Gene expression by human monocytes from peripheral blood in response to exposure to metals

With increasing life expectancy and active lifestyles, the longevity of arthroplasties has become an important problem in orthopaedic surgery and will remain so until novel approaches to joint preservation have been developed. The sensitivity of the recipient to the metal alloys may be one of the factors limiting the lifespan of implants. In the present study, the response of human monocytes from peripheral blood to an exposure to metal ions was investigated, using the method of real-time polymerase chain reaction (PCR)-based low-density arrays. Upon stimulation with bivalent (Co2+ and Ni2+) and trivalent (Ti3+) cations and with the calcium antagonist LaCl3, the strength of the elicited monocytic response was in the order of Co2+ > or = Ni2+ > Ti3+ > or = LaCl3. The transcriptional regulation of the majority of genes affected by the exposure of monocytes to Co2+ and Ni2+ was similar. Some genes critically involved in the processes of inflammation and bone resorption, however, were found to be differentially regulated by these bivalent cations. The data demonstrate that monocytic gene expression is adapted in response to metal ions and that this response is, in part, specific for the individual metals. It is suggested that metal alloys used in arthroplasties may affect the extent of inflammation and bone resorption in the peri-implant tissues in dependence of their chemical composition.

ASSOCIATON TESTS THAT ACCOMMODATE GENOTYPING ERRORS

High-throughput SNP arrays provide estimates of genotypes for up to one million loci, often used in genome-wide association studies. While these estimates are typically very accurate, genotyping errors do occur, which can influence in particular the most extreme test statistics and p-values. Estimates for the genotype uncertainties are also available, although typically ignored. In this manuscript, we develop a framework to incorporate these genotype uncertainties in case-control studies for any genetic model. We verify that using the assumption of a “local alternative” in the score test is very reasonable for effect sizes typically seen in SNP association studies, and show that the power of the score test is simply a function of the correlation of the genotype probabilities with the true genotypes. We demonstrate that the power to detect a true association can be substantially increased for difficult to call genotypes, resulting in improved inference in association studies.

A Faster Circular Binary Segmentation Algorithm for the Analysis of Array CGH Data

Motivation: Array CGH technologies enable the simultaneous measurement of DNA copy number for thousands of sites on a genome. We developed the circular binary segmentation (CBS) algorithm to divide the genome into regions of equal copy number (Olshen {\it et~al}, 2004). The algorithm tests for change-points using a maximal $t$-statistic with a permutation reference distribution to obtain the corresponding $p$-value. The number of computations required for the maximal test statistic is $O(N^2),$ where $N$ is the number of markers. This makes the full permutation approach computationally prohibitive for the newer arrays that contain tens of thousands markers and highlights the need for a faster. algorithm. Results: We present a hybrid approach to obtain the $p$-value of the test statistic in linear time. We also introduce a rule for stopping early when there is strong evidence for the presence of a change. We show through simulations that the hybrid approach provides a substantial gain in speed with only a negligible loss in accuracy and that the stopping rule further increases speed. We also present the analysis of array CGH data from a breast cancer cell line to show the impact of the new approaches on the analysis of real data. Availability: An R (R Development Core Team, 2006) version of the CBS algorithm has been implemented in the ``DNAcopy'' package of the Bioconductor project (Gentleman {\it et~al}, 2004). The proposed hybrid method for the $p$-value is available in version 1.2.1 or higher and the stopping rule for declaring a change early is available in version 1.5.1 or higher.

An introduction to low-level analysis methods of DNA microarray data

This article gives an overview over the methods used in the low--level analysis of gene expression data generated using DNA microarrays. This type of experiment allows to determine relative levels of nucleic acid abundance in a set of tissues or cell populations for thousands of transcripts or loci simultaneously. Careful statistical design and analysis are essential to improve the efficiency and reliability of microarray experiments throughout the data acquisition and analysis process. This includes the design of probes, the experimental design, the image analysis of microarray scanned images, the normalization of fluorescence intensities, the assessment of the quality of microarray data and incorporation of quality information in subsequent analyses, the combination of information across arrays and across sets of experiments, the discovery and recognition of patterns in expression at the single gene and multiple gene levels, and the assessment of significance of these findings, considering the fact that there is a lot of noise and thus random features in the data. For all of these components, access to a flexible and efficient statistical computing environment is an essential aspect.

