Systems biology approaches for prioritizing therapeutic gene targets

Rational approaches to therapy of complex diseases may be improved by predictive modelling of underlying disease mechanisms. Formulating and implementing such models requires the integration of heterogeneous information from different sources and usually entails considerable effort. We need new concepts and resources making knowledge on causal regulatory interactions of genes and physiological states in a disease context available. Dedicated ontologies and text mining methods can be of great use for guiding and supporting the process of model construction and model evaluation

A minimal model of peptide binding predicts ensemble properties of serum antibodies

<p/> <p>Background</p> <p>The importance of peptide microarrays as a tool for serological diagnostics has strongly increased over the last decade. However, interpretation of the binding signals is still hampered by our limited understanding of the technology. This is in particular true for arrays probed with antibody mixtures of unknown complexity, such as sera. To gain insight into how signals depend on peptide amino acid sequences, we probed random-sequence peptide microarrays with sera of healthy and infected mice. We analyzed the resulting antibody binding profiles with regression methods and formulated a minimal model to explain our findings.</p> <p>Results</p> <p>Multivariate regression analysis relating peptide sequence to measured signals led to the definition of amino acid-associated weights. Although these weights do not contain information on amino acid position, they predict up to 40-50% of the binding profiles' variation. Mathematical modeling shows that this position-independent ansatz is only adequate for highly diverse random antibody mixtures which are not dominated by a few antibodies. Experimental results suggest that sera from healthy individuals correspond to that case, in contrast to sera of infected ones.</p> <p>Conclusions</p> <p>Our results indicate that position-independent amino acid-associated weights predict linear epitope binding of antibody mixtures only if the mixture is random, highly diverse, and contains no dominant antibodies. The discovered ensemble property is an important step towards an understanding of peptide-array serum-antibody binding profiles. It has implications for both serological diagnostics and B cell epitope mapping.</p

PPINGUIN: Peptide Profiling Guided Identification of Proteins improves quantitation of iTRAQ ratios

<p>Abstract</p> <p>Background</p> <p>Recent development of novel technologies paved the way for quantitative proteomics. One of the most important among them is iTRAQ, employing isobaric tags for relative or absolute quantitation. Despite large progress in technology development, still many challenges remain for derivation and interpretation of quantitative results. One of these challenges is the consistent assignment of peptides to proteins.</p> <p>Results</p> <p>We have developed Peptide Profiling Guided Identification of Proteins (PPINGUIN), a statistical analysis workflow for iTRAQ data addressing the problem of ambiguous peptide quantitations. Motivated by the assumption that peptides uniquely derived from the same protein are correlated, our method employs clustering as a very early step in data processing prior to protein inference. Our method increases experimental reproducibility and decreases variability of quantitations of peptides assigned to the same protein. Giving further support to our method, application to a type 2 diabetes dataset identifies a list of protein candidates that is in very good agreement with previously performed transcriptomics meta analysis. Making use of quantitative properties of signal patterns identified, PPINGUIN can reveal new isoform candidates.</p> <p>Conclusions</p> <p>Regarding the increasing importance of quantitative proteomics we think that this method will be useful in practical applications like model fitting or functional enrichment analysis. We recommend to use this method if quantitation is a major objective of research.</p

Evidence for Elevated Cerebrospinal Fluid ERK1/2 Levels in Alzheimer Dementia

Cerebrospinal fluid (CSF) samples from 33 patients with Alzheimer dementia (AD), 21 patients with mild cognitive impairment who converted to AD during followup (MCI-AD), 25 patients with stable mild cognitive impairment (MCI-stable), and 16 nondemented subjects (ND) were analyzed with a chemiluminescence immunoassay to assess the levels of the mitogen-activated protein kinase ERK1/2 (extracellular signal-regulated kinase 1/2). The results were evaluated in relation to total Tau (tTau), phosphorylated Tau (pTau), and beta-amyloid 42 peptide (Aβ42). CSF-ERK1/2 was significantly increased in the AD group as compared to stable MCI patients and the ND group. Western blot analysis of a pooled cerebrospinal fluid sample revealed that both isoforms, ERK1 and ERK2, and low amounts of doubly phosphorylated ERK2 were detectable. As a predictive diagnostic AD biomarker, CSF-ERK1/2 was inferior to tTau, pTau, and Aβ42

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics

Global quantification of mammalian gene expression control

Gene expression is a multistep process that involves the transcription, translation and turnover of messenger RNAs and proteins. Although it is one of the most fundamental processes of life, the entire cascade has never been quantified on a genome-wide scale. Here we simultaneously measured absolute mRNA and protein abundance and turnover by parallel metabolic pulse labelling for more than 5,000 genes in mammalian cells. Whereas mRNA and protein levels correlated better than previously thought, corresponding half-lives showed no correlation. Using a quantitative model we have obtained the first genome-scale prediction of synthesis rates of mRNAs and proteins. We find that the cellular abundance of proteins is predominantly controlled at the level of translation. Genes with similar combinations of mRNA and protein stability shared functional properties, indicating that half-lives evolved under energetic and dynamic constraints. Quantitative information about all stages of gene expression provides a rich resource and helps to provide a greater understanding of the underlying design principles

A “Crossomics” Study Analysing Variability of Different Components in Peripheral Blood of Healthy Caucasoid Individuals

Background: Different immunotherapy approaches for the treatment of cancer and autoimmune diseases are being developed and tested in clinical studies worldwide. Their resulting complex experimental data should be properly evaluated, therefore reliable normal healthy control baseline values are indispensable. Methodology/Principal Findings: To assess intra- and inter-individual variability of various biomarkers, peripheral blood of 16 age and gender equilibrated healthy volunteers was sampled on 3 different days within a period of one month. Complex "crossomics'' analyses of plasma metabolite profiles, antibody concentrations and lymphocyte subset counts as well as whole genome expression profiling in CD4(+)T and NK cells were performed. Some of the observed age, gender and BMI dependences are in agreement with the existing knowledge, like negative correlation between sex hormone levels and age or BMI related increase in lipids and soluble sugars. Thus we can assume that the distribution of all 39.743 analysed markers is well representing the normal Caucasoid population. All lymphocyte subsets, 20% of metabolites and less than 10% of genes, were identified as highly variable in our dataset. Conclusions/Significance: Our study shows that the intra- individual variability was at least two-fold lower compared to the inter-individual one at all investigated levels, showing the importance of personalised medicine approach from yet another perspective

Mycobacterium tuberculosis gene expression profiling within the context of protein networks.

As one of the world's most successful intracellular pathogens, Mycobacterium tuberculosis, the causative agent of human tuberculosis, is responsible for two to three million deaths annually. The pathogenicity of M. tuberculosis relies on its ability to survive and persist within host macrophage cells during infection. It is of central importance, therefore, to identify genes and pathways that are involved in the survival and persistence of M. tuberculosis within these cells. Utilizing genome-wide DNA arrays we have identified M. tuberculosis genes that are specifically induced during macrophage infection. To better understand the cellular context of these differentially expressed genes, we have also combined our array analyses with computational methods of protein network identification. Our combined approach reveals certain signatures of M. tuberculosis residing within macrophage cells, including the induction of genes involved in DNA damage repair, fatty acid degradation, iron metabolism, and cell wall metabolism