Separation and identification of mouse brain tissue microproteins using top‐down method with high resolution nanocapillary liquid chromatography mass spectrometry

Microproteins and endogenous peptides in the brain contain important substances that have critical roles in diverse biological processes, contributing to signal transduction and intercellular signaling. However, variability in their physical or chemical characteristics, such as molecule size, hydrophobicity, and charge states, complicate the simultaneous analysis of these compounds, although this would be highly beneficial for the field of neuroscience research. Here, we present a top‐down analytical method for simultaneous analysis of microproteins and endogenous peptides using high‐resolution nanocapillary LC‐MS/MS. This method is detergent‐free and digestion‐free, which allows for extracting and preserving intact microproteins and peptides for direct LC‐MS analysis. Both higher energy collision dissociation and electron‐transfer dissociation fragmentations were used in the LC‐MS analysis to increase the identification rate, and bioinformatics tools ProteinGoggle and PEAKS Studio software were utilized for database search. In total, we identified 471 microproteins containing 736 proteoforms, including brain‐derived neurotrophic factor and a number of fibroblast growth factors. In addition, we identified 599 peptides containing 151 known or potential neuropeptides such as somatostatin‐28 and neuropeptide Y. Our approach bridges the gap for the characterization of brain microproteins and peptides, which permits quantification of a diversity of signaling molecules for biomarker discovery or therapy diagnosis in the future.     

Defining parameters for homology-tolerant database searching.

De novo interpretation of tandem mass spectrometry (MS/MS) spectra provides sequences for searching protein databases when limited sequence information is present in the database. Our objective was to define a strategy for this type of homology-tolerant database search. Homology searches, using MS-Homology software, were conducted with 20, 10, or 5 of the most abundant peptides from 9 proteins, based either on precursor trigger intensity or on total ion current, and allowing for 50%, 30%, or 10% mismatch in the search. Protein scores were corrected by subtracting a threshold score that was calculated from random peptides. The highest (p < .01) corrected protein scores (i.e., above the threshold) were obtained by submitting 20 peptides and allowing 30% mismatch. Using these criteria, protein identification based on ion mass searching using MS/MS data (i.e., Mascot) was compared with that obtained using homology search. The highest-ranking protein was the same using Mascot, homology search using the 20 most intense peptides, or homology search using all peptides, for 63.4% of 112 spots from two-dimensional polyacrylamide gel electrophoresis gels. For these proteins, the percent coverage was greatest using Mascot compared with the use of all or just the 20 most intense peptides in a homology search (25.1%, 18.3%, and 10.6%, respectively). Finally, 35% of de novo sequences completely matched the corresponding known amino acid sequence of the matching peptide. This percentage increased when the search was limited to the 20 most intense peptides (44.0%). After identifying the protein using MS-Homology, a peptide mass search may increase the percent coverage of the protein identified.     

树鼩神经肽Y的分子克隆及其灵长类类似物的同源性比较

树鼩由于与灵长类动物有较密切的亲缘关系和其个体小,以及繁殖周期短等特性而倍受关注,尤其是作为医用实验动物的研究,近年来已受到越来越多的重视,但树鼩的分类地位还一直有所争论。该研究从树鼩脑cDNA文库中克隆得到编码树鼩神经肽Y(neuropeptide Y,NPY)前体序列,序列比对发现该序列与灵长类NPY序列同源性高达96.9%。将该序列与GenBank数据库中其他物种的NPY序列构建系统进化树,发现树鼩与灵长类处于同一分支。该研究结果揭示了树鼩与灵长类较近的亲缘关系。     

Extensive characterization of peptides from Panax ginseng C. A. Meyer using mass spectrometric approach

Panax ginseng is an important herb that has clear effects on the treatment of diverse diseases. Until now, the natural peptide constitution of this herb remains unclear. Here, we conduct an extensive characterization of Ginseng peptidome using MS‐based data mining and sequencing. The screen on the charge states of precursor ions indicated that Ginseng is a peptide‐rich herb in comparison of a number of commonly used herbs. The Ginseng peptides were then extracted and submitted to nano‐LC‐MS/MS analysis using different fragmentation modes, including CID, high‐energy collisional dissociation, and electron transfer dissociation. Further database search and de novo sequencing allowed the identification of total 308 peptides, some of which might have important biological activities. This study illustrates the abundance and sequences of endogenous Ginseng peptides, thus providing the information of more candidates for the screening of active compounds for future biological research and drug discovery studies.     

