Genome-wide DNA methylation analysis for diabetic nephropathy in type 1 diabetes mellitus

BACKGROUND: Diabetic nephropathy is a serious complication of diabetes mellitus and is associated with considerable morbidity and high mortality. There is increasing evidence to suggest that dysregulation of the epigenome is involved in diabetic nephropathy. We assessed whether epigenetic modification of DNA methylation is associated with diabetic nephropathy in a case-control study of 192 Irish patients with type 1 diabetes mellitus (T1D). Cases had T1D and nephropathy whereas controls had T1D but no evidence of renal disease. METHODS: We performed DNA methylation profiling in bisulphite converted DNA from cases and controls using the recently developed Illumina Infinium(R) HumanMethylation27 BeadChip, that enables the direct investigation of 27,578 individual cytosines at CpG loci throughout the genome, which are focused on the promoter regions of 14,495 genes. RESULTS: Singular Value Decomposition (SVD) analysis indicated that significant components of DNA methylation variation correlated with patient age, time to onset of diabetic nephropathy, and sex. Adjusting for confounding factors using multivariate Cox-regression analyses, and with a false discovery rate (FDR) of 0.05, we observed 19 CpG sites that demonstrated correlations with time to development of diabetic nephropathy. Of note, this included one CpG site located 18 bp upstream of the transcription start site of UNC13B, a gene in which the first intronic SNP rs13293564 has recently been reported to be associated with diabetic nephropathy. CONCLUSION: This high throughput platform was able to successfully interrogate the methylation state of individual cytosines and identified 19 prospective CpG sites associated with risk of diabetic nephropathy. These differences in DNA methylation are worthy of further follow-up in replication studies using larger cohorts of diabetic patients with and without nephropathy

Intra- and inter-individual genetic differences in gene expression

Genetic variation is known to influence the amount of mRNA produced by a gene. Given that the molecular machines control mRNA levels of multiple genes, we expect genetic variation in the components of these machines would influence multiple genes in a similar fashion. In this study we show that this assumption is correct by using correlation of mRNA levels measured independently in the brain, kidney or liver of multiple, genetically typed, mice strains to detect shared genetic influences. These correlating groups of genes (CGG) have collective properties that account for 40-90% of the variability of their constituent genes and in some cases, but not all, contain genes encoding functionally related proteins. Critically, we show that the genetic influences are essentially tissue specific and consequently the same genetic variations in the one animal may up-regulate a CGG in one tissue but down-regulate the same CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. The implication of this study is that this class of genetic variation can result in complex inter- and intra-individual and tissue differences and that this will create substantial challenges to the investigation of phenotypic outcomes, particularly in humans where multiple tissues are not readily available.

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Classification across gene expression microarray studies

<p>Abstract</p> <p>Background</p> <p>The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing.</p> <p>Results</p> <p>For each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In particular, the better predictive results of DV in across platform classification indicate higher robustness of the classifier when trained on single channel data and applied to gene expression ratios.</p> <p>Conclusions</p> <p>We present a systematic evaluation of strategies for the integration of independent microarray studies in a classification task. Our findings in across studies classification may guide further research aiming on the construction of more robust and reliable methods for stratification and diagnosis in clinical practice.</p

Advanced Genomic Data Mining

As data banks increase their size, one of the current challenges in bioinformatics is to be able to query them in a sensible way. Information is contained in differen

Interpretation of multiple probe sets mapping to the same gene in Affymetrix GeneChips