QUANTIFYING UNCERTAINTY IN GENOTYPE CALLS

Genome-wide association studies (GWAS) are used to discover genes underlying complex, heritable disorders for which less powerful study designs have failed in the past. The number of GWAS has skyrocketed recently with findings reported in top journals and the mainstream media. Mircorarrays are the genotype calling technology of choice in GWAS as they permit exploration of more than a million single nucleotide polymorphisms (SNPs)simultaneously. The starting point for the statistical analyses used by GWAS, to determine association between loci and disease, are genotype calls (AA, AB, or BB). However, the raw data, microarray probe intensities, are heavily processed before arriving at these calls. Various sophisticated statistical procedures have been proposed for transforming raw data into genotype calls. We find that variability in microarray output quality across different SNPs, different arrays, and different sample batches has substantial inuence on the accuracy of genotype calls made by existing algorithms. Failure to account for these sources of variability, GWAS run the risk of adversely affecting the quality of reported findings. In this paper we present solutions based on a multi-level mixed model. Software implementation of the method described in this paper is available as free and open source code in the crlmm R/BioConductor.

Stochastic Models Based on Molecular Hybridization Theory for Short Oligonucleotide Microarrays

High density oligonucleotide expression arrays are a widely used tool for the measurement of gene expression on a large scale. Affymetrix GeneChip arrays appear to dominate this market. These arrays use short oligonucleotides to probe for genes in an RNA sample. Due to optical noise, non-specific hybridization, probe-specific effects, and measurement error, ad-hoc measures of expression, that summarize probe intensities, can lead to imprecise and inaccurate results. Various researchers have demonstrated that expression measures based on simple statistical models can provide great improvements over the ad-hoc procedure offered by Affymetrix. Recently, physical models based on molecular hybridization theory, have been proposed as useful tools for prediction of, for example, non-specific hybridization. These physical models show great potential in terms of improving existing expression measures. In this paper we demonstrate that the system producing the measured intensities is too complex to be fully described with these relatively simple physical models and we propose empirically motivated stochastic models that compliment the above mentioned molecular hybridization theory to provide a comprehensive description of the data. We discuss how the proposed model can be used to obtain improved measures of expression useful for the data analysts.

A HIDDEN MARKOV MODEL FOR JOINT ESTIMATION OF GENOTYPE AND COPY NUMBER IN HIGH-THROUGHPUT SNP CHIPS

Amplifications and deletions of chromosomal DNA, as well as copy-neutral loss of heterozygosity have been associated with diseases processes. High-throughput single nucleotide polymorphism (SNP) arrays are useful for making genome-wide estimates of copy number and genotype calls. Because neighboring SNPs in high throughput SNP arrays are likely to have dependent copy number and genotype due to the underlying haplotype structure and linkage disequilibrium, hidden Markov models (HMM) may be useful for improving genotype calls and copy number estimates that do not incorporate information from nearby SNPs. We improve previous approaches that utilize a HMM framework for inference in high throughput SNP arrays by integrating copy number, genotype calls, and the corresponding confidence scores when available. Using simulated data, we demonstrate how confidence scores control smoothing in a probabilistic framework. Software for fitting HMMs to SNP array data is available in the R package ICE.

Mechanisms of intrinsic beating variability in cardiac cell cultures and model pacemaker networks

Heart rate variability (HRV) exhibits fluctuations characterized by a power law behavior of its power spectrum. The interpretation of this nonlinear HRV behavior, resulting from interactions between extracardiac regulatory mechanisms, could be clinically useful. However, the involvement of intrinsic variations of pacemaker rate in HRV has scarcely been investigated. We examined beating variability in spontaneously active incubating cultures of neonatal rat ventricular myocytes using microelectrode arrays. In networks of mathematical model pacemaker cells, we evaluated the variability induced by the stochastic gating of transmembrane currents and of calcium release channels and by the dynamic turnover of ion channels. In the cultures, spontaneous activity originated from a mobile focus. Both the beat-to-beat movement of the focus and beat rate variability exhibited a power law behavior. In the model networks, stochastic fluctuations in transmembrane currents and stochastic gating of calcium release channels did not reproduce the spatiotemporal patterns observed in vitro. In contrast, long-term correlations produced by the turnover of ion channels induced variability patterns with a power law behavior similar to those observed experimentally. Therefore, phenomena leading to long-term correlated variations in pacemaker cellular function may, in conjunction with extracardiac regulatory mechanisms, contribute to the nonlinear characteristics of HRV.

