Integrating protein structures and precomputed genealogies in the Magnum database: Examples with cellular retinoid binding proteins

Background

Gene expression microarray experiments are expensive to conduct and guidelines for acceptable quality control at intermediate steps before and after the samples are hybridised to chips are vague. We conducted an experiment hybridising RNA from human brain to 117 U133A Affymetrix GeneChips and used these data to explore the relationship between 4 pre-chip variables and 22 post-chip outcomes and quality control measures.

Results

We found that the pre-chip variables were significantly correlated with each other but that this correlation was strongest between measures of RNA quality and cRNA yield. Post-mortem interval was negatively correlated with these variables. Four principal components, reflecting array outliers, array adjustment, hybridisation noise and RNA integrity, explain about 75% of the total post-chip measure variability. Two significant canonical correlations existed between the pre-chip and post-chip variables, derived from MAS 5.0, dChip and the Bioconductor packages affy and affyPLM. The strongest (CANCOR 0.838, p < 0.0001) correlated RNA integrity and yield with post chip quality control (QC) measures indexing 3'/5' RNA ratios, bias or scaling of the chip and scaling of the variability of the signal across the chip. Post-mortem interval was relatively unimportant. We also found that the RNA integrity number (RIN) could be moderately well predicted by post-chip measures B_ACTIN35, GAPDH35 and SF.

Conclusion

We have found that the post-chip variables having the strongest association with quantities measurable before hybridisation are those reflecting RNA integrity. Other aspects of quality, such as noise measures (reflecting the execution of the assay) or measures reflecting data quality (outlier status and array adjustment variables) are not well predicted by the variables we were able to determine ahead of time. There could be other variables measurable pre-hybridisation which may be better associated with expression data quality measures. Uncovering such connections could create savings on costly microarray experiments by eliminating poor samples before hybridisation.     

Characterization of the expression ratio noise structure in high-density oligonucleotide arrays

Background

High-density oligonucleotide microarrays provide a powerful tool for assessing differential mRNA expression levels. Characterizing the noise resulting from the enzymatic and hybridization steps, called type I noise, is essential for attributing significance measures to the differential expression scores. We introduce scoring functions for expression ratios, and associated quality measures. Both the PM (Perfect Match) probes and PM-MM differentials (MM is the single MisMatch) are considered as raw intensities. We then characterize the log-ratio noise structure using robust estimates of their intensity dependent variance.

Results

We show the relationships between the obtained ratios and their quality measures. The complementarity of PM and PM-MM methods is emphasized by the probe sets signal to noise measures. Using a large set of replicate experiments, we demonstrate that the noise structure in the log-ratios very closely follows a local log-normal distribution for both the PM and PM-MM cases. Therefore, significance relative to the type I noise can be quantified reliably using the local STD. We discuss the intensity dependence of the STD and show that ratio scores >1.25 are significant in the mid- to high-intensity range.

Conclusions

The ratio noise structure inherent to high-density oligonucleotide arrays can be well described in terms of local log-normal ratio distributions with characteristic intensity dependence. Therefore, robust estimates of the local STD of these distributions provide a simple and powerful way for assessing significance (relative to type I noise) in differential gene expression. This approach will be helpful for improving the reliability of predictions from hybridization experiments in general.     

Chondrosarcoma of the spine: A rare case with unusual presentation

Background

TMAs are becoming a useful tool for research and quality control methods, mostly for immunohistochemistry and in situ hybridization.

Methods

A new technique that allows building TMA blocks with more than 300 tissue cores without using a recipient paraffin block for the tissue cores and without using a commercial TMA builder instrument is described. This technique is based on the construction of TMA needles modifying conventional hypodermic needles to punch tissue cores from donor blocks, which are attached by double-side adhesive tape on a computer-generated paper grid used to align the cores on the block mould, which is filled with liquid paraffin.

Results

More than two hundred TMA blocks were constructed using this method, utilized in immunohistochemistry and histochemistry as positive and negative controls and also in research.

