East Weddell Sea echinoids from the JR275 expedition

Information regarding the echinoids in this dataset is based on the Agassiz Trawl (AGT) and epibenthic sledge (EBS) samples collected during the British Antarctic Survey cruise JR275 on the RRS James Clark Ross in the austral summer 2012. A total of 56 (1 at the South Orkneys and 55 in the Eastern Weddell Sea) Agassiz Trawl and 18 (2 at the South Orkneys and 16 in the Eastern Weddell Sea) epibenthic sledge deployments were performed at depths ranging from ~280 to ~2060 m. This presents a unique collection for the Antarctic benthic biodiversity assessment of an important group of benthic invertebrates. In total 487 specimens belonging to six families, 15 genera, and 22 morphospecies were collected. The species richness per station varied between one and six. Total species richness represents 27% of the 82 echinoid species ever recorded in the Southern Ocean (David et al. 2005b, Pierrat et al. 2012, Saucède et al. 2014). The Cidaridae (sub-family Ctenocidarinae) and Schizasteridae are the two most speciose families in the dataset. They comprise seven and nine species respectively. This is illustrative of the overall pattern of echinoid diversity in the Southern Ocean where 65% of Antarctic species belong to the families Schizasteridae and Cidaridae (Pierrat et al. 2012).     

The DegraBase: A Database of Proteolysis in Healthy and Apoptotic Human Cells

Proteolysis is a critical post-translational modification for regulation of cellular processes. Our lab has previously developed a technique for specifically labeling unmodified protein N termini, the α-aminome, using the engineered enzyme, subtiligase. Here we present a database, called the DegraBase (http://wellslab.ucsf.edu/degrabase/), which compiles 8090 unique N termini from 3206 proteins directly identified in subtiligase-based positive enrichment mass spectrometry experiments in healthy and apoptotic human cell lines. We include both previously published and unpublished data in our analysis, resulting in a total of 2144 unique α-amines identified in healthy cells, and 6990 in cells undergoing apoptosis. The N termini derive from three general categories of proteolysis with respect to cleavage location and functional role: translational N-terminal methionine processing (∼10% of total proteolysis), sites close to the translational N terminus that likely represent removal of transit or signal peptides (∼25% of total), and finally, other endoproteolytic cuts (∼65% of total). Induction of apoptosis causes relatively little change in the first two proteolytic categories, but dramatic changes are seen in endoproteolysis. For example, we observed 1706 putative apoptotic caspase cuts, more than double the total annotated sites in the CASBAH and MEROPS databases. In the endoproteolysis category, there are a total of nearly 3000 noncaspase nontryptic cleavages that are not currently reported in the MEROPS database. These studies significantly increase the annotation for all categories of proteolysis in human cells and allow public access for investigators to explore interesting proteolytic events in healthy and apoptotic human cells.Annotation of the human α-aminome, the full set of unmodified protein N termini, can provide a wealth of information regarding protein turnover, protein trafficking, and protease activity (1). The vast majority of protein N termini in eukaryotic cells are cotranslationally blocked by acetylation through the action of N-acetyl transferases (2). Free α-amines occur on some proteins that are never N-terminally acetylated, and can also be regenerated by signal or transit peptide removal during protein trafficking, and endo- or exoproteolysis during protein maturation and signaling. Thus, there has been considerable effort to develop unbiased proteomic methods to characterize the α-aminome in healthy and diseased states (3–8).We have developed a positive enrichment method in which the α-amines of intracellular (8) or extracellular proteins (9) can be specifically and directly tagged and captured, without pretreatment or protection, using subtiligase, an engineered peptide ligase (Fig. 1A) (10, 11). Following purification, tryptic digestion, and LC-MS/MS, the protein sequence and exact site of proteolysis are readily identified. We have applied this approach to study proteolysis by caspases, cysteine-class aspartyl specific proteases, during cellular apoptosis (8, 12–14), and inflammatory response (15). These studies, in a variety of cell types and apoptotic inducers, have revealed much about the targets, substrate recognition, timing, logic, and evolution of caspase cleavage events. These efforts have generated a huge amount of data that requires systematic compilation, organization, and normalization so that it can be shared and queried easily by all investigators and compared with other databases describing proteolytic events (16–18).Open in a separate windowFig. 1.Experimental schema, database design, and database summary. (A), For all experiments, human cells were grown under standard conditions, either with or without treatment with apoptosis inducing agents. Cells are lysed and proteins biotinylated on their free α-amines using subtiligase, followed by purification and identification by LC-MS/MS. N termini identifications from every experiment were entered into the database to create the untreated and apoptotic datasets, and a subset apoptotic caspase-cleaved dataset for apoptotic N termini following aspartic acid cleavage. (B), The DegraBase database is structured around four main tables linking the experimental data to the MS identifications and external database information at both the N terminus and protein level (for more details see Supplemental File S1). (C), Summary statistics of the DegraBase for all experiments in the DegraBase and for both the untreated and apoptotic datasets (more details in Supplemental Table S1A). The blue box highlights the apoptotic caspase-cleaved dataset within the apoptotic dataset.Here we present the results of both previously published and new experiments that detect α-amines in both untreated and apoptotic human cells. These studies reveal new translational N-terminal processing, signal and transit peptide removal, and other proteolytic events associated with normal protein maturation and function in healthy cells. Comparing these data to the apoptotic dataset reveals that the greatest changes in apoptosis are caused by endoproteolysis, owing to the induction of caspases as well as other proteases. We find a total of 1706 putative caspase sites in nearly 1300 different human proteins. We further find an additional 2900 noncaspase, nontryptic, nontransit, and nonsignal peptide cleavage sites in 1415 proteins.In addition to the analyses described here, we provide a publically available database, the DegraBase, that is dynamic, expandable, searchable, and readily accessible (http://wellslab.ucsf.edu/degrabase/). With this database, investigators can query all 8090 unique α-amines detected with high confidence from 26,043 peptide observations in both previously published (8, 12, 13) and new subtiligase α-aminome labeling experiments. The DegraBase substantially expands annotated intracellular proteolytic events in healthy and apoptotic cells.     

