A computational framework for the inference of protein complex remodeling from whole-proteome measurements.

Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at defined quantitative ratios. Typically, global changes in protein complex composition are assessed with experimental approaches that tend to be time consuming. Here, we have developed a computational algorithm for the detection of altered protein complexes based on the systematic assessment of subunit ratios from quantitative proteomic measurements. We applied it to measurements from breast cancer cell lines and patient biopsies and were able to identify strong remodeling of HDAC2 epigenetic complexes in more aggressive forms of cancer. The presented algorithm is available as an R package and enables the inference of changes in protein complex states by extracting functionally relevant information from bottom-up proteomic datasets.

M. tuberculosis relies on trace oxygen to maintain energy homeostasis and survive in hypoxic environments.

The bioenergetic mechanisms by which Mycobacterium tuberculosis survives hypoxia are poorly understood. Current models assume that the bacterium shifts to an alternate electron acceptor or fermentation to maintain membrane potential and ATP synthesis. Counterintuitively, we find here that oxygen itself is the principal terminal electron acceptor during hypoxic dormancy. M. tuberculosis can metabolize oxygen efficiently at least two orders of magnitude below the concentration predicted to occur in hypoxic lung granulomas. Despite a difference in apparent affinity for oxygen, both the cytochrome bcc:aa3 and cytochrome bd oxidase respiratory branches are required for hypoxic respiration. Simultaneous inhibition of both oxidases blocks oxygen consumption, reduces ATP levels, and kills M. tuberculosis under hypoxia. The capacity of mycobacteria to scavenge trace levels of oxygen, coupled with the absence of complex regulatory mechanisms to achieve hierarchal control of the terminal oxidases, may be a key determinant of long-term M. tuberculosis survival in hypoxic lung granulomas.

Ex Vivo Drug Testing in Patient-derived Papillary Renal Cancer Cells Reveals EGFR and the BCL2 Family as Therapeutic Targets.

BACKGROUND: Immune checkpoint inhibitors and antiangiogenic agents are used for first-line treatment of advanced papillary renal cell carcinoma (pRCC) but pRCC response rates to these therapies are low. OBJECTIVE: To generate and characterise a functional ex vivo model to identify novel treatment options in advanced pRCC. DESIGN, SETTING, AND PARTICIPANTS: We established patient-derived cell cultures (PDCs) from seven pRCC samples from patients and characterised them via genomic analysis and drug profiling. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Comprehensive molecular characterisation in terms of copy number analysis and whole-exome sequencing confirmed the concordance of pRCC PDCs with the original tumours. We evaluated their sensitivity to novel drugs by generating drug scores for each PDC. RESULTS AND LIMITATIONS: PDCs confirmed pRCC-specific copy number variations such as gains in chromosomes 7, 16, and 17. Whole-exome sequencing revealed that PDCs retained mutations in pRCC-specific driver genes. We performed drug screening with 526 novel and oncological compounds. Whereas exposure to conventional drugs showed low efficacy, the results highlighted EGFR and BCL2 family inhibition as the most effective targets in our pRCC PDCs. CONCLUSIONS: High-throughput drug testing on newly established pRCC PDCs revealed that inhibition of EGFR and BCL2 family members could be a therapeutic strategy in pRCC. PATIENT SUMMARY: We used a new approach to generate patient-derived cells from a specific type of kidney cancer. We showed that these cells have the same genetic background as the original tumour and can be used as models to study novel treatment options for this type of kidney cancer.

Enzyme expression kinetics by Escherichia coli during transition from rich to minimal media depends on proteome reserves.

Bacterial fitness depends on adaptability to changing environments. In rich growth medium, which is replete with amino acids, Escherichia coli primarily expresses protein synthesis machineries, which comprise ~40% of cellular proteins and are required for rapid growth. Upon transition to minimal medium, which lacks amino acids, biosynthetic enzymes are synthesized, eventually reaching ~15% of cellular proteins when growth fully resumes. We applied quantitative proteomics to analyse the timing of enzyme expression during such transitions, and established a simple positive relation between the onset time of enzyme synthesis and the fractional enzyme 'reserve' maintained by E. coli while growing in rich media. We devised and validated a coarse-grained kinetic model that quantitatively captures the enzyme recovery kinetics in different pathways, solely on the basis of proteomes immediately preceding the transition and well after its completion. Our model enables us to infer regulatory strategies underlying the 'as-needed' gene expression programme adopted by E. coli.

