High-Abundance Protein-Guided Hybrid Spectral Library for Data-Independent Acquisition Metaproteomics.

Metaproteomics offers a direct avenue to identify microbial proteins in microbiota, enabling the compositional and functional characterization of microbiota. Due to the complexity and heterogeneity of microbial communities, in-depth and accurate metaproteomics faces tremendous limitations. One challenge in metaproteomics is the construction of a suitable protein sequence database to interpret the highly complex metaproteomic data, especially in the absence of metagenomic sequencing data. Herein, we present a high-abundance protein-guided hybrid spectral library strategy for in-depth data independent acquisition (DIA) metaproteomic analysis (HAPs-hyblibDIA). A dedicated high-abundance protein database of gut microbial species is constructed and used to mine the taxonomic information on microbiota samples. Then, a sample-specific protein sequence database is built based on the taxonomic information using Uniprot protein sequence for subsequent analysis of the DIA data using hybrid spectral library-based DIA analysis. We evaluated the accuracy and sensitivity of the method using synthetic microbial community samples and human gut microbiome samples. It was demonstrated that the strategy can successfully identify taxonomic compositions of microbiota samples and that the peptides identified by HAPs-hyblibDIA overlapped greatly with the peptides identified using a metagenomic sequencing-derived database. At the peptide and species level, our results can serve as a complement to the results obtained using a metagenomic sequencing-derived database. Furthermore, we validated the applicability of the HAPs-hyblibDIA strategy in a cohort of human gut microbiota samples of colorectal cancer patients and controls, highlighting its usability in biomedical research.

Comprehensive Evaluation and Optimization of the Data-Dependent LC-MS/MS Workflow for Deep Proteome Profiling.

Data-dependent liquid chromatography-tandem mass spectrometry (LC-MS/MS) is widely used in proteomic analyses. A well-performed LC-MS/MS workflow, which involves multiple procedures and interdependent metrics, is a prerequisite for deep proteome profiling. Researchers have previously evaluated LC-MS/MS performance mainly based on the number of identified peptides and proteins. However, this is not a comprehensive approach. This motivates us to develop MSRefine, which aims to evaluate and optimize the performance of the LC-MS/MS workflow for data-dependent acquisition (DDA) proteomics. It extracts 47 kinds of metrics, scores the metrics, and reports visual results, assisting users in evaluating the workflow, locating problems, and providing optimizing strategies. In this study, we compared and analyzed multiple pairs of datasets spanning different samples, methods, and instruments and demonstrated that the comprehensive visual metrics and scores in MSRefine enable us to evaluate the performance of the various experiments and provide optimal strategies for the identification of more peptides and proteins.

Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics.

BACKGROUND: Microbiota are closely associated with human health and disease. Metaproteomics can provide a direct means to identify microbial proteins in microbiota for compositional and functional characterization. However, in-depth and accurate metaproteomics is still limited due to the extreme complexity and high diversity of microbiota samples. It is generally recommended to use metagenomic data from the same samples to construct the protein sequence database for metaproteomic data analysis. Although different metagenomics-based database construction strategies have been developed, an optimization of gene taxonomic annotation has not been reported, which, however, is extremely important for accurate metaproteomic analysis. RESULTS: Herein, we proposed an accurate taxonomic annotation pipeline for genes from metagenomic data, namely contigs directed gene annotation (ConDiGA), and used the method to build a protein sequence database for metaproteomic analysis. We compared our pipeline (ConDiGA or MD3) with two other popular annotation pipelines (MD1 and MD2). In MD1, genes were directly annotated against the whole bacterial genome database; in MD2, contigs were annotated against the whole bacterial genome database and the taxonomic information of contigs was assigned to the genes; in MD3, the most confident species from the contigs annotation results were taken as reference to annotate genes. Annotation tools, including BLAST, Kaiju, and Kraken2, were compared. Based on a synthetic microbial community of 12 species, it was found that Kaiju with the MD3 pipeline outperformed the others in the construction of protein sequence database from metagenomic data. Similar performance was also observed with a fecal sample, as well as in silico mixed datasets of the simulated microbial community and the fecal sample. CONCLUSIONS: Overall, we developed an optimized pipeline for gene taxonomic annotation to construct protein sequence databases. Our study can tackle the current taxonomic annotation reliability problem in metagenomics-derived protein sequence database and can promote the in-depth metaproteomic analysis of microbiome. The unique metagenomic and metaproteomic datasets of the 12 bacterial species are publicly available as a standard benchmarking sample for evaluating various analysis pipelines. The code of ConDiGA is open access at GitHub for the analysis of microbiota samples. Video Abstract.

An iTRAQ based quantitative proteomic strategy to explore novel secreted proteins in metastatic hepatocellular carcinoma cell lines.

Secretomics is receiving more and more considerable attention due to the key roles of secreted proteins in cancer. Most of the potential biomarkers for clinical diagnosis and treatment of cancer are secreted proteins. However, the low concentration of secreted proteins and contaminants released from dead cells are a great challenge to secretomic profiling studies. Although some bioinformatics tools such as SecretomeP and SignalP can help to annotate or predict secreted proteins, they also cause false positive or negative rates of identification especially for nonclassical secreted proteins. Therefore, an iTRAQ based quantitative proteomics strategy was set up in this work and applied in the secretomics study of metastatic HCC cell lines. A total of 94 proteins were identified as secreted and 31 of them were newly found in our data. Compared with the known secreted proteins participating in inter-cellular signalling, most of the newly identified secreted proteins were metabolic enzymes, such as PKM2 and EHHADH, whose functions focused on the synthesis/metabolism of glucose, fatty acids and amino acids. Exploring their secretion would help to further study their bio-functions in conditioned media and the effects on the interactions of cancer cells and the microenvironment. Differences between the secretomes of the two metastatic HCC cell lines were also explored in the same experiment. This strategy showed its superiority in accurately identifying secreted proteins as well as monitoring their variation under different biological conditions.

DeepFLR facilitates false localization rate control in phosphoproteomics.

Protein phosphorylation is a post-translational modification crucial for many cellular processes and protein functions. Accurate identification and quantification of protein phosphosites at the proteome-wide level are challenging, not least because efficient tools for protein phosphosite false localization rate (FLR) control are lacking. Here, we propose DeepFLR, a deep learning-based framework for controlling the FLR in phosphoproteomics. DeepFLR includes a phosphopeptide tandem mass spectrum (MS/MS) prediction module based on deep learning and an FLR assessment module based on a target-decoy approach. DeepFLR improves the accuracy of phosphopeptide MS/MS prediction compared to existing tools. Furthermore, DeepFLR estimates FLR accurately for both synthetic and biological datasets, and localizes more phosphosites than probability-based methods. DeepFLR is compatible with data from different organisms, instruments types, and both data-dependent and data-independent acquisition approaches, thus enabling FLR estimation for a broad range of phosphoproteomics experiments.

Metaproteomics profiling of the microbial communities in fermentation starters (Daqu) during multi-round production of Chinese liquor.

Introduction: The special flavor and fragrance of Chinese liquor are closely related to microorganisms in the fermentation starter Daqu. The changes of microbial community can affect the stability of liquor yield and quality. Methods: In this study, we used data-independent acquisition mass spectrometry (DIA-MS) for cohort study of the microbial communities of a total of 42 Daqu samples in six production cycles at different times of a year. The DIA MS data were searched against a protein database constructed by metagenomic sequencing. Results: The microbial composition and its changes across production cycles were revealed. Functional analysis of the differential proteins was carried out and the metabolic pathways related to the differential proteins were explored. These metabolic pathways were related to the saccharification process in liquor fermentation and the synthesis of secondary metabolites to form the unique flavor and aroma in the Chinese liquor. Discussion: We expect that the metaproteome profiling of Daqu from different production cycles will serve as a guide for the control of fermentation process of Chinese liquor in the future.

Data-independent acquisition boosts quantitative metaproteomics for deep characterization of gut microbiota.

Metaproteomics can provide valuable insights into the functions of human gut microbiota (GM), but is challenging due to the extreme complexity and heterogeneity of GM. Data-independent acquisition (DIA) mass spectrometry (MS) has been an emerging quantitative technique in conventional proteomics, but is still at the early stage of development in the field of metaproteomics. Herein, we applied library-free DIA (directDIA)-based metaproteomics and compared the directDIA with other MS-based quantification techniques for metaproteomics on simulated microbial communities and feces samples spiked with bacteria with known ratios, demonstrating the superior performance of directDIA by a comprehensive consideration of proteome coverage in identification as well as accuracy and precision in quantification. We characterized human GM in two cohorts of clinical fecal samples of pancreatic cancer (PC) and mild cognitive impairment (MCI). About 70,000 microbial proteins were quantified in each cohort and annotated to profile the taxonomic and functional characteristics of GM in different diseases. Our work demonstrated the utility of directDIA in quantitative metaproteomics for investigating intestinal microbiota and its related disease pathogenesis.

Global profiling of proteolytically modified proteins in human metastatic hepatocellular carcinoma cell lines reveals CAPN2 centered network.

Proteolysis affects every protein at some point in its life cycle. Many biomarkers of disease or cancer are stable proteolytic fragments in biological fluids. There is great interest and a challenge in proteolytically modified protein study to identify physiologic protease-substrate relationships and find potential biomarkers. In this study, two human hepatocellular carcinoma (HCC) cell lines with different metastasis potential, MHCC97L, and HCCLM6, were researched with a high-throughput and sensitive PROTOMAP platform. In total 391 proteins were found to be proteolytically processed and many of them were cleaved into persistent fragments instead of completely degraded. Fragments related to 161 proteins had different expressions in these two cell lines. Through analyzing these significantly changed fragments with bio-informatic tools, several bio-functions such as tumor cell migration and anti-apoptosis were enriched. A proteolysis network was also built up, of which the CAPN2 centered subnetwork, including SPTBN1, ATP5B, and VIM, was more active in highly metastatic HCC cell line. Interestingly, proteolytic modifications of CD44 and FN1 were found to affect their secretion. This work suggests that proteolysis plays an important role in human HCC metastasis.

Multi-Omic Profiling of Multi-Biosamples Reveals the Role of Amino Acid and Nucleotide Metabolism in Endometrial Cancer.

Background: Endometrial cancer (EC) is one of the most common gynecological cancers. The traditional diagnosis of EC relies on histopathology, which, however, is invasive and may arouse tumor spread. There have been many studies aiming to find the metabolomic biomarkers of EC to improve the early diagnosis of cancer in a non-invasive or minimally invasive way, which can also provide valuable information for understanding the disease. However, most of these studies only analyze a single type of sample by metabolomics, and cannot provide a comprehensive view of the altered metabolism in EC patients. Our study tries to gain a pathway-based view of multiple types of samples for understanding metabolomic disorders in EC by combining metabolomics and proteomics. Methods: Forty-four EC patients and forty-three controls were recruited for the research. We collected endometrial tissue, urine, and intrauterine brushing samples. Untargeted metabolomics and untargeted proteomics were both performed on the endometrial tissue samples, while only untargeted metabolomics was performed on the urine and intrauterine brushing samples. Results: By integrating the differential metabolites and proteins between EC patients and controls detected in the endometrial tissue samples, we identified several EC-related significant pathways, such as amino acid metabolism and nucleotide metabolism. The significance of these pathways and the potential of metabolite biomarker-based diagnosis were then further verified by using urine and intrauterine brushing samples. It was found that the regulation of metabolites involved in the significant pathways showed similar trends in the intrauterine brushings and the endometrial tissue samples, while opposite trends in the urine and the endometrial tissue samples. Conclusions: With multi-omics characterization of multi-biosamples, the metabolomic changes related to EC are illustrated in a pathway-based way. The network of altered metabolites and related proteins provides a comprehensive view of altered metabolism in the endometrial tissue samples. The verification of these critical pathways by using urine and intrauterine brushing samples provides evidence for the possible non-invasive or minimally invasive biopsy for EC diagnosis in the future.

Photosynthesis of Acetate by Sporomusa ovata-CdS Biohybrid System.

Sporomusa ovata, a typical electroautotrophic microorganism, has been utilized in bioelectrosynthesis for carbon dioxide fixation to multicarbon organic chemicals. However, additional photovoltaic devices are normally needed to convert photo energy to electric energy to power the carbon dioxide fixation, which restricts the overall energy conversion efficiency. Herein, we report Sporomusa ovata-CdS biohybrids for artificial photosynthesis driven by light without any other power source. The quantum yield can reach 16.8 ± 9%, and the active duration time of the system can last for 5 days. During the artificial photosynthesis, carbon dioxide is first reduced to formate and finally converted to acetate via the Wood-Ljungdahl pathway. The carbon dioxide fixation, electron transfer, energy metabolism, and reactive oxygen species damage repair processes in the biohybrid system were characterized by proteomic analysis. Key enzymes, e.g., flavoprotein, ferredoxin, formate-tetrahydrofolate ligase, 5-methyltetrahydrofolate:corrinoid iron-sulfur protein methyltransferase, thioredoxin, and rubrerythrin, were found up-regulated in the biohybrid system. The findings are helpful in understanding the mechanism of the artificial photosynthesis and useful for the development of new biohybrid systems using genetically engineered microbes in the future. The study is expected to boost the development of bioabiotic hybrid system in solar energy harvest.

Quantitative metaproteomics reveals composition and metabolism characteristics of microbial communities in Chinese liquor fermentation starters.

Introduction: Daqu, the Chinese liquor fermentation starter, contains complex microbial communities that are important for the yield, quality, and unique flavor of produced liquor. However, the composition and metabolism of microbial communities in the different types of high-temperature Daqu (i.e., white, yellow, and black Daqu) have not been well understood. Methods: Herein, we used quantitative metaproteomics based on data-independent acquisition (DIA) mass spectrometry to analyze a total of 90 samples of white, yellow, and black Daqu collected in spring, summer, and autumn, revealing the taxonomic and metabolic profiles of different types of Daqu across seasons. Results: Taxonomic composition differences were explored across types of Daqu and seasons, where the under-fermented white Daqu showed the higher microbial diversity and seasonal stability. It was demonstrated that yellow Daqu had higher abundance of saccharifying enzymes for raw material degradation. In addition, considerable seasonal variation of microbial protein abundance was discovered in the over-fermented black Daqu, suggesting elevated carbohydrate and amino acid metabolism in autumn black Daqu. Discussion: We expect that this study will facilitate the understanding of the key microbes and their metabolism in the traditional fermentation process of Chinese liquor production.

Direct MALDI-TOF profiling of gingival crevicular fluid sediments for periodontitis diagnosis.

Periodontitis is a widespread stomatological disease and represents one of the main causes of tooth loss in adults. Traditional diagnosis of periodontitis relies on the judgment by professional periodontists that cannot reveal its progression at the early stage. In this work, we characterized the gingival crevicular fluid (GCF) sediments of patients with periodontitis and healthy volunteers by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Potential protein biomarkers were selected based on the multivariate statistical analysis of the MALDI-TOF mass spectra, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) identification. Twelve potential protein biomarkers were identified from 17 patients compared to 7 healthy volunteers, including 5 microbial proteins and 7 human proteins, indicating the microbial composition and host response components related to the etiology of periodontitis. The panel of biomarkers was then verified with the GCF samples of another 11 patients. The 12 biomarkers also showed potential value in the early diagnosis of periodontitis. This work developed a rapid assay to screen periodontitis among populations. It can be popularized to non-periodontal specialists such as community general practitioners, benefiting the early and accurate monitoring of periodontitis. The identification of the potential biomarkers can also help in the understanding of the pathogenesis of periodontitis.

Mesoporous Silica as Sorbents and Enzymatic Nanoreactors for Microbial Membrane Proteomics.

The membrane proteins of microbes are at the forefront of host and parasite interactions. Having a general view of the functions of microbial membrane proteins is vital for many biomedical studies on microbiota. Nevertheless, due to the strong hydrophobicity and low concentration of membrane proteins, it is hard to efficiently enrich and digest the proteins for mass spectrometry analysis. Herein, we design an enzymatic nanoreactor for the digestion of membrane proteins using methylated well-ordered hexagonal mesoporous silica (Met-SBA-15). The material can efficiently extract hydrophobic membrane proteins and host the proteolysis in nanopores. The performance of the enzymatic nanoreactor is first demonstrated using standard hydrophobic proteins and then validated using membrane proteins extracted from Escherichia coli (E. coli) or a mixed bacterial sample of eight strains. Using the nanoreactor, 431 membrane proteins are identified from E. coli, accounting for 38.5% of all membrane proteins of the species, which is much more than that by the widely used in-solution digestion protocol. From the mixed bacterial sample of eight strains, 1395 membrane proteins are identified using the nanoreactor. On the contrary, the traditional in-solution proteolysis workflow only leads to the identification of 477 membrane proteins, demonstrating that the Met-SBA-15 can be offered as an excellent tool for microbial membrane proteome research and is expected to be used in human microbiota studies, e.g. host-microbe interactions.

Novel proteomic strategy reveal combined alpha1 antitrypsin and cathepsin D as biomarkers for colorectal cancer early screening.

Biomarkers for colorectal cancer (CRC) early diagnosis are currently lacking. The purpose of this study was to interpret molecular events in the early stage of CRC that may bring about new biomarkers for early diagnosis. Methylation isotope labeling assistant gel-enhanced liquid chromatography-mass spectrometry (GeLC-MS) strategy was developed to improve protein identification in quantitative proteome analysis between pooled early stage CRC and pooled normal counterparts. Expression of candidate biomarkers were in situ verified in a 372-dots tissue array, and their relative concentrations in sera were validated in 84 CRC patients and healthy individuals. Altogether, 501 proteins showing consistent differential expression were discovered. Function analysis highlighted the ubiquitination-proteasome and glycolysis/gluconeogenesis pathways as the most regulated pathways in CRC. Two glycol-proteins, alpha1 antitrypsin (A1AT) and cathepsin D (CTSD), which play central role in proteasome regulation, were further examined due to their possible importance in human cancers. Consistent with proteome data, CRC specimens expressed less A1AT and more CTSD than normal counterparts in both tissue and serum levels. By combining CTSD and A1AT, 96.77% of CRC tissues were distinguished from normal tissues by immunohistochemical analysis on a tissue array (P<0.0001). Combined CTSD and A1AT should be strongly considered for clinical use in early diagnosis of early stage CRC, and the methylation assistant GeLC-MS approach is competent for a global quantitative proteome study.

Metaproteomics characterizes human gut microbiome function in colorectal cancer.

Pathogenesis of colorectal cancer (CRC) is associated with alterations in gut microbiome. Previous studies have focused on the changes of taxonomic abundances by metagenomics. Variations of the function of intestinal bacteria in CRC patients compared to healthy crowds remain largely unknown. Here we collected fecal samples from CRC patients and healthy volunteers and characterized their microbiome using quantitative metaproteomic method. We have identified and quantified 91,902 peptides, 30,062 gut microbial protein groups, and 195 genera of microbes. Among the proteins, 341 were found significantly different in abundance between the CRC patients and the healthy volunteers. Microbial proteins related to iron intake/transport; oxidative stress; and DNA replication, recombination, and repair were significantly alternated in abundance as a result of high local concentration of iron and high oxidative stress in the large intestine of CRC patients. Our study shows that metaproteomics can provide functional information on intestinal microflora that is of great value for pathogenesis research, and can help guide clinical diagnosis in the future.

In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics.

Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by data-dependent acquisition (DDA) experiments are required prior to DIA analysis, which is time-consuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning-based approach to generate in silico spectral libraries for DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models using DeepDIA is comparable to that of experimental libraries, and outperforms libraries generated by global models. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that DeepDIA can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library. We expect this work expanding the toolbox for DIA proteomics.

Nanozeolite-driven approach for enrichment of secretory proteins in human hepatocellular carcinoma cells.

Given the importance of secreted proteins as a source for early detection and diagnosis of disease, secreted proteins have been arousing considerable attention. However, the analysis of secreted proteins represents a challenge for current proteomic techniques. One of the difficulties in secretomic study is to concentrate proteins from large volume of growth media, particularly, the low abundant and low molecular weight proteins (molecular weight <30 kDa). Herein, we describe a novel strategy for harvesting secretory proteins. In this approach, proteins secreted from the human hepatocellular carcinoma cell line were enriched by zeolite LTL nanocrystals, followed by 1-D SDS-PAGE for protein fractionation and then by LC-ESI-MS/MS for protein identification. In total, 1474 unique proteins were confidently identified, including 505 low molecular weight proteins, and covered a broad range of pI and molecular weight. Furthermore, this study not only offered an efficient and powerful method for the enrichment of secretory proteins but also allowed in-depth study of secretome of hepatocellular carcinoma cells. The reported work is expected to represent one of the most comprehensive secretomic analyses so far.

[A method using spectrum alignment to improve analysis efficiency of liquid chromatography combined with mass spectrometry].

In the analysis of proteins in human umbilical vein endothelial cells (HUVEC) treated with dimethyl sulfoxide (DMSO) and NEDD8-activating enzyme inhibitor (MLN4924, MLN), the Progenesis LC-MS software (Nonlinear Dynamics Ltd) was applied to liquid chromatography spectrum alignment, while spectrum similarities were figured out among several experiments of the same sample, and also among different samples. After double enzymolysis, the sample was added with digested QconCAT standard proteins. They were separated by HPLC-MS/MS, followed by spectrum alignment and data analysis. This established experiment flow offered a better identification result of more than 8 000 proteins, while the original result was about 7 000 proteins, ensuring a relatively high identification efficiency. On the basis of relative quantification with spectrum count, the described procedure can analyze the differential expression of proteins induced by DMSO and MLN. The similarities of total ion chromatograms after alignment were also compared. This method was proved to be quick and easy, with the advantages of high throughput and high sensitivity.

Improved proteomic analysis pipeline for LC-ETD-MS/MS using charge enhancing methods.

Electron transfer dissociation (ETD) is a useful and complementary activation method for peptide fragmentation in mass spectrometry. However, ETD spectra typically receive a relatively low score in the identifications of 2+ ions. To overcome this challenge, we, for the first time, systematically interrogated the benefits of combining ion charge enhancing methods (dimethylation, guanidination, m-nitrobenzyl alcohol (m-NBA) or Lys-C digestion) and differential search algorithms (Mascot, Sequest, OMSSA, pFind and X!Tandem). A simple sample (BSA) and a complex sample (AMJ2 cell lysate) were selected in benchmark tests. Clearly distinct outcomes were observed through different experimental protocol. In the analysis of AMJ2 cell lines, X!Tandem and pFind revealed 92.65% of identified spectra; m-NBA adduction led to a 5-10% increase in average charge state and the most significant increase in the number of successful identifications, and Lys-C treatment generated peptides carrying mostly triple charges. Based on the complementary identification results, we suggest that a combination of m-NBA and Lys-C strategies accompanied by X!Tandem and pFind can greatly improve ETD identification.

Comparison of alternative extraction methods for secretome profiling in human hepatocellular carcinoma cells.

Secreted proteins are important sources for early detection and diagnosis of disease, and as such have received considerable attention. The extraction of low concentration proteins from large volumes of culture media, which are rich in salts and other compounds that interfere with most proteomics techniques, presents a problem for secretome studies. Ultrafiltration, precipitation, and dialysis are three major extraction methods that can be used to overcome this problem. The present study for the first time, compared the merits and shortcomings of these three methods, without bias. Centrifugal ultrafiltration provided the best extraction efficiency, and precipitation provided the highest number of identifiable proteins. The three methods yielded closely related, but different, information on the secretome; thus, they should be considered complementary or, at least, supplementary methods. Three hundred and sixty unique proteins were identified, including 211 potential secreted proteins. Compared with previous studies, this study also identified 42 new secreted proteins. The present study not only offers a reference for the selection of secretome extraction methods, but also expands the secretome database for the investigation of hepatocellular carcinoma.