Ca(2+) signals coordinate zygotic polarization and cell cycle progression in the brown alga Fucus serratus

Zygotes of the fucoid brown algae provide excellent models for addressing fundamental questions about zygotic symmetry breaking. Although the acquisition of polarity is tightly coordinated with the timing and orientation of the first asymmetric division-with zygotes having to pass through a G1/S-phase checkpoint before the polarization axis can be fixed -the mechanisms behind the interdependence of polarization and cell cycle progression remain unclear. In this study, we combine in vivo Ca(2+) imaging, single cell monitoring of S-phase progression and multivariate analysis of high-throughput intracellular Ca(2+) buffer loading to demonstrate that Ca(2+) signals coordinate polarization and cell cycle progression in the Fucus serratus zygote. Consistent with earlier studies on this organism, and in contrast to animal models, we observe no fast Ca(2+) wave following fertilization. Rather, we show distinct slow localized Ca(2+) elevations associated with both fertilization and S-phase progression, and we show that both S-phase and zygotic polarization are dependent on pre-S-phase Ca(2+) increases. Surprisingly, this Ca(2+) requirement cannot be explained by co-dependence on a single G1/ S-phase checkpoint, as S phase and zygotic polarization are differentially sensitive to pre-S-phase Ca(2+) elevations and can be uncoupled. Furthermore, subsequent cell cycle progression through M phase is independent of localized actin polymerization and zygotic polarization. This absence of a morphogenesis checkpoint, together with the observed Ca(2+)dependences of S phase and polarization, show that the regulation of zygotic division in the brown algae differs from that in other eukaryotic model systems, such as yeast and Drosophila.

MSGR-based Low Latency Complex Matrix Inversion Architecture

This paper presents a matrix inversion architecture based on the novel Modified Squared Givens Rotations (MSGR) algorithm, which extends the original SGR method to complex valued data, and also corrects erroneous results in the original SGR method when zeros occur on the diagonal of the matrix either initially or during processing. The MSGR algorithm also avoids complex dividers in the matrix inversion, thus minimising the complexity of potential real-time implementations. A systolic array architecture is implemented and FPGA synthesis results indicate a high-throughput low-latency complex matrix inversion solution. © 2008 IEEE.

Subpixel interpolation architecture for multistandard video motion estimation

A new reconfigurable subpixel interpolation architecture for multistandard (e.g., MPEG-2, MPEG-4, H.264, and AVS) video motion estimation (ME) is presented. This exploits the mixed use of parallel and serial-input FIR filters to achieve high throughput rate and efficient silicon utilization. Silicon design studies show that this can be implemented using 34.8 × 10 3 gates with area and performance that compares very favorably with specific fixed solutions, e.g., for the H.264 standard alone. This can support SDTV and HDTV applications when implemented in 0.18 µm CMOS technology, with further performance enhancements achievable at 0.13 µm and below. © 2009 IEEE.

Predicting Cell Cycle Regulated Genes by Causal Interactions

The fundamental difference between classic and modern biology is that technological innovations allow to generate high-throughput data to get insights into molecular interactions on a genomic scale. These high-throughput data can be used to infer gene networks, e. g., the transcriptional regulatory or signaling network, representing a blue print of the current dynamical state of the cellular system. However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms. In this paper we propose a new approach to analyze the transcriptional regulatory network of yeast to predict cell cycle regulated genes. The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes. The major purpose of the present paper is to predict cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions between genes, and a list of known periodic genes. No further data are used. Our approach utilizes the causal membership of genes and the hierarchical organization of the transcriptional regulatory network leading to two groups of periodic genes with a well defined direction of information flow. We predict genes as periodic if they appear on unique shortest paths connecting two periodic genes from different hierarchy levels. Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently. This also shows that there is a wealth of information buried in the transcriptional regulatory network whose unraveling may require more elaborate concepts than it might seem at first.

Discovery of substituted maleimides as liver X receptor agonists and determination of a ligand-bound crystal structure

Substituted 3-(phenylamino)-1H-pyrrole-2,5-diones were identified from a high throughput screen as inducers of human ATP binding cassette transporter A1 expression. Mechanism of action studies led to the identification of GSK3987 (4) as an LXR ligand. 4 recruits the steroid receptor coactivator-1 to human LXR alpha and LXRP with EC(50)s of 40 nM, profiles as an LXR agonist in functional assays, and activates LXR though a mechanism that is similar to first generation LXR agonists.