BACKGROUND: Affymetrix GeneChip technology enables the parallel observations of tens of thousands of genes. It is important that the probe set annotations are reliable so that biological inferences can be made about genes which undergo differential expression. Probe sets representing the same gene might be expected to show similar fold changes/z-scores, however this is in fact not the case. RESULTS: We have made a case study of the mouse Surf4, chosen because it is a gene that was reported to be represented by the same eight probe sets on the MOE430A array by both Affymetrix and Bioconductor in early 2004. Only five of the probe sets actually detect Surf4 transcripts. Two of the probe sets detect splice variants of Surf2. We have also studied the expression changes of the eight probe sets in a public-domain microarray experiment. The transcripts for Surf4 are correlated in time, and similarly the transcripts for Surf2 are also correlated in time. However, the transcripts for Surf4 and Surf2 are not correlated. This proof of principle shows that observations of expression can be used to confirm, or otherwise, annotation discrepancies. We have also investigated groups of probe sets on the RAE230A array that are assigned to the same LocusID, but which show large variances in differential expression in any one of three different experiments on rat. The probe set groups with high variances are found to represent cases of alternative splicing, use of alternative poly(A) signals, or incorrect annotations. CONCLUSION: Our results indicate that some probe sets should not be considered as unique measures of transcription, because the individual probes map to more than one transcript dependent upon the biological condition. Our results highlight the need for care when assessing whether groups of probe sets all measure the same transcript

Relative impact of key sources of systematic noise in Affymetrix and Illumina gene-expression microarray experiments

<p>Abstract</p> <p>Background</p> <p>Systematic processing noise, which includes batch effects, is very common in microarray experiments but is often ignored despite its potential to confound or compromise experimental results. Compromised results are most likely when re-analysing or integrating datasets from public repositories due to the different conditions under which each dataset is generated. To better understand the relative noise-contributions of various factors in experimental-design, we assessed several Illumina and Affymetrix datasets for technical variation between replicate hybridisations of Universal Human Reference (UHRR) and individual or pooled breast-tumour RNA.</p> <p>Results</p> <p>A varying degree of systematic noise was observed in each of the datasets, however in all cases the relative amount of variation between standard control RNA replicates was found to be greatest at earlier points in the sample-preparation workflow. For example, 40.6% of the total variation in reported expressions were attributed to replicate extractions, compared to 13.9% due to amplification/labelling and 10.8% between replicate hybridisations. Deliberate probe-wise batch-correction methods were effective in reducing the magnitude of this variation, although the level of improvement was dependent on the sources of noise included in the model. Systematic noise introduced at the chip, run, and experiment levels of a combined Illumina dataset were found to be highly dependant upon the experimental design. Both UHRR and pools of RNA, which were derived from the samples of interest, modelled technical variation well although the pools were significantly better correlated (4% average improvement) and better emulated the effects of systematic noise, over all probes, than the UHRRs. The effect of this noise was not uniform over all probes, with low GC-content probes found to be more vulnerable to batch variation than probes with a higher GC-content.</p> <p>Conclusions</p> <p>The magnitude of systematic processing noise in a microarray experiment is variable across probes and experiments, however it is generally the case that procedures earlier in the sample-preparation workflow are liable to introduce the most noise. Careful experimental design is important to protect against noise, detailed meta-data should always be provided, and diagnostic procedures should be routinely performed prior to downstream analyses for the detection of bias in microarray studies.</p

Altered Patterns of Gene Expression Underlying the Enhanced Immunogenicity of Radiation-Attenuated Schistosomes

Schistosoma mansoni is a blood-dwelling parasitic worm that causes schistosomiasis in humans throughout Africa and parts of South America. A vaccine would enhance attempts to control and eradicate the disease that currently relies on treatment with a single drug. Although a manufactured vaccine has yet to generate high levels of protection, this can be achieved with infective parasite larvae that have been disabled by exposure to radiation. How these weakened parasites are able to induce protective immunity when normal parasites do not, is the question addressed by our experiments. We have used a technique of gene expression profiling to compare the patterns in normal and disabled parasites, over the period when they would trigger an immune response in the host. We found that only a handful of genes were differentially expressed, all of them diminished in the disabled parasite. However, a more sensitive technique to examine groups of genes revealed that those involved in nervous system and muscle function were depressed in the disabled parasites. We suggest that reduced mobility of these larvae permits them longer contact with the immune system, thus enabling a strong protective immune response to develop

ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data

<p>Abstract</p> <p>Background</p> <p>Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-seq) or ChIP followed by genome tiling array analysis (ChIP-chip) have become standard technologies for genome-wide identification of DNA-binding protein target sites. A number of algorithms have been developed in parallel that allow identification of binding sites from ChIP-seq or ChIP-chip datasets and subsequent visualization in the University of California Santa Cruz (UCSC) Genome Browser as custom annotation tracks. However, summarizing these tracks can be a daunting task, particularly if there are a large number of binding sites or the binding sites are distributed widely across the genome.</p> <p>Results</p> <p>We have developed <it>ChIPpeakAnno </it>as a Bioconductor package within the statistical programming environment R to facilitate batch annotation of enriched peaks identified from ChIP-seq, ChIP-chip, cap analysis of gene expression (CAGE) or any experiments resulting in a large number of enriched genomic regions. The binding sites annotated with <it>ChIPpeakAnno </it>can be viewed easily as a table, a pie chart or plotted in histogram form, i.e., the distribution of distances to the nearest genes for each set of peaks. In addition, we have implemented functionalities for determining the significance of overlap between replicates or binding sites among transcription factors within a complex, and for drawing Venn diagrams to visualize the extent of the overlap between replicates. Furthermore, the package includes functionalities to retrieve sequences flanking putative binding sites for PCR amplification, cloning, or motif discovery, and to identify Gene Ontology (GO) terms associated with adjacent genes.</p> <p>Conclusions</p> <p><it>ChIPpeakAnno </it>enables batch annotation of the binding sites identified from ChIP-seq, ChIP-chip, CAGE or any technology that results in a large number of enriched genomic regions within the statistical programming environment R. Allowing users to pass their own annotation data such as a different Chromatin immunoprecipitation (ChIP) preparation and a dataset from literature, or existing annotation packages, such as <it>GenomicFeatures </it>and <it>BSgenom</it>e, provides flexibility. Tight integration to the <it>biomaRt </it>package enables up-to-date annotation retrieval from the BioMart database.</p

Meta-analysis of archived DNA microarrays identifies genes regulated by hypoxia and involved in a metastatic phenotype in cancer cells

<p>Abstract</p> <p>Background</p> <p>Metastasis is a major cancer-related cause of death. Recent studies have described metastasis pathways. However, the exact contribution of each pathway remains unclear. Another key feature of a tumor is the presence of hypoxic areas caused by a lack of oxygen at the center of the tumor. Hypoxia leads to the expression of pro-metastatic genes as well as the repression of anti-metastatic genes. As many Affymetrix datasets about metastasis and hypoxia are publicly available and not fully exploited, this study proposes to re-analyze these datasets to extract new information about the metastatic phenotype induced by hypoxia in different cancer cell lines.</p> <p>Methods</p> <p>Affymetrix datasets about metastasis and/or hypoxia were downloaded from GEO and ArrayExpress. AffyProbeMiner and GCRMA packages were used for pre-processing and the Window Welch <it>t </it>test was used for processing. Three approaches of meta-analysis were eventually used for the selection of genes of interest.</p> <p>Results</p> <p>Three complementary approaches were used, that eventually selected 183 genes of interest. Out of these 183 genes, 99, among which the well known <it>JUNB</it>, <it>FOS </it>and <it>TP63</it>, have already been described in the literature to be involved in cancer. Moreover, 39 genes of those, such as <it>SERPINE1 </it>and <it>MMP7</it>, are known to regulate metastasis. Twenty-one genes including <it>VEGFA </it>and <it>ID2 </it>have also been described to be involved in the response to hypoxia. Lastly, DAVID classified those 183 genes in 24 different pathways, among which 8 are directly related to cancer while 5 others are related to proliferation and cell motility. A negative control composed of 183 random genes failed to provide such results. Interestingly, 6 pathways retrieved by DAVID with the 183 genes of interest concern pathogen recognition and phagocytosis.</p> <p>Conclusion</p> <p>The proposed methodology was able to find genes actually known to be involved in cancer, metastasis and hypoxia and, thus, we propose that the other genes selected based on the same methodology are of prime interest in the metastatic phenotype induced by hypoxia.</p