Biomedical subjects

Marcel J T Reinders

Publications and source records attributed to Marcel J T Reinders.

14 recordsLinked to original sources

PubMed2025

Domain mapping of disease mutations reveals pathogenic SORL1 variants in Alzheimer's disease.

BACKGROUND: Protein truncating variants (PTVs) in SORL1 are observed almost exclusively in Alzheimer’s Disease (AD) cases, but the effect of rare SORL1 missense variants is unclear. METHODS: To identify high-priority missense variants (HPVs), we applied ‘domain mapping of disease mutations’ for the 637 unique coding SORL1 variants detected in 18,959 AD-cases and 21,893 non-demented controls. RESULTS: In this sample, PTVs and HPVs associated with respectively a 35- and 10-fold increased risk of early onset AD and 17- and 6-fold increased risk of overall AD. The median age at onset (AAO) of PTV- and HPV-carriers was 62 and 64 years, and APOE-genotype contributed to AAO-variability. The median AAO of PTV- and HPV-carriers is ~8–10 years earlier than wild-type SORL1 carriers, matched for APOE-genotype. Specific HPVs are highly penetrant and lead to earlier AAOs than PTVs, suggesting possible dominant negative effects. CONCLUSION: Our results justify a debate on whether HPV carriers should be considered for clinical counseling.

Humans↗

PubMed2016

Gene co-expression analysis identifies brain regions and cell types involved in migraine pathophysiology: a GWAS-based study using the Allen Human Brain Atlas.

Migraine is a common disabling neurovascular brain disorder typically characterised by attacks of severe headache and associated with autonomic and neurological symptoms. Migraine is caused by an interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified over a dozen genetic loci associated with migraine. Here, we integrated migraine GWAS data with high-resolution spatial gene expression data of normal adult brains from the Allen Human Brain Atlas to identify specific brain regions and molecular pathways that are possibly involved in migraine pathophysiology. To this end, we used two complementary methods. In GWAS data from 23,285 migraine cases and 95,425 controls, we first studied modules of co-expressed genes that were calculated based on human brain expression data for enrichment of genes that showed association with migraine. Enrichment of a migraine GWAS signal was found for five modules that suggest involvement in migraine pathophysiology of: (i) neurotransmission, protein catabolism and mitochondria in the cortex; (ii) transcription regulation in the cortex and cerebellum; and (iii) oligodendrocytes and mitochondria in subcortical areas. Second, we used the high-confidence genes from the migraine GWAS as a basis to construct local migraine-related co-expression gene networks. Signatures of all brain regions and pathways that were prominent in the first method also surfaced in the second method, thus providing support that these brain regions and pathways are indeed involved in migraine pathophysiology.

Atlases as Topic↗

PubMed2006

Gene therapy: is IL2RG oncogenic in T-cell development?

The gene IL2RG encodes the gamma-chain of the interleukin-2 receptor and is mutated in patients with X-linked severe combined immune deficiency (X-SCID). Woods et al. report the development of thymus tumours in a mouse model of X-SCID after correction by lentiviral overexpression of IL2RG and claim that these were caused by IL2RG itself. Here we find that retroviral overexpression of IL2RG in human CD34+ cells has no effect on T-cell development, whereas overexpression of the T-cell acute lymphoblastic leukaemia (T-ALL) oncogene LMO2 leads to severe abnormalities. Retroviral expression of IL2RG may therefore not be directly oncogenic--rather, the restoration of normal signalling by the interleukin-7 receptor to X-SCID precursor cells allows progression of T-cell development to stages that are permissive for the pro-leukaemic effects of ectopic LMO2.

Adaptor Proteins, Signal Transducing↗

PubMed2006

A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets.