Integrative systems analysis identifies genetic and dietary modulators of bile acid homeostasis.

Bile acids (BAs) are complex and incompletely understood enterohepatic-derived hormones that control whole-body metabolism. Here, we profiled postprandial BAs in the liver, feces, and plasma of 360 chow- or high-fat-diet-fed BXD male mice and demonstrated that both genetics and diet strongly influence BA abundance, composition, and correlation with metabolic traits. Through an integrated systems approach, we mapped hundreds of quantitative trait loci that modulate BAs and identified both known and unknown regulators of BA homeostasis. In particular, we discovered carboxylesterase 1c (Ces1c) as a genetic determinant of plasma tauroursodeoxycholic acid (TUDCA), a BA species with established disease-preventing actions. The association between Ces1c and plasma TUDCA was validated using data from independent mouse cohorts and a Ces1c knockout mouse model. Collectively, our data are a unique resource to dissect the physiological importance of BAs as determinants of metabolic traits, as underscored by the identification of CES1C as a master regulator of plasma TUDCA levels.

Expression Dysregulation as a Mediator of Fitness Costs in Antibiotic Resistance.

Antimicrobial resistance (AMR) poses a threat to global health and the economy. Rifampicin-resistant Mycobacterium tuberculosis accounts for a third of the global AMR burden. Gaining the upper hand on AMR requires a deeper understanding of the physiology of resistance. AMR often results in a fitness cost in the absence of drug. Identifying the molecular mechanisms underpinning this cost could help strengthen future treatment regimens. Here, we used a collection of M. tuberculosis strains that provide an evolutionary and phylogenetic snapshot of rifampicin resistance and subjected them to genome-wide transcriptomic and proteomic profiling to identify key perturbations of normal physiology. We found that the clinically most common rifampicin resistance-conferring mutation, RpoB Ser450Leu, imparts considerable gene expression changes, many of which are mitigated by the compensatory mutation in RpoC Leu516Pro. However, our data also provide evidence for pervasive epistasis-the same resistance mutation imposed a different fitness cost and functionally distinct changes to gene expression in genetically unrelated clinical strains. Finally, we report a likely posttranscriptional modulation of gene expression that is shared in most of the tested strains carrying RpoB Ser450Leu, resulting in an increased abundance of proteins involved in central carbon metabolism. These changes contribute to a more general trend in which the disruption of the composition of the proteome correlates with the fitness cost of the RpoB Ser450Leu mutation in different strains.

From coarse to fine: the absolute Escherichia coli proteome under diverse growth conditions.

Accurate measurements of cellular protein concentrations are invaluable to quantitative studies of gene expression and physiology in living cells. Here, we developed a versatile mass spectrometric workflow based on data-independent acquisition proteomics (DIA/SWATH) together with a novel protein inference algorithm (xTop). We used this workflow to accurately quantify absolute protein abundances in Escherichia coli for > 2,000 proteins over > 60 growth conditions, including nutrient limitations, non-metabolic stresses, and non-planktonic states. The resulting high-quality dataset of protein mass fractions allowed us to characterize proteome responses from a coarse (groups of related proteins) to a fine (individual) protein level. Hereby, a plethora of novel biological findings could be elucidated, including the generic upregulation of low-abundant proteins under various metabolic limitations, the non-specificity of catabolic enzymes upregulated under carbon limitation, the lack of large-scale proteome reallocation under stress compared to nutrient limitations, as well as surprising strain-dependent effects important for biofilm formation. These results present valuable resources for the systems biology community and can be used for future multi-omics studies of gene regulation and metabolic control in E. coli.

Complex-centric proteome profiling by SEC-SWATH-MS for the parallel detection of hundreds of protein complexes.

