Mapping early serum proteome signatures of liver regeneration in living donor liver transplant cases

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

The liver is the only solid organ capable of regenerating itself to regain 100% of its mass and function after liver injury and/or partial hepatectomy (PH). This exceptional property represents a therapeutic opportunity for severe liver disease patients. However, liver regeneration (LR) might fail due to poorly understood causes. Here, we have investigated the regulation of liver proteome and phosphoproteome at a short time after PH (9 h), to depict a detailed mechanistic background of the early LR phase. Furthermore, we analyzed the dynamic changes of the serum proteome and metabolome of healthy living donor liver transplant (LDLT) donors at different time points after surgery. The molecular profiles from both analyses were then correlated. Insulin and FXR‐FGF15/19 signaling were stimulated in mouse liver after PH, leading to the activation of the main intermediary kinases (AKT and ERK). Besides, inhibition of the hippo pathway led to an increased expression of its target genes and of one of its intermediary proteins (14‐3‐3 protein), contributing to cell proliferation. In association with these processes, metabolic reprogramming coupled to enhanced mitochondrial activity cope for the energy and biosynthetic requirements of LR. In human serum of LDLT donors, we identified 56 proteins and 13 metabolites statistically differential which recapitulate some of the main cellular processes orchestrating LR in its early phase. These results provide mechanisms and protein mediators of LR that might prove useful for the follow‐up of the regenerative process in the liver after PH as well as preventing the occurrence of complications associated with liver resection.

Related collections

Most cited references 59

Record: found
Abstract: found
Article: not found

MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

Jürgen Cox, Matthias Mann (2008)

Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.

0 comments Cited 3069 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

The MaxQuant computational platform for mass spectrometry-based shotgun proteomics.

Stefka Tyanova, Tikira Temu, Juergen Cox (2016)

MaxQuant is one of the most frequently used platforms for mass-spectrometry (MS)-based proteomics data analysis. Since its first release in 2008, it has grown substantially in functionality and can be used in conjunction with more MS platforms. Here we present an updated protocol covering the most important basic computational workflows, including those designed for quantitative label-free proteomics, MS1-level labeling and isobaric labeling techniques. This protocol presents a complete description of the parameters used in MaxQuant, as well as of the configuration options of its integrated search engine, Andromeda. This protocol update describes an adaptation of an existing protocol that substantially modifies the technique. Important concepts of shotgun proteomics and their implementation in MaxQuant are briefly reviewed, including different quantification strategies and the control of false-discovery rates (FDRs), as well as the analysis of post-translational modifications (PTMs). The MaxQuant output tables, which contain information about quantification of proteins and PTMs, are explained in detail. Furthermore, we provide a short version of the workflow that is applicable to data sets with simple and standard experimental designs. The MaxQuant algorithms are efficiently parallelized on multiple processors and scale well from desktop computers to servers with many cores. The software is written in C# and is freely available at http://www.maxquant.org.

0 comments Cited 1314 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

MissForest--non-parametric missing value imputation for mixed-type data.

Daniel Stekhoven, Peter Bühlmann (2012)

Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. The package missForest is freely available from http://stat.ethz.ch/CRAN/. stekhoven@stat.math.ethz.ch; buhlmann@stat.math.ethz.ch