Conclusion

This technique has the following advantages: it is easy to reproduce, affordable, quick and creates uniform blocks with more than 300 cores aligned, adherent and easy to cut, with negligible losses during cutting and immunohistochemistry and in situ hybridization procedures.     

Reference genes for quantitative reverse transcription-polymerase chain reaction expression studies in wild and cultivated peanut

Background

Molecular genetic studies on rare tumour entities, such as bone tumours, often require the use of decalcified, formalin-fixed, paraffin-embedded tissue (dFFPE) samples. Regardless of which decalcification procedure is used, this introduces a vast breakdown of DNA that precludes the possibility of further molecular genetic testing. We set out to establish a robust protocol that would overcome these intrinsic hurdles for bone tumour research.

Findings

The goal of our study was to establish a protocol, using a modified DNA isolation procedure and quality controls, to select decalcified samples suitable for array-CGH testing. Archival paraffin blocks were obtained from 9 different pathology departments throughout Europe, using different fixation, embedding and decalcification procedures, in order to preclude a bias for certain lab protocols. Isolated DNA samples were subjected to direct chemical labelling and enzymatic labelling systems and were hybridised on a high resolution oligonucleotide chip containing 44,000 reporter elements. Genomic alterations (gains and losses) were readily detected in most of the samples analysed. For example, both homozygous deletions of 0.6 Mb and high level of amplifications of 0.7 Mb were identified.

Conclusions

We established a robust protocol for molecular genetic testing of dFFPE derived DNA, irrespective of fixation, decalcification or sample type used. This approach may greatly facilitate further genetic testing on rare tumour entities where archival decalcified, formalin fixed samples are the only source.     

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background

One important preprocessing step in the analysis of microarray data is background subtraction. In high-density oligonucleotide arrays this is recognized as a crucial step for the global performance of the data analysis from raw intensities to expression values.

Results

We propose here an algorithm for background estimation based on a model in which the cost function is quadratic in a set of fitting parameters such that minimization can be performed through linear algebra. The model incorporates two effects: 1) Correlated intensities between neighboring features in the chip and 2) sequence-dependent affinities for non-specific hybridization fitted by an extended nearest-neighbor model.

Conclusion

The algorithm has been tested on 360 GeneChips from publicly available data of recent expression experiments. The algorithm is fast and accurate. Strong correlations between the fitted values for different experiments as well as between the free-energy parameters and their counterparts in aqueous solution indicate that the model captures a significant part of the underlying physical chemistry.     

Use of a multi-virus array for the study of human viral and retroviral pathogens: gene expression studies and ChIP-chip analysis

Background

Since the discovery of human immunodeficiency virus (HIV-1) twenty years ago, AIDS has become one of the most studied diseases. A number of viruses have subsequently been identified to contribute to the pathogenesis of HIV and its opportunistic infections and cancers. Therefore, a multi-virus array containing eight human viruses implicated in AIDS pathogenesis was developed and its efficacy in various applications was characterized.

Results

The amplified open reading frames (ORFs) of human immunodeficiency virus type 1, human T cell leukemia virus types 1 and 2, hepatitis C virus, Epstein-Barr virus, human herpesvirus 6A and 6B, and Kaposi's sarcoma-associated herpesvirus were spotted on glass slides and hybridized to DNA and RNA samples. Using a random priming method for labeling genomic DNA or cDNA probes, we show specific detection of genomic viral DNA from cells infected with the human herpesviruses, and effectively demonstrate the inhibitory effects of a cellular cyclin dependent kinase inhibitor on viral gene expression in HIV-1 and KSHV latently infected cells. In addition, we coupled chromatin immunoprecipitation with the virus chip (ChIP-chip) to study cellular protein and DNA binding.