RNA Interference Screen to Identify Kinases That Suppress Rescue of ΔF508-CFTR

Cystic Fibrosis (CF) is an autosomal recessive disorder caused by mutations in the gene encoding the Cystic fibrosis transmembrane conductance regulator (CFTR). ΔF508-CFTR, the most common disease-causing CF mutant, exhibits folding and trafficking defects and is retained in the endoplasmic reticulum, where it is targeted for proteasomal degradation. To identify signaling pathways involved in ΔF508-CFTR rescue, we screened a library of endoribonuclease-prepared short interfering RNAs (esiRNAs) that target ∼750 different kinases and associated signaling proteins. We identified 20 novel suppressors of ΔF508-CFTR maturation, including the FGFR1. These were subsequently validated by measuring channel activity by the YFP halide-sensitive assay following shRNA-mediated knockdown, immunoblotting for the mature (band C) ΔF508-CFTR and measuring the amount of surface ΔF508-CFTR by ELISA. The role of FGFR signaling on ΔF508-CFTR trafficking was further elucidated by knocking down FGFRs and their downstream signaling proteins: Erk1/2, Akt, PLCγ-1, and FRS2. Interestingly, inhibition of FGFR1 with SU5402 administered to intestinal organoids (mini-guts) generated from the ileum of ΔF508-CFTR homozygous mice resulted in a robust ΔF508-CFTR rescue. Moreover, combination of SU5402 and VX-809 treatments in cells led to an additive enhancement of ΔF508-CFTR rescue, suggesting these compounds operate by different mechanisms. Chaperone array analysis on human bronchial epithelial cells harvested from ΔF508/ΔF508-CFTR transplant patients treated with SU5402 identified altered expression of several chaperones, an effect validated by their overexpression or knockdown experiments. We propose that FGFR signaling regulates specific chaperones that control ΔF508-CFTR maturation, and suggest that FGFRs may serve as important targets for therapeutic intervention for the treatment of CF.Cystic fibrosis (CF)¹ is a pleiotropic disease caused by an abnormal ion transport in the secretory epithelia lining the tubular organs of the body such as lungs, intestines, pancreas, liver, and male reproductive tract. In the airways of CF patients, reduced Cl⁻ and bicarbonate secretion caused by lack of functional Cystic fibrosis transmembrane conductance regulator (CFTR) on the apical surface, and hyper-absorption of Na⁺ because of elevated activity of ENaC (1), lead to a dehydration of the airway surface liquid (ASL). This reduces the viscosity of the mucus layer and the deposited layer of thickened mucus creates an environment that promotes bacterial colonization, which eventually leads to chronic infection of the lungs and death (2, 3).CFTR is a transmembrane protein that functions as a cAMP-regulated, ATP-dependent Cl⁻ channel that also allows passage of bicarbonate through its pore (4, 5). It also possesses ATPase activity important for Cl⁻ conductance (6, 7). The CFTR structure is predicted to consist of five domains: two membrane spanning domains (MSD1, MSD2), each composed of six putative transmembrane helices, two nucleotide binding domains (NBD1, NBD2), and a unique regulatory (R) region (8).More than 1900 CFTR mutations have been identified to date (www.genet.sickkids.on.ca/cftr). The most common mutation is a deletion of phenylalanine at position 508 (ΔF508 or ΔF508-CFTR) in NBD1 (9). The ΔF508 mutation causes severe defects in the processing and function of CFTR. The protein exhibits impaired trafficking from the endoplasmic reticulum (ER) to the plasma membrane (PM), impaired intramolecular interactions between NBD1 and the transmembrane domain, and cell surface instability (10–15). Nevertheless, the ΔF508 defect can be corrected, because treating cells expressing ΔF508-CFTR with low temperature or chemical chaperones (e.g. glycerol) can restore some surface expression of the mutant (11, 16).Numerous small molecules that can at least partially correct (or potentiate) the ΔF508-CFTR defect have been identified to date (17–27), and some were already tested in clinical trials (e.g. sildenafil, VX-809/Lumacaftor), or have made it to the clinic (VX-770/Kalydeco/Ivacaftor) (http://www.cff.org/research/DrugDevelopmentPipeline/). However, the need to identify new ΔF508-CFTR correctors remains immense as the most promising corrector, VX-809, has proven ineffective in alleviating lung disease of CF patients when administered alone (27). Thus, our group developed a high-content technology aimed at identifying proteins and small molecules that correct the trafficking and functional defects of ΔF508-CFTR (28). We successfully used this approach to carry out three separate high-content screens: a protein overexpression screen (28), a small-molecule kinase inhibitor screen (29) and a kinome RNA interference (RNAi) screen, described here.     

Comprehensive Identification of Proteins from MALDI Imaging

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a powerful tool for the visualization of proteins in tissues and has demonstrated considerable diagnostic and prognostic value. One main challenge is that the molecular identity of such potential biomarkers mostly remains unknown. We introduce a generic method that removes this issue by systematically identifying the proteins embedded in the MALDI matrix using a combination of bottom-up and top-down proteomics. The analyses of ten human tissues lead to the identification of 1400 abundant and soluble proteins constituting the set of proteins detectable by MALDI IMS including >90% of all IMS biomarkers reported in the literature. Top-down analysis of the matrix proteome identified 124 mostly N- and C-terminally fragmented proteins indicating considerable protein processing activity in tissues. All protein identification data from this study as well as the IMS literature has been deposited into MaTisse, a new publically available database, which we anticipate will become a valuable resource for the IMS community.Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS)¹ is an emerging technique that can be described as a multi-color molecular microscope as it allows visualizing the distribution of many molecules as mass to charge (m/z) signals in parallel in situ (1). Originally described some 15 years ago (2) the method has been successfully adapted to different analyte classes including small molecule drugs (3), metabolites (4), lipids (5), proteins (6), and peptides (7) using e.g. formalin fixed paraffin embedded (FFPE) as well as fresh frozen tissue (8). Because the tissue stays intact in the process, MALDI IMS is compatible with histochemistry (9) as well as immunohistochemistry and thus adds an additional dimension of molecular information to classical microscopy based tissue analysis (10). Imaging of proteins is appealing as it conceptually allows determining the localization and abundance of proteoforms (11) that naturally occur in the tissue under investigation including modifications such as phosphorylation, acetylation, or ubiquitination, protease mediated cleavage or truncation (12). Therefore a proteinous m/z species detected by MALDI IMS can be viewed as an in situ molecular probe of a particular biological process. In turn, m/z abundance patterns that discriminate different physiological or pathological conditions might be used as diagnostic or even prognostic markers (13, 14). In recent years, MALDI IMS of proteins has been successfully applied to different cancer types from the brain (15), breast (16, 17), kidney (18), prostate (19), and skin (20). Furthermore, the technique has been applied in the context of colon inflammation (21), embryonic development (22), Alzheimer''s disease (23), and amyotrophic lateral sclerosis (24). With a few notable exceptions (13, 14, 16–18, 20, 24–30), the identity of the proteins constituting the observed characteristic m/z patters has generally remained elusive. This not only precludes the validation of the putative biomarkers by, for example, immunohistochemistry, but also the elucidation of the biological processes that might underlie the observed phenotype.Here, we introduce a straightforward extraction and identification method for proteins embedded in the MALDI matrix layer that represent the molecular species amenable to MALDI IMS. Using a bottom-up proteomics approach including tryptic digestion and liquid chromatography tandem mass spectrometry (LC-MS/MS), we first created an inventory list of proteins derived from this layer, which we term the MALDI matrix proteome. Although the bottom-up approach breaks the link between the identified proteins and the m/z species detected in MALDI IMS, the list of identified proteins serves as the pool of proteins from which all potential biomarkers are most likely derived. Indeed we detected >90% of all human MALDI IMS biomarkers reported in the literature by analyzing just ten human tissues. In addition, the results demonstrate that the same inventory can be used as a focused database for direct top-down sequencing and identification of proteins extracted from the MALDI matrix layer. The proposed method is generic and can be applied to any MALDI IMS study, which is why we believe that one of the major challenges in identifying MALDI IMS biomarkers has now been overcome. In addition, we provide a list of all proteins and peptides identified in the MALDI matrices and tissues studied here as well as a comprehensive list of m/z species identified in the literature dealing with MALDI imaging of humans and rodents. This information has been compiled in MaTisse (http://www.wzw.tum.de/bioanalytik/matisse), a new publically available and searchable database, which we believe will become a valuable tool for the MALDI imaging community.     