Development of a Diagnostic Genetic Test for Simplex and Autosomal Recessive Retinitis Pigmentosa

PURPOSE: Retinitis pigmentosa (RP) causes hereditary blindness in adults (prevalence, approximately 1 in 4000). Each of the more than 30 causative genes identified to date are responsible for only a small percentage of cases. Genetic diagnosis via traditional methods is problematic, and a single test with a higher probability of detecting the causative mutation would be very beneficial for the clinician. The goal of this study therefore was to develop a high-throughput screen capable of detecting both known mutations and novel mutations within all genes implicated in autosomal recessive or simplex RP. DESIGN: Evaluation of diagnostic technology. PARTICIPANTS AND CONTROLS: Participants were 56 simplex and autosomal recessive RP patients, with 360 population controls unscreened for ophthalmic disease. METHODS: A custom genechip capable of resequencing all exons containing known mutations in 19 disease-associated genes was developed (RP genechip). A second, commercially available arrayed primer extension (APEX) system was used to screen 501 individual previously reported variants. The ability of these high-throughput approaches to identify pathogenic variants was assessed in a cohort of simplex and autosomal recessive RP patients. MAIN OUTCOME MEASURES: Number of mutations and potentially pathogenic variants identified. RESULTS: The RP genechip identified 44 sequence variants: 5 previously reported mutations; 22 known single nucleotide polymorphisms (SNPs); 11 novel, potentially pathogenic variants; and 6 novel SNPs. There was strong concordance with the APEX array, but only the RP genechip detected novel variants. For example, identification of a novel mutation in CRB1 revealed a patient, who also had a single previously known CRB1 mutation, to be a compound heterozygote. In some individuals, potentially pathogenic variants were discovered in more than one gene, consistent with the existence of disease modifier effects resulting from mutations at a second locus. CONCLUSIONS: The RP genechip provides the significant advantage of detecting novel variants and could be expected to detect at least one pathogenic variant in more than 50% of patients. The APEX array provides a reliable method to detect known pathogenic variants in autosomal recessive RP and simplex RP patients and is commercially available. High-throughput genotyping for RP is evolving into a clinically useful genetic diagnostic tool.

Reconfigurable system-on-a-chip motion estimation architecture for multi-standard video coding

A new domain-specific, reconfigurable system-on-a-chip (SoC) architecture is proposed for video motion estimation. This has been designed to cover most of the common block-based video coding standards, including MPEG-2, MPEG-4, H.264, WMV-9 and AVS. The architecture exhibits simple control, high throughput and relatively low hardware cost when compared with existing circuits. It can also easily handle flexible search ranges without any increase in silicon area and can be configured prior to the start of the motion estimation process for a specific standard. The computational rates achieved make the circuit suitable for high-end video processing applications, such as HDTV. Silicon design studies indicate that circuits based on this approach incur only a relatively small penalty in terms of power dissipation and silicon area when compared with implementations for specific standards. Indeed, the cost/performance achieved exceeds that of existing but specific solutions and greatly exceeds that of general purpose field programmable gate array (FPGA) designs.

Monoclonal antibody development for acrylamide-adducted human haemoglobin; A biomarker of dietary acrylamide exposure

The spontaneous formation of the neurotoxic carcinogen acrylamide in a wide range of cooked foods has recently been discovered, leading to dietary exposure estimates of 30.8 mu g of acrylamide day(-1) for an average 77 kg human male. This is considerably higher than the European legal limit of acrylamide in drinking water, which is approximately 0.2 mu g of acrylamide person(-1) day(-1). A recent study of 62,573 women over 11.3 years has observed an increased risk of postmenopausal endometrial and ovarian cancer (but not breast cancer) with increasing dietary acrylamide intake, demonstrating significant risk to human health. As individual acrylamide exposure is affected by dietary habits, cooking methods, and cigarette consumption; accurate extrapolation from estimated dietary exposure is extremely difficult. Quantifying biomarkers of acrylamide exposure therefore remains the most effective means of rapidly determining individual exposure to acrylamide, and correlating exposure with lifestyle choices. Current methodologies for the analysis of blood biomarkers of acrylamide are focused on expensive, slower chromatographic techniques such as GC and LC coupled to mass spectrometry. This paper describes the first successful development of two monoclonal antibodies specific to acrylamide-adducted haemoglobin (IC50 of 94 ng ml(-1) and 198 ng ml(-1)), that are suitable for use in a high-throughput biomarker immunoassay to determine individual acrylamide exposure. Further development of acrylamide-haemoglobin standards with defined levels of acrylamide adduction will enable a fully quantitative assay, and allow sensitivity comparisons with alternative chromatographic methods of analysis. (C) 2008 Elsevier B.V. All rights reserved.

Inferring the conservative causal core of gene regulatory networks

Background
Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.

Results
In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently.