BACKGROUND: Gene selection is an important step when building predictors of disease state based on gene expression data. Gene selection generally improves performance and identifies a relevant subset of genes. Many univariate and multivariate gene selection approaches have been proposed. Frequently the claim is made that genes are co-regulated (due to pathway dependencies) and that multivariate approaches are therefore per definition more desirable than univariate selection approaches. Based on the published performances of all these approaches a fair comparison of the available results can not be made. This mainly stems from two factors. First, the results are often biased, since the validation set is in one way or another involved in training the predictor, resulting in optimistically biased performance estimates. Second, the published results are often based on a small number of relatively simple datasets. Consequently no generally applicable conclusions can be drawn. RESULTS: In this study we adopted an unbiased protocol to perform a fair comparison of frequently used multivariate and univariate gene selection techniques, in combination with a ränge of classifiers. Our conclusions are based on seven gene expression datasets, across several cancer types. CONCLUSION: Our experiments illustrate that, contrary to several previous studies, in five of the seven datasets univariate selection approaches yield consistently better results than multivariate approaches. The simplest multivariate selection approach, the Top Scoring method, achieves the best results on the remaining two datasets. We conclude that the correlation structures, if present, are difficult to extract due to the small number of samples, and that consequently, overly-complex gene selection algorithms that attempt to extract these structures are prone to overtraining.

Algorithms↗

PubMed2006

The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies.

BACKGROUND: Intensity values measured by Affymetrix microarrays have to be both normalized, to be able to compare different microarrays by removing non-biological variation, and summarized, generating the final probe set expression values. Various pre-processing techniques, such as dChip, GCRMA, RMA and MAS have been developed for this purpose. This study assesses the effect of applying different pre-processing methods on the results of analyses of large Affymetrix datasets. By focusing on practical applications of microarray-based research, this study provides insight into the relevance of pre-processing procedures to biology-oriented researchers. RESULTS: Using two publicly available datasets, i.e., gene-expression data of 285 patients with Acute Myeloid Leukemia (AML, Affymetrix HG-U133A GeneChip) and 42 samples of tumor tissue of the embryonal central nervous system (CNS, Affymetrix HuGeneFL GeneChip), we tested the effect of the four pre-processing strategies mentioned above, on (1) expression level measurements, (2) detection of differential expression, (3) cluster analysis and (4) classification of samples. In most cases, the effect of pre-processing is relatively small compared to other choices made in an analysis for the AML dataset, but has a more profound effect on the outcome of the CNS dataset. Analyses on individual probe sets, such as testing for differential expression, are affected most; supervised, multivariate analyses such as classification are far less sensitive to pre-processing. CONCLUSION: Using two experimental datasets, we show that the choice of pre-processing method is of relatively minor influence on the final analysis outcome of large microarray studies whereas it can have important effects on the results of a smaller study. The data source (platform, tissue homogeneity, RNA quality) is potentially of bigger importance than the choice of pre-processing method.

Algorithms↗

PubMed2005

Maximum significance clustering of oligonucleotide microarrays.

MOTIVATION: Affymetrix high-density oligonucleotide microarrays measure the expression of DNA transcripts using probesets, i.e. multiple probes per transcript. Usually, these multiple measurements are transformed into a single probeset expression level before data analysis proceeds; any information on variability is lost. In this paper we demonstrate how individual probe measurements can be used in a statistic for differential expression. Furthermore, we show how this statistic can serve as a criterion for clustering microarrays. RESULTS: A novel clustering algorithm using this maximum significance criterion is demonstrated to be more efficient with the measured data than competing techniques for dealing with repeated measurements, especially when the sample size is small.

Algorithms↗

PubMed2005

Ig gene rearrangement steps are initiated in early human precursor B cell subsets and correlate with specific transcription factor expression.

The role of specific transcription factors in the initiation and regulation of Ig gene rearrangements has been studied extensively in mouse models, but data on normal human precursor B cell differentiation are limited. We purified five human precursor B cell subsets, and assessed and quantified their IGH, IGK, and IGL gene rearrangement patterns and gene expression profiles. Pro-B cells already massively initiate D(H)-J(H) rearrangements, which are completed with V(H)-DJ(H) rearrangements in pre-B-I cells. Large cycling pre-B-II cells are selected for in-frame IGH gene rearrangements. The first IGK/IGL gene rearrangements were initiated in pre-B-I cells, but their frequency increased enormously in small pre-B-II cells, and in-frame selection was found in immature B cells. Transcripts of the RAG1 and RAG2 genes and earlier defined transcription factors, such as E2A, early B cell factor, E2-2, PAX5, and IRF4, were specifically up-regulated at stages undergoing Ig gene rearrangements. Based on the combined Ig gene rearrangement status and gene expression profiles of consecutive precursor B cell subsets, we identified 16 candidate genes involved in initiation and/or regulation of Ig gene rearrangements. These analyses provide new insights into early human precursor B cell differentiation steps and represent an excellent template for studies on oncogenic transformation in precursor B acute lymphoblastic leukemia and B cell differentiation blocks in primary Ab deficiencies.

