Blocking HIV-1 entry by a gp120 surface binding inhibitor

We report the mode of action of a proteomimetic compound that binds to the exterior surface of gp120 and blocks HIV-1 entry into cells. Using a one cycle time-of-addition study and antibody competition binding studies, we have determined that the compound blocks HIV-1 entry through modulation of key protein-protein interactions mediated by gp120. The compound exhibits anti-HIV-1 replication activities against several pseudotype viruses derived from primary isolates and the resistant strains isolated from existing drug candidates with equal potency. Together, these data provide evidence that the proteomimetic compound represents a novel class of HIV-1 viral entry inhibitor that functions through protein surface recognition in analogy to an antibody.     

Discovery of potent and selective DP1 receptor antagonists in the azaindole series

Azaindole based structures were evaluated as DP1 receptor antagonists. This work has lead to the discovery of potent, selective and distinct DP1 receptor antagonists.     

Integrin‐linked kinase is involved in TNF‐α‐induced inducible nitric‐oxide synthase expression in myoblasts

Tumor necrosis factor‐α (TNF‐α) is a pleiotropic cytokine produced by activated macrophages. Nitric oxide (NO) is a highly reactive nitrogen radical implicated in inflammatory responses. We investigated the signaling pathway involved in inducible nitric oxide synthase (iNOS) expression and NO production stimulated by TNF‐α in cultured myoblasts. TNF‐α stimulation caused iNOS expression and NO production in myoblasts (G7 cells). TNF‐α‐mediated iNOS expression was attenuated by integrin‐linked kinase (ILK) inhibitor (KP392) and siRNA. Pretreatment with Akt inhibitor, mammalian target of rapamycin (mTOR) inhibitor (rapamycin), NF‐κB inhibitor (PDTC), and IκB protease inhibitor (TPCK) also inhibited the potentiating action of TNF‐α. Stimulation of cells with TNF‐α increased ILK kinase activity. TNF‐α also increased the Akt and mTOR phosphorylation. TNF‐α mediated an increase of NF‐κB‐specific DNA–protein complex formation, p65 translocation into nucleus, NF‐κB‐luciferase activity was inhibited by KP392, Akt inhibitor, and rapamycin. Our results suggest that TNF‐α increased iNOS expression and NO production in myoblasts via the ILK/Akt/mTOR and NF‐κB signaling pathway. J. Cell. Biochem. 109: 1244–1253, 2010. © 2010 Wiley‐Liss, Inc.     

Discovery of a synthetic dual inhibitor of HIV and HCV infection based on a tetrabutoxy-calix[4]arene scaffold

A potential anti-HIV and HCV drug candidate is highly desirable as coinfection has become a worldwide public health challenge. A potent compound based on a tetrabutoxy-calix[4]arene scaffold that possesses dual inhibition for both HIV and HCV is described. Structural activity relationship studies demonstrate the effects of lower-rim alkylation in maintaining cone conformation and upper-rim interacting head groups on the calix[4]arene play key roles for its potent dual antiviral activities.     

Thrombin enhanced migration and MMPs expression of human chondrosarcoma cells involves PAR receptor signaling pathway

Thrombin is a multifunctional protease that can activate hemostasis and coagulation through the cleavage of fibrinogen to form fibrin clots. Thrombin also plays a crucial role in migration and metastasis of human cancer cells. However, the effect of thrombin on migration activity in human chondrosarcoma cells is mostly unknown. Here, we found that thrombin increased the migration and expression of matrix metalloproteinase (MMP)‐2 and MMP‐13 in human chondrosarcoma cells (JJ012 and SW1353 cells). By using pharmacological inhibitors or activators or genetic inhibition by the protease‐activated receptor (PAR), we found that the PAR1 and PAR4 receptor but not PAR3 receptor are involved in thrombin‐mediated cell migration and MMPs expression. Thrombin‐mediated migration and MMPs up‐regulation was attenuated by phospholipase C (PLC), protein kinase C, and c‐Src inhibitor. Activations of PLCβ, PKCα, c‐Src, and NF‐κB pathways after thrombin treatment was demonstrated, and thrombin‐induced MMPs expression and migration activity was inhibited by the specific inhibitors and mutants of PLC, PKC, c‐Src, and NF‐κB cascades. Taken together, our results indicated that thrombin enhances the migration of chondrosarcoma cells by increasing MMP‐2 and MMP‐13 expression through the PAR/PLC/PKCα/c‐Src/NF‐κB signal transduction pathway. J. Cell. Physiol. 223:737–745, 2010. © 2010 Wiley‐Liss, Inc.     

