Multidimensional Proteomic Landscape Reveals Distinct Activated Pathways Between Human Brain Tumors.

Brain metastases (BrMs) and gliomas are two typical human brain tumors with high incidence of mortalities and distinct clinical challenges, yet the understanding of these two types of tumors remains incomplete. Here, a multidimensional proteomic landscape of BrMs and gliomas to infer tumor-specific molecular pathophysiology at both tissue and plasma levels is presented. Tissue sample analysis reveals both shared and distinct characteristics of brain tumors, highlighting significant disparities between BrMs and gliomas with differentially activated upstream pathways of the PI3K-Akt signaling pathway that have been scarcely discussed previously.

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.

Metaproteomics profiling of the microbial communities in fermentation starters () 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 . 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 samples in six production cycles at different times of a year.

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.

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.

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

Introduction: , 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 (i.e.

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.

Photosynthesis of Acetate by -CdS Biohybrid System.

, 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 -CdS biohybrids for artificial photosynthesis driven by light without any other power source.

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.

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.

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).

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.

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.

[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.

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.

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).

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.

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.

A robust new strategy for high-molecular-weight proteome research: a 2-hydroxyethyl agarose/polyacrylamide gel enhanced separation and ZnO-PMMA nanobeads assisted identification.

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 silver staining. Second, ZnO-polymethyl methacrylate (ZnO-PMMA) nanobeads were applied here for enriching and desalting the peptides from the HMW proteins.

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.

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).