Quantitative profiling of posttranslational modifications of pathological tau via sarkosyl fractionation and mass spectrometry.

Tau protein aggregation is associated with posttranslational modifications (PTMs) in 75% of all dementia cases. The distribution of tau pathology and the presence of specific tau phosphorylation sites of interest are typically visualized and measured using antibodies. However, previous knowledge of the target epitopes is required. Additionally, antibodies can be used in a semi-quantitative manner but cannot be used to determine the absolute amount of tau or the extent of the modifications at specific sites or domains. Here we present a discovery assay that characterizes the global qualitative and quantitative tau modification landscape of a sample without a priori knowledge. Our workflow uses sarkosyl fractionation to extract the pathological tau species from sample-limited brain specimens, followed by mass spectrometry (MS) to characterize and quantify tau PTMs. The two-step MS-based proteomics approach includes an exploratory tau PTM analysis and a targeted full-length expressed stable isotope-labeled tau assay, which monitors specific unmodified tau peptides using a heavy isotope-labeled internal standard as a reference. This enables the absolute quantification of the respective tau peptides and the total tau amount in the sample, thus providing the modification extent of tau PTMs. This approach provides precise, comprehensive, qualitative and quantitative tau PTM profiling of the sample. It also enables the detailed molecular comparison of tau across multiple experiments, including a comparison between neurodegenerative diseases, stages of the disease, human patient heterogeneity and characterization of animal models. The approach is useful for studying the molecular features of pathological tau in neurodegeneration. The procedure requires 7-8 d and is suitable for users with expertise in targeted and untargeted MS-based protein analysis.

Common mouse models of tauopathy reflect early but not late human disease.

BACKGROUND: Mouse models that overexpress human mutant Tau (P301S and P301L) are commonly used in preclinical studies of Alzheimer's Disease (AD) and while several drugs showed therapeutic effects in these mice, they were ineffective in humans. This leads to the question to which extent the murine models reflect human Tau pathology on the molecular level. METHODS: We isolated insoluble, aggregated Tau species from two common AD mouse models during different stages of disease and characterized the modification landscape of the aggregated Tau using targeted and untargeted mass spectrometry-based proteomics. The results were compared to human AD and to human patients that suffered from early onset dementia and that carry the P301L Tau mutation. RESULTS: Both mouse models accumulate insoluble Tau species during disease. The Tau aggregation is driven by progressive phosphorylation within the proline rich domain and the C-terminus of the protein. This is reflective of early disease stages of human AD and of the pathology of dementia patients carrying the P301L Tau mutation. However, Tau ubiquitination and acetylation, which are important to late-stage human AD are not represented in the mouse models. CONCLUSION: AD mouse models that overexpress human Tau using risk mutations are a suitable tool for testing drug candidates that aim to intervene in the early formation of insoluble Tau species promoted by increased phosphorylation of Tau.

Transcriptomic and proteomic profiling of young and old mice in the bleomycin model reveals high similarity.

The most common preclinical, in vivo model to study lung fibrosis is the bleomycin-induced lung fibrosis model in 2- to 3-mo-old mice. Although this model resembles key aspects of idiopathic pulmonary fibrosis (IPF), there are limitations in its predictability for the human disease. One of the main differences is the juvenile age of animals that are commonly used in experiments, resembling humans of around 20 yr. Because IPF patients are usually older than 60 yr, aging appears to play an important role in the pathogenesis of lung fibrosis. Therefore, we compared young (3 months) and old mice (21 months) 21 days after intratracheal bleomycin instillation. Analyzing lung transcriptomics (mRNAs and miRNAs) and proteomics, we found most pathways to be similarly regulated in young and old mice. However, old mice show imbalanced protein homeostasis as well as an increased inflammatory state in the fibrotic phase compared to young mice. Comparisons with published human transcriptomic data sets (GSE47460, GSE32537, and GSE24206) revealed that the gene signature of old animals correlates significantly better with IPF patients, and it also turned human healthy individuals better into "IPF patients" using an approach based on predictive disease modeling. Both young and old animals show similar molecular hallmarks of IPF in the bleomycin-induced lung fibrosis model, although old mice more closely resemble several features associated with IPF in comparison to young animals.

Biomarker discovery for chronic liver diseases by multi-omics - a preclinical case study.

Nonalcoholic steatohepatitis (NASH) is a major cause of liver fibrosis with increasing prevalence worldwide. Currently there are no approved drugs available. The development of new therapies is difficult as diagnosis and staging requires biopsies. Consequently, predictive plasma biomarkers would be useful for drug development. Here we present a multi-omics approach to characterize the molecular pathophysiology and to identify new plasma biomarkers in a choline-deficient L-amino acid-defined diet rat NASH model. We analyzed liver samples by RNA-Seq and proteomics, revealing disease relevant signatures and a high correlation between mRNA and protein changes. Comparison to human data showed an overlap of inflammatory, metabolic, and developmental pathways. Using proteomics analysis of plasma we identified mainly secreted proteins that correlate with liver RNA and protein levels. We developed a multi-dimensional attribute ranking approach integrating multi-omics data with liver histology and prior knowledge uncovering known human markers, but also novel candidates. Using regression analysis, we show that the top-ranked markers were highly predictive for fibrosis in our model and hence can serve as preclinical plasma biomarkers. Our approach presented here illustrates the power of multi-omics analyses combined with plasma proteomics and is readily applicable to human biomarker discovery.