Sequence and expression of murine cDNAs encoding Xlr3a and Xlr3b, defining a new X-linked lymphocyte-regulated Xlr gene subfamily

Using a subtractive cDNA approach we have identified two nearly identical genes, Xlr3a and Xlr3b (X-linked lymphocyte regulated), expressed at a consistently high level in 14 out of 14 murine plasmacytoma cell lines, at a high level in 1 out of 8 B-lymphoma cell lines, and at a very low level in 2 out of the 8 B-lymphoma cell lines. The messages are not detected in 10 pre-B-lymphoma cell lines. These genes express 2.0-kb mRNAs that encode 226-amino-acid proteins that are extremely basic, with an estimated pI of 8.1 and 9.0, respectively. By sequence comparison they are homologous to Xlr1, an acidic nuclear protein that is produced in lymphoid cell lines corresponding to the late stages of lymphocyte differentiation. Xlr2 is a highly homologous gene that is expressed in differentiating male germ cells. Xlr3a and Xlr3b are members of a new subfamily in the Xlr multigene family. Like Xlrl, they are up-regulated during B-cell terminal differentiation in normal and neoplastic B-cells, and cross-hybridize with a message in testis RNA. Also, like Xlrl, they do not cross-hybridize with human genomic DNA.     

Prostaglandins, cyclic nucleotides and cell function

Based upon the ability of the E-prostaglandins to stimulate cyclic AMP formation in a dose-related manner and the correlation between this property and their affinity for a membraneous receptor, the action of these prostaglandins was proposed to be expressed largely via cyclic AMP. The failure of the F-prostaglandins to demonstrate significant activity in these two parameters led to the suggestion that they must act at another receptor via a different mediator. The recent isolation of a receptor unique to PGF_2α and the demonstrated ability of this prostaglandin to increase tissue cyclic GMP levels are consistent with this concept that the E-prostaglandins and PGF_2α play distinctly different roles in cell regulation.     

Endothelial cell prostacyclin production induced by activated neutrophils

A bovine aortic endothelial cell (EC) line released prostacyclin (greater than 1 pmol/10(+5) EC cells) when incubated with fMet-Leu-Phe (FMLP)-stimulated rat and human neutrophils (PMNs). This prostaglandin (PG) I2 was shown to come from the ECs and not from the PMNs by radioactive, high-performance liquid chromatography, and immunochemical criteria. Both FMLP-stimulated rat peritoneal and human peripheral PMNs as well as their stimulated cell-free supernatants and unstimulated sonicates could elicit the release of PGI2 from ECs. Since phorbol myristate acetate stimulated PMN adherence but elicited little PGI2 release from ECs, the PGI2 stimulation in ECs is unrelated to PMN adhesion. The addition of catalase and superoxide dismutase to FMLP-stimulated PMNs enhanced rather than reduced PGI2 formation, indicating that activated oxygen products of the PMN are not responsible for the induction of PGI2. Incubation of ECs with leukotriene (LT) B4, LTC4, or LTD4 did not trigger PGI2 release nor did aspirin pretreatment of the PMNs reduce the PGI2 induction. These data suggest that arachidonic acid metabolites of the PMNs were not responsible for the PGI2 induction. Available data indicates that the PMN factor that stimulates PGI2 from ECs is either released concomitantly with the azurophilic granules or is closely related to this event.     

