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Purpose Drawing upon the conservation of resources theory, this study aims to investigate whether, how and when salespeople’s substantive attribution of the organization’s corporate social responsibility (CSR) affects value-based selling (VBS). The authors argue that salespeople’s substantive CSR attribution increase value-based selling through two mechanisms (i.e. by lowering emotional exhaustion and increasing empathy), and treatment by customers can increase or decrease the strength of these relationships. Design/methodology/approach B2B salespeople working in various industries in China were recruited through snowball sampling to participate in the study. There were 462 volunteers (57.58% women; aged 30–55; tenure ranging from six months to 15 years) who provided valid self-report questionnaires. Findings Hierarchical multiple regression supported the association between salespeople’s substantive CSR attribution and VBS. The results showed that salespeople’s emotional state (i.e. emotional exhaustion and empathy) mediated the association between substantive CSR attribution and VBS. As expected, salespeople’s experiences of customer incivility weakened the mediating effect of emotional exhaustion; contrary to expectations, customer-initiated interpersonal justice weakened the mediation effect of empathy. Originality/value This study makes a unique contribution to the existing marketing literature by first investigating the role of salespeople’s attribution of CSR motives in facilitating their VBS, which answers the call to identify factors that predict VBS. In addition, to the best of the authors’ knowledge, the authors are the first to test salespeople’s emotions as a mechanism of the link between their CSR attributions and selling behaviors.

Precise and automated analysis of site-specific O-glycosylation on single proteins is crucial for comprehensive characterization of some important glycoproteins, such as tumor biomarkers and recombinant drug proteins. Mass spectrometry has been proven to be a powerful technique for protein sequencing and N-glycosylation analysis. However, challenges remain in developing computational tools for intact O-glycopeptide analysis, which has greatly hindered the development of mass-spectrometry-based O-glycosylation analysis. Herein, an integrated strategy together with a dedicated automated computational tool termed AOGP was developed for intact O-glycopeptide analysis on single proteins. AOGP utilized de novo sequencing for O-glycans and a database search strategy for peptide backbones. The false discovery rate (FDR) of the identification results was controlled and validated by a mixed Gaussian distribution estimation method. AOGP exhibited superior performance in identifying intact O-glycopeptides of the human erythropoietin with a total of 188 O-glycopeptide spectra reported under 1% FDR. AOGP is developed in Python, is fully open-sourced, and is equipped with a user-friendly interface. Such an easy-operating and robust tool would greatly facilitate O-glycosylation analysis on single proteins in tumor biomarker and recombinant drug protein development.

Interpreting large-scale glycoproteomic data for intact glycopeptide identification has been tremendously advanced by software tools. However, software tools for quantitative analysis of intact glycopeptides remain lagging behind, which greatly hinders exploring the differential expression and functions of site-specific glycosylation in organisms. Here, we report pGlycoQuant, a generic software tool for accurate and convenient quantitative intact glycopeptide analysis, supporting both primary and tandem mass spectrometry quantitation for multiple quantitative strategies. pGlycoQuant enables intact glycopeptide quantitation with very low missing values via a deep residual network, thus greatly expanding the quantitative function of several powerful search engines, currently including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. The pGlycoQuant-based site-specific N-glycoproteomic study conducted here quantifies 6435 intact N-glycopeptides in three hepatocellular carcinoma cell lines with different metastatic potentials and, together with in vitro molecular biology experiments, illustrates core fucosylation at site 979 of the L1 cell adhesion molecule (L1CAM) as a potential regulator of HCC metastasis. pGlycoQuant is freely available at https://github.com/expellir-arma/pGlycoQuant/releases/. We have demonstrated pGlycoQuant to be a powerful tool for the quantitative analysis of site-specific glycosylation and the exploration of potential glycosylation-related biomarker candidates, and we expect further applications in glycoproteomic studies.

Precise and automated analysis of site-specific

The precise and large-scale identification of intact glycopeptides is a critical step in glycoproteomics. Owing to the complexity of glycosylation, the current overall throughput, data quality and accessibility of intact glycopeptide identification lack behind those in routine proteomic analyses. Here, we propose a workflow for the precise high-throughput identification of intact N-glycopeptides at the proteome scale using stepped-energy fragmentation and a dedicated search engine. pGlyco 2.0 conducts comprehensive quality control including false discovery rate evaluation at all three levels of matches to glycans, peptides and glycopeptides, improving the current level of accuracy of intact glycopeptide identification. The N-glycoproteome of samples metabolically labeled with 15N/13C were analyzed quantitatively and utilized to validate the glycopeptide identification, which could be used as a novel benchmark pipeline to compare different search engines. Finally, we report a large-scale glycoproteome dataset consisting of 10,009 distinct site-specific N-glycans on 1988 glycosylation sites from 955 glycoproteins in five mouse tissues., Protein glycosylation is a heterogeneous post-translational modification that generates greater proteomic diversity that is difficult to analyze. Here the authors describe pGlyco 2.0, a workflow for the precise one step identification of intact N-glycopeptides at the proteome scale.

