Antibody signatures in patients with histopathologically defined multiple sclerosis patterns

Early active multiple sclerosis (MS) lesions can be classified histologically into three main immunopathological patterns of demyelination (patterns I-III), which suggest pathogenic heterogeneity and may predict therapy response. Patterns I and II show signs of immune-mediated demyelination, but only pattern II is associated with antibody/complement deposition. In pattern III lesions, which include Baló's concentric sclerosis, primary oligodendrocyte damage was proposed. Serum antibody reactivities could reflect disease pathogenesis and thus distinguish histopathologically defined MS patterns. We established a customized microarray with more than 700 peptides that represent human and viral antigens potentially relevant for inflammatory demyelinating CNS diseases, and tested sera from 66 patients (pattern I n = 12; II n = 29; III n = 25, including 8 with Baló's), healthy controls, patients with Sjögren's syndrome and stroke patients. Cell-based assays were performed for aquaporin 1 (AQP1) and AQP4 antibody detection. No single peptide showed differential binding among study cohorts. Because antibodies can react with different peptides from one protein, we also analyzed groups of peptides. Patients with pattern II showed significantly higher reactivities to Nogo-A peptides as compared to patterns I (p = 0.02) and III (p = 0.02). Pattern III patients showed higher reactivities to AQP1 (compared to pattern I p = 0.002, pattern II p = 0.001) and varicella zoster virus (VZV, compared to pattern II p = 0.05). In patients with Baló's, AQP1 reactivity was also significantly higher compared to patients without Baló's (p = 0.04), and the former revealed distinct antibody signatures. Histologically, Baló's patients showed loss of AQP1 and AQP4 in demyelinating lesions, but no antibodies binding conformational AQP1 or AQP4 were detected. In summary, higher reactivities to Nogo-A peptides in pattern II patients could be relevant for enhanced axonal repair and remyelination. Higher reactivities to AQP1 peptides in pattern III patients and its subgroup of Baló's patients possibly reflect astrocytic damage. Finally, latent VZV infection may cause peripheral immune activation

Serum peptide reactivities may distinguish neuromyelitis optica subgroups and multiple sclerosis

Objective: To assess in an observational study whether serum peptide antibody reactivities may distinguish aquaporin-4 (AQP4) antibody (Ab)–positive and -negative neuromyelitis optica spectrum disorders (NMOSD) and relapsing-remitting multiple sclerosis (RRMS). Methods: We screened 8,700 peptides that included human and viral antigens of potential relevance for inflammatory demyelinating diseases and random peptides with pooled sera from different patient groups and healthy controls to set up a customized microarray with 700 peptides. With this microarray, we tested sera from 66 patients with AQP4-Ab-positive (n = 16) and AQP4-Ab-negative (n = 19) NMOSD, RRMS (n = 11), and healthy controls (n = 20). Results: Differential peptide reactivities distinguished NMOSD subgroups from RRMS in 80% of patients. However, the 2 NMOSD subgroups were not well-discriminated, although those patients are clearly separated by their antibody reactivities against AQP4 in cell-based assays. Elevated reactivities to myelin and Epstein-Barr virus peptides were present in RRMS and to AQP4 and AQP1 peptides in AQP4-Ab-positive NMOSD. Conclusions: While AQP4-Ab-positive and -negative NMOSD subgroups are not well-discriminated by peptide antibody reactivities, our findings suggest that peptide antibody reactivities may have the potential to distinguish between both NMOSD subgroups and MS. Future studies should thus concentrate on evaluating peptide antibody reactivities for the differentiation of AQP4-Ab-negative NMOSD and MS

GOSim – an R-package for computation of information theoretic GO similarities between terms and gene products

