Translocation of soil organic matter following reservoir impoundment in boreal systems: Implications for in situ productivity, Limnol

To evaluate the effect of reservoir flooding on carbon cycling over time, we studied sedimentary environments in three natural lakes that existed prior to impoundment and that have been incorporated in the larger lentic system of a 70-yr-old boreal reservoir in Quebec. Elemental and biomarker analyses were determined in all core intervals and three soil profiles to characterize source inputs of organic matter to sediments. Following impoundment, a twoto threefold increase in lignin concentrations (5 to 10–25 mg [10 g dry wt] �1) associated with similar decreases in selected terrigenous biomarker ratios (cinnamyl phenols: vanillyl phenols, C: V, 1.0–2.0 to 0.2–0.5; 3,5-dihydroxybenzoic acid: vanillyl phenols, 3,5-Bd: V, 0.2–0.5 to 0.1–0.2; and p-hydroxyl phenols: sum of vanillyl and syringyl phenols, P: [V�S], 0.3–0.7 to 0.1–0.4) illustrate the effect of soil erosion and subsequent translocation of surface soil organic matter (SOM) to sedimentary deposits. Using a mixing model based on mass-normalized biomarker yields in identified surface and mineral soil end-members, we show that although the proportion of mineral-derived SOM predominates in the receiving sedimentary systems during preflooding conditions (97–99 % of allochthonous inputs), translocated surface SOM increased in postflooding sediments to comprise up to 5–30 % of the total allochthonous organic matter inputs. Using a similar quantitative mixing model based on elemental C and N contents in the autochthonous and the mixed allochthonous end-members, we estimated that concomitant to the redistributio

Spectrum-to-Spectrum Searching Using a Proteome-wide Spectral Library*

The unambiguous assignment of tandem mass spectra (MS/MS) to peptide sequences remains a key unsolved problem in proteomics. Spectral library search strategies have emerged as a promising alternative for peptide identification, in which MS/MS spectra are directly compared against a reference library of confidently assigned spectra. Two problems relate to library size. First, reference spectral libraries are limited to rediscovery of previously identified peptides and are not applicable to new peptides, because of their incomplete coverage of the human proteome. Second, problems arise when searching a spectral library the size of the entire human proteome. We observed that traditional dot product scoring methods do not scale well with spectral library size, showing reduction in sensitivity when library size is increased. We show that this problem can be addressed by optimizing scoring metrics for spectrum-to-spectrum searches with large spectral libraries. MS/MS spectra for the 1.3 million predicted tryptic peptides in the human proteome are simulated using a kinetic fragmentation model (MassAnalyzer version2.1) to create a proteome-wide simulated spectral library. Searches of the simulated library increase MS/MS assignments by 24% compared with Mascot, when using probabilistic and rank based scoring methods. The proteome-wide coverage of the simulated library leads to 11% increase in unique peptide assignments, compared with parallel searches of a reference spectral library. Further improvement is attained when reference spectra and simulated spectra are combined into a hybrid spectral library, yielding 52% increased MS/MS assignments compared with Mascot searches. Our study demonstrates the advantages of using probabilistic and rank based scores to improve performance of spectrum-to-spectrum search strategies

Identification of a Family of Fatty-Acid-Speciated Sonic Hedgehog Proteins, Whose Members Display Differential Biological Properties

Hedgehog (HH) proteins are proteolytically processed into a biologically active form that is covalently modified by cholesterol and palmitate. However, most studies of HH biogenesis have characterized protein from cells in which HH is overexpressed. We purified Sonic Hedgehog (SHH) from cells expressing physiologically relevant levels and showed that it was more potent than SHH isolated from overexpressing cells. Furthermore, the SHH in our preparations was modified with a diverse spectrum of fatty acids on its amino termini, and this spectrum of fatty acids varied dramatically depending on the growth conditions of the cells. The fatty acid composition of SHH affected its trafficking to lipid rafts as well as its potency. Our results suggest that HH proteins exist as a family of diverse lipid-speciated proteins that might be altered in different physiological and pathological contexts in order to regulate distinct properties of HH proteins

IsoformResolver: A Peptide-Centric Algorithm for Protein Inference

When analyzing proteins in complex samples using tandem mass spectrometry of peptides generated by proteolysis, the inference of proteins can be ambiguous, even with well-validated peptides. Unresolved questions include whether to show all possible proteins vs a minimal list, what to do when proteins are inferred ambiguously, and how to quantify peptides that bridge multiple proteins, each with distinguishing evidence. Here we describe IsoformResolver, a peptide-centric protein inference algorithm that clusters proteins in two ways, one based on peptides experimentally identified from MS/MS spectra, and the other based on peptides derived from an <i>in silico</i> digest of the protein database. MS/MS-derived protein groups report minimal list proteins in the context of all possible proteins, without redundantly listing peptides. <i>In silico</i>-derived protein groups pull together functionally related proteins, providing stable identifiers. The peptide-centric grouping strategy used by IsoformResolver allows proteins to be displayed together when they share peptides in common, providing a comprehensive yet concise way to organize protein profiles. It also summarizes information on spectral counts and is especially useful for comparing results from multiple LC–MS/MS experiments. Finally, we examine the relatedness of proteins within IsoformResolver groups and compare its performance to other protein inference software

N- and O‑Glycosylation Analysis of Etanercept Using Liquid Chromatography and Quadrupole Time-of-Flight Mass Spectrometry Equipped with Electron-Transfer Dissociation Functionality