Most catalytic, structural and regulatory functions of the cell are carried out by functional modules, typically complexes containing or consisting of proteins. The composition and abundance of these complexes and the quantitative distribution of specific proteins across different modules are therefore of major significance in basic and translational biology. However, detection and quantification of protein complexes on a proteome-wide scale is technically challenging. We have recently extended the targeted proteomics rationale to the level of native protein complex analysis (complex-centric proteome profiling). The complex-centric workflow described herein consists of size exclusion chromatography (SEC) to fractionate native protein complexes, data-independent acquisition mass spectrometry to precisely quantify the proteins in each SEC fraction based on a set of proteotypic peptides and targeted, complex-centric analysis where prior information from generic protein interaction maps is used to detect and quantify protein complexes with high selectivity and statistical error control via the computational framework CCprofiler (https://github.com/CCprofiler/CCprofiler). Complex-centric proteome profiling captures most proteins in complex-assembled state and reveals their organization into hundreds of complexes and complex variants observable in a given cellular state. The protocol is applicable to cultured cells and can potentially also be adapted to primary tissue and does not require any genetic engineering of the respective sample sources. At present, it requires ~8 d of wet-laboratory work, 15 d of mass spectrometry measurement time and 7 d of computational analysis.

Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines.

Here we describe a proteomic data resource for the NCI-60 cell lines generated by pressure cycling technology and SWATH mass spectrometry. We developed the DIA-expert software to curate and visualize the SWATH data, leading to reproducible detection of over 3,100 SwissProt proteotypic proteins and systematic quantification of pathway activities. Stoichiometric relationships of interacting proteins for DNA replication, repair, the chromatin remodeling NuRD complex, ß-catenin, RNA metabolism, and prefoldins are more evident than that at the mRNA level. The data are available in CellMiner (discover.nci.nih.gov/cellminercdb and discover.nci.nih.gov/cellminer), allowing casual users to test hypotheses and perform integrative, cross-database analyses of multi-omic drug response correlations for over 20,000 drugs. We demonstrate the value of proteome data in predicting drug response for over 240 clinically relevant chemotherapeutic and targeted therapies. In summary, we present a novel proteome resource for the NCI-60, together with relevant software tools, and demonstrate the benefit of proteome analyses.

Identification of genes involved in glaucoma pathogenesis using combined network analysis and empirical studies.

Glaucoma is a leading cause of blindness. We aimed in this study to identify genes that may make subtle and cumulative contributions to glaucoma pathogenesis. To this end, we identified molecular interactions and pathways that include transcription factors (TFs) FOXC1, PITX2, PAX6 and NFKB1 and various microRNAs including miR-204 known to have relevance to trabecular meshwork (TM) functions and/or glaucoma. TM tissue is involved in glaucoma pathogenesis. In-house microarray transcriptome results and data sources were used to identify target genes of the regulatory molecules. Bioinformatics analyses were done to filter TM and glaucoma relevant genes. These were submitted to network-creating softwares to define interactions, pathways and a network that would include the genes. The network was stringently scrutinized and minimized, then expanded by addition of microarray data and data on TF and microRNA-binding sites. Selected features of the network were confirmed by empirical studies such as dual luciferase assays, real-time PCR and western blot experiments and apoptosis assays. MYOC, WDR36, LTPBP2, RHOA, CYP1B1, OPA1, SPARC, MEIS2, PLEKHG5, RGS5, BBS5, ALDH1A1, NOMO2, CXCL6, FMNL2, ADAMTS5, CLOCK and DKK1 were among the genes included in the final network. Pathways identified included those that affect ECM properties, IOP, ciliary body functions, retinal ganglion cell viability, apoptosis, focal adhesion and oxidative stress response. The identification of many genes potentially involved in glaucoma pathology is consistent with its being a complex disease. The inclusion of several known glaucoma-related genes validates the approach used.

A new class of protein biomarkers based on subcellular distribution: application to a mouse liver cancer model.