Efficient detection of aberrant glycoproteins in serum is particularly important for biomarker discovery. However, direct quantitation of glycoproteins in serum remains technically challenging because of the extraordinary complexity of the serum proteome. In the current work, we proposed a straightforward and highly efficient strategy by using the nonglycopeptides releasing from the specifically enriched glycoproteins for targeted glycoprotein quantification. With this so-called nonglycopeptide-based mass spectrometry (NGP-MS) strategy, a powerful and nondiscriminatory pipeline for hepatocellular carcinoma (HCC) glycoprotein biomarker discovery, verification, and validation has been developed. First, a data set of 234 NGPs was strictly established for multiple-reaction monitoring (MRM) quantification in serum. Second, the NGPs enriched from 20 HCC serum mixtures and 20 normal serum mixtures were labeled with mTRAQ reagents (Δ0 and Δ8, respectively) to find the differentially expressed glycoproteins in HCC. A total of 97 glycoprotein candidates were preliminarily screened and submitted for absolute quantitation with NGP-based stable-isotope-labeled (SID)-MRM in the individual samples of 38 HCC serum and 24 normal controls. Finally, 21 glycoproteins were absolutely quantified with high quality. The diagnostic sensitivity results showed that three glycoproteins, β-2-glycoprotein 1 (APOH), α-1-acid glycoprotein 2 (ORM2), and complement C3 (C3), could be used for the discrimination between HCC patients and healthy people. A novel glycoprotein biomarker panel [APOH, ORM2, C3, and α-fetoprotein (AFP)] has proven to outperform AFP, the known HCC serum biomarker, alone, in this study. We believe that this strategy and the panel of glycoproteins might hold great clinical value for HCC detection in the future.

The emitter clogging is the most common hardware failure of nano-electrospray ionization, to improve the durability and electrospray stability of fused silica emitters, we demonstrate a means of fabricating nano-electrospray emitters with controllable aperture size and gradually-narrowed channel on the tip. We simulated the fluid morphologies in the emitter channels by computational fluid dynamics and found more stable flow on aperture-controllable nano-electrospray emitter. Besides, we found the unstable flow sections of commercial emitters match the actual clogging sections very well, indicating the main cause of emitter clogging is unstable flow. We further tested the emitters by nano-LC-MS based proteome analysis. Compared with the commercial emitter, aperture-controllable nano-electrospray emitters promoted the total ion chromatogram intensity by 25%, the number of identified proteins by 6.58%, and the number of identified peptides by 7.87%. In total, 989 proteins were identified from 1 μg of extracted mouse cardiac proteins. After the optimization by using mouse samples, we analyzed clinical auricular dextral tissues from patients undergoing cardiac surgery and found 16 proteins related to atrial fibrillation. Overall, aperture-controllable nano-electrospray emitter exhibits better sensitivity and reproducibility in the application of nano-LC-MS cardiac proteome analysis.

N-glycosylation is deeply involved in many biological processes, and approximately 50% of mammalian proteins are predicted to be glycosylated. Many large-scale studies have been carried out to reveal the glycosylation status involved in different physiological pathologies across species. However, the lack of a highly specific and high-throughput N-glycosylated enrichment method not only results in extended time requirements but also limits the depth of mapping when handling a large number of samples. In this study, we firstly optimized traditional zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) enrichment and found that using of 70% acetonitrile (ACN), 0.1% trifluoroacetic acid (TFA) as the enrichment buffer, 2800 g as the washing speed and 600 μL as the washing volume achieved the best specificity, which is higher than 75%. On this basis, we developed a multi-parallel enrichment strategy assisted by a filter-coated 96-well plate, which achieved high specificity and high throughput simultaneously. This strategy allowed us to enrich large numbers of fractionated samples from hepatocellular carcinoma (HCC) cell lines in less than 2 h. Its good specificity helped us achieve in-depth mapping of the N-glycoproteome in metastatic HCC cell lines. A total of 5466 N-glycosites from 2383 glycoproteins were identified, among which 1900 N-glycosites were unannotated in UniProt. The in-depth glycoproteome mapping provides insight into the N-glycosylation status in HCC cell lines with differences in metastatic potential and contributes to biomarker discovery.

GREDIL for quantitative N-glycomics largely improves the stability of N-glycan 18O-labeling and greatly decreases the interference of isotopic cluster overlap.

Efficient glycopeptides enrichment prior to mass spectrometry analysis is essential for glycoproteome study. ZIC-HILIC (zwitterionic hydrophilic interaction liquid chromatography) based glycopeptides enrichment approaches have been attracting more attention for several benefits like easy operating, high enrichment specificity and intact glycopeptide retained. In this study, Poly (amidoamine) dendrimer (PAMAM) was adopted for the synthesis of zwitterionically functionalized (ZICF) materials for glycopeptide enrichment. The multiple branched structure and good solubility of ZICF-PAMAM enables a sufficient interaction with glycopeptides. The ZICF-PAMAM combined with the FASP-mode enrichment strategy exhibits more superior performance compared with the existing methods. It has the minimum detectable concentration of femtomolar level and high recovery rate of over 90.01%, and can efficiently enrich glycopeptides from complex biological samples even for merely 0.1 μL human serum. The remarkable glycopeptides enrichment capacity of ZICF-PAMAM highlights the potential application in in-depth glycoproteome research, which may open up new opportunities for the development of glycoproteomics.