High identification rates of endogenous neuropeptides from mouse brain

Mass spectrometry-based neuropeptidomics is one of the most powerful approaches for identification of endogenous neuropeptides in the brain. Until now, however, the identification rate of neuropeptides in neuropeptidomics is relatively low and this severely restricts insights into their biological function. In the present study, we developed a high accuracy mass spectrometry-based approach to enhance the identification rates of neuropeptides from brain tissue. Our integrated approach used mixing on column for loading aqueous and organic extracts to reduce the loss of peptides during sample treatment and used charge state-directed tandem mass spectrometry to increase the number of peptides subjected to high mass accuracy fragmentation. This approach allowed 206 peptides on average to be identified from a single mouse brain sample that was prepared using 15 μL of solutions per 1 mg of tissue. In total, we identified more than 500 endogenous peptides from mouse hypothalamus and whole brain samples. Our identification rate is about two to four times higher compared to previously reported studies conducted on mice or other species. The hydrophobic peptides, such as neuropeptide Y and galanin, could be presented and detected with hydrophilic peptides in the same LC-MS run, allowing a high coverage of peptide characterization over an organism. This will advance our understanding of the roles of diverse peptides and their links in the brain functions.     

昆明城郊中国树鼩群体线粒体DNA遗传多样性

由于树鼩是灵长类动物的近亲,且具有体型小、繁殖周期短、饲养管理成本低等优点,长期以来被认为有望替代灵长类动物用于人类疾病的动物模型研究.然而,目前对树鼩的群体遗传结构还知之甚少,这极大地限制了其在疾病动物模型研究的应用,也是其品系资源创制的瓶颈.本研究通过分析80只采自于云南省昆明周边地区的野生树鼩(Tupaia belangeri chinensis)线粒体DNA(mtDNA)多态性,结合国外报道的2个树鼩(Tupaia belangeri)序列比较后发现,在604 bp的mtDNA控制区片段中兵检测到29个核苷酸替代变异,这些变异共界定了13种单倍型,表现较高的群体遗传多样度.另外,昆明地区的树鼩与国外报道的2个树鼩间存在较大的遗传分化,mtDNA控制区单倍型之间的核苷酸替换数大于18个,远高于昆明地区树鼢群体内部不同单倍型之间的差异.选择含有代表性的mtDNA控制区单倍型的17个昆明地区树鼩个体进一步测定了细胞色素b基因片段(1134 bp),结合前人报道的数据分析,结果进一步支持mtDNA控制区数据反映的遗传格局及揭示的昆明地区树询与国外报道树鼩之间的明显差异.本研究结果提示,昆明地区树鼩与国外树鼩之间存在较大遗传差异,在将树鼩用于人类疾病动物模型研究中要注意这些遗传差别.昆明城郊的树鼩群体具有较高的遗传多样度,在开展近交系建立等工作时须考虑选取群体内部具有代表性的mtDNA世系.

Abstract：

Due to their special phylogenetic position in the Euarchontoglires and close affinity to primates, tree shrews have been proposed as an alternative experimental animal to primates in biomedical research. However, the population genetic structure of tree shrews has largely remained unknown and this has hindered the development of tree shrew breeding and selection. Here we sampled 80 Chinese tree shrews (Tupaia belangeri chinensis) in Kunming, China, and analyzed partial mtDNA control region sequence variation. Based on our samples and two published sequences from northern tree shrews (T. belangeri), we identified 29 substitutions in the mtDNA control region fragment (～ 604 bp)across 82 individuals and defined 13 hapiotypes. Seventeen samples were selected for sequencing of the cytochrome b (Cyt b; 1134 bp) gene based on control region sequence variation and were analyzed in combination with 34 published sequences to solidify the phylogenetic pattern obtained from control region data. Overall, tree shrews from Kunming have high genetic diversity and present a remarkable long genetic distance to the two reported northern tree shrews outside China. Our results provide some caution when using tree shrews to establish animal models because of this apparent genetic difference. In addition, the high genetic diversity of Chinese tree shrews inhabiting Kunming suggests that systematic genetic investigations should be conducted before establishing an inbred strain for medical and biological research.     