Dynamic changes of cardiac conduction during rapid pacing

Slow conduction and unidirectional conduction block (UCB) are key mechanisms of reentry. Following abrupt changes in heart rate, dynamic changes of conduction velocity (CV) and structurally determined UCB may critically influence arrhythmogenesis. Using patterned cultures of neonatal rat ventricular myocytes grown on microelectrode arrays, we investigated the dynamics of CV in linear strands and the behavior of UCB in tissue expansions following an abrupt decrease in pacing cycle length (CL). Ionic mechanisms underlying rate-dependent conduction changes were investigated using the Pandit-Clark-Giles-Demir model. In linear strands, CV gradually decreased upon a reduction of CL from 500 ms to 230-300 ms. In contrast, at very short CLs (110-220 ms), CV first decreased before increasing again. The simulations suggested that the initial conduction slowing resulted from gradually increasing action potential duration (APD), decreasing diastolic intervals, and increasing postrepolarization refractoriness, which impaired Na(+) current (I(Na)) recovery. Only at very short CLs did APD subsequently shorten again due to increasing Na(+)/K(+) pump current secondary to intracellular Na(+) accumulation, which caused recovery of CV. Across tissue expansions, the degree of UCB gradually increased at CLs of 250-390 ms, whereas at CLs of 180-240 ms, it first increased and subsequently decreased. In the simulations, reduction of inward currents caused by increasing intracellular Na(+) and Ca(2+) concentrations contributed to UCB progression, which was reversed by increasing Na(+)/K(+) pump activity. In conclusion, CV and UCB follow intricate dynamics upon an abrupt decrease in CL that are determined by the interplay among I(Na) recovery, postrepolarization refractoriness, APD changes, ion accumulation, and Na(+)/K(+) pump function.

A comprehensive test system to identify suitable antibodies against p53 for immunohistochemical analysis of canine tissues

The tumour suppressor p53 is commonly detected in tissues of companion animals by means of antibodies raised against the human protein. The following three-step procedure was devised to test the suitability of such antibodies for immunohistochemistry on canine tissues. (1) Western blot and immunohistochemical analyses on bacterially expressed recombinant canine protein to assess human-to-canine cross-reactivity. (2) Immunohistochemistry of cultured, UVB-irradiated canine keratinocytes to evaluate suitability for detection of endogenous p53. (3) Immunohistochemistry on tissue arrays to further substantiate suitability of the antibodies on a panel of normal and neoplastic human and canine tissues. Five of six antibodies cross-reacted with recombinant canine p53. Three of these (PAb122, PAb240, CM-1) also immunolabelled stabilized wild type p53 in cell cultures and elicited a consistent, characteristic labelling pattern in a subset of tumours. However, two alternative batches of polyclonal antibody CM-1 failed to detect p53 in cell cultures, while showing a characteristic labelling pattern of a completely different subset of tumours and unspecific labelling of normal tissues. The test system described is well suited to the selection of antibodies for immunohistochemical p53 detection. The results emphasize the need to include appropriate controls, especially for polyclonal antibodies.

Gene expression variation between distinct areas of breast cancer measured from paraffin-embedded tissue cores

BACKGROUND: Diagnosis and prognosis in breast cancer are mainly based on histology and immunohistochemistry of formalin-fixed, paraffin-embedded (FFPE) material. Recently, gene expression analysis was shown to elucidate the biological variance between tumors and molecular markers were identified that led to new classification systems that provided better prognostic and predictive parameters. Archived FFPE samples represent an ideal source of tissue for translational research, as millions of tissue blocks exist from routine diagnostics and from clinical studies. These should be exploited to provide clinicians with more accurate prognostic and predictive information. Unfortunately, RNA derived from FFPE material is partially degraded and chemically modified and reliable gene expression measurement has only become successful after implementing novel and optimized procedures for RNA isolation, demodification and detection. METHODS: In this study we used tissue cylinders as known from the construction of tissue microarrays. RNA was isolated with a robust protocol recently developed for RNA derived from FFPE material. Gene expression was measured by quantitative reverse transcription PCR. RESULTS: Sixteen tissue blocks from 7 patients diagnosed with multiple histological subtypes of breast cancer were available for this study. After verification of appropriate localization, sufficient RNA yield and quality, 30 tissue cores were available for gene expression measurement on TaqMan(R) Low Density Arrays (16 invasive ductal carcinoma (IDC), 8 ductal carcinoma in situ (DCIS) and 6 normal tissue), and 14 tissue cores were lost. Gene expression values were used to calculate scores representing the proliferation status (PRO), the estrogen receptor status and the HER2 status. The PRO scores measured from entire sections were similar to PRO scores determined from IDC tissue cores. Scores determined from normal tissue cores consistently revealed lower PRO scores than cores derived from IDC or DCIS of the same block or from different blocks of the same patient. CONCLUSION: We have developed optimized protocols for RNA isolation from histologically distinct areas. RNA prepared from FFPE tissue cores is suitable for gene expression measurement by quantitative PCR. Distinct molecular scores could be determined from different cores of the same tumor specimen.