IDEAL-Q, an Automated Tool for Label-free Quantitation Analysis Using an Efficient Peptide Alignment Approach and Spectral Data Validation

In this study, we present a fully automated tool, called IDEAL-Q, for label-free quantitation analysis. It accepts raw data in the standard mzXML format as well as search results from major search engines, including Mascot, SEQUEST, and X!Tandem, as input data. To quantify as many identified peptides as possible, IDEAL-Q uses an efficient algorithm to predict the elution time of a peptide unidentified in a specific LC-MS/MS run but identified in other runs. Then, the predicted elution time is used to detect peak clusters of the assigned peptide. Detected peptide peaks are processed by statistical and computational methods and further validated by signal-to-noise ratio, charge state, and isotopic distribution criteria (SCI validation) to filter out noisy data. The performance of IDEAL-Q has been evaluated by several experiments. First, a serially diluted protein mixed with Escherichia coli lysate showed a high correlation with expected ratios and demonstrated good linearity (R² = 0.996). Second, in a biological replicate experiment on the THP-1 cell lysate, IDEAL-Q quantified 87% (1,672 peptides) of all identified peptides, surpassing the 45.7% (909 peptides) achieved by the conventional identity-based approach, which only quantifies peptides identified in all LC-MS/MS runs. Manual validation on all 11,940 peptide ions in six replicate LC-MS/MS runs revealed that 97.8% of the peptide ions were correctly aligned, and 93.3% were correctly validated by SCI. Thus, the mean of the protein ratio, 1.00 ± 0.05, demonstrates the high accuracy of IDEAL-Q without human intervention. Finally, IDEAL-Q was applied again to the biological replicate experiment but with an additional SDS-PAGE step to show its compatibility for label-free experiments with fractionation. For flexible workflow design, IDEAL-Q supports different fractionation strategies and various normalization schemes, including multiple spiked internal standards. User-friendly interfaces are provided to facilitate convenient inspection, validation, and modification of quantitation results. In summary, IDEAL-Q is an efficient, user-friendly, and robust quantitation tool. It is available for download.Quantitative analysis of protein expression promises to provide fundamental understanding of the biological changes or biomarker discoveries in clinical applications. In recent years, various stable isotope labeling techniques, e.g. ICAT (1), enzymatic labeling using ¹⁸O/¹⁶O (2, 3), stable isotope labeling by amino acids in cell culture (4), and isobaric tagging for relative and absolute quantitation (2, 5), coupled with LC-MS/MS have been widely used for large scale quantitative proteomics. However, several factors, such as the limited number of samples, the complexity of procedures in isotopic labeling experiments, and the high cost of reagents, limit the applicability of isotopic labeling techniques to high throughput analysis. Unlike the labeling approaches, the label-free quantitation approach quantifies protein expression across multiple LC-MS/MS analyses directly without using any labeling technique (7–9). Thus, it is particularly useful for analyzing clinical specimens in highly multiplexed quantitation (10, 11); theoretically, it can be used to compare any number of samples. Despite these significant advantages, data analysis in label-free experiments is an intractable problem because of the experimental procedures. First, although high reproducibility in LC is considered a critical prerequisite, variations, including the aging of separation columns, changes in sample buffers, and fluctuations in temperature, will cause a chromatographic shift in retention time for analytes in different LC-MS/MS runs and thus complicate the analysis. In addition, under the label-free approach, many technical replicate analyses across a large number of samples are often acquired; however, comparing a large number of data files further complicates data analysis and renders lower quantitation accuracy than that derived by labeling methods. Hence, an accurate, automated computation tool is required to effectively solve the problem of chromatographic shift, analyze a large amount of experimental data, and provide convenient user interfaces for manual validation of quantitation results.The rapid emergence of new label-free techniques for biomarker discovery has inspired the development of a number of bioinformatics tools in recent years. For example, Scaffold (Proteome Software) and Census (12) process PepXML search results to quantify relative protein expression based on spectral counting (13–15), which uses the number of MS/MS spectra assigned to a protein to determine the relative protein amount. Spectral counting has demonstrated a high correlation with protein abundance; however, to achieve good quantitation accuracy with the technique, high speed MS/MS data acquisition is required. Moreover, manipulations of the exclusion/inclusion strategy also affect the accuracy of spectral counting significantly. Because peptide level quantitation is also important for post-translational modification