Daniel Chamrad

Alterations in the Mouse and Human Proteome Caused by Huntington’s Disease

Huntington’s disease is an autosomal dominantly inherited disease that usually starts in midlife and inevitably leads to death. In our effort to identify proteins involved in processes upstream or downstream of the disease-causing huntingtin, we studied the proteome of a well established mouse model by large gel two-dimensional electrophoresis. We could demonstrate for the first time at the protein level that α1-antitrypsin and αB-crystalline both decrease in expression over the course of disease. Importantly, the α1-antitrypsin decrease in the brain precedes that in liver and testes in mice. Reduced expression of the serine protease inhibitors α1-antitrypsin and contraspin was found in liver, heart, and testes close to terminal disease. Decreased expression of the chaperone αB-crystallin was found exclusively in the brain. In three brain regions obtained post-mortem from Huntington’s disease patients, α1-antitrypsin expression was also altered. Reduced expression of the major urinary proteins not found in the brain was seen in the liver of affected mice, demonstrating that the disease exerts its influence outside the brain of transgenic mice at the protein level. Maintaining α1-antitrypsin and αB-crystallin availability during the course of Huntington’s disease might prevent neuronal cell death and therefore could be useful in delaying the disease progression.

Retention time alignment algorithms for LC/MS data must consider non-linear shifts

MOTIVATION Proteomics has particularly evolved to become of high interest for the field of biomarker discovery and drug development. Especially the combination of liquid chromatography and mass spectrometry (LC/MS) has proven to be a powerful technique for analyzing protein mixtures. Clinically orientated proteomic studies will have to compare hundreds of LC/MS runs at a time. In order to compare different runs, sophisticated preprocessing steps have to be performed. An important step is the retention time (rt) alignment of LC/MS runs. Especially non-linear shifts in the rt between pairs of LC/MS runs make this a crucial and non-trivial problem. RESULTS For the purpose of demonstrating the particular importance of correcting non-linear rt shifts, we evaluate and compare different alignment algorithms. We present and analyze two versions of a new algorithm that is based on regression techniques, once assuming and estimating only linear shifts and once also allowing for the estimation of non-linear shifts. As an example for another type of alignment method we use an established alignment algorithm based on shifting vectors that we adapted to allow for correcting non-linear shifts also. In a simulation study, we show that rt alignment procedures that can estimate non-linear shifts yield clearly better alignments. This is even true under mild non-linear deviations. AVAILABILITY R code for the regression-based alignment methods and simulated datasets are available at http://www.statistik.tu-dortmund.de/genetik-publikationen-alignment.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

An easy-to-use Decoy Database Builder software tool, implementing different decoy strategies for false discovery rate calculation in automated MS/MS protein identifications.

One of the major challenges for large scale proteomics research is the quality evaluation of results. Protein identification from complex biological samples or experimental setups is often a manual and subjective task which lacks profound statistical evaluation. This is not feasible for high‐throughput proteomic experiments which result in large datasets of thousands of peptides and proteins and their corresponding mass spectra. To improve the quality, reliability and comparability of scientific results, an estimation of the rate of erroneously identified proteins is advisable. Moreover, scientific journals increasingly stipulate that articles containing considerable MS data should be subject to stringent statistical evaluation. We present a newly developed easy‐to‐use software tool enabling quality evaluation by generating composite target‐decoy databases usable with all relevant protein search engines. This tool, when used in conjunction with relevant statistical quality criteria, enables to reliably determine peptides and proteins of high quality, even for nonexperienced users (e.g. laboratory staff, researchers without programming knowledge). Different strategies for building decoy databases are implemented and the resulting databases are characterized and compared. The quality of protein identification in high‐throughput proteomics is usually measured by the false positive rate (FPR), but it is shown that the false discovery rate (FDR) delivers a more meaningful, robust and comparable value.

Bioinformatics in Proteomics

Proteomics technologies are under continuous improvements and new technologies are introduced. Nowadays high throughput acquisition of proteome data is possible. The young and rapidly emerging field of bioinformatics in proteomics is introducing new algorithms to handle large and heterogeneous data sets and to improve the knowledge discovery process. For example new algorithms for image analysis of two dimensional gels have been developed within the last five years. Within mass spectrometry data analysis algorithms for peptide mass fingerprinting (PMF) and peptide fragmentation fingerprinting (PFF) have been developed. Local proteomics bioinformatics platforms emerge as data management systems and knowledge bases in Proteomics. We review recent developments in bioinformatics for proteomics with emphasis on expression proteomics.

Valid data from large-scale proteomics studies

How sure can we be to have identified the right proteins in a large scale proteomics study with our mass spectrometric instrumentation? Can we expect valid data from the employed search algorithm(s)? Can we believe what our computer is telling us? Right questions—what are the answers?

In-Depth Protein Characterization by Mass Spectrometry

Within this chapter, various techniques and instructions for characterizing primary structure of proteins are presented, whereas the focus lies on obtaining as much complete sequence information of single proteins as possible. Especially, in the area of protein production, mass spectrometry-based detailed protein characterization plays an increasing important role for quality control. In comparison to typical proteomics applications, wherein it is mostly sufficient to identify proteins by few peptides, several complementary techniques have to be applied to maximize primary structure information and analysis steps have to be specifically adopted. Starting from sample preparation down to mass spectrometry analysis and finally to data analysis, some of the techniques typically applied are outlined here in a summarizing and introductory manner.