Nup53, a central nucleoporin linking the inner NPC scaffold to the pore membrane. Multisite phosphorylation of Nup53 critically contributes to its liberation from its partner nucleoporins, including the pore membrane protein NDC1. Initial steps of NPC disassembly in semi-permeabilized cells can be reconstituted by a cocktail of mitotic kinases including cyclinB-CDK1, NIMA, and PLK1, suggesting that the unzipping of nucleoporin interactions by protein phosphorylation is an important principle underlying mitotic NE permeabilization.

Respiratory Syncytial Virus (RSV) is a highly pathogenic member of the Paramyxoviridae that causes severe respiratory tract infections. Reports in the literature have indicated that to infect cells the incoming viruses either fuse their envelope directly with the plasma membrane or exploit clathrin-mediated endocytosis. To study the entry process in human tissue culture cells (HeLa, A549), we used fluorescence microscopy and developed quantitative, FACS-based assays to follow virus binding to cells, endocytosis, intracellular trafficking, membrane fusion, and infection. A variety of perturbants were employed to characterize the cellular processes involved. We found that immediately after binding to cells RSV activated a signaling cascade involving the EGF receptor, Cdc42, PAK1, and downstream effectors. This led to a series of dramatic actin rearrangements; the cells rounded up, plasma membrane blebs were formed, and there was a significant increase in fluid uptake. If these effects were inhibited using compounds targeting Na⁺/H⁺ exchangers, myosin II, PAK1, and other factors, no infection was observed. The RSV was rapidly and efficiently internalized by an actin-dependent process that had all hallmarks of macropinocytosis. Rather than fusing with the plasma membrane, the viruses thus entered Rab5-positive, fluid-filled macropinosomes, and fused with the membranes of these on the average 50 min after internalization. Rab5 was required for infection. To find an explanation for the endocytosis requirement, which is unusual among paramyxoviruses, we analyzed the fusion protein, F, and could show that, although already cleaved by a furin family protease once, it underwent a second, critical proteolytic cleavage after internalization. This cleavage by a furin-like protease removed a small peptide from the F1 subunits, and made the virus infectious.

Since its inception, proteomics has essentially operated in a discovery mode with the goal of identifying and quantifying the maximal number of proteins in a sample. Increasingly, proteomic measurements are also supporting hypothesis-driven studies, in which a predetermined set of proteins is consistently detected and quantified in multiple samples. Selected reaction monitoring (SRM) is a targeted mass spectrometric technique that supports the detection and quantification of specific proteins in complex samples at high sensitivity and reproducibility. Here, we describe ATAQS, an integrated software platform that supports all stages of targeted, SRM-based proteomics experiments including target selection, transition optimization and post acquisition data analysis. This software will significantly facilitate the use of targeted proteomic techniques and contribute to the generation of highly sensitive, reproducible and complete datasets that are particularly critical for the discovery and validation of targets in hypothesis-driven studies in systems biology.

In high-throughput mass spectrometry proteomics, peptides and proteins are not simply identified as present or not present in a sample, rather the identifications are associated with differing levels of confidence. The false discovery rate (FDR) has emerged as an accepted means for measuring the confidence associated with identifications. We have developed the Systematic Protein Investigative Research Environment (SPIRE) for the purpose of integrating the best available proteomics methods. Two successful approaches to estimating the FDR for MS protein identifications are the MAYU and our current SPIRE methods. We present here a method to combine these two approaches to estimating the FDR for MS protein identifications into an integrated protein model (IPM). We illustrate the high quality performance of this IPM approach through testing on two large publicly available proteomics datasets. MAYU and SPIRE show remarkable consistency in identifying proteins in these datasets. Still, IPM results in a more robust FDR estimation approach and additional identifications, particularly among low abundance proteins. IPM is now implemented as a part of the SPIRE system.

What is the relationship between enzymes and metabolites, the two major constituents of metabolic networks? We propose three alternative relationships between enzyme capacity and metabolite concentration alterations based on a Michaelis-Menten kinetic; that is enzyme capacities, metabolite concentrations, or both could limit the metabolic reaction rates. These relationships imply different correlations between changes in enzyme capacity and metabolite concentration, which we tested by quantifying metabolite, transcript, and enzyme abundances upon local (single-enzyme modulation) and global (GCR2 transcription factor mutant) perturbations in Saccharomyces cerevisiae. Our results reveal an inverse relationship between fold-changes in substrate metabolites and their catalyzing enzymes. These data provide evidence for the hypothesis that reaction rates are jointly limited by enzyme capacity and metabolite concentration. Hence, alteration in one network constituent can be efficiently buffered by converse alterations in the other constituent, implying a passive mechanism to maintain metabolic homeostasis upon perturbations in enzyme capacity.

