Client-centred practice in occupational therapy

Dekker, J. [Promotor]Polman, C.H. [Promotor]Steultjens, M.P.M. [Copromotor

Exploring pathway interactions in insulin resistant mouse liver

<p>Abstract</p> <p>Background</p> <p>Complex phenotypes such as insulin resistance involve different biological pathways that may interact and influence each other. Interpretation of related experimental data would be facilitated by identifying relevant pathway interactions in the context of the dataset.</p> <p>Results</p> <p>We developed an analysis approach to study interactions between pathways by integrating gene and protein interaction networks, biological pathway information and high-throughput data. This approach was applied to a transcriptomics dataset to investigate pathway interactions in insulin resistant mouse liver in response to a glucose challenge. We identified regulated pathway interactions at different time points following the glucose challenge and also studied the underlying protein interactions to find possible mechanisms and key proteins involved in pathway cross-talk. A large number of pathway interactions were found for the comparison between the two diet groups at t = 0. The initial response to the glucose challenge (t = 0.6) was typed by an acute stress response and pathway interactions showed large overlap between the two diet groups, while the pathway interaction networks for the late response were more dissimilar.</p> <p>Conclusions</p> <p>Studying pathway interactions provides a new perspective on the data that complements established pathway analysis methods such as enrichment analysis. This study provided new insights in how interactions between pathways may be affected by insulin resistance. In addition, the analysis approach described here can be generally applied to different types of high-throughput data and will therefore be useful for analysis of other complex datasets as well.</p

An evaluation of two-channel ChIP-on-chip and DNA methylation microarray normalization strategies

<p>Abstract</p> <p>Background</p> <p>The combination of chromatin immunoprecipitation with two-channel microarray technology enables genome-wide mapping of binding sites of DNA-interacting proteins (ChIP-on-chip) or sites with methylated CpG di-nucleotides (DNA methylation microarray). These powerful tools are the gateway to understanding gene transcription regulation. Since the goals of such studies, the sample preparation procedures, the microarray content and study design are all different from transcriptomics microarrays, the data pre-processing strategies traditionally applied to transcriptomics microarrays may not be appropriate. Particularly, the main challenge of the normalization of "regulation microarrays" is (i) to make the data of individual microarrays quantitatively comparable and (ii) to keep the signals of the enriched probes, representing DNA sequences from the precipitate, as distinguishable as possible from the signals of the un-enriched probes, representing DNA sequences largely absent from the precipitate.</p> <p>Results</p> <p>We compare several widely used normalization approaches (VSN, LOWESS, quantile, T-quantile, Tukey's biweight scaling, Peng's method) applied to a selection of regulation microarray datasets, ranging from DNA methylation to transcription factor binding and histone modification studies. Through comparison of the data distributions of control probes and gene promoter probes before and after normalization, and assessment of the power to identify known enriched genomic regions after normalization, we demonstrate that there are clear differences in performance between normalization procedures.</p> <p>Conclusion</p> <p>T-quantile normalization applied separately on the channels and Tukey's biweight scaling outperform other methods in terms of the conservation of enriched and un-enriched signal separation, as well as in identification of genomic regions known to be enriched. T-quantile normalization is preferable as it additionally improves comparability between microarrays. In contrast, popular normalization approaches like quantile, LOWESS, Peng's method and VSN normalization alter the data distributions of regulation microarrays to such an extent that using these approaches will impact the reliability of the downstream analysis substantially.</p

Reduction of colonic inflammation in HLA-B27 transgenic rats by feeding Marie Ménard apples, rich in polyphenols

Inflammatory bowel diseases (IBD) are immunomediated ailments affecting millions of individuals. Although diet is regarded as an important factor influencing IBD, there are no accepted dietary recommendations presently available. We administered 7.6 % lyophilised apples obtained from two cultivars (Golden Delicious and Marie Ménard, low and high in polyphenols, respectively) to HLA-B27 transgenic rats which develop spontaneous IBD. After 3 months feeding, rats fed Marie Ménard apples had reduced myeloperoxidase activity (3.6 (sem 0.3) v. 2.2 (sem 0.2) U/g tissue; P <0.05) and reduced cyclo-oxygenase-2 (P <0.05) and inducible NO synthase gene expression (P <0.01) in the colon mucosa and significantly less diarrhoea (P <0.05), compared with control rats. Cell proliferation in the colon mucosa was reduced significantly by feeding Golden Delicious apples, with a borderline effect of Marie Ménard apples. Gene expression profiling of the colon mucosa, analysed using the Whole Rat Genome 4 x 44 K Agilent Arrays, revealed a down-regulation of the pathways of PG synthesis, mitogen-activated protein kinase (MAPK) signalling and TNFalpha-NF-kappaB in Marie Ménard-fed rats. In the stools of the animals of this group we also measured a significant reduction of bacteria of the Bacteriodes fragilis group. In conclusion, the administration of Marie Ménard apples, rich in polyphenols and used at present only in the manufacturing of cider, ameliorates colon inflammation in transgenic rats developing spontaneous intestinal inflammation, suggesting the possible use of these and other apple varieties to control inflammation in IBD patient

Fatigue Profiles in Patients with Multiple Sclerosis are Based on Severity of Fatigue and not on Dimensions of Fatigue

Fatigue related to Multiple Sclerosis (MS) is considered a multidimensional symptom, manifesting in several dimensions such as physical, cognitive, and psychosocial fatigue. This study investigated in 264 patients with severe primary MS-related fatigue (median MS duration 6.8 years, mean age 48.1 years, 75% women) whether subgroups can be distinguished based on these dimensions. Subsequently, we tested whether MS-related