<p>Abstract</p> <p>Background</p> <p>With the increased availability of high throughput data, such as DNA microarray data, researchers are capable of producing large amounts of biological data. During the analysis of such data often there is the need to further explore the similarity of genes not only with respect to their expression, but also with respect to their functional annotation which can be obtained from Gene Ontology (GO).</p> <p>Results</p> <p>We present the freely available software package <it>GOSim</it>, which allows to calculate the functional similarity of genes based on various information theoretic similarity concepts for GO terms. <it>GOSim </it>extends existing tools by providing additional lately developed functional similarity measures for genes. These can e.g. be used to cluster genes according to their biological function. Vice versa, they can also be used to evaluate the homogeneity of a given grouping of genes with respect to their GO annotation. <it>GOSim </it>hence provides the researcher with a flexible and powerful tool to combine knowledge stored in GO with experimental data. It can be seen as complementary to other tools that, for instance, search for significantly overrepresented GO terms within a given group of genes.</p> <p>Conclusion</p> <p><it>GOSim </it>is implemented as a package for the statistical computing environment <it>R </it>and is distributed under GPL within the CRAN project.</p

NEAT: An efficient network enrichment analysis test

Background: Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. Results: We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves. Conclusions: NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat )

Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'

<p>Abstract</p> <p>Background</p> <p>Network inference from high-throughput data has become an important means of current analysis of biological systems. For instance, in cancer research, the functional relationships of cancer related proteins, summarised into signalling networks are of central interest for the identification of pathways that influence tumour development. Cancer cell lines can be used as model systems to study the cellular response to drug treatments in a time-resolved way. Based on these kind of data, modelling approaches for the signalling relationships are needed, that allow to generate hypotheses on potential interference points in the networks.</p> <p>Results</p> <p>We present the R-package 'ddepn' that implements our recent approach on network reconstruction from longitudinal data generated after external perturbation of network components. We extend our approach by two novel methods: a Markov Chain Monte Carlo method for sampling network structures with two edge types (activation and inhibition) and an extension of a prior model that penalises deviances from a given reference network while incorporating these two types of edges. Further, as alternative prior we include a model that learns signalling networks with the scale-free property.</p> <p>Conclusions</p> <p>The package 'ddepn' is freely available on R-Forge and CRAN <url>http://ddepn.r-forge.r-project.org</url>, <url>http://cran.r-project.org</url>. It allows to conveniently perform network inference from longitudinal high-throughput data using two different sampling based network structure search algorithms.</p

Computational cancer biology: education is a natural key to many locks

BACKGROUND: Oncology is a field that profits tremendously from the genomic data generated by high-throughput technologies, including next-generation sequencing. However, in order to exploit, integrate, visualize and interpret such high-dimensional data efficiently, non-trivial computational and statistical analysis methods are required that need to be developed in a problem-directed manner. DISCUSSION: For this reason, computational cancer biology aims to fill this gap. Unfortunately, computational cancer biology is not yet fully recognized as a coequal field in oncology, leading to a delay in its maturation and, as an immediate consequence, an under-exploration of high-throughput data for translational research. SUMMARY: Here we argue that this imbalance, favoring ’wet lab-based activities’, will be naturally rectified over time, if the next generation of scientists receives an academic education that provides a fair and competent introduction to computational biology and its manifold capabilities. Furthermore, we discuss a number of local educational provisions that can be implemented on university level to help in facilitating the process of harmonization

A comparison of four clustering methods for brain expression microarray data

Background DNA microarrays, which determine the expression levels of tens of thousands of genes from a sample, are an important research tool. However, the volume of data they produce can be an obstacle to interpretation of the results. Clustering the genes on the basis of similarity of their expression profiles can simplify the data, and potentially provides an important source of biological inference, but these methods have not been tested systematically on datasets from complex human tissues. In this paper, four clustering methods, CRC, k-means, ISA and memISA, are used upon three brain expression datasets. The results are compared on speed, gene coverage and GO enrichment. The effects of combining the clusters produced by each method are also assessed. Results k-means outperforms the other methods, with 100% gene coverage and GO enrichments only slightly exceeded by memISA and ISA. Those two methods produce greater GO enrichments on the datasets used, but at the cost of much lower gene coverage, fewer clusters produced, and speed. The clusters they find are largely different to those produced by k-means. Combining clusters produced by k-means and memISA or ISA leads to increased GO enrichment and number of clusters produced (compared to k-means alone), without negatively impacting gene coverage. memISA can also find potentially disease-related clusters. In two independent dorsolateral prefrontal cortex datasets, it finds three overlapping clusters that are either enriched for genes associated with schizophrenia, genes differentially expressed in schizophrenia, or both. Two of these clusters are enriched for genes of the MAP kinase pathway, suggesting a possible role for this pathway in the aetiology of schizophrenia. Conclusion Considered alone, k-means clustering is the most effective of the four methods on typical microarray brain expression datasets. However, memISA and ISA can add extra high-quality clusters to the set produced by k-means, so combining these three methods is the method of choice

