Proteomics analysis of prefrontal cortex of Alzheimer's disease patients revealed dysregulated proteins in the disease and novel proteins associated with amyloid-ß pathology.

BACKGROUND: Alzheimer's disease (AD) is a progressive, chronic, and neurodegenerative disease, and the most common cause of dementia worldwide. Currently, the mechanisms underlying the disease are far from being elucidated. Thus, the study of proteins involved in its pathogenesis would allow getting further insights into the disease and identifying new markers for AD diagnosis. METHODS: We aimed here to analyze protein dysregulation in AD brain by quantitative proteomics to identify novel proteins associated with the disease. 10-plex TMT (tandem mass tags)-based quantitative proteomics experiments were performed using frozen tissue samples from the left prefrontal cortex of AD patients and healthy individuals and vascular dementia (VD) and frontotemporal dementia (FTD) patients as controls (CT). LC-MS/MS analyses were performed using a Q Exactive mass spectrometer. RESULTS: In total, 3281 proteins were identified and quantified using MaxQuant. Among them, after statistical analysis with Perseus (p value < 0.05), 16 and 155 proteins were defined as upregulated and downregulated, respectively, in AD compared to CT (Healthy, FTD and VD) with an expression ratio ≥ 1.5 (upregulated) or ≤ 0.67 (downregulated). After bioinformatics analysis, ten dysregulated proteins were selected as more prone to be associated with AD, and their dysregulation in the disease was verified by qPCR, WB, immunohistochemistry (IHC), immunofluorescence (IF), pull-down, and/or ELISA, using tissue and plasma samples of AD patients, patients with other dementias, and healthy individuals. CONCLUSIONS: We identified and validated novel AD-associated proteins in brain tissue that should be of further interest for the study of the disease. Remarkably, PMP2 and SCRN3 were found to bind to amyloid-ß (Aß) fibers in vitro, and PMP2 to associate with Aß plaques by IF, whereas HECTD1 and SLC12A5 were identified as new potential blood-based biomarkers of the disease.

In-depth quantitative proteomics analysis revealed C1GALT1 depletion in ECC-1 cells mimics an aggressive endometrial cancer phenotype observed in cancer patients with low C1GALT1 expression.

BACKGROUND: Endometrial cancer (EC) is the most common cancer of the female reproductive organs. Despite the good overall prognosis of most low-grade ECs, FIGO I and FIGO II patients might experience tumor recurrence and worse prognosis. The study of alterations related to EC pathogenesis might help to get insights into underlying mechanisms involved in EC development and progression. METHODS: Core tumoral samples were used to investigate the role of C1GALT1 in EC by immunohistochemistry (IHC). ECC-1 cells were used as endometrioid EC model to investigate the effect of C1GALT1 depletion using C1GALT1 specific shRNAs. SILAC quantitative proteomics analyses and cell-based assays, PCR, qPCR, WB, dot-blot and IHC analyses were used to identify, quantify and validate dysregulation of proteins. RESULTS: Low C1GALT1 protein expression levels associate to a more aggressive phenotype of EC. Out of 5208 proteins identified and quantified by LC-MS/MS, 100 proteins showed dysregulation (log2fold-change ≥ 0.58 or ≤-0.58) in the cell protein extracts and 144 in the secretome of C1GALT1 depleted ECC-1 cells. Nine dysregulated proteins were validated. Bioinformatics analyses pointed out to an increase in pathways associated with an aggressive phenotype. This finding was corroborated by loss-of-function cell-based assays demonstrating higher proliferation, invasion, migration, colony formation and angiogenesis capacity in C1GALT1 depleted cells. These effects were associated to the overexpression of ANXA1, as demonstrated by ANXA1 transient silencing cell-based assays, and thus, correlating C1GALT and ANXA1 protein expression and biological effects. Finally, the negative protein expression correlation found by proteomics between C1GALT1 and LGALS3 was confirmed by IHC. CONCLUSION: C1GALT1 stably depleted ECC-1 cells mimic an EC aggressive phenotype observed in patients and might be useful for the identification and validation of EC markers of progression.

Targeted proteomics on its way to discovery.

For a long time, targeted and discovery proteomics covered different corners of the detection spectrum, with targeted proteomics focused on small target sets. This changed with the recent advances in highly multiplexed analysis. While discovery proteomics still pushes higher numbers of identified and quantified proteins, the advances in targeted proteomics rose to cover large parts of less complex proteomes or proteomes with low protein detection counts due to dynamic range restrictions, like the blood proteome. These new developments will impact, especially on the field of biomarker discovery and the possibility of using targeted proteomics for diagnostic purposes.

Analysis of the Dynamic Proteasome Structure by Cross-Linking Mass Spectrometry.