Comparative Cost-Effectiveness of Drugs in Early versus Late Stages of Cancer:Review of the Literature and a Case Study in Breast Cancer

Many oncological drugs that are being used in the adjuvant setting were first submitted for reimbursement in the metastatic stage, with differences in incremental cost-effectiveness ratios (ICERs) in both settings having potential implications for reimbursement and pricing. The aim of this study is to identify a possible trend in the cost-effectiveness for the early/adjuvant and late/metastatic stages of oncological drugs through review and case study.We reviewed pairs of cost-effectiveness analyses of the same oncological drug in different stages for Scotland and the Netherlands. The case study in this report was directed at trastuzumab in the Dutch situation. Using a simplified Markov model, the cost-effectiveness in early and late stage of breast cancer was calculated and compared to the findings from the review.Comparable studies were found for cetuximab, bortezomib and bosutinib. Treatments in the late stage were found to be more expensive per QALY by a factor ranging from 1.5 to 12. The case study provided a similar result; late stage treatment was more expensive by a factor 10. Using, for example, a threshold of € 80,000/QALY, the early stage of cetuximab, bosutinib and trastuzumab are deemed cost-effective, while their compared late stage is lifted over the threshold and potentially considered not cost-effective.ICERs of oncological drugs used in different stages are more unfavourable in the late stage than in the early stage. Applying a reasonable threshold may result in early stage treatment being deemed cost-effective while late stage potentially not. Authorities should be aware of this when assessing oncological drugs and interpreting the corresponding ICERs, in the situation where oncological drugs are generally most submitted for reimbursement in the late stage initially

Beyond Pathway Analysis: Identification of Active Subnetworks in Rett Syndrome

Pathway and network approaches are valuable tools in analysis and interpretation of large complex omics data. Even in the field of rare diseases, like Rett syndrome, omics data are available, and the maximum use of such data requires sophisticated tools for comprehensive analysis and visualization of the results. Pathway analysis with differential gene expression data has proven to be extremely successful in identifying affected processes in disease conditions. In this type of analysis, pathways from different databases like WikiPathways and Reactome are used as separate, independent entities. Here, we show for the first time how these pathway models can be used and integrated into one large network using the WikiPathways RDF containing all human WikiPathways and Reactome pathways, to perform network analysis on transcriptomics data. This network was imported into the network analysis tool Cytoscape to perform active submodule analysis. Using a publicly available Rett syndrome gene expression dataset from frontal and temporal cortex, classical enrichment analysis, including pathway and Gene Ontology analysis, revealed mainly immune response, neuron specific and extracellular matrix processes. Our active module analysis provided a valuable extension of the analysis prominently showing the regulatory mechanism of MECP2, especially on DNA maintenance, cell cycle, transcription, and translation. In conclusion, using pathway models for classical enrichment and more advanced network analysis enables a more comprehensive analysis of gene expression data and provides novel results

Altered sphingolipid function in Alzheimer's disease;:a gene regulatory network approach

Sphingolipids (SLs) are bioactive lipids involved in various important physiological functions. The SL pathway has been shown to be affected in several brain-related disorders, including Alzheimer's disease (AD). Recent evidence suggests that epigenetic dysregulation plays an important role in the pathogenesis of AD as well. Here, we use an integrative approach to better understand the relationship between epigenetic and transcriptomic processes in regulating SL function in the middle temporal gyrus of AD patients. Transcriptomic analysis of 252 SL-related genes, selected based on GO term annotations, from 46 AD patients and 32 healthy age-matched controls, revealed 103 differentially expressed SL-related genes in AD patients. Additionally, methylomic analysis of the same subjects revealed parallel hydroxymethylation changes in PTGIS, GBA, and ITGB2 in AD. Subsequent gene regulatory network-based analysis identified 3 candidate genes, that is, SELPLG, SPHK1 and CAV1 whose alteration holds the potential to revert the gene expression program from a diseased towards a healthy state. Together, this epigenomic and transcriptomic approach highlights the importance of SL-related genes in AD, and may provide novel biomarkers and therapeutic alternatives to traditionally investigated biological pathways in AD.</p

Aqueous humor proteome of primary open angle glaucoma: A combined dataset of mass spectrometry studies

Analysis of the proteins of the aqueous humor can help to elucidate the complex pathogenesis of primary open angle glaucoma. Thanks to advances in liquid chromatography tandem mass spectrometry (LC-MS/MS) it is now possible to identify hundreds of proteins in individual aqueous humor samples without the need to pool samples. We performed a systematic literature search to find publications that performed LC-MS/MS on aqueous humor samples of glaucoma patients and of non-glaucomatous controls. Of the seven publications that we found, we obtained the raw data of three publications. These three studies used glaucoma patients that were clinically similar (i.e. undergoing glaucoma filtration surgery) which prompted us to reanalyse and combine their data. Raw data of each study were analysed separately with the latest version of MaxQuant (version v1.6.11.0). Outcome files were exported to Microsoft Excel. Samples belonging to the same patient were averaged to obtain peptide expression values per individual. We compared the overlap of identified proteins using the VLOOKUP function of Excel and a publicly available Venn diagram software. For the peptide sequences that can belong to multiple proteins (usually of the same protein family), we initially included all possibly identified proteins. This ensured that we would not miss a potential overlap between the studies due to differences in identified peptide counts. Next, of those peptides of which we compared multiple proteins, only one unique protein was included in our analysis i.e. either the protein overlapping bet