To-date, most proteomic studies aimed at discovering tissue-based cancer biomarkers have compared the quantity of selected proteins between case and control groups. However, proteins generally function in association with other proteins to form modules localized in particular subcellular compartments in specialized cell types and tissues. Sub-cellular mislocalization of proteins has in fact been detected as a key feature in a variety of cancer cells. Here, we describe a strategy for tissue-biomarker detection based on a mitochondrial fold enrichment (mtFE) score, which is sensitive to protein abundance changes as well as changes in subcellular distribution between mitochondria and cytosol. The mtFE score integrates protein abundance data from total cellular lysates and mitochondria-enriched fractions, and provides novel information for the classification of cancer samples that is not necessarily apparent from conventional abundance measurements alone. We apply this new strategy to a panel of wild-type and mutant mice with a liver-specific gene deletion of Liver receptor homolog 1 (Lrh-1hep-/-), with both lines containing control individuals as well as individuals with liver cancer induced by diethylnitrosamine (DEN). Lrh-1 gene deletion attenuates cancer cell metabolism in hepatocytes through mitochondrial glutamine processing. We show that proteome changes based on mtFE scores outperform protein abundance measurements in discriminating DEN-induced liver cancer from healthy liver tissue, and are uniquely robust against genetic perturbation. We validate the capacity of selected proteins with informative mtFE scores to indicate hepatic malignant changes in two independent mouse models of hepatocellular carcinoma (HCC), thus demonstrating the robustness of this new approach to biomarker research. Overall, the method provides a novel, sensitive approach to cancer biomarker discovery that considers contextual information of tested proteins.

Complex-centric proteome profiling by SEC-SWATH-MS.

Proteins are major effectors and regulators of biological processes that can elicit multiple functions depending on their interaction with other proteins. The organization of proteins into macromolecular complexes and their quantitative distribution across these complexes is, therefore, of great biological and clinical significance. In this paper, we describe an integrated experimental and computational technique to quantify hundreds of protein complexes in a single operation. The method consists of size exclusion chromatography (SEC) to fractionate native protein complexes, SWATH/DIA mass spectrometry to precisely quantify the proteins in each SEC fraction, and the computational framework CCprofiler to detect and quantify protein complexes by error-controlled, complex-centric analysis using prior information from generic protein interaction maps. Our analysis of the HEK293 cell line proteome delineates 462 complexes composed of 2,127 protein subunits. The technique identifies novel sub-complexes and assembly intermediates of central regulatory complexes while assessing the quantitative subunit distribution across them. We make the toolset CCprofiler freely accessible and provide a web platform, SECexplorer, for custom exploration of the HEK293 proteome modularity.

Systems proteomics approaches to study bacterial pathogens: application to Mycobacterium tuberculosis.

Significant developments and improvements in basic and clinical research notwithstanding, infectious diseases still claim at least 13 million lives annually. Classical research approaches have deciphered many molecular mechanisms underlying infection. Today it is increasingly recognized that multiple molecular mechanisms cooperate to constitute a complex system that is used by a given pathogen to interfere with the biochemical processes of the host. Therefore, systems-level approaches now complement the standard molecular biology techniques to investigate pathogens and their interactions with the human host. Here we review omic studies in Mycobacterium tuberculosis, the causative agent of tuberculosis, with a particular focus on proteomic methods and their application to the bacilli. Likewise, the discussed methods are directly portable to other bacterial pathogens.

Elucidation of host-pathogen protein-protein interactions to uncover mechanisms of host cell rewiring.

Infectious diseases are the result of molecular cross-talks between hosts and their pathogens. These cross-talks are in part mediated by host-pathogen protein-protein interactions (HP-PPI). HP-PPI play crucial roles in infections, as they may tilt the balance either in favor of the pathogens' spread or their clearance. The identification of host proteins targeted by viral or bacterial pathogenic proteins necessary for the infection can provide insights into their underlying molecular mechanisms of pathogenicity, and potentially even single out pharmacological intervention targets. Here, we review the available methods to study HP-PPI, with a focus on recent mass spectrometry based methods to decipher bacterial-human infectious diseases and examine their relevance in uncovering host cell rewiring by pathogens.

A Case for a Human Immuno-Peptidome Project Consortium.

A multidisciplinary group of researchers gathered at the Hönggerberg Campus at ETH Zurich, Switzerland, for the first meeting on the Human Immuno-Peptidome Project (https://hupo.org/human-immuno-peptidome-project/). The long-term goal of this project is to map the entire repertoire of peptides presented by human leukocyte antigen molecules using mass spectrometry technologies, and make its robust analysis accessible to any immunologist. Here we outline the specific challenges identified toward this goal, and within this framework, describe the structure of a multipronged program aimed at addressing these challenges and implementing solutions at a community-wide level. Pillars of that program are: (1) method and technology development, (2) standardization, (3) effective data sharing, and (4) education. If successful, this community-driven endeavor might provide a roadmap toward new paradigms in immunology.

