Transgenic mice with -6A haplotype of the human angiotensinogen gene have increased blood pressure compared with -6G haplotype

Hypertension is a serious risk factor for cardiovascular disease, and the angiotensinogen (AGT) gene locus is associated with human essential hypertension. The human AGT (hAGT) gene has an A/G polymorphism at -6, and the -6A allele is associated with increased blood pressure. However, transgenic mice containing 1.2 kb of the promoter with -6A of the hAGT gene show neither increased plasma AGT level nor increased blood pressure compared with -6G. We have found that the hAGT gene has three additional SNPs (A/G at -1670, C/G at -1562, and T/G at -1561). Variants -1670A, -1562C, and -1561T almost always occur with -6A, and variants -1670G, -1562G, and -1561G almost always occur with -6G. Therefore, the hAGT gene may be subdivided into either -6A or -6G haplotypes. We show that these polymorphisms affect the binding of HNF-1α and glucocorticoid receptor to the promoter, and a reporter construct containing a 1.8-kb hAGT gene promoter with -6A haplotype has 4-fold increased glucocorticoid-induced promoter activity as compared with -6G haplotype. In order to understand the physiological significance of these haplotypes in an in vivo situation, we have generated double transgenic mice containing either the -6A or -6G haplotype of the hAGT gene and the human renin gene. Our ChIP assay shows that HNF-1α and glucocorticoid receptor have stronger affinity for the chromatin obtained from the liver of transgenic mice containing -6A haplotype. Our studies also show that transgenic mice containing -6A haplotype have increased plasma AGT level and increased blood pressure as compared with -6G haplotype. Our studies explain the molecular mechanism involved in association of the -6A allele of the hAGT gene with hypertension.     

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.     

TimeTree: a public knowledge-base of divergence times among organisms

Biologists and other scientists routinely need to know times of divergence between species and to construct phylogenies calibrated to time (timetrees). Published studies reporting time estimates from molecular data have been increasing rapidly, but the data have been largely inaccessible to the greater community of scientists because of their complexity. TimeTree brings these data together in a consistent format and uses a hierarchical structure, corresponding to the tree of life, to maximize their utility. Results are presented and summarized, allowing users to quickly determine the range and robustness of time estimates and the degree of consensus from the published literature. AVAILABILITY: TimeTree is available at http://www.timetree.net     

Neuropharmacological actions of panchagavya formulation containing Emblica officinalis Gaerth and Glycyrrhiza glabra Linn in mice

A panchagavya Ayurvedic formulation containing E. officinalis, G. glabra, and cow's ghee was evaluated for its effect on pentobarbital-induced sleeping time, pentylenetetrazol-induced seizures, maximal electroshock-induced seizures, spontaneous motor activity, rota-rod performance (motor coordination) and antagonism to amphetamine in mice. The formulation (300, 500 mg/kg, po) produced a significant prolongation of pentobarbital-induced sleeping time and reduced spontaneous locomotor activity. The formulation also significantly antagonised the amphetamine induced hyper-locomotor activity (500, 750 mg/kg, po) and protected mice against tonic convulsions induced by maximal electroshock (500, 750 mg/kg, po). The formulation slightly prolonged the phases of seizure activity but did not protect mice against lethality induced by pentylenetetrazole. The formulation did not show neurotoxicity. The results suggest that the panchagavya formulation is sedative in nature.     

Naturally occurring proteolytic antibodies: selective immunoglobulin M-catalyzed hydrolysis of HIV gp120