studies, the accuracy of spectral counting on peptide level quantitation deserves further study.Another type of quantitation analysis determines peptide abundance by MS¹ peak signals. According to some studies, MS¹ peak signals across different LC-MS/MS runs can be highly reproducible and correlate well with protein abundance in complex biological samples (7–9). Quantitation analysis methods based on MS¹ peak signals can be classified into three categories: identity-based, pattern-based, and hybrid-based methods (16). Identity-based methods (7–9) depend on the results of MS/MS sequencing to identify and detect peptide signals in MS¹ data. However, because the data acquisition speed of MS scanning is insufficient, a considerable number of low abundance peptides may not be selected for limited MS/MS sequencing. Only a few peptides can be repetitively identified in all LC-MS/MS runs and subsequently quantified; thus, only a small fraction of identified peptides are quantified, resulting in a small number of quantifiable peptides/proteins.In contrast to identity-based methods, pattern-based methods (17–23), including the publicly available MSight (20), MZmine (21, 22), and msInspect (23), tend to quantify all peptide peaks in MS¹ data to increase the number of quantifiable peptides. These methods first detect all peaks in each MS¹ data and then align the detected peaks across different LC-MS/MS runs. However, in pattern-based methods, efficient detection and alignment of the peaks between each pair of LC-MS/MS runs are a major challenge. To align the peaks, several methods based on dynamic programming or image pattern recognition have been proposed (24–26). The algorithms applied in these methods require intensive computation, and their computation time increases dramatically as the number of compared samples increases because all the LC-MS/MS runs must be processed. Therefore, pattern-based approaches are infeasible for processing a large number of samples. Furthermore, pattern recognition algorithms may fail on data containing noise or overlapping peptide signal (i.e. co-eluting peptides). The hybrid-based quantitation approach (16, 27–30) combines a pattern recognition algorithm with peptide identification results to align shifted peptides for quantitation. The pioneering accurate mass and time tag strategy (27) takes advantage of very sensitive, highly accurate mass measurement instruments with a wide dynamic range, e.g. FTICR-MS and TOF-MS, for quantitation analysis. PEPPeR (16) and SuperHirn (28) apply pattern recognition algorithms to align peaks and use the peptide identification results as landmarks to improve the alignment. However, because these methods still align all peaks in MS¹ data, they suffer the same computation time problem as pattern-based methods.To resolve the computation-intensive problem in the hybrid approach, we present a fully automated software system, called IDEAL-Q, for label-free quantitation including differential protein expression and protein modification analysis. Instead of using computation-intensive pattern recognition methods, IDEAL-Q uses a computation-efficient fragmental regression method for identity-based alignment of all confidently identified peptides in a local elution time domain. It then performs peptide cross-assignment by mapping predicted elution time profiles across multiple LC-MS experiments. To improve the quantitation accuracy, IDEAL-Q applies three validation criteria to the detected peptide peak clusters to filter out noisy signals, false peptide peak clusters, and co-eluting peaks. Because of the above key features, i.e. fragmental regression and stringent validation, IDEAL-Q can substantially increase the number of quantifiable proteins as well as the quantitation accuracy compared with other extracted ion chromatogram (XIC)¹ -based tools. Notably, to accommodate different designs, IDEAL-Q supports various built-in normalization procedures, including normalization based on multiple internal standards, to eliminate systematic biases. It also adapts to different fractionation strategies for in-depth proteomics profiling.We evaluated the performance of IDEAL-Q on three levels: 1) quantitation of a standard protein mixture, 2) large scale proteome quantitation using replicate cell lysate, and 3) proteome scale quantitative analysis of protein expression that incorporates an additional fractionation step. We demonstrated that IDEAL-Q can quantify up to 89% of identified proteins (703 proteins) in the replicate THP-1 cell lysate. Moreover, by manual validation of the entire 11,940 peptide ions corresponding to 1,990 identified peptides, 93% of peptide ions were accurately quantified. In another experiment on replicate data containing huge chromatographic shifts obtained from two independent LC-MS/MS instruments, IDEAL-Q demonstrated its robust quantitation and its ability to rectify such shifts. Finally, we applied IDEAL-Q to the THP-1 replicate experiment with an additional SDS-PAGE fractionation step. Equipped with user-friendly visualization interfaces and convenient data output for publication, IDEAL-Q represents a generic, robust, and comprehensive tool for label-free quantitative proteomics.     