The spectral networks paradigm in high throughput mass spectrometry

High-throughput proteomics is made possible by a combination of modern mass spectrometry instruments capable of generating many millions of tandem mass (MS(2)) spectra on a daily basis and the increasingly sophisticated associated software for their automated identification. Despite the growing accumulation of collections of identified spectra and the regular generation of MS(2) data from related peptides, the mainstream approach for peptide identification is still the nearly two decades old approach of matching one MS(2) spectrum at a time against a database of protein sequences. Moreover, database search tools overwhelmingly continue to require that users guess in advance a small set of 4-6 post-translational modifications that may be present in their data in order to avoid incurring substantial false positive and negative rates. The spectral networks paradigm for analysis of MS(2) spectra differs from the mainstream database search paradigm in three fundamental ways. First, spectral networks are based on matching spectra against other spectra instead of against protein sequences. Second, spectral networks find spectra from related peptides even before considering their possible identifications. Third, spectral networks determine consensus identifications from sets of spectra from related peptides instead of separately attempting to identify one spectrum at a time. Even though spectral networks algorithms are still in their infancy, they have already delivered the longest and most accurate de novo sequences to date, revealed a new route for the discovery of unexpected post-translational modifications and highly-modified peptides, enabled automated sequencing of cyclic non-ribosomal peptides with unknown amino acids and are now defining a novel approach for mapping the entire molecular output of biological systems that is suitable for analysis with tandem mass spectrometry. Here we review the current state of spectral networks algorithms and discuss possible future directions for automated interpretation of spectra from any class of molecules.     

Mass spectrometry-based proteomics combined with bioinformatic tools for bacterial classification

Timely classification and identification of bacteria is of vital importance in many areas of public health. We present a mass spectrometry (MS)-based proteomics approach for bacterial classification. In this method, a bacterial proteome database is derived from all potential protein coding open reading frames (ORFs) found in 170 fully sequenced bacterial genomes. Amino acid sequences of tryptic peptides obtained by LC-ESI MS/MS analysis of the digest of bacterial cell extracts are assigned to individual bacterial proteomes in the database. Phylogenetic profiles of these peptides are used to create a matrix of sequence-to-bacterium assignments. These matrixes, viewed as specific assignment bitmaps, are analyzed using statistical tools to reveal the relatedness between a test bacterial sample and the microorganism database. It is shown that, if a sufficient amount of sequence information is obtained from the MS/MS experiments, a bacterial sample can be classified to a strain level by using this proteomics method, leading to its positive identification.     

Tandem mass spectrometry for the detection of plant pathogenic fungi and the effects of database composition on protein inferences

LC-MS/MS has demonstrated potential for detecting plant pathogens. Unlike PCR or ELISA, LC-MS/MS does not require pathogen-specific reagents for the detection of pathogen-specific proteins and peptides. However, the MS/MS approach we and others have explored does require a protein sequence reference database and database-search software to interpret tandem mass spectra. To evaluate the limitations of database composition on pathogen identification, we analyzed proteins from cultured Ustilago maydis, Phytophthora sojae, Fusarium graminearum, and Rhizoctonia solani by LC-MS/MS. When the search database did not contain sequences for a target pathogen, or contained sequences to related pathogens, target pathogen spectra were reliably matched to protein sequences from nontarget organisms, giving an illusion that proteins from nontarget organisms were identified. Our analysis demonstrates that when database-search software is used as part of the identification process, a paradox exists whereby additional sequences needed to detect a wide variety of possible organisms may lead to more cross-species protein matches and misidentification of pathogens.     