Notes: β(1-3)Glucanosyltransferase Gel4p Is Essential for Aspergillus fumigatus

The β(1-3)glucanosyltransferase GEL family of Aspergillus fumigatus contains 7 genes, among which only 3 are expressed during mycelial growth. The role of the GEL4 gene was investigated in this study. Like the other Gelps, it encodes a glycosylphosphatidylinositol (GPI)-anchored protein. In contrast to the other β(1-3)glucanosyltransferases analyzed to date, it is essential for this fungal species.β(1-3)Glucan is the main component of the fungal cell wall (11). In fungi, β(1-3)glucans are synthesized by a plasma membrane-bound glucan synthase complex. Neosynthesized glucans are then extruded into the periplasmic space (2, 3, 9), where they become branched and covalently linked to other cell wall components, resulting in the formation of three-dimensional rigid structures. In the search of transglycosidase in the filamentous fungus Aspergillus fumigatus, β(1-3)glucanosyltransferases were identified and classified as a unique family (GH72) in the Carbohydrate-Active enZYmes database (http://www.cazy.org/). These enzymes cleave the β(1-3) bond of a β(1-3)glucan oligosaccharide with at least 10 glucose units and transfer the newly formed reducing end (>5 glucose units) to the nonreducing end of another β(1-3)glucan oligosaccharide, resulting in the elongation of the β(1-3)glucans. This reaction can proceed in vitro until the neosynthetized β(1-3)glucan becomes insoluble. Initially demonstrated biochemically, the requirement for long-chain β(1-3)glucan oligosaccharide has now been confirmed by the analysis of the first crystal structure obtained in this transglycosidase family (7, 8). First discovered in Aspergillus fumigatus and named Gelp for glucan elongase, this activity has been found in all fungal species investigated to date and could be assigned to orthologous proteins, such as Gasp or Phrp, that were known to be involved in cell wall integrity but were endowed with an unknown biochemical function (12, 13, 14).     

Megadalton Complexes in the Chloroplast Stroma of Arabidopsis thaliana Characterized by Size Exclusion Chromatography,Mass Spectrometry,and Hierarchical Clustering

To characterize MDa-sized macromolecular chloroplast stroma protein assemblies and to extend coverage of the chloroplast stroma proteome, we fractionated soluble chloroplast stroma in the non-denatured state by size exclusion chromatography with a size separation range up to ∼5 MDa. To maximize protein complex stability and resolution of megadalton complexes, ionic strength and composition were optimized. Subsequent high accuracy tandem mass spectrometry analysis (LTQ-Orbitrap) identified 1081 proteins across the complete native mass range. Protein complexes and assembly states above 0.8 MDa were resolved using hierarchical clustering, and protein heat maps were generated from normalized protein spectral counts for each of the size exclusion chromatography fractions; this complemented previous analysis of stromal complexes up to 0.8 MDa (Peltier, J. B., Cai, Y., Sun, Q., Zabrouskov, V., Giacomelli, L., Rudella, A., Ytterberg, A. J., Rutschow, H., and van Wijk, K. J. (2006) The oligomeric stromal proteome of Arabidopsis thaliana chloroplasts. Mol. Cell. Proteomics 5, 114–133). This combined experimental and bioinformatics analyses resolved chloroplast ribosomes in different assembly and functional states (e.g. 30, 50, and 70 S), which enabled the identification of plastid homologues of prokaryotic ribosome assembly factors as well as proteins involved in co-translational modifications, targeting, and folding. The roles of these ribosome-associating proteins will be discussed. Known RNA splice factors (e.g. CAF1/WTF1/RNC1) as well as uncharacterized proteins with RNA-binding domains (pentatricopeptide repeat, RNA recognition motif, and chloroplast ribosome maturation), RNases, and DEAD box helicases were found in various sized complexes. Chloroplast DNA (>3 MDa) was found in association with the complete heteromeric plastid-encoded DNA polymerase complex, and a dozen other DNA-binding proteins, e.g. DNA gyrase, topoisomerase, and various DNA repair enzymes. The heteromeric ≥5-MDa pyruvate dehydrogenase complex and the 0.8–1-MDa acetyl-CoA carboxylase complex associated with uncharacterized biotin carboxyl carrier domain proteins constitute the entry point to fatty acid metabolism in leaves; we suggest that their large size relates to the need for metabolic channeling. Protein annotations and identification data are available through the Plant Proteomics Database, and mass spectrometry data are available through Proteomics Identifications database.Chloroplasts are essential plant organelles of prokaryotic origin that perform a variety of metabolic and signaling functions. Best known for their role in photosynthesis, they also carry out the biosynthesis of many primary and secondary metabolites like lipids, amino acids, vitamins, nucleotides, tetrapyrroles, and hormones (1). Subcellular localization prediction by TargetP (2) combined with a correction for false positive and false negative rates suggested that all non-green plastid types and chloroplasts together contain some 3500 proteins in Arabidopsis thaliana (3). More than 95% of the chloroplast proteins are nucleus-encoded and post-translationally imported into the chloroplast (4–6). Over the last decade, several studies were published that aimed to identify (subfractions of) the Arabidopsis chloroplast proteome (e.g. Refs. 7–10). The precise number of bona fide chloroplast proteins from these proteomics studies is probably somewhere around 1000–1300; comparing this number with the predicted chloroplast proteome indicates that ∼50% of the proteome has still not been observed. Recently, we concluded that when compared with the predicted Arabidopsis chloroplast proteome the chloroplast proteome identified to date is particularly underrepresented (40–70%) for proteins involved in signaling, stress, development, unassigned function, and DNA/RNA metabolism (9). To probe deeper into the chloroplast proteome, enrichment for low abundance proteins prior to MS analysis is required.Many biochemical functions are executed by protein assemblies. Several studies have catalogued the assembly states of chloroplast proteins in plants. Separation of the oligomeric Arabidopsis stromal proteome by two-dimensional native gel electrophoresis (CN¹-PAGE) profiled 240 non-redundant proteins and captured information for 124 complexes (11). However, native gel electrophoresis has a practical size limit, and only protein complexes below ∼1000 kDa can be effectively separated, thereby missing megadalton-sized complexes. Several megadalton-sized complexes in plants have been characterized by targeted purification schemes, including the spinach 30 and 50 S ribosomal particles (12–14), cytosolic ribosomes (15, 16), the tobacco plastid-encoded RNA polymerase (PEP) complex (17), maize mitochondrial pyruvate dehydrogenase complex (PDC) (18), and pea chloroplast acetyl-CoA carboxylase (ACCase) complex (19). Proteome characterization of a membrane-depleted, Triton-insoluble, and high density pellet from pea plastids was highly enriched for the chloroplast PDC as well as proteins involved in plastid gene expression and carbon fixation (20). However, because no subsequent fractionation was performed, specific protein associations could not be resolved.To extend chloroplast proteome coverage and characterize MDa-sized macromolecular assemblies to complement the previous CN-PAGE analysis of complexes up to 0.8 MDa, we fractionated the soluble chloroplast stroma by size exclusion chromatography (SEC) with a particular focus on complexes greater than 0.8 MDa. Proteins were identified by mass spectrometry analysis using an LTQ-Orbitrap, a high accuracy and high sensitivity hybrid instrument (21, 22). SEC migration profiles for identified proteins were generated from matched spectral counts. Hierarchical clustering and protein heat maps of the SEC migration profiles revealed that the identified protein complexes include 30, 50, and 70 S ribosomal particles; PDC; PEP; and ACCase, indicating successful MDa size fractionation. In addition, many “new” proteins were detected, and they were enriched for functions in plastid gene expression, in particular putative ribosomal biogenesis factors. Finally, protein annotations and identification data are available via the Plant Proteomics Database (PPDB) at http://ppdb.tc.cornell.edu/, and mass spectrometry data with their metadata were deposited in the Proteomics Identifications database (PRIDE) (http://www.ebi.ac.uk/pride/) under accession numbers 11459–11568.The concept of using chromatography (or other continuous fractionation techniques) of protein complexes (or other types of cellular protein fractions) with mass spectrometry-based quantification to determine co-localization has been applied using stable isotope labeling (23, 24) or label-free techniques (25, 26). When combined with cluster analysis (this study and Ref. 24), principle component analysis (23), or correlation of normalized elution profiles (this study and Refs. 25 and 26), this strategy is clearly a powerful tool and is widely applicable to other subcellular proteomes.     