The 26S proteasome is a macromolecular complex that degrades proteins maintaining cell homeostasis; thus, determining its structure is a priority to understand its function. Although the 20S proteasome's structure has been known for some years, the highly dynamic nature of the 19S regulatory particle has presented a challenge to structural biologists. Advances in cryo-electron microscopy (cryo-EM) made it possible to determine the structure of the 19S regulatory particle and showed at least seven different conformational states of the proteasome. However, there are still many questions to be answered. Cross-linking mass spectrometry (CLMS) is now routinely used in integrative structural biology studies, and it promises to take integrative structural biology to the next level, answering some of these questions.

Ubiquitin Chain Analysis by Parallel Reaction Monitoring.

Assessment of the global profile of ubiquitin chain topologies within a proteome is of interest to answer a wide range of biological questions. The protocol outlined here takes advantage of the di-glycine (-GG) modification left after the tryptic digestion of ubiquitin incorporated in a chain. By quantifying these topology-characteristic peptides the relative abundance of each ubiquitin chain topology can be determined. The steps required to quantify these peptides by a parallel reaction monitoring experiment are reported taking into consideration the stabilization of ubiquitin chains. Preparation of heavy controls, cell lysis, and digestion are described along with the appropriate mass spectrometer setup and data analysis workflow. An example data set with perturbations in ubiquitin topology is presented, accompanied by examples of how optimization of the protocol can affect results. By following the steps outlined, a user will be able to perform a global assessment of the ubiquitin topology landscape within their biological context.

Analysis of ubiquitin signaling and chain topology cross-talk.

Protein ubiquitination is a powerful post-translational modification implicated in many cellular processes. Although ubiquitination is associated with protein degradation, depending on the topology of polyubiquitin chains, protein ubiquitination is connected to non-degradative events in DNA damage response, cell cycle control, immune response, trafficking, intracellular localization, and vesicle fusion events. It has been shown that a ubiquitin chain can contain two or more topologies at the same time. These branched chains add another level of complexity to ubiquitin signaling, increasing its versatility and specificity. Mass spectrometry-based proteomics has been playing an important role in the identification of all types of ubiquitin chains and linkages. This review aims to provide an overview of ubiquitin chain topology and associated signaling pathways and discusses the MS-based proteomic methodologies used to determine such topologies. SIGNIFICANCE: Ubiquitination plays important roles in many cellular processes. Proteins can be monoubiquitinated or polyubiquitinated forming non-branched or branched chains in a high number of possible combinations, each associated with different cellular processes. The detection and the topology of ubiquitin chains is thus of extreme importance in order to explain such processes. Advances in mass spectrometry based proteomics allowed for the discovery and topology mapping of many ubiquitin chains. This review revisits the state of the art in ubiquitin chain identification by mass spectrometry and gives an insight on the implication of such chains in many cellular processes.

An integrated workflow for crosslinking mass spectrometry.

We present a concise workflow to enhance the mass spectrometric detection of crosslinked peptides by introducing sequential digestion and the crosslink identification software xiSEARCH. Sequential digestion enhances peptide detection by selective shortening of long tryptic peptides. We demonstrate our simple 12-fraction protocol for crosslinked multi-protein complexes and cell lysates, quantitative analysis, and high-density crosslinking, without requiring specific crosslinker features. This overall approach reveals dynamic protein-protein interaction sites, which are accessible, have fundamental functional relevance and are therefore ideally suited for the development of small molecule inhibitors.

Optimizing the Parameters Governing the Fragmentation of Cross-Linked Peptides in a Tribrid Mass Spectrometer.

We compared the five different ways of fragmentation available on a tribrid mass spectrometer and optimized their collision energies with regard to optimal sequence coverage of cross-linked peptides. We created a library of bis(sulfosuccinimidyl)suberate (BS3/DSS) cross-linked precursors, derived from the tryptic digests of three model proteins (Human Serum Albumin, creatine kinase, and myoglobin). This enabled in-depth targeted analysis of the fragmentation behavior of 1065 cross-linked precursors using the five fragmentation techniques: collision-induced dissociation (CID), beam-type CID (HCD), electron-transfer dissociation (ETD), and the combinations ETciD and EThcD. EThcD gave the best sequence coverage for cross-linked m/z species with high charge density, while HCD was optimal for all others. We tested the resulting data-dependent decision tree against collision energy-optimized single methods on two samples of differing complexity (a mix of eight proteins and a highly complex ribosomal cellular fraction). For the high complexity sample the decision tree gave the highest number of identified cross-linked peptide pairs passing a 5% false discovery rate (on average ∼21% more than the second best, HCD). For the medium complexity sample, the higher speed of HCD proved decisive. Currently, acquisition speed plays an important role in allowing the detection of cross-linked peptides against the background of linear peptides. Enrichment of cross-linked peptides will reduce this role and favor methods that provide spectra of higher quality. Data are available via ProteomeXchange with identifier PXD006131.