Addressing Accuracy and Precision Issues in iTRAQ Quantitation

iTRAQ (isobaric tags for relative or absolute quantitation) is a mass spectrometry technology that allows quantitative comparison of protein abundance by measuring peak intensities of reporter ions released from iTRAQ-tagged peptides by fragmentation during MS/MS. However, current data analysis techniques for iTRAQ struggle to report reliable relative protein abundance estimates and suffer with problems of precision and accuracy. The precision of the data is affected by variance heterogeneity: low signal data have higher relative variability; however, low abundance peptides dominate data sets. Accuracy is compromised as ratios are compressed toward 1, leading to underestimation of the ratio. This study investigated both issues and proposed a methodology that combines the peptide measurements to give a robust protein estimate even when the data for the protein are sparse or at low intensity. Our data indicated that ratio compression arises from contamination during precursor ion selection, which occurs at a consistent proportion within an experiment and thus results in a linear relationship between expected and observed ratios. We proposed that a correction factor can be calculated from spiked proteins at known ratios. Then we demonstrated that variance heterogeneity is present in iTRAQ data sets irrespective of the analytical packages, LC-MS/MS instrumentation, and iTRAQ labeling kit (4-plex or 8-plex) used. We proposed using an additive-multiplicative error model for peak intensities in MS/MS quantitation and demonstrated that a variance-stabilizing normalization is able to address the error structure and stabilize the variance across the entire intensity range. The resulting uniform variance structure simplifies the downstream analysis. Heterogeneity of variance consistent with an additive-multiplicative model has been reported in other MS-based quantitation including fields outside of proteomics; consequently the variance-stabilizing normalization methodology has the potential to increase the capabilities of MS in quantitation across diverse areas of biology and chemistry.Different techniques are being used and developed in the field of proteomics to allow quantitative comparison of samples between one state and another. These can be divided into gel- (1–4) or mass spectrometry-based (5–8) techniques. Comparative studies have found that each technique has strengths and weaknesses and plays a complementary role in proteomics (9, 10). There is significant interest in stable isotope labeling strategies of proteins or peptides as with every measurement there is the potential to use an internal reference allowing relative quantitation comparison, which significantly increases sensitivity of detection of change in abundance. Isobaric labeling techniques such as tandem mass tags (11, 12) or isobaric tags for relative or absolute quantitation (iTRAQ)¹ (13, 14) allow multiplexing of four, six and eight separately labeled samples within one experiment. In contrast to most other quantitative proteomics methods where precursor ion intensities are measured, here the measurement and ensuing quantitation of iTRAQ reporter ions occurs after fragmentation of the precursor ion. Differentially labeled peptides are selected in MS as a single mass precursor ion as the size difference of the tags is equalized by a balance group. The reporter ions are only liberated in MS/MS after the reporter ion and balance groups fragment from the labeled peptides during CID. iTRAQ has been applied to a wide range of biological applications from bacteria under nitrate stress (15) to mouse models of cerebellar dysfunction (16).For the majority of MS-based quantitation methods (including MS/MS-based methods like iTRAQ), the measurements are made at the peptide level and then combined to compute a summarized value for the protein from which they arose. An advantage is that the protein can be identified and quantified from data of multiple peptides often with multiple values per distinct peptide, thereby enhancing confidence in both identity and the abundance. However, the question arises of how to summarize the peptide readings to obtain an estimate of the protein ratio. This will involve some sort of averaging, and we need to consider the distribution of the data, in particular the following three aspects. (i) Are the data centered around a single mode (which would be related to the true protein quantitation), or are there phenomena that make them multimodal? (ii) Are the data approximately symmetric (non-skewed) around the mode? (iii) Are there outliers? In the case of multimodality, it is recommended that an effort be made to separate the various phenomena into their separate variables and to dissect the multimodality. Li et al. (17) developed ASAP ratio for ICAT data that includes a complex data combination strategy. Peptide abundance ratios are calculated by combining data from multiple fractions across MS runs and then averaging across peptides to give an abundance ratio for each parent protein. GPS Explorer, a software package developed for iTRAQ, assumes normality in the peptide ratio for a protein once an outlier filter is applied (18). The iTRAQ package ProQuant assumes that peptide ratio data for a protein follow a log-normal distribution (19). Averaging can be via mean (20), weighted average (21, 22), or weighted correlation (23). Some of these methods try to take into account the varying precision of the peptide measurements. There are many different ideas of how to process peptide