高通量质谱法用于小鼠脑部神经肽的鉴定

神经肽在参与调控人体各种生理功能上发挥着重要的作用,如痛觉、睡眠、情绪、学习与记忆等生理活动都受到神经肽的影响。神经肽主要存在于机体的神经组织内,其他体液和器官中也有少量的分布。目前对全脑组织神经肽高通量鉴定的研究仍不足,高通量检测这些神经肽对了解神经肽的组成和功能具有重要的意义。本研究通过对小鼠全脑组织内源性肽段的萃取,运用液相串联质谱(LC-MS/MS)技术对全脑组织的神经肽进行检测,共鉴定到1 830条内源性肽段和99条预测神经肽肽段。这些内源性肽段的鉴定在疾病的治疗和机制研究以及药物的研发方面提供了参考价值,也为研究新的神经肽及其功能奠定了基础。     

Quantitative proteome analysis using differential stable isotopic labeling and microbore LC-MALDI MS and MS/MS

We demonstrate an approach for global quantitative analysis of protein mixtures using differential stable isotopic labeling of the enzyme-digested peptides combined with microbore liquid chromatography (LC) matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS). Microbore LC provides higher sample loading, compared to capillary LC, which facilitates the quantification of low abundance proteins in protein mixtures. In this work, microbore LC is combined with MALDI MS via a heated droplet interface. The compatibilities of two global peptide labeling methods (i.e., esterification to carboxylic groups and dimethylation to amine groups of peptides) with this LC-MALDI technique are evaluated. Using a quadrupole-time-of-flight mass spectrometer, MALDI spectra of the peptides in individual sample spots are obtained to determine the abundance ratio among pairs of differential isotopically labeled peptides. MS/MS spectra are subsequently obtained from the peptide pairs showing significant abundance differences to determine the sequences of selected peptides for protein identification. The peptide sequences determined from MS/MS database search are confirmed by using the overlaid fragment ion spectra generated from a pair of differentially labeled peptides. The effectiveness of this microbore LC-MALDI approach is demonstrated in the quantification and identification of peptides from a mixture of standard proteins as well as E. coli whole cell extract of known relative concentrations. It is shown that this approach provides a facile and economical means of comparing relative protein abundances from two proteome samples.     

Characterization of the human salivary proteome by capillary isoelectric focusing/nanoreversed-phase liquid chromatography coupled with ESI-tandem MS

Saliva is a readily available body fluid with great diagnostic potential. The foundation for saliva-based diagnostics, however, is the development of a complete catalog of secreted and "leaked" proteins detectable in saliva. By employing a capillary isoelectric focusing-based multidimensional separation platform coupled with electrospray ionization tandem mass spectrometry (MS), a total of 5338 distinct peptides were sequenced, leading to the identification of 1381 distinct proteins. A search of bacterial protein sequences also identified many peptides unique to several organisms and unique to the NCBI nonredundant database. To the best of our knowledge, this proteome study represents the largest catalog of proteins measured from a single saliva sample to date. Data analysis was performed on individual MS/MS spectra using the highly specific peptide identification algorithm, OMSSA. Searches were conducted against a decoyed SwissProt human database to control the false-positive rate at 1%. Furthermore, the well-curated SwissProt sequences represent perhaps the least redundant human protein sequence database (12,484 records versus the 50,009 records found in the International Protein Index human database), therefore minimizing multiple protein inferences from single peptides. This combined bioanalytical and bioinformatic approach has established a solid foundation for building up the human salivary proteome for the realization of the diagnostic potential of saliva.     

Analysis of the resolution limitations of peptide identification algorithms

Proteome identification using peptide-centric proteomics techniques is a routinely used analysis technique. One of the most powerful and popular methods for the identification of peptides from MS/MS spectra is protein database matching using search engines. Significance thresholding through false discovery rate (FDR) estimation by target/decoy searches is used to ensure the retention of predominantly confident assignments of MS/MS spectra to peptides. However, shortcomings have become apparent when such decoy searches are used to estimate the FDR. To study these shortcomings, we here introduce a novel kind of decoy database that contains isobaric mutated versions of the peptides that were identified in the original search. Because of the supervised way in which the entrapment sequences are generated, we call this a directed decoy database. Since the peptides found in our directed decoy database are thus specifically designed to look quite similar to the forward identifications, the limitations of the existing search algorithms in making correct calls in such strongly confusing situations can be analyzed. Interestingly, for the vast majority of confidently identified peptide identifications, a directed decoy peptide-to-spectrum match can be found that has a better or equal match score than the forward match score, highlighting an important issue in the interpretation of peptide identifications in present-day high-throughput proteomics.     