We report the selective catalytic cleavage of the HIV coat protein gp120, a B cell superantigen, by IgM antibodies (Abs) from uninfected humans and mice that had not been previously exposed to gp120. The rate of IgM-catalyzed gp120 cleavage was greater than of other polypeptide substrates, including the bacterial superantigen protein A. The kinetic parameters of gp120 cleavage varied over a broad range depending on the source of the IgMs, and turnover numbers as great as 2.1/min were observed, suggesting that different Abs possess distinct gp120 recognition properties. IgG Abs failed to cleave gp120 detectably. The Fab fragment of a monoclonal IgM cleaved gp120, suggesting that the catalytic activity belongs to the antibody combining site. The electrophoretic profile of gp120 incubated with a monoclonal human IgM suggested hydrolysis at several sites. One of the cleavage sites was identified as the Lys(432)-Ala(433) peptide bond, located within the region thought to be the Ab-recognizable superantigenic determinant. A covalently reactive peptide analog (CRA) corresponding to gp120 residues 421-431 with a C-terminal amidino phosphonate diester mimetic of the Lys(432)-Ala(433) bond was employed to probe IgM nucleophilic reactivity. The peptidyl CRA inhibited the IgM-catalyzed cleavage of gp120 and formed covalent IgM adducts at levels exceeding a control hapten CRA devoid of the peptide sequence. These observations suggest that IgMs can selectively cleave gp120 by a nucleophilic mechanism and raise the possibility of their role as defense enzymes.     

Light-mediated regulation defines a minimal promoter region of TOP2

Light signaling has been demonstrated to be an important factor for plant growth and development; however, its role in the regulation of DNA replication and cell cycle has just started to be unraveled. In this work, we have demonstrated that the TOP2 promoter of Pisum sativum (pea) is activated by a broad spectrum of light including far-red light (FR), red light (RL) and blue light (BL). Deletion analyses of the TOP2 promoter in transformed plants, Arabidopsis thaliana and Nicotiana tobaccum (tobacco), define a minimal promoter region that is induced by RL, FR and BL, and is essential and sufficient for light-mediated activation. The minimal promoter of TOP2 follows the phytochrome- mediated low-fluence response similar to complex light regulated promoters. DNA–protein interaction studies reveal the presence of a DNA binding activity specific to a 106 bp region of the minimal promoter that is crucial for light-mediated activation. These results altogether indicate a direct involvement of light signaling in the regulation of expression of TOP2, one of the components of the DNA replication/cell cycle machinery.     

Evidence for the existence of a new receptor for CGRP,which is not CRLR

Receptors for calcitonin gene-related peptide (CGRP), a neuropeptide known to be the most potent vasodilator, are abundantly expressed in cerebellum. A monoclonal antibody to cerebellar CGRP receptors specifically detects a 66 kDa protein from rat cerebellum and other rat and human tissues, but not from SK-N-MC cells which express calcitonin receptor-like receptor (CRLR), a recently described component of CGRP receptors. In contrast, mRNA expression for CRLR was abundant in SK-N-MC cells, but it was undetectable in rat cerebellum. Furthermore, the antibody could not detect any immunoreactive protein in HEK 293 cells transiently transfected with CRLR and receptor activity-modifying protein 1 (RAMP(1)) indicating the possible existence of another CGRP receptor, which does not involve CRLR. Due to the absence of biochemical or structural data on the existence of a CGRP(2) receptor and the new data provided in this paper, we suggest to identify the two CGRP receptors as CGRP-A and CGRP-B.     

Antioxidants prevent UV-induced apoptosis by inhibiting mitochondrial cytochrome c release and caspase activation in Spodoptera frugiperda (Sf9) cells

Oxidative stress has been shown to be associated with apoptosis (programmed cell death) in a number of cell systems. We earlier reported in vitro cultured Spodoptera frugiperda (Sf9) cells as a model system to study oxidative stress induced apoptosis (J Biosciences 24 (1999) 13) and the inhibition of UV-induced apoptosis by the baculovirus antiapoptotic p35 protein that acts as a sink to sequester reactive oxygen species (Proc Natl Acad Sci USA 96 (1999) 4838). We now show that UV-induced apoptosis in Sf9 cells, is preceded by the release of mitochondrial cytochrome c into the cytosol and consequent activation of Sf-caspase-1. The inhibitory effect of different antioxidants including scavengers of oxygen radicals such as butylated hydroxyanisole (BHA), alpha tocopherol acetate, benzoate and reduced-glutathione (GSH) on ultra violet B (UVB)-induced apoptosis in cultured Sf9 cells was assessed. Both, cytochrome c release as well as Sf-caspase-1 activation was inhibited by pre-treatment with antioxidants such as BHA and alpha tocopherol acetate, suggesting that these antioxidants inhibit apoptosis by acting quite upstream in the apoptosis cascade at the mitochondrial level, as well as downstream at the caspase level.