data, but as yet no systematic study has been completed to guide analysis and ensure the methods being utilized are appropriate.The quality of a quantitation method can be considered in terms of precision, which refers to how well repeated measurements agree with each other, and accuracy, which refers to how much they on average deviate from the true value. Both of these types of variability are inherent to the measurement process. Precision is affected by random errors, non-reproducible and unpredictable fluctuations around the true value. (In)accuracy, by contrast, is caused by systematic biases that go consistently in the same direction. In iTRAQ, systematic biases can arise because of inconsistencies in iTRAQ labeling efficiency and protein digestion (22). Typically, ratiometric normalization has been used to address this tag bias where all peptide ratios are multiplied by a global normalization factor determined to center the ratio distribution on 1 (19, 22). Even after such normalization, concerns have been raised that iTRAQ has imperfect accuracy with ratios shrunken toward 1, and this underestimation has been reported across multiple MS platforms (23–27). It has been suggested that this underestimation arises from co-eluting peptides with similar m/z values, which are co-selected during ion selection and co-fragmented during CID (23, 27). As the majority of these will be at a 1:1 ratio across the reporter ion tags (as required for normalization in iTRAQ experiments), they will contribute a background value equally to each of the iTRAQ reporter ion signals and diminish the computed ratios.With regard to random errors, iTRAQ data are seen to exhibit heterogeneity of variance; that is the variance of the signal depends on its mean. In particular, the coefficient of variation (CV) is higher in data from low intensity peaks than in data from high intensity peaks (16, 22, 23). This has also been observed in other MS-based quantitation techniques when quantifying from the MS signal (28–30). Different approaches have been proposed to model the variance heterogeneity. Pavelka et al. (31) used a power law global error model in conjunction with quantitation data derived from spectral counts. Other authors have proposed that the higher CV at low signal arises from the majority of MS instrumentation measuring ion counts as whole numbers (32). Anderle et al. (28) described a two-component error model in which Poisson statistics of ion counts measured as whole numbers dominate at the low intensity end of the dynamic range and multiplicative effects dominate at the high intensity end and demonstrated its fit to label-free LC-MS quantitation data. Previously, in the 1990s, Rocke and Lorenzato (29) proposed a two-component additive-multiplicative error model in an environmental toxin monitoring study utilizing gas chromatography MS.How can the variance heterogeneity be addressed in the data analysis? Some of the current approaches include outlier removal (18, 25), weighted means (21, 22), inclusion filters (16, 22), logarithmic transformation (19), and weighted correlation analysis (23). Outlier removal methods, for example using Dixon''s test, assume a normal distribution for which there is little empirical basis. The inclusion filter method, where low intensity data are excluded, reduces the protein coverage considerably if the heterogeneity is to be significantly reduced. The weighted mean method results in higher intensity readings contributing more to the weighted mean than readings from low intensity readings. Filtering, outlier removal, and weighted methods are of limited use for peptides for which only a few low intensity readings were made; however, such cases typically dominate the data sets. Even with a logarithmic transformation, heterogeneity has been reported for iTRAQ data (16, 19, 22). Current methods struggle to address the issue and to maintain sensitivity.Here we investigate the data analysis issues that relate to precision and accuracy in quantitation and propose a robust methodology that is designed to make use of all data without ad hoc filtering rules. The additive-multiplicative model mentioned above motivates the so-called generalized logarithm transformation, a transformation that addresses heterogeneity of variance by approximately stabilizing the variance of the transformed signal across its whole dynamic range (33). Huber et al. (33) provided an open source software package, variance-stabilizing normalization (VSN), that determines the data-dependent transformation parameters. Here we report that the application of this transformation is beneficial for the analysis of iTRAQ data. We investigated the error structure of iTRAQ quantitation data using different peak identification and quantitation packages, LC-MS/MS data collection systems, and both the 4-plex and 8-plex iTRAQ systems. The usefulness of the VSN transformation to address heterogeneity of variance was demonstrated. Furthermore, we considered the correlations between multiple, peptide-level readings for the same protein and proposed a method to summarize them to a protein abundance estimate. We considered same-same comparisons to assess the magnitude of experimental variability and then used a set of complex biological samples whose biology has been well characterized to assess the power of the method to detect true differential abundance. We assessed the accuracy of the system with a four-protein mixture at known ratios spanning a -fold change expression range of 1–4. From this, we proposed a methodology to address the accuracy issues of iTRAQ.     

