Relative abundance (%) of the glycans identified at Asn107 of human α1-antitrypsin ^a.

a<p>Approximate abundance was estimated using MS signal intensities from a single analysis.</p

MS and MS/MS spectra of deglycosylated peptides and desialylated glycans of human transferrin.

<p>(A) Mass spectrum of deglycosylated peptides of human transferrin, m/z 1477.681, 2516.072 and 3530.644 were identified as deglycosylated peptides. (B) MS/MS spectrum of m/z 2516 indicated by arrow in Figure 3A. (C) Mass spectrum of desialylated glycans of human transferrin. The MS signals were [M+Na]⁺ ions in average values, and corresponding structures were also shown.</p

MS and MS/MS spectra of glycopeptides from 10 pmol gel-separated human transferrin.

<p>(A) Mass spectrum of the glycopeptides of human transferrin. (B) The enlarged view of Figure 2A, which shows low-abundance glycopeptides more clearly. (C) Mass spectrum of the parent ion of m/z 3684.6 obtained in TOF/TOF mode. The difference between 3682.3, 3391.6 and 3100.5 is about 291. (D) MS/MS spectrum from the precursor indicated by arrow in Figure 2C. The mass of [M_pep+H]⁺ was 1476.7. Glycan structures were deduced based on the difference between adjacent signals and its biosynthesis process.</p

2-DE image of 10 µL human serum and mass spectra of glycopeptides of 2-DE separated α1-antitrypsin.

<p>(A) 2-DE image of 10 µL human serum. Two largest spots of α1-antitrypsin were chosen for glycosylation analysis and named as A1PI-1 and A1PI-2. (B)–(C): Mass spectra of the glycopeptides of A1PI-1 and A1PI-2 in the mass range of 5500-7000 Dalton. Data were shown in average value. Compared with A1PI-1, the relative abundance of diantennary N-glycans indicated by arrows in A1PI-2 increased significantly, while the triantennary N-glycans decreased significantly.</p

The mass of potential glycopeptides of human transferrin calculated from that of deglycosylated peptides and desialylated glycans.

<p>M_PG: Mass of potential glycopeptides.</p><p>Note: The measured masses of the glycopeptides of human transferrin are 3393.0, 3684.6, 4049.4, 4142.9, 4432.1, 4576.6, 4724.0, 4871.2, 5088.7, 5234.1, 5381.8, 5525.9, 5738.8 and 5884.1. The values in bold are identified glycopeptides.</p

Additional file 1 of Similar proteome expression profiles of the aggregated lymphoid nodules area and Peyer’s patches in Bactrian camel

Supplementary Material

Correction of Errors in Tandem Mass Spectrum Extraction Enhances Phosphopeptide Identification

The tandem mass spectrum extraction of phosphopeptides is more difficult and error-prone than that of unmodified peptides due to their lower abundance, lower ionization efficiency, the cofragmentation with other high-abundance peptides, and the use of MS³ on MS² fragments with neutral losses. However, there are still no established methods to evaluate its correctness. Here we propose to identify and correct these errors via the combinatorial use of multiple spectrum extraction tools. We evaluated five free and two commercial extraction tools using Mascot and phosphoproteomics raw data from LTQ FT Ultra, in which RawXtract 1.9.9.2 identified the highest number of unique phosphopeptides (peptide expectation value <0.05). Surprisingly, ProteoWizzard (v. 3.0.3476) extracted wrong precursor mass for most MS³ spectra. Comparison of the top three free extraction tools showed that only 54% of the identified spectra were identified consistently from all three tools, indicating that some errors might happen during spectrum extraction. Manual check of 258 spectra not identified from all three tools revealed 405 errors of spectrum extraction with 7.4% in selecting wrong precursor charge, 50.6% in selecting wrong precursor mass, and 42.1% in exporting MS/MS fragments. We then corrected the errors by selecting the best extracted MGF file for each spectrum among the three tools for another database search. With the errors corrected, it results in the 22.4 and 12.2% increase in spectrum matches and unique peptide identification, respectively, compared with the best single method. Correction of errors in spectrum extraction improves both the sensitivity and confidence of phosphopeptide identification. Data analysis on nonphosphopeptide spectra indicates that this strategy applies to unmodified peptides as well. The identification of errors in spectrum extraction will promote the improvement of spectrum extraction tools in future

Data_Sheet_1_Comparing the metabolic pathways of different clinical phases of bipolar disorder through metabolomics studies.docx

