Association of statins with mortality in type 2 diabetes patients with intensive glycemic therapy.

AIMS: Intensive glycemic therapy could lead to increased mortality in patients with type 2 diabetes mellitus (T2DM). But it remains unclear whether statins use improves prognosis in T2DM patients with intensive glycemic therapy. METHODS: Using data from Action to Control Cardiovascular Risk in Diabetes trial and performing propensity score matching and Cox proportional hazards regression, we explored the relationship between statin use and the risk of mortality in intensive-therapy group. RESULTS: In the intensive-therapy group, total mortality (TM) in patients with statins treatment is lower than those without statins (hazard ratio (HR), 0.68; 95% confidence interval (CI) 0.49-0.95; P = 0.022); the effects of statins on cardiovascular mortality (CM) and primary outcomes (PO), however, were negligible (CM: HR 0.96; 95% CI 0.61-1.51; P = 0.854; PO: HR 0.88; 95% CI 0.65-1.19; P = 0.415). Besides, the risk of TM, CM and PO in patients with the intensive therapy combined with statins use was similar to those in the standard group (TM: P = 0.445; CM: P = 0.362; PO: P = 0.637). CONCLUSIONS: Statins may alleviate the risk of TM in T2DM patients receiving intensive glycemic therapy.

Multimodal Magnetic Resonance Imaging and Therapeutic Intervention With Yi-nao-jie-yu Decoction in a Rat Model of Post-stroke Depression.

Post-stroke depression (PSD) is the most common neuropsychiatric complication after a stroke, though its neuropathological characteristics have not been fully elucidated. Comprehensive and non-invasive magnetic resonance (MR) assessment techniques are urgently needed for current research, as diffusion tensor imaging (DTI), arterial spin labeling (ASL), and magnetic resonance spectroscopy (MRS) can allow for a comprehensive assessment of neuropathological changes in the brain. These techniques can provide information about microscopic tissue integrity, cerebral perfusion, and cerebral metabolism, and can serve as powerful tools for investigating neurophysiological changes associated with PSD. Yi-nao-jie-yu decoction (YNJYD) is a Chinese herbal formulation based on the theory of traditional Chinese medicine, with demonstrated clinical efficacy in the treatment of PSD. The aim of this study was to use these MR techniques to evaluate changes in PSD and YNJYD-treated rats. This is the first experimental study in animals to investigate neuropathological changes associated with PSD using a combination of multiple MR techniques, including DTI, ASL, and MRS. In addition, we investigated the effect of YNJYD in a rat model of PSD by assessing changes in brain tissue microstructure, brain metabolism, and cerebral perfusion. First, depressive-like behaviors of PSD rats were assessed by the open field test (OFT), sucrose preference test (SPT), and Morris water maze (MWM) test, and then the integrity of the rats' microstructure was assessed by DTI, the levels of regional cerebral perfusion were assessed by ASL, and changes in the relative concentrations of brain metabolites were determined by MRS. The results showed that OFT and SPT scores were significantly reduced in PSD rats, as was performance in the MWM; these PSD-associated changes were attenuated in rats administered YNJYD, with improved depressive-like behaviors evidenced by increased OFT and SPT scores and improved performance in the MWM task. Furthermore, we found that PSD rats had lower perfusion levels in the prefrontal cortex (PFC) and hippocampus (HP), microstructural damage, and abnormal changes in the concentrations of brain metabolites; YNJYD exerted therapeutic effects on PSD rats by improving microcirculation in the PFC and HP, regulating glutamatergic systems and membrane phospholipid metabolism, and repairing microstructural damage.

Integrating Cross-Linking Experiments with Ab Initio Protein-Protein Docking.

Ab initio protein-protein docking algorithms often rely on experimental data to identify the most likely complex structure. We integrated protein-protein docking with the experimental data of chemical cross-linking followed by mass spectrometry. We tested our approach using 19 cases that resulted from an exhaustive search of the Protein Data Bank for protein complexes with cross-links identified in our experiments. We implemented cross-links as constraints based on Euclidean distance or void-volume distance. For most test cases, the rank of the top-scoring near-native prediction was improved by at least twofold compared with docking without the cross-link information, and the success rate for the top 5 predictions nearly tripled. Our results demonstrate the delicate balance between retaining correct predictions and eliminating false positives. Several test cases had multiple components with distinct interfaces, and we present an approach for assigning cross-links to the interfaces. Employing the symmetry information for these cases further improved the performance of complex structure prediction.

In vivo protein interaction network analysis reveals porin-localized antibiotic inactivation in Acinetobacter baumannii strain AB5075.