Charge induced enhancement of adsorption for hydrogen storage materials

The rising concerns about environmental pollution and global warming have facilitated research interest in hydrogen energy as an alternative energy source. To apply hydrogen for transportations, several issues have to be solved, within which hydrogen storage is the most critical problem. Lots of materials and devices have been developed; however, none is able to meet the DOE storage target. The primary issue for hydrogen physisorption is a weak interaction between hydrogen and the surface of solid materials, resulting negligible adsorption at room temperature. To solve this issue, there is a need to increase the interaction between the hydrogen molecules and adsorbent surface. In this study, intrinsic electric dipole is investigated to enhance the adsorption energy. The results from the computer simulation of single ionic compounds with hydrogen molecules to form hydrogen clusters showed that electrical charge of substances plays an important role in generation of attractive interaction with hydrogen molecules. In order to further examine the effects of static interaction on hydrogen adsorption, activated carbon with a large surface area was impregnated with various ionic salts including LiCl, NaCl, KCl, KBr, and NiCl and their performance for hydrogen storage was evaluated by using a volumetric method. Corresponding computer simulations have been carried out by using DFT (Density Functional Theory) method combined with point charge arrays. Both experimental and computational results prove that the adsorption capacity of hydrogen and its interaction with the solid materials increased with electrical dipole moment. Besides the intrinsic dipole, an externally applied electric field could be another means to enhance hydrogen adsorption. Hydrogen adsorption under an applied electric field was examined by using porous nickel foil as electrodes. Electrical signals showed that adsorption capacity increased with the increasing of gas pressure and external electric voltage. Direct measurement of the amount of hydrogen adsorption was also carried out with porous nickel oxides and magnesium oxides using the piezoelectric material PMN-PT as the charge supplier due to the pressure. The adsorption enhancement from the PMN-PT generated charges is obvious at hydrogen pressure between 0 and 60 bars, where the hydrogen uptake is increased at about 35% for nickel oxide and 25% for magnesium oxide. Computer simulation reveals that under the external electric field, the electron cloud of hydrogen molecules is pulled over to the adsorbent site and can overlap with the adsorbent electrons, which in turn enhances the adsorption energy Experiments were also carried out to examine the effects of hydrogen spillover with charge induced enhancement. The results show that the overall storage capacity in nickel oxide increased remarkably by a factor of 4.

Irradiation of intracerebral 9L gliosarcoma by a single array of microplanar x-ray beams from a synchrotron: balance between curing and sparing

The purpose of this work was the understanding of microbeam radiation therapy at the ESRF in order to find the best compromise between curing of tumors and sparing of normal tissues, to obtain a better understanding of survival curves and to report its efficiency. This method uses synchrotron-generated x-ray microbeams. Rats were implanted with 9L gliosarcomas and the tumors were diagnosed by MRI. They were irradiated 14 days after implantation by arrays of 25 microm wide microbeams in unidirectional mode, with a skin entrance dose of 625 Gy. The effect of using 200 or 100 microm center-to-center spacing between the microbeams was compared. The median survival time (post-implantation) was 40 and 67 days at 200 and 100 microm spacing, respectively. However, 72% of rats irradiated at 100 microm spacing showed abnormal clinical signs and weight patterns, whereas only 12% of rats were affected at 200 microm spacing. In parallel, histological lesions of the normal brain were found in the 100 microm series only. Although the increase in lifespan was equal to 273% and 102% for the 100 and 200 microm series, respectively, the 200 microm spacing protocol provides a better sparing of healthy tissue and may prove useful in combination with other radiation modalities or additional drugs.