This study identified the metabolic biomarkers for different clinical phases of bipolar disorder (BD) through metabolomics. BD patients were divided into three groups: patients with BD and depressive episodes (BE, n = 59), patients with BD and mania/hypomania episodes (BH, n = 16), patients with BD and mixed episodes (BM, n = 10), and healthy controls (HC, n = 10). Serum from participants was collected for metabolomic sequencing, biomarkers from each group were screened separately by partial least squares analysis, and metabolic pathways connected to the biomarkers were identified. Compared with the controls, 3-D-hydroxyacetic acid and N-acetyl-glycoprotein showed significant differences in the BE, BH, and BM groups. This study suggests that different clinical types of BD share the same metabolic pathways, such as pyruvate, glycolysis/gluconeogenesis, and ketone body metabolisms. In particular, abnormal glycine, serine, and threonine metabolism was specific to BM; β-glucose, glycerol, lipids, lactate, and acetoacetate metabolites were specific to depressive episodes; the guanidine acetic acid metabolites specific to BH; and the acetic and ascorbic acids were metabolites specific to manic and BM. We screened potential biomarkers for different clinical phases of BD, which aids in BD typing and provides a theoretical basis for exploring the molecular mechanisms of BD.</p

Correction of Errors in Tandem Mass Spectrum Extraction Enhances Phosphopeptide Identification

The tandem mass spectrum extraction of phosphopeptides is more difficult and error-prone than that of unmodified peptides due to their lower abundance, lower ionization efficiency, the cofragmentation with other high-abundance peptides, and the use of MS³ on MS² fragments with neutral losses. However, there are still no established methods to evaluate its correctness. Here we propose to identify and correct these errors via the combinatorial use of multiple spectrum extraction tools. We evaluated five free and two commercial extraction tools using Mascot and phosphoproteomics raw data from LTQ FT Ultra, in which RawXtract 1.9.9.2 identified the highest number of unique phosphopeptides (peptide expectation value <0.05). Surprisingly, ProteoWizzard (v. 3.0.3476) extracted wrong precursor mass for most MS³ spectra. Comparison of the top three free extraction tools showed that only 54% of the identified spectra were identified consistently from all three tools, indicating that some errors might happen during spectrum extraction. Manual check of 258 spectra not identified from all three tools revealed 405 errors of spectrum extraction with 7.4% in selecting wrong precursor charge, 50.6% in selecting wrong precursor mass, and 42.1% in exporting MS/MS fragments. We then corrected the errors by selecting the best extracted MGF file for each spectrum among the three tools for another database search. With the errors corrected, it results in the 22.4 and 12.2% increase in spectrum matches and unique peptide identification, respectively, compared with the best single method. Correction of errors in spectrum extraction improves both the sensitivity and confidence of phosphopeptide identification. Data analysis on nonphosphopeptide spectra indicates that this strategy applies to unmodified peptides as well. The identification of errors in spectrum extraction will promote the improvement of spectrum extraction tools in future

Enhancement of Arsenic Adsorption during Mineral Transformation from Siderite to Goethite: Mechanism and Application

Synthesized siderite was used to remove As­(III) and As­(V) from water solutions under anoxic conditions and oxic conditions. Results showed that As adsorption on synthetic siderite under anoxic conditions was around 10 mg/g calculated with Langmuir isotherm. However, the calculated As adsorption on synthetic siderite under oxic conditions ranged between 115 and 121 mg/g, which was around 11 times higher than that under anoxic conditions. It was found that 75% siderite was transformed into goethite during oxic adsorption. However, synthetic goethite had lower As adsorption capacity than siderite under oxic conditions, although its adsorption capacity was a little higher than siderite under anoxic conditions. It suggested that the coexistence of goethite and siderite bimineral during mineral transformation probably contributed to the robust adsorption capacity of siderite under oxic conditions. Results of extended X-ray absorption fine structure (EXAF) spectroscopy indicated both As­(III) and As­(V) formed inner-sphere complexes on the surface of As-treated solid regardless of substrates, including the bidentate binuclear corner-sharing (²C) complexes and the monodentate mononuclear corner-sharing (¹V) complexes. Monodenate (¹V) and bidentate (²C) complexes would be related to high As adsorption capacity of siderite under oxic conditions. It showed that more Fe atoms were coordinated with As atom in the monodentate complexes and the bidentate complexes of As­(V)/As­(III)-treated siderite under oxic conditions, in comparison with As­(V)/As­(III)-treated siderite under anoxic conditions and As­(V)/As­(III)-treated goethite. Calcinations of natural siderite resulting in the coexistence of goethite and siderite greatly increased As adsorption on the solid, which confirmed that the coexistence of bimineral during mineral transformation from siderite to goethite greatly enhanced As adsorption capacity of siderite adsorbent. The observation can be applied for modification of natural siderite for As removal from high As waters