Selective chemokine receptor usage by central nervous system myeloid cells in CCR2-red fluorescent protein knock-in mice

Background

Monocyte subpopulations distinguished by differential expression of chemokine receptors CCR2 and CX3CR1 are difficult to track in vivo, partly due to lack of CCR2 reagents.

Methodology/Principal Findings

We created CCR2-red fluorescent protein (RFP) knock-in mice and crossed them with CX3CR1-GFP mice to investigate monocyte subset trafficking. In mice with experimental autoimmune encephalomyelitis, CCR2 was critical for efficient intrathecal accumulation and localization of Ly6C^hi/CCR2^hi monocytes. Surprisingly, neutrophils, not Ly6C^lo monocytes, largely replaced Ly6C^hi cells in the central nervous system of these mice. CCR2-RFP expression allowed the first unequivocal distinction between infiltrating monocytes/macrophages from resident microglia.

Conclusion/Significance

These results refine the concept of monocyte subsets, provide mechanistic insight about monocyte entry into the central nervous system, and present a novel model for imaging and quantifying inflammatory myeloid populations.     

Substantial decrease of heat-shock protein 90 precedes the decline of sperm motility during cooling of boar spermatozoa

The decline in boar semen quality after cryopreservation may be attributed to changes in intracellular proteins. Thus, the aim of the present study was to evaluate the change of protein profiles in boar spermatozoa during the process of cooling and after cryopreservation. A total of 9 sexually mature boars (mean age = 25.5+/-12.3 mo) was used. Samples for protein analysis were collected before chilling, after cooling to 15 degrees C, after cooling to 5 degrees C, following thawing after freezing to -100 degrees C, and following thawing after 1 wk of cryopreservation at -196 degrees C. Semen characteristics evaluated included progressive motility and the percentage of morphologically normal spermatozoa. Total proteins from 5x10(6) spermatozoa were separated and analyzed by SDS-PAGE. The results revealed that there was a substantial decrease of a 90 kDa protein in the frozen-thawed spermatozoa. Western blot analysis demonstrated that this protein was 90 kDa heat-shock protein (HSP90). Time course study showed that the decrease of HSP90 in spermatozoa initially occurred in the first hour during cooling to 5 degrees C. When compared with the fresh spermatozoa before chilling, there was a 64% decrease of HSP90 in spermatozoa after cooling to 5 degrees C. However, the motility and percentage of normal spermatozoa did not significantly decrease during this period of treatment. Both declined substantially as the semen was thawed after freezing from -100 degrees C. The results indicated that the decrease of HSP90 precedes the decline of semen characteristics. The length of time between a decrease of HSP90 and the decline in sperm motility was estimated to be 2 to 3 h. Taken together, the above results suggested that a substantial decrease of HSP90 might be associated with a decline in sperm motility during cooling of boar spermatozoa.