Homologous recombination (HR) is essential for high-fidelity DNA repair during mitotic proliferation and meiosis. Yet, context-specific modifications must tailor the recombination machinery to avoid (mitosis) or enforce (meiosis) the formation of reciprocal exchanges-crossovers-between recombining chromosomes. To obtain molecular insight into how crossover control is achieved, we affinity purified 7 DNA-processing enzymes that channel HR intermediates into crossovers or noncrossovers from vegetative cells or cells undergoing meiosis. Using mass spectrometry, we provide a global characterization of their composition and reveal mitosis- and meiosis-specific modules in the interaction networks. Functional analyses of meiosis-specific interactors of MutLγ-Exo1 identified Rtk1, Caf120, and Chd1 as regulators of crossing-over. Chd1, which transiently associates with Exo1 at the prophase-to-metaphase I transition, enables the formation of MutLγ-dependent crossovers through its conserved ability to bind and displace nucleosomes. Thus, rewiring of the HR network, coupled to chromatin remodeling, promotes context-specific control of the recombination outcome.

Cells balance glycolysis with respiration to support their metabolic needs in different environmental or physiological contexts. With abundant glucose, many cells prefer to grow by aerobic glycolysis or fermentation. Using 161 natural isolates of fission yeast, we investigated the genetic basis and phenotypic effects of the fermentation-respiration balance. The laboratory and a few other strains depended more on respiration. This trait was associated with a single nucleotide polymorphism in a conserved region of Pyk1, the sole pyruvate kinase in fission yeast. This variant reduced Pyk1 activity and glycolytic flux. Replacing the "low-activity" pyk1 allele in the laboratory strain with the "high-activity" allele was sufficient to increase fermentation and decrease respiration. This metabolic rebalancing triggered systems-level adjustments in the transcriptome and proteome and in cellular traits, including increased growth and chronological lifespan but decreased resistance to oxidative stress. Thus, low Pyk1 activity does not lead to a growth advantage but to stress tolerance. The genetic tuning of glycolytic flux may reflect an adaptive trade-off in a species lacking pyruvate kinase isoforms.

Nitrogen-containing bisphosphonates (N-BPs) are therapeutic agents used to treat osteoporosis and promote osteoblast and osteocyte survival. The molecular mechanisms underlying this effect have been extensively studied, but the global changes induced by N-BPs at the protein level are not known. In this context, we investigated the effect of 10(-7)M Risedronate for 1h and 48h on MLO-Y4 osteocytic cells, through a quantitative, label free shotgun proteomic analysis. We described herein a preliminary proteome map of untreated MLO-Y4 cells, composed of 353 protein species. Moreover, we identified 10 and 15 differentially expressed proteins after 1h and 48h of Risedronate treatment, respectively. Among these, PARK7/DJ-1 protein levels were induced up to 3 times and this event was associated with the activation of the pro-survival Akt pathway that we propose as a novel player in the effect of N-BPs on osteocytes. Risedronate was also able to induce the expression and the secretion of the growth factor pro-granulin. In addition, protein prenylation inhibition appeared to be involved in the modulation of MLO-Y4 proteome by RIS in a protein-specific manner. In conclusion, these findings unveil novel functions targeted by N-BPs in osteocytes and could be useful to design novel pharmaceutical compounds.

Parkinson's disease (PD), Alzheimer's disease (AD) and Amyotrophic lateral sclerosis (ALS) are neurodegenerative diseases hallmarked by the formation of toxic protein aggregates. However, targeting these aggregates therapeutically have thus far shown no success. The treatment of AD has remained particularly problematic since no new drugs have been approved in the last 15 years. Therefore, novel therapeutic targets need to be identified and explored. Here, through the integration of genomic and proteomic data, a set of proteins with strong links to α-synuclein-aggregating neurodegenerative diseases was identified. We propose 17 protein targets that are likely implicated in neurodegeneration and could serve as potential targets. The human phosphatidylinositol 5-phosphatase synaptojanin-1, which has already been independently confirmed to be implicated in Parkinson's and Alzheimer's disease, was among those identified. Despite its involvement in PD and AD, structural aspects are currently missing at the molecular level. We present the first atomistic model of the 5-phosphatase domain of synaptojanin-1 and its binding to its substrate phosphatidylinositol 4,5-bisphosphate (PIP2). We determine structural information on the active site including membrane-embedded molecular dynamics simulations. Deficiency of charge within the active site of the protein is observed, which suggests that a second divalent cation is required to complete dephosphorylation of the substrate. The findings in this work shed light on the protein's binding to phosphatidylinositol 4,5-bisphosphate (PIP2) and give additional insight for future targeting of the protein active site, which might be of interest in neurodegenerative diseases where synaptojanin-1 is overexpressed.

Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identify the protein targets of a compound and also detect the interaction surfaces between ligands and protein targets without prior labeling or modification. To address this limitation, we here develop LiP-Quant, a drug target deconvolution pipeline based on limited proteolysis coupled with mass spectrometry that works across species, including in human cells. We use machine learning to discern features indicative of drug binding and integrate them into a single score to identify protein targets of small molecules and approximate their binding sites. We demonstrate drug target identification across compound classes, including drugs targeting kinases, phosphatases and membrane proteins. LiP-Quant estimates the half maximal effective concentration of compound binding sites in whole cell lysates, correctly discriminating drug binding to homologous proteins and identifying the so far unknown targets of a fungicide research compound.

Biological processes are regulated by intermolecular interactions and chemical modifications that do not affect protein levels, thus escaping detection in classical proteomic screens. We demonstrate here that a global protein structural readout based on limited proteolysis-mass spectrometry (LiP-MS) detects many such functional alterations, simultaneously and in situ, in bacteria undergoing nutrient adaptation and in yeast responding to acute stress. The structural readout, visualized as structural barcodes, captured enzyme activity changes, phosphorylation, protein aggregation, and complex formation, with the resolution of individual regulated functional sites such as binding and active sites. Comparison with prior knowledge, including other 'omics data, showed that LiP-MS detects many known functional alterations within well-studied pathways. It suggested distinct metabolite-protein interactions and enabled identification of a fructose-1,6-bisphosphate-based regulatory mechanism of glucose uptake in E. coli. The structural readout dramatically increases classical proteomics coverage, generates mechanistic hypotheses, and paves the way for in situ structural systems biology.

Calreticulin (CALR) is an endoplasmic reticulum (ER)-retained chaperone that assists glycoproteins in obtaining their structure. CALR mutations occur in patients with myeloproliferative neoplasms (MPNs), and the ER retention of CALR mutants (CALR MUT) is reduced due to a lacking KDEL sequence. Here, we investigate the impact of CALR mutations on protein structure and protein levels in MPNs by subjecting primary patient samples and CALR-mutated cell lines to limited proteolysis-coupled mass spectrometry (LiP-MS). Especially glycoproteins are differentially expressed and undergo profound structural alterations in granulocytes and cell lines with homozygous, but not with heterozygous, CALR mutations. Furthermore, homozygous CALR mutations and loss of CALR equally perturb glycoprotein integrity, suggesting that loss-of-function attributes of mutated CALR chaperones (CALR MUT) lead to glycoprotein maturation defects. Finally, by investigating the misfolding of the CALR glycoprotein client myeloperoxidase (MPO), we provide molecular proof of protein misfolding in the presence of homozygous CALR mutations.

The complexity of many cellular and organismal traits results from the integration of genetic and environmental factors via molecular networks. Network structure and effect propagation are best understood at the level of functional modules, but so far, no concept has been established to include the global network state. Here, we show when and how genetic perturbations lead to molecular changes that are confined to small parts of a network versus when they lead to modulation of network states. Integrating multi-omics profiling of genetically heterogeneous budding and fission yeast strains with an array of cellular traits identified a central state transition of the yeast molecular network that is related to PKA and TOR (PT) signaling. Genetic variants affecting this PT state globally shifted the molecular network along a single-dimensional axis, thereby modulating processes including energy and amino acid metabolism, transcription, translation, cell cycle control, and cellular stress response. We propose that genetic effects can propagate through large parts of molecular networks because of the functional requirement to centrally coordinate the activity of fundamental cellular processes.

Metabolic activity is intimately linked to T cell fate and function. Using high-resolution mass spectrometry, we generated dynamic metabolome and proteome profiles of human primary naive T cells following activation. We discovered critical changes in the arginine metabolism that led to a drop in intracellular L-arginine concentration. Elevating L-arginine levels induced global metabolic changes including a shift from glycolysis to oxidative phosphorylation in activated T cells and promoted the generation of central memory-like cells endowed with higher survival capacity and, in a mouse model, anti-tumor activity. Proteome-wide probing of structural alterations, validated by the analysis of knockout T cell clones, identified three transcriptional regulators (BAZ1B, PSIP1, and TSN) that sensed L-arginine levels and promoted T cell survival. Thus, intracellular L-arginine concentrations directly impact the metabolic fitness and survival capacity of T cells that are crucial for anti-tumor responses.

The ability to reliably and reproducibly measure any protein of the human proteome in any tissue or cell type would be transformative for understanding systems-level properties as well as specific pathways in physiology and disease. Here, we describe the generation and verification of a compendium of highly specific assays that enable quantification of 99.7% of the 20,277 annotated human proteins by the widely accessible, sensitive, and robust targeted mass spectrometric method selected reaction monitoring, SRM. This human SRMAtlas provides definitive coordinates that conclusively identify the respective peptide in biological samples. We report data on 166,174 proteotypic peptides providing multiple, independent assays to quantify any human protein and numerous spliced variants, non-synonymous mutations, and post-translational modifications. The data are freely accessible as a resource at http://www.srmatlas.org/, and we demonstrate its utility by examining the network response to inhibition of cholesterol synthesis in liver cells and to docetaxel in prostate cancer lines.