Clustering-based approaches to SAGE data mining

Serial analysis of gene expression (SAGE) is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical applications, such as the prediction of new gene functions and the identification of biomarkers in human cancer research. Clustering techniques have become fundamental approaches in these applications. This paper reviews relevant clustering techniques specifically designed for this type of data. It places an emphasis on current limitations and opportunities in this area for supporting biologically-meaningful data mining and visualisation

Impact of RNA degradation on gene expression profiling

<p>Abstract</p> <p>Background</p> <p>Gene expression profiling is a highly sensitive technique which is used for profiling tumor samples for medical prognosis. RNA quality and degradation influence the analysis results of gene expression profiles. The impact of this influence on the profiles and its medical impact is not fully understood. As patient samples are very valuable for clinical studies, it is necessary to establish criteria for the RNA quality to be able to use these samples in later analysis.</p> <p>Methods</p> <p>To investigate the effects of RNA integrity on gene expression profiling, whole genome expression arrays were used. We used tumor biopsies from patients diagnosed with locally advanced rectal cancer. To simulate degradation, the isolated total RNA of all patients was subjected to heat-induced degradation in a time-dependent manner. Expression profiling was then performed and data were analyzed bioinformatically to assess the differences.</p> <p>Results</p> <p>The differences introduced by RNA degradation were largely outweighed by the biological differences between the patients. Only a relatively small number of probes (275 out of 41,000) show a significant effect due to degradation. The genes that show the strongest effect due to RNA degradation were, especially, those with short mRNAs and probe positions near the 5' end.</p> <p>Conclusions</p> <p>Degraded RNA from tumor samples (RIN > 5) can still be used to perform gene expression analysis. A much higher biological variance between patients is observed compared to the effect that is imposed by degradation of RNA. Nevertheless there are genes, very short ones and those with the probe binding side close to the 5' end that should be excluded from gene expression analysis when working with degraded RNA. These results are limited to the Agilent 44 k microarray platform and should be carefully interpreted when transferring to other settings.</p

Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions

<p>Abstract</p> <p>Background</p> <p>Modern gene perturbation techniques, like RNA interference (RNAi), enable us to study effects of targeted interventions in cells efficiently. In combination with mRNA or protein expression data this allows to gain insights into the behavior of complex biological systems.</p> <p>Results</p> <p>In this paper, we propose Deterministic Effects Propagation Networks (DEPNs) as a special Bayesian Network approach to reverse engineer signaling networks from a combination of protein expression and perturbation data. DEPNs allow to reconstruct protein networks based on combinatorial intervention effects, which are monitored via changes of the protein expression or activation over one or a few time points. Our implementation of DEPNs allows for latent network nodes (i.e. proteins without measurements) and has a built in mechanism to impute missing data. The robustness of our approach was tested on simulated data. We applied DEPNs to reconstruct the <it>ERBB </it>signaling network in <it>de novo </it>trastuzumab resistant human breast cancer cells, where protein expression was monitored on Reverse Phase Protein Arrays (RPPAs) after knockdown of network proteins using RNAi.</p> <p>Conclusion</p> <p>DEPNs offer a robust, efficient and simple approach to infer protein signaling networks from multiple interventions. The method as well as the data have been made part of the latest version of the R package "nem" available as a supplement to this paper and via the Bioconductor repository.</p