A Presumptive Developmental Role for a Sea Urchin Cyclin B Splice Variant

We show that a splice variant–derived cyclin B is produced in sea urchin oocytes and embryos. This splice variant protein lacks highly conserved sequences in the COOH terminus of the protein. It is found strikingly abundant in growing oocytes and cells committed to differentiation during embryogenesis. Cyclin B splice variant (CBsv) protein associates weakly in the cell with Xenopus cdc2 and with budding yeast CDC28p. In contrast to classical cyclin B, CBsv very poorly complements a triple CLN deletion in budding yeast, and its microinjection prevents an initial step in MPF activation, leading to an important delay in oocyte meiosis reinitiation. CBsv microinjection in fertilized eggs induces cell cycle delay and abnormal development. We assume that CBsv is produced in growing oocytes to keep them in prophase, and during embryogenesis to slow down cell cycle in cells that will be committed to differentiation.Cyclins are a conserved family of proteins that play a central role in eukaryotic cell division cycle progression, as regulatory subunits of cyclin dependent kinases (CDKs, whose catalytic subunits are homologues of the fission yeast cdc2 protein).¹ CDKs are downstream targets of convergent cascades of regulations at critical points of the cell cycle. M-phase–promoting factor (MPF, formerly maturation promoting factor, reference 21), the factor responsible for M-phase entry and progression in mitosis, has been purified three times by biochemical means (7, 19, 36). MPF from starfish, Xenopus, and carp oocytes has been found to be a heterodimer composed of one molecule of cdc2 and one molecule of cyclin B (CB). B type cyclins are archetypal mitotic cyclins, evolutively and functionally related to fission yeast cdc13p. Among CDKs, the regulation of MPF is by far the best understood today. Cyclin B is required for activity, as well for activation and for inhibition of MPF. The cdc2 monomer has never been found active. Its activation is conferred by the CAK-dependent T161-phosphorylation that requires cyclin B association (4, 28, 33). Inhibition of MPF during S- and G2-phases and also by the DNA replication checkpoint mechanism is achieved by wee1-catalyzed phosphorylation of the tyrosine 15 in cyclin B–bound molecules of cdc2 (9, 22). Cyclin B is also likely required for activation of the protein phosphatase cdc25p that specifically dephosphorylates tyrosine 15 and allows MPF amplification and entry into mitosis (5, 37). Finally, targeted proteolysis of cyclin B by an ubiquitin-dependent pathway is the mechanism by which MPF is inactivated and the cell returns to interphase (8). Therefore, the major part of MPF regulation is accounted for by cyclin B synthesis and proteolysis. This was emphasized in simplified early embryogenesis cycles that are composed of a succession of M- and S-phases without intervening G-phases. Cycles in acellular Xenopus egg extracts are driven by MPF as a basic oscillator, whose periodic activity is scheduled strictly by oscillating abundance of cyclin B (24). Accordingly, during the cleavage period of Xenopus embryogenesis, cdc2 tyrosine 15 is never found phosphorylated (3) and checkpoint mechanisms are downregulated.Site-directed mutagenesis as well as protein crystallization have allowed the mapping of some sequences in cyclins involved in these regulations. Crystal structure of the homologous dimer cdk2–cyclin A showed that the cyclin interacts with the cdk via sequences distributed along the so-called cyclin box, a sequence well conserved among all cyclins (14). In the NH₂ terminus of mitotic cyclins A and B, a destruction box is required to allow ubiquitination of the protein and its targeted proteolysis in anaphase (8). Mutants that are deleted for this box are stable in mitosis, and their overexpression triggers mitotic arrest. Also in the NH₂-terminal region of B type cyclins, a cytoplasmic retention signal (CRS) is presumed to account for differential early prophase localization of nuclear cyclin A and cytoplasmic cyclin B (27). A chimeric cyclin A with the first amino acids of cyclin B remains cytoplasmic until early prophase. Further on, at the beginning of the cyclin box, conserved amino acids in the P-box are thought to be involved in the specific activation of cdc2 by cdc25 (37). Finally, two reports showed that a short COOH-terminal deletion of recombinant cyclins A or B abolished the binding to cdc2 (17, 34), although this region was not found to be directly involved in the physical interaction between cyclin A and cdk2 (14).Here we show that such a COOH-terminal truncation, which removes universally conserved amino acids, is naturally realized in a splice variant of sea urchin cyclin B. Moreover, immunofluorescence experiments suggest this splice variant plays a role in embryogenesis and behaves like a marker of cell lineages in postcleavage embryos.     

A Quantitative Proteomics Design for Systematic Identification of Protease Cleavage Events

We present here a novel proteomics design for systematic identification of protease cleavage events by quantitative N-terminal proteomics, circumventing the need for time-consuming manual validation. We bypass the singleton detection problem of protease-generated neo-N-terminal peptides by introducing differential isotopic proteome labeling such that these substrate reporter peptides are readily distinguished from all other N-terminal peptides. Our approach was validated using the canonical human caspase-3 protease and further applied to mouse cathepsin D and E substrate processing in a mouse dendritic cell proteome, identifying the largest set of protein protease substrates ever reported and gaining novel insight into substrate specificity differences of these cathepsins.Several protocols for proteome-wide identification of protease processing events were recently published. They all follow strategies in which N-terminal peptides, including neo-N-terminal peptides generated by protease action, are enriched from whole proteome digests before identification (e.g. Refs. 1–4). LC-MS/MS analyses of these peptides often yield hundreds of processing events identified in a single experiment (e.g. Refs. 3–5). The N-terminal COFRADIC1 technology developed in our laboratory (6) has been successful in identifying cleavage events of both canonical (e.g. caspases-3 and -7 (7)) and non-canonical proteases (e.g. HtrA2/Omi (8)). Differential stable isotopic labeling in particular, necessary to univocally distinguish genuine neo-N-terminal peptides, allows analyzing control and protease-treated proteomes in a single run. However, this also introduces the most important bottleneck of the technology: verifying whether the peptide envelope of a neo-N-terminal peptide only carries the isotopic label of the protease-treated sample (see Fig. 1A) often had to be done manually for each identified peptide. This “singleton detection problem” can to some extent be automated by software routines such as ProteinProspector (http://prospector.ucsf.edu/prospector/mshome.htm), the MASCOT Distiller Quantitation Toolbox (www.matrixscience.com/distiller.html), and ICPLQuant (9), although these often need specific or proprietary data formats or can only handle MALDI-MS data (9), and researchers still need to individually check correct calling of a neo-N-terminal peptide (10).Open in a separate windowFig. 1.Manual versus automated annotation of protease cleavage events. A, in a typical setup, a heavy (H) labeled proteome is used for protease treatment, and the light (L) labeled proteome serves as a control. Following mixing and N-terminal COFRADIC sorting, neo-N-terminal peptides generated by the added protease are present as singletons, whereas all other N-terminal peptides are present as couples with (light/heavy) ratios around 1 (0 in log₂ scale). B, a mixture of light and heavy labeled proteins (mixed in a 1:1 ratio) is treated with a protease, and as a result, neo-N-terminal peptides generated by the action of the added protease are now present in light/heavy ratios distributed around 1 (0 in log₂ scale) and are clearly distinct from all other N-terminal peptides that come in ratios around 3 (1.58 in log₂ scale). Both types of peptides are readily quantified, circumventing the need for manual validation.To fully overcome this singleton detection problem, here we present and validate a method for highly automated, software-based quantification and annotation of protein processing events on a proteomics scale based on stable isotopic labeling and positional proteomics. We illustrate its strength by generating the largest set of cathepsin D and E substrates hitherto reported. Furthermore, differences in the specificity profiles of these non-canonical proteases are illustrated by the validation of a cleavage event specific for cathepsin E in filamin-A.     

