A highly efficient method for extracting peptides from a single mouse hypothalamus

Detection of dynorphin 1-17 biotransformation fragments in human nasal polyps by UPLC-QTOF-MS

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a persistent inflammation of the sinonasal mucosa. CRSwNP treatments are associated with inconsistent efficacy and recurrence of symptoms. Dynorphin 1-17 (DYN 1-17) and its fragments have been shown to modulate the immune response in various inflammatory conditions. This study aimed to investigate the effect of different pH and degrees of inflammation on DYN 1-17 metabolism in human CRSwNP tissues. DYN 1-17 was incubated with grade 3 and grade 4 inflamed tissues of CRSwNP patients at pH 5.5 and pH 7.4 over a range of incubation periods. The resulting fragments were identified using an ultra-performance liquid chromatography (UPLC) system coupled to quadrupole-time of flight (QTOF) mass spectrometry based on their accurate mass. The rate of DYN 1-17 fragmentation was slower at pH 5.5 in comparison to pH 7.4. The extent and rate of metabolism of DYN 1-17 were much lower in grade 3 inflamed tissue (31-32 fragments) than in grade 4 (34-41 fragments). N-Terminal fragments (DYN 1-15, 1-11, 1-10, and 1-6) were metabolized slower at pH 5.5 as compared to pH 7.4. DYN 1-12, 1-8, 2-10, 4-10, 5-10, and 8-14 were only observed under the inflammatory pH while DYN 5-17 and 6-17 were only identified upon incubation with grade 4 CRSwNP tissues. DYN 1-17 metabolism was significantly affected by the pH level and the severity of the inflammation of CRSwNP tissues, indicating the potential roles of DYN 1-17 and its fragments in modulating the inflammatory response and their avenue as therapeutics in future studies.

Characterization of genotype IV hepatitis E virus-like particles expressed in E.coli

HEV (Hepatitis E virus) is an infectious disease transmitted between humans and animals, which poses a severe threat to the biological safety and property throughout the world. The disease is especially severe in patients with potential liver cirrhosis and women during pregnancy. There is no specific and thorough HEV treatment at present. The development of hepatitis E virus vaccine is vital to the prevention of viral hepatitis worldwide. Since HEV cannot grow adequately in vitro, vaccine developed by devitalized virus particles does not work. Exploration of HEV-like structures is essential for the development of functional vaccines against HEV infection. ORF2 encodes the structural proteins of HEV, some of which can automatically assemble into virus-like particles (VLP) in this experiment, the recombinant capsid protein p27 was expressed in E. coli and the VLP formed by p27 was used to immunize mice. The results showed that the VLP formed by recombinant P27 had similar particle size to that of HEV; the immune dose produced by p27 was positively correlated with the immune effect. Compared with other genetic engineering subunit vaccines, P27 protein has a better application prospect.

Proteomic-driven identification of short open reading frame-encoded peptides

Accumulating evidence has shown that a large number of short open reading frames (sORFs) also have the ability to encode proteins. The discovery of sORFs opens up a new research area, leading to the identification and functional study of sORF encoded peptides (SEPs) at the omics level. Besides bioinformatics prediction and ribosomal profiling, mass spectrometry (MS) has become a significant tool as it directly detects the sequence of SEPs. Though MS-based proteomics methods have proved to be effective for qualitative and quantitative analysis of SEPs, the detection of SEPs is still a great challenge due to their low abundance and short sequence. To illustrate the progress in method development, we described and discussed the main steps of large-scale proteomics identification of SEPs, including SEP extraction and enrichment, MS detection, data processing and quality control, quantification, and function prediction and validation methods. This article is protected by copyright. All rights reserved.

A novel peptide identified from visceral ganglia induces oocyte maturation, spermatozoa active motility, and spawning in the pen shell Atrinapectinata

The identification of novel peptides that regulate reproduction is essential for studying reproductive physiology in bivalves. Therefore, we aimed to identify peptides that affect the reproductive physiology of bivalves. We identified an oocyte maturation-, sperm motility-, and spawning-inducing peptide from the visceral ganglia of the pen shell, Atrina pectinata. The peptide consisted of 26 amino acid residues (GFDSINFPGTIDGFKDYSSNKKERLL). This peptide induced oocyte maturation and sperm motility activation at less than 1 nM upon the treatment of gonad fragments and induced spawning at 1 nmol when injected into mature individuals. Mature eggs and sperms artificially spawned by peptide administration were fertilized, and we confirmed that the development proceeded normally to veliger (D-shape) larvae. These results indicated that GFDSINFPGTIDGFKDYSSNKKERLL stimulated the gonads of pen shells and induced oocyte maturation, sperm motility activation, and spawning.

LC-MS peak assignment based on unanimous selection by six machine learning algorithms

Recent mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance results in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm.