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Microbiota are closely associated to 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. One of the main challenges is constructing a protein sequence database that best fits the microbiota sample. Herein, we proposed an accurate taxonomic annotation pipeline from metagenomic data for deep metaproteomic coverage, namely contigs directed gene annotation (ConDiGA). We mixed 12 known bacterial species to derive a synthetic microbial community to benchmark metagenomic and metaproteomic pipelines. With the optimized taxonomic annotation strategy by ConDiGA, we built a protein sequence database from the metagenomic data for metaproteomic analysis and identified about 12,000 protein groups, which was very close to the result obtained with the reference proteome protein sequence database of the 12 species. We also demonstrated the practicability of the method in real fecal samples, achieved deep proteome coverage of human gut microbiome, and compared the function and taxonomy of gut microbiota at metagenomic level and metaproteomic level. Our study can tackle the current taxonomic annotation reliability problem in metagenomics-derived protein sequence database for metaproteomics. The unique dataset of metagenomic and the metaproteomic data of the 12 bacterial species is 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 real microbiota samples.

Summary Highly efficient inorganic light absorbers combined with highly specific biocatalysts, i.e., chemolithoautotrophic microbes, have recently been proposed for solar-powered biomanufacturing to pave the way in artificial photosynthesis. In this work, we studied the global protein and metabolite changes of a Moorella thermoacetica-CdS (cadmium sulfide quantum dots) hybrid system during light harvesting. The Wood-Ljungdahl pathway (WLP) that has been considered as the main approach for CO2 fixation was found to be highly activated by CdS. Targeted metabolite quantification revealed that energy-metabolism-associated glycolysis and the tricarboxylic acid (TCA) cycle were also activated. Therefore, we propose an energy-conservation scheme to the M. thermoacetica-CdS hybrid system, i.e., glycolysis and the TCA cycle along with the oxidation of acetyl-coenzyme A (CoA) and NADH, in addition to the well-documented chemiosmotic proton gradient-driven ATP production by ATPase. We expect that the study can be helpful to guide the design of effective and biocompatible photo-biocatalytic systems in the future.

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.

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.

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., Data-independent acquisition (DIA) is an emerging technology in proteomics but it typically relies on spectral libraries built by data-dependent acquisition (DDA). Here, the authors use deep learning to generate in silico spectral libraries directly from protein sequences that enable more comprehensive DIA experiments than DDA-based libraries.

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.

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.

A new mass spectrometry based analysis strategy has been established here for high-molecular-weight (HMW) proteome research. First, a 2-hydroxyethyl agarose/polyacrylamide (HEAG/PAM) electrophoresis gel was designed for the first time to realize an easy-handling separation method with high spatial resolution for HMW proteins, good reproducibility and mass spectrometry-compatible sliver staining. Second, ZnO–polymethyl methacrylate (ZnO–PMMA) nanobeads were applied here for enriching and desalting the peptides from the HMW proteins. Third, the peptides were analyzed by matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) with the presence of the ZnO–PMMA nanobeads, and their MS signals were enhanced markedly. The success rate of identification for HMW proteins was significantly increased due to high enriching efficiency and salt tolerance capability as well as signal enhancing capability of the ZnO–PMMA nanobeads. We believe that this analysis strategy will inspire and accelerate the HMW proteome studies.

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 (P0.0001). 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.

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

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.

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.

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.