The nosocomial pathogen Acinetobacter baumannii is a frequent cause of hospital-acquired infections worldwide and is a challenge for treatment due to its evolved resistance to antibiotics, including carbapenems. Here, to gain insight on A. baumannii antibiotic resistance mechanisms, we analyse the protein interaction network of a multidrug-resistant A. baumannii clinical strain (AB5075). Using in vivo chemical cross-linking and mass spectrometry, we identify 2,068 non-redundant cross-linked peptide pairs containing 245 intra- and 398 inter-molecular interactions. Outer membrane proteins OmpA and YiaD, and carbapenemase Oxa-23 are hubs of the identified interaction network. Eighteen novel interactors of Oxa-23 are identified. Interactions of Oxa-23 with outer membrane porins OmpA and CarO are verified with co-immunoprecipitation analysis. Furthermore, transposon mutagenesis of oxa-23 or interactors of Oxa-23 demonstrates changes in meropenem or imipenem sensitivity in strain AB5075. These results provide a view of porin-localized antibiotic inactivation and increase understanding of bacterial antibiotic resistance mechanisms.

XLinkDB 2.0: integrated, large-scale structural analysis of protein crosslinking data.

MOTIVATION: Large-scale chemical cross-linking with mass spectrometry (XL-MS) analyses are quickly becoming a powerful means for high-throughput determination of protein structural information and protein-protein interactions. Recent studies have garnered thousands of cross-linked interactions, yet the field lacks an effective tool to compile experimental data or access the network and structural knowledge for these large scale analyses. We present XLinkDB 2.0 which integrates tools for network analysis, Protein Databank queries, modeling of predicted protein structures and modeling of docked protein structures. The novel, integrated approach of XLinkDB 2.0 enables the holistic analysis of XL-MS protein interaction data without limitation to the cross-linker or analytical system used for the analysis. AVAILABILITY AND IMPLEMENTATION: XLinkDB 2.0 can be found here, including documentation and help: http://xlinkdb.gs.washington.edu/ CONTACT: : jimbruce@uw.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quantitative interactome analysis reveals a chemoresistant edgotype.

Chemoresistance is a common mode of therapy failure for many cancers. Tumours develop resistance to chemotherapeutics through a variety of mechanisms, with proteins serving pivotal roles. Changes in protein conformations and interactions affect the cellular response to environmental conditions contributing to the development of new phenotypes. The ability to understand how protein interaction networks adapt to yield new function or alter phenotype is limited by the inability to determine structural and protein interaction changes on a proteomic scale. Here, chemical crosslinking and mass spectrometry were employed to quantify changes in protein structures and interactions in multidrug-resistant human carcinoma cells. Quantitative analysis of the largest crosslinking-derived, protein interaction network comprising 1,391 crosslinked peptides allows for 'edgotype' analysis in a cell model of chemoresistance. We detect consistent changes to protein interactions and structures, including those involving cytokeratins, topoisomerase-2-alpha, and post-translationally modified histones, which correlate with a chemoresistant phenotype.

Regional Isolation Drives Bacterial Diversification within Cystic Fibrosis Lungs.

Bacterial lineages that chronically infect cystic fibrosis (CF) patients genetically diversify during infection. However, the mechanisms driving diversification are unknown. By dissecting ten CF lung pairs and studying ∼12,000 regional isolates, we were able to investigate whether clonally related Pseudomonas aeruginosa inhabiting different lung regions evolve independently and differ functionally. Phylogenetic analysis of genome sequences showed that regional isolation of P. aeruginosa drives divergent evolution. We investigated the consequences of regional evolution by studying isolates from mildly and severely diseased lung regions and found evolved differences in bacterial nutritional requirements, host defense and antibiotic resistance, and virulence due to hyperactivity of the type 3 secretion system. These findings suggest that bacterial intermixing is limited in CF lungs and that regional selective pressures may markedly differ. The findings also may explain how specialized bacterial variants arise during infection and raise the possibility that pathogen diversification occurs in other chronic infections characterized by spatially heterogeneous conditions.

Dynamic Proteome Response of Pseudomonas aeruginosa to Tobramycin Antibiotic Treatment.