Delayed effects of transcriptional responses in Mycobacterium tuberculosis exposed to nitric oxide suggest other mechanisms involved in survival.

Mycobacterium tuberculosis has succeeded as a human pathogen for tens of thousands of years thanks to its ability to resist and adapt to the adverse conditions it encounters upon infection. Bacterial adaptation to stress is commonly viewed in the context of transcriptional regulation, with the implicit expectation that an initial transcriptomic response is tightly coupled to an ensuing proteomic response. However, after challenging M. tuberculosis with nitric oxide we found that the rapid transcriptional responses, detectable within minutes of nitric oxide exposure, typically took several hours to manifest on the protein level. Furthermore, early proteomic responses were dominated by the degradation of a set of proteins, specifically those containing damaged iron-sulphur clusters. Overall, our findings are consistent with transcriptional responses participating mostly in late-stage recovery rather than in generating an immediate resistance to nitric oxide stress, suggesting that survival of M. tuberculosis under acute stress is contingent on mechanisms other than transcriptional regulation. These findings provide a revised molecular understanding of an important human pathogen.

HCSD: the human cancer secretome database.

The human cancer secretome database (HCSD) is a comprehensive database for human cancer secretome data. The cancer secretome describes proteins secreted by cancer cells and structuring information about the cancer secretome will enable further analysis of how this is related with tumor biology. The secreted proteins from cancer cells are believed to play a deterministic role in cancer progression and therefore may be the key to find novel therapeutic targets and biomarkers for many cancers. Consequently, huge data on cancer secretome have been generated in recent years and the lack of a coherent database is limiting the ability to query the increasing community knowledge. We therefore developed the Human Cancer Secretome Database (HCSD) to fulfil this gap. HCSD contains >80 000 measurements for about 7000 nonredundant human proteins collected from up to 35 high-throughput studies on 17 cancer types. It has a simple and user friendly query system for basic and advanced search based on gene name, cancer type and data type as the three main query options. The results are visualized in an explicit and interactive manner. An example of a result page includes annotations, cross references, cancer secretome data and secretory features for each identified protein.

The potential roles of actin in the nucleus.

Over the past few decades, actin's presence in the nucleus has been demonstrated. Actin is a key protein necessary for different nuclear processes. Although actin is well known for its functional role in dynamic behavior of the cytoskeleton, emerging studies are now highlighting new roles for actin. At the present time there is no doubt about the presence of actin in the nucleus. A number of studies have uncovered the functional involvement of actin in nuclear processes. Actin as one of the nuclear components has its own structured and functional rules, such as nuclear matrix association, chromatin remodeling, transcription by RNA polymerases I, II, III and mRNA processing. In this historical review, we attempt to provide an overview of our current understanding of the functions of actin in the nucleus.

MicroRNAs that target RGS5.

OBJECTIVES: An earlier meta-analysis on gene expression data derived from four microarray, two cDNA library, and one SAGE experiment had identified RGS5 as one of only ten non-housekeeping genes that were reported to be expressed in human trabecular meshwork (TM) cells by all studies. RGS5 encodes regulator of G-protein signaling-5. The TM tissue is the route of aqueous fluid outflow, and is relevant to the pathology of glaucoma. MicroRNAs constitute the most recently identified components of the cellular machinery for gene regulation in eukaryotic cells. Given our long standing interest in glaucoma, we aimed to identify miRNAs that may target RGS5. MATERIALS AND METHODS: Eight miRNAs were selected for study using bioinformatics tools and available data on miRNAs expressed in the eye. Their effects were assessed using the dual luciferase assay. 3'-UTR segments of RGS5 mRNA were cloned downstream of a luciferase coding gene in psiCHECK2 vectors, and these were co-transfected with each of the miRNAs into HEK293 cells. RESULTS: The outcomes evidenced that one or more of the segments are in fact targeted by miR-7, miR-9, miR-96, miR-23a, miR-23b, miR-204, and miR-211. Down regulations by the miRNAs were statistically significant. The effect of miR-204 is considered particularly important as this miRNA is well known to regulate eye development and to affect multiple ocular functions. CONCLUSION: Our results justify further studies on regulation of RGS5 expression and RGS5 downstream functions by these miRNAs.