Serum Glycoprotein Biomarker Discovery and Qualification Pipeline Reveals Novel Diagnostic Biomarker Candidates for Esophageal Adenocarcinoma

We report an integrated pipeline for efficient serum glycoprotein biomarker candidate discovery and qualification that may be used to facilitate cancer diagnosis and management. The discovery phase used semi-automated lectin magnetic bead array (LeMBA)-coupled tandem mass spectrometry with a dedicated data-housing and analysis pipeline; GlycoSelector (http://glycoselector.di.uq.edu.au). The qualification phase used lectin magnetic bead array-multiple reaction monitoring-mass spectrometry incorporating an interactive web-interface, Shiny mixOmics (http://mixomics-projects.di.uq.edu.au/Shiny), for univariate and multivariate statistical analysis. Relative quantitation was performed by referencing to a spiked-in glycoprotein, chicken ovalbumin. We applied this workflow to identify diagnostic biomarkers for esophageal adenocarcinoma (EAC), a life threatening malignancy with poor prognosis in the advanced setting. EAC develops from metaplastic condition Barrett''s esophagus (BE). Currently diagnosis and monitoring of at-risk patients is through endoscopy and biopsy, which is expensive and requires hospital admission. Hence there is a clinical need for a noninvasive diagnostic biomarker of EAC. In total 89 patient samples from healthy controls, and patients with BE or EAC were screened in discovery and qualification stages. Of the 246 glycoforms measured in the qualification stage, 40 glycoforms (as measured by lectin affinity) qualified as candidate serum markers. The top candidate for distinguishing healthy from BE patients'' group was Narcissus pseudonarcissus lectin (NPL)-reactive Apolipoprotein B-100 (p value = 0.0231; AUROC = 0.71); BE versus EAC, Aleuria aurantia lectin (AAL)-reactive complement component C9 (p value = 0.0001; AUROC = 0.85); healthy versus EAC, Erythroagglutinin Phaseolus vulgaris (EPHA)-reactive gelsolin (p value = 0.0014; AUROC = 0.80). A panel of 8 glycoforms showed an improved AUROC of 0.94 to discriminate EAC from BE. Two biomarker candidates were independently verified by lectin magnetic bead array-immunoblotting, confirming the validity of the relative quantitation approach. Thus, we have identified candidate biomarkers, which, following large-scale clinical evaluation, can be developed into diagnostic blood tests. A key feature of the pipeline is the potential for rapid translation of the candidate biomarkers to lectin-immunoassays.Biomarkers play a central role in health care by enabling accurate diagnosis and prognosis; hence there is extensive research on the identification and development of novel biomarkers. However, despite numerous biomarker publications over the years (1), only a handful of new cancer biomarkers have successfully completed the journey from discovery, qualification, to verification and validation (2–4). One possible way to overcome this challenge is to develop an integrated biomarker pipeline that facilitates the smooth and successful transition from discovery to validation (5–10). The first and foremost consideration in an integrated pipeline is the sample source. In general, most of the proteomics based workflows use tissues or proximal fluids during the discovery phase, with the goal of extending the findings to plasma. Although this approach avoid the high complexity serum/plasma proteome and the associated requisite multi-dimensional sample separation in discovery stages, it often leads to failure when the candidates are not detected in plasma because of the limited sensitivity of the available analytical methods, or the absence of candidates in the plasma (11). To overcome this pitfall, we have developed an integrated glycoprotein biomarker pipeline, which can simply and rapidly isolate glycosylated proteins from serum to enable high throughput analysis of differentially glycosylated proteins in discovery and qualification stages.The workflow utilizes naturally occurring glycan binding proteins, lectins, in a semi-automated high throughput workflow called lectin magnetic bead array-tandem mass spectrometry (LeMBA-MS/MS)¹ (12, 13). Although lectins have been well-utilized in glycobiology and biomarker discovery (14–17), the LeMBA-MS/MS workflow demonstrates several unique features. First, serum glycoproteins are isolated in a single-step using 20 individual lectin-coated magnetic beads in microplate format. Second, we have optimized the concentrations of salts and detergents for sample denaturation to avoid co-isolation of protein complexes without adversely affecting lectin pull-down efficiency. Third, a liquid handler is used for sample processing to facilitate high-throughput screening and increase reproducibility. In addition, we have optimized on-bead trypsin digestion and incorporated lectin-exclusion lists during nano-LC-MS/MS to identify nonglycosylated peptides from the isolated glycoproteins. With these innovations, LeMBA-MS/MS demonstrates nanomolar sensitivity and linearity, and applicability across species (12). Compared with existing single, serial or multi-lectin affinity chromatography (18, 19), LeMBA-MS/MS offers the capability to simultaneously screen 20 lectins in a semi-automated, high throughput manner. On the other hand, because LeMBA-MS/MS identifies the nonglycosylated peptides, it cannot be used for glycan site assignment and glycan structure elucidation (20–23). However, the main advantage of LeMBA, we believe, is as a part of an integrated translational biomarker pipeline leading to lectin immunoassays. The lack of glycan structure details is not critical for clinical translation, as exemplified by the alpha-fetoprotein-L3 (AFP-L3) test, which measures the Lens culinaris agglutinin (LCA) binding fraction of serum alpha-fetoprotein (24, 25), and has been approved by the U.S. Food and Drug Administration for detection of hepatocellular carcinoma.In this study, we report the extension of the glycoprotein biomarker pipeline to the qualification phase with LeMBA-MRM-MS, and introduce statistical analysis pipelines GlycoSelector (http://glycoselector.di.uq.edu.au/) and Shiny mixOmics (http://mixomics-projects.di.uq.edu.au/Shiny) for the discovery and qualification phases, respectively. The utility of this integrated serum glycoprotein biomarker pipeline is demonstrated using esophageal adenocarcinoma (EAC) with unmet clinical need for an in vitro diagnostic test. EAC is a lethal malignancy of the lower esophagus with very poor 5-year survival rate of less than 25% (26). EAC is becoming increasingly common and its incidence is associated with the prevalent precursor metaplastic condition Barrett''s esophagus (BE), but with a low annual conversion rate of up to 1% (27). A common set of risk factors are described for BE and EAC, include gastroesophageal reflux disease (GERD), obesity, male gender, and smoking (28, 29). The current endoscopy-biopsy based diagnosis is invasive and costly, leading to an ineffective surveillance program. A blood test employing serum biomarkers that can distinguish patients with EAC from those with either BE or healthy tissue would, potentially, change the paradigm for the way in which BE and EAC are managed in the population (30). Serum glycan profiling studies have shown differential expression of glycan structures between healthy, BE, early dysplastic and EAC patients (31–35). However, diagnostic serum glycoproteins showing differential glycosylation hence differential lectin binding remain to be discovered, making it a suitable disease model for this study.     

Distribution of millipedes along an altitudinal gradient in the south of Lake Teletskoye,Altai Mts,Russia (Diplopoda)

The distribution of millipedes along an altitudinal gradient in the south of Lake Teletskoye, Altai, Russia based on new samples from the Kyga Profile sites, as well as on partly published and freshly revised material (Mikhaljova et al. 2007, 2008, 2014, Nefedieva and Nefediev 2008, Nefediev and Nefedieva 2013, Nefedieva et al. 2014), is established. The millipede diversity is estimated to be at least 15 species and subspecies from 10 genera, 6 families and three orders. The bulk of species diversity is confined both to low- and mid-mountain chern taiga forests and high-mountain shrub tundras, whereas the highest numbers, reaching up to 130 ind./m², is shown in subalpine Pinus sibirica sparse growths. Based on clustering studied localities on species diversity similarity two groups of sites are defined: low-mountain sites and subalpine sparse growths of Pinus sibirica ones.     