Effect of cleavage enzyme, search algorithm and decoy database on mass spectrometric identification of wheat gluten proteins

While tandem mass spectrometry (MS/MS) is routinely used to identify proteins from complex mixtures, certain types of proteins present unique challenges for MS/MS analyses. The major wheat gluten proteins, gliadins and glutenins, are particularly difficult to distinguish by MS/MS. Each of these groups contains many individual proteins with similar sequences that include repetitive motifs rich in proline and glutamine. These proteins have few cleavable tryptic sites, often resulting in only one or two tryptic peptides that may not provide sufficient information for identification. Additionally, there are less than 14,000 complete protein sequences from wheat in the current NCBInr release. In this paper, MS/MS methods were optimized for the identification of the wheat gluten proteins. Chymotrypsin and thermolysin as well as trypsin were used to digest the proteins and the collision energy was adjusted to improve fragmentation of chymotryptic and thermolytic peptides. Specialized databases were constructed that included protein sequences derived from contigs from several assemblies of wheat expressed sequence tags (ESTs), including contigs assembled from ESTs of the cultivar under study. Two different search algorithms were used to interrogate the database and the results were analyzed and displayed using a commercially available software package (Scaffold). We examined the effect of protein database content and size on the false discovery rate. We found that as database size increased above 30,000 sequences there was a decrease in the number of proteins identified. Also, the type of decoy database influenced the number of proteins identified. Using three enzymes, two search algorithms and a specialized database allowed us to greatly increase the number of detected peptides and distinguish proteins within each gluten protein group.     

ProbIDtree: an automated software program capable of identifying multiple peptides from a single collision-induced dissociation spectrum collected by a tandem mass spectrometer

In MS/MS experiments with automated precursor ion, selection only a fraction of sequencing attempts lead to the successful identification of a peptide. A number of reasons may contribute to this situation. They include poor fragmentation of the selected precursor ion, the presence of modified residues in the peptide, mismatches with sequence databases, and frequently, the concurrent fragmentation of multiple precursors in the same CID attempt. Current database search engines are incapable of correctly assigning the sequences of multiple precursors to such spectra. We have developed a search engine, ProbIDtree, which can identify multiple peptides from a CID spectrum generated by the concurrent fragmentation of multiple precursor ions. This is achieved by iterative database searching in which the submitted spectra are generated by subtracting the fragment ions assigned to a tentatively matched peptide from the acquired spectrum and in which each match is assigned a tentative probability score. Tentatively matched peptides are organized in a tree structure from which their adjusted probability scores are calculated and used to determine the correct identifications. The results using MALDI-TOF-TOF MS/MS data demonstrate that multiple peptides can be effectively identified simultaneously with high confidence using ProbIDtree.     

Extending the coverage of spectral libraries: A neighbor‐based approach to predicting intensities of peptide fragmentation spectra

Searching spectral libraries in MS/MS is an important new approach to improving the quality of peptide and protein identification. The idea relies on the observation that ion intensities in an MS/MS spectrum of a given peptide are generally reproducible across experiments, and thus, matching between spectra from an experiment and the spectra of previously identified peptides stored in a spectral library can lead to better peptide identification compared to the traditional database search. However, the use of libraries is greatly limited by their coverage of peptide sequences: even for well‐studied organisms a large fraction of peptides have not been previously identified. To address this issue, we propose to expand spectral libraries by predicting the MS/MS spectra of peptides based on the spectra of peptides with similar sequences. We first demonstrate that the intensity patterns of dominant fragment ions between similar peptides tend to be similar. In accordance with this observation, we develop a neighbor‐based approach that first selects peptides that are likely to have spectra similar to the target peptide and then combines their spectra using a weighted K‐nearest neighbor method to accurately predict fragment ion intensities corresponding to the target peptide. This approach has the potential to predict spectra for every peptide in the proteome. When rigorous quality criteria are applied, we estimate that the method increases the coverage of spectral libraries available from the National Institute of Standards and Technology by 20–60%, although the values vary with peptide length and charge state. We find that the overall best search performance is achieved when spectral libraries are supplemented by the high quality predicted spectra.     