Genetically susceptible bacteria become antibiotic tolerant during chronic infections, and the mechanisms responsible are poorly understood. One factor that may contribute to differential sensitivity in vitro and in vivo is differences in the time-dependent tobramycin concentration profile experienced by the bacteria. Here, we examine the proteome response induced by subinhibitory concentrations of tobramycin in Pseudomonas aeruginosa cells grown under planktonic conditions. These efforts revealed increased levels of heat shock proteins and proteases were present at higher dosage treatments (0.5 and 1 µg/ml), while less dramatic at 0.1 µg/ml dosage. In contrast, many metabolic enzymes were significantly induced by lower dosages (0.1 and 0.5 µg/ml) but not at 1 µg/ml dosage. Time course proteome analysis further revealed that the increase of heat shock proteins and proteases was most rapid from 15 min to 60 min, and the increased levels sustained till 6 h (last time point tested). Heat shock protein IbpA exhibited the greatest induction by tobramycin, up to 90-fold. Nevertheless, deletion of ibpA did not enhance sensitivity to tobramycin. It seemed possible that the absence of sensitization could be due to redundant functioning of IbpA with other proteins that protect cells from tobramycin. Indeed, inactivation of two heat shock chaperones/proteases in addition to ibpA in double mutants (ibpA/clpB, ibpA/PA0779 and ibpA/hslV) did increase tobramycin sensitivity. Collectively, these results demonstrate the time- and concentration-dependent nature of the P. aeruginosa proteome response to tobramycin and that proteome modulation and protein redundancy are protective mechanisms to help bacteria resist antibiotic treatments.

Probing the protein interaction network of Pseudomonas aeruginosa cells by chemical cross-linking mass spectrometry.

In pathogenic Gram-negative bacteria, interactions among membrane proteins are key mediators of host cell attachment, invasion, pathogenesis, and antibiotic resistance. Membrane protein interactions are highly dependent upon local properties and environment, warranting direct measurements on native protein complex structures as they exist in cells. Here we apply in vivo chemical cross-linking mass spectrometry, to reveal the first large-scale protein interaction network in Pseudomonas aeruginosa, an opportunistic human pathogen, by covalently linking interacting protein partners, thereby fixing protein complexes in vivo. A total of 626 cross-linked peptide pairs, including previously unknown interactions of many membrane proteins, are reported. These pairs not only define the existence of these interactions in cells but also provide linkage constraints for complex structure predictions. Structures of three membrane proteins, namely, SecD-SecF, OprF, and OprI are predicted using in vivo cross-linked sites. These findings improve understanding of membrane protein interactions and structures in cells.

XLink-DB: database and software tools for storing and visualizing protein interaction topology data.

As large-scale cross-linking data becomes available, new software tools for data processing and visualization are required to replace manual data analysis. XLink-DB serves as a data storage site and visualization tool for cross-linking results. XLink-DB accepts data generated with any cross-linker and stores them in a relational database. Cross-linked sites are automatically mapped onto PDB structures if available, and results are compared to existing protein interaction databases. A protein interaction network is also automatically generated for the entire data set. The XLink-DB server, including examples, and a help page are available for noncommercial use at http://brucelab.gs.washington.edu/crosslinkdbv1/ . The source code can be viewed and downloaded at https://sourceforge.net/projects/crosslinkdb/?source=directory .

In vivo protein interaction network identified with a novel real-time cross-linked peptide identification strategy.

Protein interaction topologies are critical determinants of biological function. Large-scale or proteome-wide measurements of protein interaction topologies in cells currently pose an unmet challenge that could dramatically improve understanding of complex biological systems. A primary impediment includes direct protein topology and interaction measurements from living systems since interactions that lack biological significance may be introduced during cell lysis. Furthermore, many biologically relevant protein interactions will likely not survive the lysis/sample preparation and may only be measured with in vivo methods. As a step toward meeting this challenge, a new mass spectrometry method called Real-time Analysis for Cross-linked peptide Technology (ReACT) has been developed that enables assignment of cross-linked peptides "on-the-fly". Using ReACT, 708 unique cross-linked (<5% FDR) peptide pairs were identified from cross-linked E. coli cells. These data allow assembly of the first protein interaction network that also contains topological features of every interaction, as it existed in cells during cross-linker application. Of the identified interprotein cross-linked peptide pairs, 40% are derived from known interactions and provide new topological data that can help visualize how these interactions exist in cells. Other identified cross-linked peptide pairs are from proteins known to be involved within the same complex, but yield newly discovered direct physical interactors. ReACT enables the first view of these interactions inside cells, and the results acquired with this method suggest cross-linking can play a major role in future efforts to map the interactome in cells.

Protein interactions, post-translational modifications and topologies in human cells.

The unique and remarkable physicochemical properties of protein surface topologies give rise to highly specific biomolecular interactions, which form the framework through which living systems are able to carry out their vast array of functions. Technological limitations undermine efforts to probe protein structures and interactions within unperturbed living systems on a large scale. Rapid chemical stabilization of proteins and protein complexes through chemical cross-linking offers the alluring possibility to study details of the protein structure to function relationships as they exist within living cells. Here we apply the latest technological advances in chemical cross-linking combined with mass spectrometry to study protein topologies and interactions from living human cells identifying a total of 368 cross-links. These include cross-links from all major cellular compartments including membrane, cytosolic and nuclear proteins. Intraprotein and interprotein cross-links were also observed for core histone proteins, including several cross-links containing post-translational modifications which are known histone marks conferring distinct epigenetic functions. Excitingly, these results demonstrate the applicability of cross-linking to make direct topological measurements on post-translationally modified proteins. The results presented here provide new details on the structures of known multi-protein complexes as well as evidence for new protein-protein interactions.