Faithful DNA replication and repair requires the activity of cullin 4-based E3 ubiquitin ligases (CRL4), but the underlying mechanisms remain poorly understood. The budding yeast Cul4 homologue, Rtt101, in complex with the linker Mms1 and the putative substrate adaptor Mms22 promotes progression of replication forks through damaged DNA. Here we characterized the interactome of Mms22 and found that the Rtt101(Mms22) ligase associates with the replisome progression complex during S-phase via the amino-terminal WD40 domain of Ctf4. Moreover, genetic screening for suppressors of the genotoxic sensitivity of rtt101Δ cells identified a cluster of replication proteins, among them a component of the fork protection complex, Mrc1. In contrast to rtt101Δ and mms22Δ cells, mrc1Δ rtt101Δ and mrc1Δ mms22Δ double mutants complete DNA replication upon replication stress by facilitating the repair/restart of stalled replication forks using a Rad52-dependent mechanism. Our results suggest that the Rtt101(Mms22) E3 ligase does not induce Mrc1 degradation, but specifically counteracts Mrc1's replicative function, possibly by modulating its interaction with the CMG (Cdc45-MCM-GINS) complex at stalled forks.

Decades of biochemical research have identified most of the enzymes that catalyze metabolic reactions in the yeast Saccharomyces cerevisiae. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood. As an important stepping stone toward such understanding, we exploit the power of proteomics assays based on selected reaction monitoring (SRM) mass spectrometry to quantify abundance changes of the 228 proteins that constitute the central carbon and amino-acid metabolic network in the yeast Saccharomyces cerevisiae, at five different metabolic steady states. Overall, 90% of the targeted proteins, including families of isoenzymes, were consistently detected and quantified in each sample, generating a proteomic data set that represents a nutritionally perturbed biological system at high reproducibility. The data set is near comprehensive because we detect 95-99% of all proteins that are required under a given condition. Interpreted through flux balance modeling, the data indicate that S. cerevisiae retains proteins not necessarily used in a particular environment. Further, the data suggest differential functionality for several metabolic isoenzymes.

Systems biology studies require the capability to quantify with high precision proteins spanning a broad range of abundances across multiple samples. However, the broad range of protein expression in cells often precludes the detection of low-abundance proteins. Different sample processing techniques can be applied to increase proteome coverage. Among these, combinatorial (hexa)peptide ligand libraries (CPLLs) bound to solid matrices have been used to specifically capture and detect low-abundance proteins in complex samples. To assess whether CPLL capture can be applied in systems biology studies involving the precise quantitation of proteins across a multitude of samples, we evaluated its performance across the whole range of protein abundances in Saccharomyces cerevisiae. We used selected reaction monitoring assays for a set of target proteins covering a broad abundance range to quantitatively evaluate the precision of the approach and its capability to detect low-abundance proteins. Replicated CPLL-isolates showed an average variability of ~10% in the amount of the isolated proteins. The high reproducibility of the technique was not dependent on the abundance of the protein or the amount of beads used for the capture. However, the protein-to-bead ratio affected the enrichment of specific proteins. We did not observe a normalization effect of CPLL beads on protein abundances. However, CPLLs enriched for and depleted specific sets of proteins and thus changed the abundances of proteins from a whole proteome extract. This allowed the identification of ~400 proteins otherwise undetected in an untreated sample, under the experimental conditions used. CPLL capture is thus a useful tool to increase protein identifications in proteomic experiments, but it should be coupled to the analysis of untreated samples, to maximize proteome coverage. Our data also confirms that CPLL capture is reproducible and can be confidently used in quantitative proteomic experiments.

Metabolite-protein interactions control a variety of cellular processes, thereby playing a major role in maintaining cellular homeostasis. Metabolites comprise the largest fraction of molecules in cells, but our knowledge of the metabolite-protein interactome lags behind our understanding of protein-protein or protein-DNA interactomes. Here, we present a chemoproteomic workflow for the systematic identification of metabolite-protein interactions directly in their native environment. The approach identified a network of known and novel interactions and binding sites in Escherichia coli, and we demonstrated the functional relevance of a number of newly identified interactions. Our data enabled identification of new enzyme-substrate relationships and cases of metabolite-induced remodeling of protein complexes. Our metabolite-protein interactome consists of 1,678 interactions and 7,345 putative binding sites. Our data reveal functional and structural principles of chemical communication, shed light on the prevalence and mechanisms of enzyme promiscuity, and enable extraction of quantitative parameters of metabolite binding on a proteome-wide scale.