Broad characterization of endogenous peptides in the tree shrew visual system

Endogenous neuropeptides, acting as neurotransmitters or hormones in the brain, carry out important functions including neural plasticity, metabolism and angiogenesis. Previous neuropeptide studies have focused on peptide-rich brain regions such as the striatum or hypothalamus. Here we present an investigation of peptides in the visual system, composed of brain regions that are generally less rich in peptides, with the aim of providing the first broad overview of peptides involved in mammalian visual functions. We target three important parts of the visual system: the primary visual cortex (V1), lateral geniculate nucleus (LGN) and superior colliculus (SC). Our study is performed in the tree shrew, a close relative of primates. Using a combination of data dependent acquisition and targeted LC-MS/MS based neuropeptidomics; we identified a total of 52 peptides from the tree shrew visual system. A total of 26 peptides, for example GAV and neuropeptide K were identified in the visual system for the first time. Out of the total 52 peptides, 27 peptides with high signal-to-noise-ratio (>10) in extracted ion chromatograms (EIC) were subjected to label-free quantitation. We observed generally lower abundance of peptides in the LGN compared to V1 and SC. Consistently, a number of individual peptides showed high abundance in V1 (such as neuropeptide Y or somatostatin 28) and in SC (such as somatostatin 28 AA1-12). This study provides the first in-depth characterization of peptides in the mammalian visual system. These findings now permit the investigation of neuropeptide-regulated mechanisms of visual perception.     

Sequence similarity-driven proteomics in organisms with unknown genomes by LC-MS/MS and automated de novo sequencing

LC-MS/MS analysis on a linear ion trap LTQ mass spectrometer, combined with data processing, stringent, and sequence-similarity database searching tools, was employed in a layered manner to identify proteins in organisms with unsequenced genomes. Highly specific stringent searches (MASCOT) were applied as a first layer screen to identify either known (i.e. present in a database) proteins, or unknown proteins sharing identical peptides with related database sequences. Once the confidently matched spectra were removed, the remainder was filtered against a nonannotated library of background spectra that cleaned up the dataset from spectra of common protein and chemical contaminants. The rectified spectral dataset was further subjected to rapid batch de novo interpretation by PepNovo software, followed by the MS BLAST sequence-similarity search that used multiple redundant and partially accurate candidate peptide sequences. Importantly, a single dataset was acquired at the uncompromised sensitivity with no need of manual selection of MS/MS spectra for subsequent de novo interpretation. This approach enabled a completely automated identification of novel proteins that were, otherwise, missed by conventional database searches.     

Identification of phosphopeptides with unknown cleavage specificity by a de novo sequencing assisted database search strategy

In theory, proteases with broad cleavage specificity could be applied to digest protein samples to improve the phosphoproteomic analysis coverage. However, in practice this approach is seldom employed. This is because the identification of phosphopeptides without enzyme specificity by conventional database search strategy is extremely difficult due to the huge search space. In this study, we investigated the performance of a de novo sequencing assisted database search strategy for the identification of such phosphopeptides. Firstly, we compared the performance of conventional database search strategy and the de novo sequencing assisted database search strategy for the identification of peptides and phosphopeptides without stetting enzyme specificity. It was found that the identification sensitivity dropped significantly for the conventional one while it was only slightly decreased for the new approach. Then, this new search strategy was applied to identify phosphopeptides generated by Proteinase K digestion, which resulted in the identification of 717 phosphopeptides. Finally, this strategy was utilized for the identification of serum endogenous phosphopeptides, which were generated in vivo by different kinds of proteases and kinases, and the identification of 68 unique serum endogenous phosphopepitdes was successfully achieved.