Adolescent↗

PubMed2005

New insights on human T cell development by quantitative T cell receptor gene rearrangement studies and gene expression profiling.

To gain more insight into initiation and regulation of T cell receptor (TCR) gene rearrangement during human T cell development, we analyzed TCR gene rearrangements by quantitative PCR analysis in nine consecutive T cell developmental stages, including CD34+ lin- cord blood cells as a reference. The same stages were used for gene expression profiling using DNA microarrays. We show that TCR loci rearrange in a highly ordered way (TCRD-TCRG-TCRB-TCRA) and that the initiating Ddelta2-Ddelta3 rearrangement occurs at the most immature CD34+CD38-CD1a- stage. TCRB rearrangement starts at the CD34+CD38+CD1a- stage and complete in-frame TCRB rearrangements were first detected in the immature single positive stage. TCRB rearrangement data together with the PTCRA (pTalpha) expression pattern show that human TCRbeta-selection occurs at the CD34+CD38+CD1a+ stage. By combining the TCR rearrangement data with gene expression data, we identified candidate factors for the initiation/regulation of TCR recombination. Our data demonstrate that a number of key events occur earlier than assumed previously; therefore, human T cell development is much more similar to murine T cell development than reported before.

Animals↗

PubMed2005

Microarray analysis reveals expression regulation of Wnt antagonists in differentiating osteoblasts.

Wnt signaling has been implicated in regulating bone formation by controlling osteoblast proliferation and function. Although stabilization of beta-catenin by Wnt has been shown to increase alkaline phosphatase expression and osteoblast differentiation, the precise role of Wnt signaling during the process of osteoblast differentiation is largely unknown. In this study, we used microarray technology to investigate expression regulation of Wnt signaling components during in vitro osteoblast differentiation. Expression was analyzed during bone morphogenetic protein 2 (BMP2)-induced osteoblast differentiation of murine C2C12 and MC3T3 cells and data were compared with expression in BMP2-treated NIH3T3 fibroblasts. During osteoblast differentiation, particularly strong expression regulation of the Wnt antagonists Sfrp2 (secreted frizzled related protein 2) and Wif1 (Wnt inhibitory factor 1) was observed in the late phase of differentiation. In situ expression analysis in murine tail vertebrae supported Wif1 expression during late phase bone cell differentiation, since Wif1 was found to be expressed in vivo in trabecular, but not in cortical bone. We further analyzed the effects of continuous activation of Wnt signaling by lithium chloride and observed that osteoblast differentiation was reduced, as measured by expression of osteoblast marker genes encoding alkaline phosphatase, osteocalcin, and osterix, as well as by the amount of calcium release. Taken together, our data indicate that endogenous expression of Wnt antagonists by osteoblasts provides a negative Wnt feedback loop which is essential in controlling osteoblast maturation.

3T3 Cells↗

PubMed2005

A protocol for building and evaluating predictors of disease state based on microarray data.

MOTIVATION: Microarray gene expression data are increasingly employed to identify sets of marker genes that accurately predict disease development and outcome in cancer. Many computational approaches have been proposed to construct such predictors. However, there is, as yet, no objective way to evaluate whether a new approach truly improves on the current state of the art. In addition no 'standard' computational approach has emerged which enables robust outcome prediction. RESULTS: An important contribution of this work is the description of a principled training and validation protocol, which allows objective evaluation of the complete methodology for constructing a predictor. We review the possible choices of computational approaches, with specific emphasis on predictor choice and reporter selection strategies. Employing this training-validation protocol, we evaluated different reporter selection strategies and predictors on six gene expression datasets of varying degrees of difficulty. We demonstrate that simple reporter selection strategies (forward filtering and shrunken centroids) work surprisingly well and outperform partial least squares in four of the six datasets. Similarly, simple predictors, such as the nearest mean classifier, outperform more complex classifiers. Our training-validation protocol provides a robust methodology to evaluate the performance of new computational approaches and to objectively compare outcome predictions on different datasets.

Algorithms↗

PubMed2005

An expression profile for diagnosis of lymph node metastases from primary head and neck squamous cell carcinomas.