Musite,a Tool for Global Prediction of General and Kinase-specific Phosphorylation Sites

Reversible protein phosphorylation is one of the most pervasive post-translational modifications, regulating diverse cellular processes in various organisms. High throughput experimental studies using mass spectrometry have identified many phosphorylation sites, primarily from eukaryotes. However, the vast majority of phosphorylation sites remain undiscovered, even in well studied systems. Because mass spectrometry-based experimental approaches for identifying phosphorylation events are costly, time-consuming, and biased toward abundant proteins and proteotypic peptides, in silico prediction of phosphorylation sites is potentially a useful alternative strategy for whole proteome annotation. Because of various limitations, current phosphorylation site prediction tools were not well designed for comprehensive assessment of proteomes. Here, we present a novel software tool, Musite, specifically designed for large scale predictions of both general and kinase-specific phosphorylation sites. We collected phosphoproteomics data in multiple organisms from several reliable sources and used them to train prediction models by a comprehensive machine-learning approach that integrates local sequence similarities to known phosphorylation sites, protein disorder scores, and amino acid frequencies. Application of Musite on several proteomes yielded tens of thousands of phosphorylation site predictions at a high stringency level. Cross-validation tests show that Musite achieves some improvement over existing tools in predicting general phosphorylation sites, and it is at least comparable with those for predicting kinase-specific phosphorylation sites. In Musite V1.0, we have trained general prediction models for six organisms and kinase-specific prediction models for 13 kinases or kinase families. Although the current pretrained models were not correlated with any particular cellular conditions, Musite provides a unique functionality for training customized prediction models (including condition-specific models) from users'' own data. In addition, with its easily extensible open source application programming interface, Musite is aimed at being an open platform for community-based development of machine learning-based phosphorylation site prediction applications. Musite is available at http://musite.sourceforge.net/.With many genomes being sequenced at an increasingly fast pace, a key and challenging issue is inferring protein function and downstream regulatory networks. As a pervasive regulatory mechanism, reversible protein phosphorylation plays an important role in signaling networks (1). Annotation of phosphorylation and other modification sites in proteomes is a critical first step toward decoding such signaling networks.In recent years, protein phosphorylation data have accumulated rapidly due to large scale mass spectrometry studies of protein phosphorylation in different organisms (2–9) and development of associated web resources (10–18). In particular, there are currently about 100,000 annotated phosphorylation sites in all organisms in UniProt/Swiss-Prot (V57.8). About 27,000 of these sites are from human. Nevertheless, our knowledge of protein phosphorylation is still limited. The majority of proteins are estimated to be phosphorylated at multiple sites (>100,000 sites in the human proteome alone) (19). Furthermore, our understanding of phosphorylation events in signaling networks is even more lacking, largely due to the lag in elucidating kinase-substrate interactions. For example, fewer than 5,000 (5%) of reported phosphorylation sites in UniProt/Swiss-Prot are annotated for their cognate protein kinases.Despite improvements in phosphopeptide enrichment and mass spectrometry analysis, experimental identification of phosphorylation sites in a global manner is still a difficult, expensive, and time-consuming task. In addition, high throughput proteomics techniques have some limitations. Because only proteotypic peptides are observed, mass spectrometry tends to provide fractional sequence coverage for proteins. Detection of low abundance proteins is also problematic. Consequently, a significant portion of phosphorylation sites are missed by current techniques. Moreover, it is even harder to characterize kinase-substrate interactions experimentally. Hence, in silico prediction of phosphorylation events can be highly valuable in many cases. As genome and proteome data in various organisms have been increasing dramatically, comprehensive and accurate prediction of protein phosphorylation sites is becoming more advantageous for proteome annotation and large scale experimental design. For example, in hypothesis-driven experiments, the researchers may want to use prediction tools to focus on putative phosphorylation sites above a high stringency level.More than a dozen phosphorylation site prediction tools have been developed; they can be divided into two categories: tools for general phosphorylation site prediction and tools for kinase-specific phosphorylation site prediction. DISPHOS (20), NetPhos (21), and scan-x (22) fall into the first category. The latter category includes Scansite (23), NetPhosK (24), GPS (25), KinasePhos (26), Predikin (27), CRPhos (28), AutoMotif (29), pkaPS (30), PPSP (31), PhoScan (32), PredPhospho (33), and NetPhorest (34). More information about these tools is given in supplemental Table S1. Although kinase-specific prediction is of interest because of its essential role in constructing signaling networks, general prediction is also important because the majority of phosphorylation sites remain undiscovered, and the kinase-specific predictors may only be able to unveil a small fraction of them.Despite the availability of various phosphorylation site prediction tools, they have limitations when applied to whole proteomes. The most important issue of phosphorylation site prediction is accuracy. Because different training data and techniques were used with these programs, prediction performance varies greatly among them as discussed later. Another notable issue is that most tools were only released as web servers and have restrictions for the data uploaded by users (see supplemental Table S1). This makes large scale predictions a laborious or impossible task. Besides web servers, GPS 2.1 (25) and PhoScan (32) were also released as stand-alone tools, capable of handling large data sets, but both tools only support kinase-specific predictions. NetPhos 2.0 and NetPhosK 1.0 were also released as both web servers and stand-alone applications under Unix/Linux, but prediction performance could be improved as we demonstrate in this study. In Schwartz et al. (22), proteome scale scans on human, mouse, fly, and yeast were performed using motif-x (35) and scan-x, and the prediction results were accessible. However, the tool scan-x has not been publicly released (as of May 26, 2010), and hence “on-the-fly” predictions for user-uploaded sequences are not possible. Another concern regarding the current tools is the stringency control of predictions. User control on the prediction stringency is important, especially for large scale predictions, because typically a user is interested only in predictions above a certain confidence threshold, and different users may have different requirements on the threshold. However, current tools either preset the threshold and do not support stringency adjustment or only support several predefined stringency levels from which a user can choose that may not meet every user''s requirement.To address the limitations of existing tools and to take advantage of the large magnitude of experimentally verified phosphorylation sites, we developed a bioinformatics tool, Musite, specifically designed for large scale prediction of both general and kinase-specific phosphorylation sites. As a stand-alone application, Musite can be easily used to perform large scale phosphorylation site prediction in an automated fashion. We modeled phosphorylation site prediction as an unbalanced binary classification problem and solve it with a comprehensive machine-learning approach. Reliable experimental phosphoproteomics data in multiple organisms were collected from several sources and utilized to train phosphorylation site prediction models by a comprehensive machine-learning procedure termed bootstrap aggregating. Three sets of features (k nearest neighbor (KNN)1 scores, disorder scores, and amino acid frequencies) were extracted from the collected data and combined using support vector machine (SVM) to make predictions. KNN scores capture local sequence similarity around sites phosphorylated by the same kinase or kinase family whether or not the kinase-substrate interactions are known. Disorder scores reflect the higher probability of phosphorylated residues to be in disordered regions, which are segments of proteins that lack a stable tertiary structure. Phosphorylation sites have been shown to be preferentially located within disordered regions (20, 36); this was confirmed on phosphoproteomics data in six organisms by this study.Applications of Musite on several proteomes yielded tens of thousands of putative phosphorylation sites with high stringency. Cross-validation tests and comparisons with other tools show that Musite performs better on general predictions and at least comparably with existing methods on kinase-specific predictions. In Musite V1.0, we have trained general prediction models for six organisms and kinase-specific prediction models for 13 kinases or kinase families. It is noted, however, that using the current pretrained models users cannot correlate prediction results with any particular cellular condition. To do so, users can utilize a unique functionality in Musite for training customized prediction models from their own condition-specific phosphorylation data. In addition, Musite supports continuous stringency adjustment to meet different confidence requirements for users. Taken together, Musite provides a valuable tool for biologists to predict phosphorylation sites up to the whole proteome level. In addition, with its open source, well designed, and easily extensible application programming interface (API), Musite is also beneficial to bioinformaticians as a platform to build their own machine learning-based applications for phosphorylation site prediction.     