Conclusions

An amplicon based virus chip representing eight human viruses was successfully used to identify each virus with little cross hybridization. Furthermore, the identity of both viruses was correctly determined in co-infected cells. The utility of the virus chip was demonstrated by a variety of expression studies. Additionally, this is the first demonstrated use of ChIP-chip analysis to show specific binding of proteins to viral DNA, which, importantly, did not require further amplification for detection.     

Secretory expression of a novel human spermatozoa antigen in <Emphasis Type="Italic">E</Emphasis>. <Emphasis Type="Italic">coli</Emphasis> and its application to a protein chip

Objective

To produce a recombinant spermatozoa antigen peptide using the E. coli: PhoA system on a protein chip for screening anti-sperm antibodies (ASA).

Results

The purity of the recombinant spermatozoa antigen exceeded 95% after two-step purification, as assessed using SDS-PAGE and HPLC. The diagnostic performance of a protein chip coated with the recombinant antigen peptide was evaluated by examining ASA in 51 infertile patients in comparison with a commercial ELISA kit. The area under the receiver operating characteristic curve (AUC) was 0.944, which indicated that the protein chip coated with recombinant spermatozoa antigen peptide was consistent with ELISA for ASA detection.

Conclusion

A recombinant spermatozoa antigen was expressed in the E. coli PhoA secretory expression system and its potential application for clinical ASA detection was validated.

     

Working without a blindfold: the critical role of diagnostics in malaria control

Background

The insertion of parasite antigens into the host erythrocyte membrane and the structure and distribution of Plasmodium falciparum adhesion receptors on that membrane are poorly understood. Laser scanning confocal microscopy (LSCM) and a novel labelling and fixation method have been used to obtain high resolution immuno-fluorescent images of erythrocyte surface PfEMP1 and internal antigens which allow analysis of the accumulation of PfEMP1 on the erythrocyte membrane during asexual development.

Methods

A novel staining technique has been developed which permits distinction between erythrocyte surface PfEMP1 and intracellular PfEMP1, in parasites whose nuclear material is exceptionally well resolved. Primary antibody detection by fluorescence is carried out on the live parasitized erythrocyte. The surface labelled cells are then fixed using paraformaldehyde and permeabilized with a non-ionic detergent to permit access of antibodies to internal parasite antigens. Differentiation between surface and internal antigens is achieved using antibodies labelled with different fluorochromes and confocal microscopy

Results

Surface exposed PfEMP1 is first detectable by antibodies at the trophozoite stage of intracellular parasite development although the improved detection method indicates that there are differences between different laboratory isolates in the kinetics of accumulation of surface-exposed PfEMP1.

Conclusion

A sensitive method for labelling surface and internal PfEMP1 with up to three different fluorochromes has been developed for laser scanning confocal optical microscopy and the analysis of the developmental expression of malaria adhesion antigens.     

AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides

Background

A tremendous amount of efforts have been devoted to identifying genes for diagnosis and prognosis of diseases using microarray gene expression data. It has been demonstrated that gene expression data have cluster structure, where the clusters consist of co-regulated genes which tend to have coordinated functions. However, most available statistical methods for gene selection do not take into consideration the cluster structure.

Results

We propose a supervised group Lasso approach that takes into account the cluster structure in gene expression data for gene selection and predictive model building. For gene expression data without biological cluster information, we first divide genes into clusters using the K-means approach and determine the optimal number of clusters using the Gap method. The supervised group Lasso consists of two steps. In the first step, we identify important genes within each cluster using the Lasso method. In the second step, we select important clusters using the group Lasso. Tuning parameters are determined using V-fold cross validation at both steps to allow for further flexibility. Prediction performance is evaluated using leave-one-out cross validation. We apply the proposed method to disease classification and survival analysis with microarray data.

Conclusion

We analyze four microarray data sets using the proposed approach: two cancer data sets with binary cancer occurrence as outcomes and two lymphoma data sets with survival outcomes. The results show that the proposed approach is capable of identifying a small number of influential gene clusters and important genes within those clusters, and has better prediction performance than existing methods.