Metastasis is the process by which cancers spread to distinct sites in the body. It is the principal cause of death in individuals suffering from cancer. For some types of cancer, early detection of metastasis at lymph nodes close to the site of the primary tumor is pivotal for appropriate treatment. Because it can be difficult to detect lymph node metastases reliably, many individuals currently receive inappropriate treatment. We show here that DNA microarray gene-expression profiling can detect lymph node metastases for primary head and neck squamous cell carcinomas that arise in the oral cavity and oropharynx. The predictor, established with an 82-tumor training set, outperforms current clinical diagnosis when independently validated. The 102 predictor genes offer unique insights into the processes underlying metastasis. The results show that the metastatic state can be deciphered from the primary tumor gene-expression pattern and that treatment can be substantially improved.

Carcinoma, Squamous Cell↗

PubMed2005

Edge-based image restoration.

In this paper, we propose a new image inpainting algorithm that relies on explicit edge information. The edge information is used both for the reconstruction of a skeleton image structure in the missing areas, as well as for guiding the interpolation that follows. The structure reconstruction part exploits different properties of the edges, such as the colors of the objects they separate, an estimate of how well one edge continues into another one, and the spatial order of the edges with respect to each other. In order to preserve both sharp and smooth edges, the areas delimited by the recovered structure are interpolated independently, and the process is guided by the direction of the nearby edges. The novelty of our approach lies primarily in exploiting explicitly the constraint enforced by the numerical interpretation of the sequential order of edges, as well as in the pixel filling method which takes into account the proximity and direction of edges. Extensive experiments are carried out in order to validate and compare the algorithm both quantitatively and qualitatively. They show the advantages of our algorithm and its readily application to real world cases.

Algorithms↗

PubMed2005

The nearest subclass classifier: a compromise between the nearest mean and nearest neighbor classifier.

We present the Nearest Subclass Classifier (NSC), which is a classification algorithm that unifies the flexibility of the nearest neighbor classifier with the robustness of the nearest mean classifier. The algorithm is based on the Maximum Variance Cluster algorithm and, as such, it belongs to the class of prototype-based classifiers. The variance constraint parameter of the cluster algorithm serves to regularize the classifier, that is, to prevent overfitting. With a low variance constraint value, the classifier turns into the nearest neighbor classifier and, with a high variance parameter, it becomes the nearest mean classifier with the respective properties. In other words, the number of prototypes ranges from the whole training set to only one per class. In the experiments, we compared the NSC with regard to its performance and data set compression ratio to several other prototype-based methods. On several data sets, the NSC performed similarly to the k-nearest neighbor classifier, which is a well-established classifier in many domains. Also concerning storage requirements and classification speed, the NSC has favorable properties, so it gives a good compromise between classification performance and efficiency.

Algorithms↗

PubMed2002

Molecular classification of breast carcinomas by comparative genomic hybridization: a specific somatic genetic profile for BRCA1 tumors.

In approximately 70% of the families with a high frequency of early-onset breast and/or ovarian cancer, BRCA1 or BRCA2 germline mutations cannot be identified with the current screening regime. Therefore, we used data mining to identify a somatic genetic signature to differentiate BRCA1 mutation carriers from non-BRCA1 carriers based on the genetic characteristics of their breast carcinomas. For this purpose, we developed a molecular classifier, which assigns a given tumor to either the BRCA1 or control group based on somatic genetic profiles as revealed by comparative genomic hybridization. This was performed on breast tumors selected from two groups of patients: 28 proven BRCA1 germline mutation carriers; and a control group consisting of 42 breast tumors from patients with unknown BRCA1 or BRCA2 status. We show that BRCA1 breast carcinomas exhibit specific somatic genetic aberrations and can be distinguished from control tumors with an accuracy of 84% (sensitivity of 96% and specificity of 76%). Chromosomal bands used by this classifier include regions on chromosomes 3p, 3q, and 5q. The classifier miss-assigned one patient with a BRCA1 mutation to the non-BRCA1 class. The germline mutation in this patient is a 62bp deletion in the last exon of BRCA1 (5622del62). Possibly, this mutation may give a different phenotypic effect than do mutations in other regions of the gene. Validation on an independent set of BRCA1 and sporadic tumors showed that the BRCA1 classifier correctly identified all 6 BRCA1 tumors and assigned 4 of the 19 control patients to the BRCA1 class. The resulting accuracy on the validation set is 84%.

Breast Neoplasms↗