Cloning and Characterization of the Glycoside Hydrolases That Remove Xylosyl Groups from 7-β-xylosyl-10-deacetyltaxol and Its Analogues

Paclitaxel, a natural antitumor compound, is produced by yew trees at very low concentrations, causing a worldwide shortage of this important anticancer medicine. These plants also produce significant amounts of 7-β-xylosyl-10-deacetyltaxol, which can be bio-converted into 10-deacetyltaxol for the semi-synthesis of paclitaxel. Some microorganisms can convert 7-β-xylosyl-10-deacetyltaxol into 10-deacetyltaxol, but the bioconversion yield needs to be drastically improved for industrial applications. In addition, the related β-xylosidases of these organisms have not yet been defined. We set out to discover an efficient enzyme for 10-deacetyltaxol production. By combining the de novo sequencing of β-xylosidase isolated from Lentinula edodes with RT-PCR and the rapid amplification of cDNA ends, we cloned two cDNA variants, Lxyl-p1–1 and Lxyl-p1–2, which were previously unknown at the gene and protein levels. Both variants encode a specific bifunctional β-d-xylosidase/β-d-glucosidase with an identical ORF length of 2412 bp (97% identity). The enzymes were characterized, and their 3.6-kb genomic DNAs (G-Lxyl-p1–1, G-Lxyl-p1–2), each harboring 18 introns, were also obtained. Putative substrate binding motifs, the catalytic nucleophile, the catalytic acid/base, and potential N-glycosylation sites of the enzymes were predicted. Kinetic analysis of both enzymes showed kcat/K_m of up to 1.07 s⁻¹mm⁻¹ against 7-β-xylosyl-10-deacetyltaxol. Importantly, at substrate concentrations of up to 10 mg/ml (oversaturated), the engineered yeast could still robustly convert 7-β-xylosyl-10-deacetyltaxol into 10-deacetyltaxol with a conversion rate of over 85% and a highest yield of 8.42 mg/ml within 24 h, which is much higher than those reported previously. Therefore, our discovery might lead to significant progress in the development of new 7-β-xylosyl-10-deacetyltaxol-converting enzymes for more efficient use of 7-β-xylosyltaxanes to semi-synthesize paclitaxel and its analogues. This work also might lead to further studies on how these enzymes act on 7-β-xylosyltaxanes and contribute to the growing database of glycoside hydrolases.The protection and sustainable utilization of natural resources are among the most important and global problems of the 21st century. Paclitaxel (Taxol^®) is mainly isolated from slow-growing yew trees (genus Taxus, family Taxaceae) and is known as a “blockbuster drug ” showing unique active mechanisms (1), with prominent activity against various cancers (including ovarian, breast, lung, head, and neck carcinomas and the AIDS-related Kaposi sarcoma) (2). However, the source of paclitaxel has always been a top concern, because its content in the plant is extremely low, and it is isolated in “large ” amounts (∼0.02%) only from the bark of the tree (3). A 100-year-old tree might yield 3 kg of bark, which provides enough paclitaxel for one 300-mg dose (4). To preserve the Taxus resource and alleviate some of the pressure on the source, several approaches have been employed to prepare paclitaxel or its analog Taxotere, including chemical semi-synthesis from the precursor 10-deacetylbaccatin III (DB),¹ which is readily available from the twigs of yew trees such as Taxus baccata (5, 6); isolation from the twigs of nursery trees including T. chinensis var. mairei and T. media (hybrid); paclitaxel-producing endophytic strain fermentation (7, 8); and Taxus cell and tissue culture (9). The first two approaches might partially relieve this pressure, but they still cannot meet the growing market demand.7-β-xylosyltaxanes are much more abundant and are extracted simultaneously with paclitaxel and DB from various species of yew (10–12), but generally they are dealt with as byproducts. Among these analogues, 7-β-xylosyl-10-deacetyltaxol (XDT) can be obtained with a yield of as much as 0.5% (from dried stem bark) (13). These 7-β-xylosyltaxanes can be hydrolyzed via chemical or biological methods to give the corresponding 7-hydroxyltaxanes, including 10-deacetyltaxol (DT) and DB, for the semi-synthesis of paclitaxel. In contrast to the chemical approach, which utilizes periodate or other oxidizing agents and a substituted hydrazine in the reactions to remove the sugar, the biological approach is an enzymatic process that releases the d-xylose from 7-xylosyltaxanes through the specific β-xylosidase and is therefore considered to be environmentally friendly.β-xylosidases (EC3.2.1.37) belong to glycoside hydrolase (GH) or glycosidase (EC3.2.1.X) families 3, 30, 39, 43, 52, and 54 (14). However, the filamentous fungal β-xylosidases have hitherto been described as belonging only to GH families 3, 43, and 54 (15). Many kinds of β-xylosidases have been purified from different organisms, such as bacteria (16–19), fungi (20–23), and plants (24, 25). Some β-xylosidase genes, such as those from bacteria (18, 19, 26, 27) or from fungi (23, 28–30), have been cloned and characterized. However, none of these enzymes have been reported to be active against 7-β-xylosyltaxanes. In fact, a lot of commercially available xylosidases, xylanases, and other glycosidases do not have any activity specific for removing xylose from 7-β-xylosyltaxanes (31). Some bacterial isolates, such as Moraxella sp. (ATCC 55475) (31, 32), Cellulosimicrobium cellulans XZ-5 (CCTCC No. M207130) (33), and Enterobacter sp. (CGMCC 2487) (34), have been reported to have the ability to convert XDT to DT. But these strains gave low yields of DT (0.23, 0.4, and 0.76 mg/ml, respectively (31–34)), which is probably due to the ubiquitous low enzyme levels in the native organisms. The related β-xylosidases of these organisms have not yet been defined. Our lab discovered that a fungal species, Lentinula edodes, could transform XDT into DT, but a similarly low yield was also observed (supplemental Fig. S1). Thus, cloning and characterization of the specific enzyme from the fungus might lead to a new biocatalytic route of preparation for 7-hylosyltaxanes for the semi-synthesis of paclitaxel or its analogues. Here, we present a strategy in which we combine protein de novo sequencing with RT-PCR and the rapid amplification of cDNA ends (RACE) to mine the targeted β-xylosidase gene from this fungus. Moreover, yeast engineered with such a heterologous gene can robustly convert 7-β-xylosyltaxanes into 7-hydroxyltaxanes.     