Complete plastid genome sequences of Drimys, Liriodendron, and Piper : implications for the phylogenetic relationships of magnoliids

Background  

The magnoliids with four orders, 19 families, and 8,500 species represent one of the largest clades of early diverging angiosperms. Although several recent angiosperm phylogenetic analyses supported the monophyly of magnoliids and suggested relationships among the orders, the limited number of genes examined resulted in only weak support, and these issues remain controversial. Furthermore, considerable incongruence resulted in phylogenetic reconstructions supporting three different sets of relationships among magnoliids and the two large angiosperm clades, monocots and eudicots. We sequenced the plastid genomes of three magnoliids, Drimys (Canellales), Liriodendron (Magnoliales), and Piper (Piperales), and used these data in combination with 32 other angiosperm plastid genomes to assess phylogenetic relationships among magnoliids and to examine patterns of variation of GC content.     

Induction of tolerance to innocuous inhaled antigen relies on a CCR7-dependent dendritic cell-mediated antigen transport to the bronchial lymph node

Allergic airway diseases such as asthma are caused by a failure of the immune system to induce tolerance against environmental Ags. The underlying molecular and cellular mechanisms that initiate tolerance are only partly understood. In this study, we demonstrated that a CCR7-dependent migration of both CD103+ and CD103- lung dendritic cells (DC) to the bronchial lymph node (brLN) is indispensable for this process. Although inhaled Ag is amply present in the brLN of CCR7-deficient mice, T cells cannot be tolerized because of the impaired migration of Ag-carrying DC and subsequent transport of Ag from the lung to the draining lymph node. Consequently, the repeated inhalation of Ag protects wild-type but not CCR7-deficient mice from developing allergic airway diseases. Thus, the continuous DC-mediated transport of inhaled Ag to the brLN is critical for the induction of tolerance to innocuous Ags.     

COASTAL DIATOM‐ENVIRONMENT RELATIONSHIPS IN THE BRACKISH BALTIC SEA1

High‐quality calibration data sets are required when diatom assemblages are used for monitoring ecological change or reconstructing palaeo‐environments. The quality of such data sets can be validated, in addition to other criteria, by the percentage of significant unimodal species responses as a measure of the length of an environmental gradient. This study presents diatom‐environment relationships analyzed from a robust data set of diatom communities living on submerged stones along a 2,000 km long coastline in the Baltic Sea area, including 524 samples taken at 135 sites and covering a salinity gradient from 0.4 to 11.4. Altogether, 487 diatom taxa belonging to 102 genera were recorded. Detrended canonical correspondence analysis showed that salinity was the overriding environmental factor regulating diatom community composition, while exposure to wave action and nutrient concentrations were of secondary importance. Modeling the abundances of the 58 most common diatom taxa yielded significant relationships with salinity for 57 taxa. Twenty‐three taxa showing monotonic responses were species with optimum distributions in freshwater or marine waters. Thirty‐four taxa showing unimodal responses were brackish‐water species with maximum distributions at different salinities. Separate analyses for small (cell biovolume <1,000 μm³) and large (≥1,000 μm³) taxa yielded similar results. In previous studies along shorter salinity gradients, large and small epilithic diatom taxa responded differently. From our large data, we conclude that counts of large diatom taxa alone seem sufficient for indicating salinity changes in coastal environments with high precision.     

Archazolid A binds to the equatorial region of the c-ring of the vacuolar H+-ATPase

The macrolactone archazolid is a novel, highly specific V-ATPase inhibitor with an IC(50) value in the low nanomolar range. The binding site of archazolid is presumed to overlap with the binding site of the established plecomacrolide V-ATPase inhibitors bafilomycin and concanamycin in subunit c of the membrane-integral V(O) complex. Using a semi-synthetic derivative of archazolid for photoaffinity labeling of the V(1)V(O) holoenzyme we confirmed binding of archazolid to the V(O) subunit c. For the plecomacrolide binding site a model has been published based on mutagenesis studies of the c subunit of Neurospora crassa, revealing 11 amino acids that are part of the binding pocket at the interface of two adjacent c subunits (Bowman, B. J., McCall, M. E., Baertsch, R., and Bowman, E. J. (2006) J. Biol. Chem. 281, 31885-31893). To investigate the contribution of these amino acids to the binding of archazolid, we established in Saccharomyces cerevisiae mutations that in N. crassa had changed the IC(50) value for bafilomycin 10-fold or more and showed that out of the amino acids forming the plecomacrolide binding pocket only one amino acid (tyrosine 142) contributes to the binding of archazolid. Using a fluorescent derivative of N,N'-dicyclohexylcarbodiimide, we found that the binding site for archazolid comprises the essential glutamate within helix 4 of subunit c. In conclusion the archazolid binding site resides within the equatorial region of the V(O) rotor subunit c. This hypothesis was supported by an additional subset of mutations within helix 4 that revealed that leucine 144 plays a role in archazolid binding.