In vivo application of photocleavable protein interaction reporter technology.

In vivo protein structures and protein-protein interactions are critical to the function of proteins in biological systems. As a complementary approach to traditional protein interaction identification methods, cross-linking strategies are beginning to provide additional data on protein and protein complex topological features. Previously, photocleavable protein interaction reporter (pcPIR) technology was demonstrated by cross-linking pure proteins and protein complexes and the use of ultraviolet light to cleave or release cross-linked peptides to enable identification. In the present report, the pcPIR strategy is applied to Escherichia coli cells, and in vivo protein interactions and topologies are measured. More than 1600 labeled peptides from E. coli were identified, indicating that many protein sites react with pcPIR in vivo. From those labeled sites, 53 in vivo intercross-linked peptide pairs were identified and manually validated. Approximately half of the interactions have been reported using other techniques, although detailed structures exist for very few. Three proteins or protein complexes with detailed crystallography structures are compared to the cross-linking results obtained from in vivo application of pcPIR technology.

Cross-linking measurements of in vivo protein complex topologies.

Identification and measurement of in vivo protein interactions pose critical challenges in the goal to understand biological systems. The measurement of structures and topologies of proteins and protein complexes as they exist in cells is particularly challenging, yet critically important to improve understanding of biological function because proteins exert their intended function only through the structures and interactions they exhibit in vivo. In the present study, protein interactions in E. coli cells were identified in our unbiased cross-linking approach, yielding the first in vivo topological data on many interactions and the largest set of identified in vivo cross-linked peptides produced to date. These data show excellent agreement with protein and complex crystal structures where available. Furthermore, our unbiased data provide novel in vivo topological information that can impact understanding of biological function, even for cases where high resolution structures are not yet available.

In vivo identification of the outer membrane protein OmcA-MtrC interaction network in Shewanella oneidensis MR-1 cells using novel hydrophobic chemical cross-linkers.

Outer membrane (OM) cytochromes OmcA (SO1779) and MtrC (SO1778) are the integral components of electron transfer used by Shewanella oneidensis for anaerobic respiration of metal (hydr)oxides. Here the OmcA-MtrC interaction was identified in vivo using a novel hydrophobic chemical cross-linker (MRN) combined with immunoprecipitation techniques. In addition, identification of other OM proteins from the cross-linked complexes allows first visualization of the OmcA-MtrC interaction network. Further experiments on omcA and mtrC mutant cells showed OmcA plays a central role in the network interaction. For comparison, two commercial cross-linkers were also used in parallel, and both resulted in fewer OM protein identifications, indicating the superior properties of MRN for identification of membrane protein interactions. Finally, comparison experiments of in vivo cross-linking and cell lysate cross-linking resulted in significantly different protein interaction data, demonstrating the importance of in vivo cross-linking for study of protein-protein interactions in cells.

Informatics strategies for large-scale novel cross-linking analysis.

The detection of protein interactions in biological systems represents a significant challenge for today's technology. Chemical cross-linking provides the potential to impart new chemical bonds in a complex system that result in mass changes in a set of tryptic peptides detected by mass spectrometry. However, system complexity and cross-linking product heterogeneity have precluded widespread chemical cross-linking use for large-scale identification of protein-protein interactions. The development of mass spectrometry identifiable cross-linkers called protein interaction reporters (PIRs) has enabled on-cell chemical cross-linking experiments with product type differentiation. However, the complex datasets resultant from PIR experiments demand new informatics capabilities to allow interpretation. This manuscript details our efforts to develop such capabilities and describes the program X-links, which allows PIR product type differentiation. Furthermore, we also present the results from Monte Carlo simulation of PIR-type experiments to provide false discovery rate estimates for the PIR product type identification through observed precursor and released peptide masses. Our simulations also provide peptide identification calculations based on accurate masses and database complexity that can provide an estimation of false discovery rates for peptide identification. Overall, the calculations show a low rate of false discovery of PIR product types due to random mass matching of approximately 12% with 10 ppm mass measurement accuracy and spectral complexity resulting from 100 peptides. In addition, consideration of a reduced database resulting from stage 1 analysis of Shewanella oneidensis MR-1 containing 367 proteins resulted in a significant reduction of expected identification false discovery rate estimation compared to that from the entire Shewanella oneidensis MR-1 proteome.