Identification and Validation of Specific Markers of Bacillus anthracis Spores by Proteomics and Genomics Approaches

Bacillus anthracis is the causative bacteria of anthrax, an acute and often fatal disease in humans. The infectious agent, the spore, represents a real bioterrorism threat and its specific identification is crucial. However, because of the high genomic relatedness within the Bacillus cereus group, it is still a real challenge to identify B. anthracis spores confidently. Mass spectrometry-based tools represent a powerful approach to the efficient discovery and identification of such protein markers. Here we undertook comparative proteomics analyses of Bacillus anthracis, cereus and thuringiensis spores to identify proteoforms unique to B. anthracis. The marker discovery pipeline developed combined peptide- and protein-centric approaches using liquid chromatography coupled to tandem mass spectrometry experiments using a high resolution/high mass accuracy LTQ-Orbitrap instrument. By combining these data with those from complementary bioinformatics approaches, we were able to highlight a dozen novel proteins consistently observed across all the investigated B. anthracis spores while being absent in B. cereus/thuringiensis spores. To further demonstrate the relevance of these markers and their strict specificity to B. anthracis, the number of strains studied was extended to 55, by including closely related strains such as B. thuringiensis 9727, and above all the B. cereus biovar anthracis CI, CA strains that possess pXO1- and pXO2-like plasmids. Under these conditions, the combination of proteomics and genomics approaches confirms the pertinence of 11 markers. Genes encoding these 11 markers are located on the chromosome, which provides additional targets complementary to the commonly used plasmid-encoded markers. Last but not least, we also report the development of a targeted liquid chromatography coupled to tandem mass spectrometry method involving the selection reaction monitoring mode for the monitoring of the 4 most suitable protein markers. Within a proof-of-concept study, we demonstrate the value of this approach for the further high throughput and specific detection of B. anthracis spores within complex samples.Bacillus anthracis is a highly virulent bacterium, which is the etiologic agent of anthrax, an acute and often lethal disease of animals and humans (1). According to the Centers for Disease Control and Prevention, B. anthracis is classified as a category A agent, the highest rank of potential bioterrorism agents (http://www.bt.cdc.gov/agent/agentlist-category.asp). The infectious agent of anthrax, the spore, was used as a bioterrorism weapon in 2001 in the United States when mailed letters containing B. anthracis spores caused 22 cases of inhalational and/or cutaneous anthrax, five of which were lethal (2). These events have emphasized the need for rapid and accurate detection of B. anthracis spores.Bacillus anthracis is a member of the genus Bacillus, Gram-positive, rod-shaped bacteria characterized by the ability to form endospores under aerobic or facultative anaerobic conditions (3). The genus Bacillus is a widely heterogeneous group encompassing 268 validly described species to date (http://www.bacterio.net/b/bacillus.html, last accessed on August 9^th 2013). B. anthracis is part of the B. cereus group which consists of six distinct species: B. anthracis, B. cereus, B. thuringiensis, B. mycoides, B. pseudomycoides, and B. weihenstephanensis (4, 5). The latter three species are generally regarded as nonpathogenic whereas B. cereus and B. thuringiensis could be opportunistic or pathogenic to mammals or insects (5, 6). B. cereus is a ubiquitous species that lives in soil but is also found in foods of plant and animal origin, such as dairy products (7). Its occurrence has also been linked to food poisoning and it can cause diarrhea and vomiting (6, 8). B. thuringiensis is primarily an insect pathogen, also present in soil, and often used as a biopesticide (9).B. anthracis is highly monomorphic, that is, shows little genetic variation (10), and primarily exists in the environment as a highly stable, dormant spore in the soil (1). Specific identification of B. anthracis is challenging because of its high genetic similarity (sequence similarity >99%) with B. cereus and B. thuringiensis (5, 11). The fact that these closely related species are rather omnipresent in the environment further complicates identification of B. anthracis. The main difference among these three species is the presence in B. anthracis of the two virulence plasmids pXO1 and pXO2 (1), which are responsible for its pathogenicity. pXO1 encodes a tripartite toxin (protective antigen (PA), lethal factor (LF), and edema factor (EF)) which causes edema and death (1), whereas pXO2 encodes a poly-γ-d-glutamate capsule which protects the organism from phagocytosis (1). B. anthracis identification often relies on the detection of the genes encoded by these two plasmids via nucleic acid-based assays (12–14). Nevertheless, the occasionally observed loss of the pXO2 plasmid within environmental species may impair the robustness of detection (1). In addition, in recent years a series of findings has shown that the presence of pXO1 and pXO2 is not a unique feature of B. anthracis. Indeed, Hu et al. have demonstrated that ∼7% of B. cereus/B. thuringiensis species can have a pXO1-like plasmid and ∼1.5% a pXO2-like plasmid (15). This was particularly underlined for some virulent B. cereus strains (i.e. B. cereus strains G9241, B. cereus biovar anthracis strains CA and CI) (16–20).Because of these potential drawbacks, the use of chromosome-encoded genes would be preferable for the specific detection of B. anthracis. Such genes (rpoB, gyrA, gyrB, plcR, BA5345, and BA813) have been reported as potential markers (21–25), but concerns have also been raised about their ability to discriminate B. anthracis efficiently from closely related B. cereus strains (26). Ahmod et al. have recently pointed out, by in silico database analysis, that a specific sequence deletion (indel) occurs in the yeaC gene and exploited it for the specific identification of B. anthracis (27). Nevertheless, a few B. anthracis strains (e.g. B. anthracis A1055) do not have this specific deletion and so may lead to false-negative results (27).In the last few years, protein profiling by MS, essentially based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF MS), has emerged as an alternative (or a complement) to genotypic or phenotypic methods for the fast and efficient identification of microorganisms (28, 29). Such an approach is based on the reproducible acquisition of global bacterial protein fingerprints/patterns. The combination of MS-based protein patterns and chemometric/bioinformatic tools has been demonstrated to efficiently differentiate members of the B. cereus group from other Bacillus species (30). However, the specific discrimination of B. anthracis from the closely related B. cereus and B. thuringiensis remains difficult (30). This study of Lasch and coworkers, performed on vegetative cells, identified a few ribosomal and spore proteins as being responsible for this clustering (30). Closer inspection of the data revealed that B. anthracis identification was essentially based on one particular isoform of the small acid-soluble spore protein B (SASP-B)¹ (30–34), which is exclusively expressed in spores, as the samples were shown to contain residual spores. However, the specificity of SASP-B has recently been questioned as the published genomes of B. cereus biovar anthracis CI and B. thuringiensis BGSC 4CC1 strains have been shown to share the same SASP-B isoform as B. anthracis (35). Altogether these results underline that the quest for specific markers of B. anthracis needs to be pursued.Mass spectrometry also represents a powerful tool for the discovery and identification of protein markers (36, 37). In the case of B. anthracis, this approach has more commonly been used for the comprehensive characterization of given bacterial proteomes. For example, the proteome of vegetative cells with variable plasmid contents has been extensively studied (38–40), as the proteomes of mature spores (41, 42) and of germinating spores (43, 44). Only one recent study, based on a proteo-genomic approach, was initiated to identify protein markers of B. anthracis (45). In this work, potential markers were characterized but using a very limited number of B. cereus group strains (three B. cereus and two B. thuringiensis). Moreover, this study was done on vegetative cells, whereas the spore proteome is drastically different. To our knowledge, no study has characterized and validated relevant protein markers specific to B. anthracis spores, which constitute the dissemination form of B. anthracis and are often targeted by first-line immunodetection methods (46).Here we report comparative proteomics analyses of Bacillus anthracis/cereus/thuringiensis spores, undertaken to identify proteoforms unique to B. anthracis. Preliminary identification was performed on a limited set of Bacillus species both at the peptide (after enzymatic digestion) and protein levels by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) using a high resolution/high mass accuracy LTQ-Orbitrap instrument. The pertinence of 11 markers was further demonstrated using proteomics and genomics approaches on a representative larger set of up to 55 different strains, including the closely related B. cereus biovar anthracis CI, CA, and B. thuringiensis 9727. Lastly, as a proof-of-concept study, we also report for four B. anthracis markers the implementation of a targeted LC-MS/MS method using selected reaction monitoring (SRM), based on the extension of a previous one focused on SASP-B (35). Preliminary results regarding method usefulness for the high throughput and accurate detection of B. anthracis spores in complex samples were also obtained and will be reported herein.