Etanercept is a highly glycosylated therapeutic Fc-fusion protein that contains multiple N- and O-glycosylation sites. An in-depth characterization of the glycosylation of etanercept was carried out using liquid chromatography/mass spectrometry (LC/MS) methods in a systematic approach in which we analyzed the N- and O-linked glycans and located the occupied O-glycosylation sites. Etanercept was first treated with peptide <i>N</i>-glycosidase F to release the <i>N</i>-glycans. The <i>N</i>-glycan pool was labeled with a 2-aminobenzamide (2-AB) fluorescence tag and separated using ultraperformance liquid chromatography–hydrophilic interaction liquid chromatography (UPLC-HILIC). Preliminary structures were assigned using Glycobase. These assignments, which included monosaccharide sequence and linkage information, were confirmed by exoglycosidase array digestions of aliquots of the <i>N</i>-glycan pool. The removal of the <i>N</i>-glycans from etanercept facilitated the selective characterization of <i>O</i>-glycopeptides and enabled the <i>O</i>-glycans to be identified. These were predominantly of the core 1 subtype (HexHexNAc O-structure) attached to Ser/Thr residues. α2→3,6,8,9 sialidase was used to remove the sialic acid residues on the <i>O</i>-glycans allowing the use of an automated LC/MS^E protocol to identify the <i>O</i>-glycopeptides. Electron-transfer dissociation (ETD) was then used to pinpoint the 12 occupied O-glycosylation sites. The determination of <i>N</i>- and <i>O</i>-glycans and O-glycosylation sites in etanercept provides a basis for future studies addressing the biological importance of specific protein glycosylations in the production of safe and efficacious biotherapeutics

A Simulated MS/MS Library for Spectrum-to-spectrum Searching in Large Scale Identification of Proteins*S⃞

Identifying peptides from mass spectrometric fragmentation data (MS/MS spectra) using search strategies that map protein sequences to spectra is computationally expensive. An alternative strategy uses direct spectrum-to-spectrum matching against a reference library of previously observed MS/MS that has the advantage of evaluating matches using fragment ion intensities and other ion types than the simple set normally used. However, this approach is limited by the small sizes of the available peptide MS/MS libraries and the inability to evaluate the rate of false assignments. In this study, we observed good performance of simulated spectra generated by the kinetic model implemented in MassAnalyzer (Zhang, Z. (2004) Prediction of low-energy collision-induced dissociation spectra of peptides. Anal. Chem. 76, 3908–3922; Zhang, Z. (2005) Prediction of low-energy collision-induced dissociation spectra of peptides with three or more charges. Anal. Chem. 77, 6364–6373) as a substitute for the reference libraries used by the spectrum-to-spectrum search programs X!Hunter and BiblioSpec and similar results in comparison with the spectrum-to-sequence program Mascot. We also demonstrate the use of simulated spectra for searching against decoy sequences to estimate false discovery rates. Although we found lower score discrimination with spectrum-to-spectrum searches than with Mascot, particularly for higher charge forms, comparable peptide assignments with low false discovery rate were achieved by examining consensus between X!Hunter and Mascot, filtering results by mass accuracy, and ignoring score thresholds. Protein identification results are comparable to those achieved when evaluating consensus between Sequest and Mascot. Run times with large scale data sets using X!Hunter with the simulated spectral library are 7 times faster than Mascot and 80 times faster than Sequest with the human International Protein Index (IPI) database. We conclude that simulated spectral libraries greatly expand the search space available for spectrum-to-spectrum searching while enabling principled analyses and that the approach can be used in consensus strategies for large scale studies while reducing search times

Late Outcomes of Transcatheter Aortic Valve Replacement in High-Risk Patients The FRANCE-2 Registry

International audienceBACKGROUND Transcatheter aortic valve replacement (TAVR) has revolutionized management of high-risk patients with severe aortic stenosis. However, survival and the incidence of severe complications have been assessed in relatively small populations and/or with limited follow-up. OBJECTIVES This report details late clinical outcome and its determinants in the FRANCE-2 (FRench Aortic National CoreValve and Edwards) registry. METHODS The FRANCE-2 registry prospectively included all TAVRs performed in France. Follow-up was scheduled at 30 days, at 6 months, and annually from 1 to 5 years. Standardized VARC (Valve Academic Research Consortium) outcome definitions were used. RESULTS A total of 4,201 patients were enrolled between January 2010 and January 2012 in 34 centers. Approaches were transarterial (transfemoral 73%, transapical 18%, subclavian 6%, and transaortic or transcarotid 3%) or, in 18% of patients, transapical. Median follow-up was 3.8 years. Vital status was available for 97.2% of patients at 3 years. The 3-year all-cause mortality was 42.0% and cardiovascular mortality was 17.5%. In a multivariate model, predictors of 3-year all-cause mortality were male sex (p = 2 of 4 (p < 0.001). Severe events according to VARC criteria occurred mainly during the first month and subsequently in < 2% of patients/year. Mean gradient, valve area, and residual aortic regurgitation were stable during follow-up. CONCLUSIONS The FRANCE-2 registry represents the largest database available on late results of TAVR. Late mortality is largely related to noncardiac causes. Incidence rates of severe events are low after the first month. Valve performance remains stable over time. (J Am Coll Cardiol 2016; 68: 1637-47) (C) 2016 by the American College of Cardiology Foundation