Conclusions
For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

Comparison of ELISA and SPR biosensor technology for the detection of paralytic shellfish poisoning toxins

An enzyme labeled immunosorbent assay (ELISA) and surface plasmon resonance (SPR) biosensor assay for the detection of paralytic shellfish poisoning (PSP) toxins were developed and a comparative evaluation was performed. A polyclonal antibody (BC67) used in both assay formats was raised to saxitoxin–jeffamine–BSA in New Zealand white rabbits. Each assay format was designed as an inhibition assay. Shellfish samples (n = 54) were evaluated by each method using two simple rapid extraction procedures and compared to the AOAC high performance liquid chromatography (HPLC) and the mouse bioassay (MBA). The results of each assay format were comparable with the HPLC and MBA methods and demonstrate that an antibody with high sensitivity and broad specificity to PSP toxins can be applied to different immunological techniques. The method of choice will depend on the end-users needs. The reduced manual labor and simplicity of operation of the SPR biosensor compared to ELISA, ease of sample extraction and superior real time semi-quantitative analysis are key features that could make this technology applicable in a high-throughput monitoring unit.

Impact of Sample Heterogeneity on Methylation Analysis

The recent emergence of high-throughput arrays for methylation analysis has made the influence of tumor content on the interpretation of methylation levels increasingly pertinent. However, to what degree does tumor content have an influence, and what degree of tumor content makes a specimen acceptable for accurate analysis remains unclear. Taking a systematic approach, we analyzed 98 unselected formalin-fixed and paraffin-embedded gastric tumors and matched normal tissue samples using the Illumina GoldenGate methylation assay. Unsupervised hierarchical clustering showed 2 separate clusters with a significant difference in average tumor content levels. The probes identified to be significantly differentially methylated between the tumors and normals also differed according to the tumor content of the samples included, with the sensitivity of identifying the

TRARESA: a tissue microarray-based hospital system for biomarker validation and discovery

Formalin fixed and paraffin embedded tissue (FFPE) collections in pathology departments are the largest resource for retrospective biomedical research studies. Based on the literature analysis of FFPE related research, as well as our own technical validation, we present the Translational Research Arrays (TRARESA), a tissue microarray centred, hospital based, translational research conceptual framework for both validation and/or discovery of novel biomarkers. TRARESA incorporates the analysis of protein, DNA and RNA in the same samples, correlating with clinical and pathological parameters from each case, and allowing (a) the confirmation of new biomarkers, disease hypotheses and drug targets, and (b) the postulation of novel hypotheses on disease mechanisms and drug targets based on known biomarkers. While presenting TRARESA, we illustrate the use of such a comprehensive approach. The conceptualisation of the role of FFPE-based studies in translational research allows the utilisation of this commodity, and adds to the hypothesis-generating armamentarium of existing high-throughput technologies.

Novel breast cancer biomarkers identified by integrative proteomic and gene expression mapping

Proteomic and transcriptomic platforms both play important roles in cancer research, with differing strengths and limitations. Here, we describe a proteo-transcriptomic integrative strategy for discovering novel cancer biomarkers, combining the direct visualization of differentially expressed proteins with the high-throughput scale of gene expression profiling. Using breast cancer as a case example, we generated comprehensive two-dimensional electrophoresis (2DE)/mass spectrometry (MS) proteomic maps of cancer (MCF-7 and HCC-38) and control (CCD-1059Sk) cell lines, identifying 1724 expressed protein spots representing 484 different protein species. The differentially expressed cell-line proteins were then mapped to mRNA transcript databases of cancer cell lines and primary breast tumors to identify candidate biomarkers that were concordantly expressed at the gene expression level. Of the top nine selected biomarker candidates, we reidentified ANX1, a protein previously reported to be differentially expressed in breast cancers and normal tissues, and validated three other novel candidates, CRAB, 6PGL, and CAZ2, as differentially expressed proteins by immunohistochemistry on breast tissue microarrays. In total, close to half (4/9) of our protein biomarker candidates were successfully validated. Our study thus illustrates how the systematic integration of proteomic and transcriptomic data from both cell line and primary tissue samples can prove advantageous for accelerating cancer biomarker discovery.

Reliability of tissue microarrays in detecting protein expression and gene amplification in breast cancer

Tissue microarrays allow high throughput molecular profiling of diagnostic or predictive markers in cancer specimens and rapid validation of novel potential candidates identified from genomic and proteomic analyses in a large number of tumor samples. To validate the use of tissue microarray technology for all the main biomarkers routinely used to decide breast cancer prognostication and postsurgical adjuvant therapy, we constructed a tissue microarray from 97 breast tumors, with a single 0.6 mm core per specimen. Inummostaining; of tissue microarray sections and conventional full sections of each tumor were performed using well-characterized prognostic markers (estrogen receptor ER, progesterone receptor PR and c-erbB2). The full section versus tissue microarray concordance for these stains was 97% for ER, 98% for PR, and 97% for c-erbB2, respectively, with a strong statistical association (kappa value more than 0.90). Fluorescence in situ hybridization analysis for HER-2/neu gene amplification from the single-core tissue microarray was technically successful in about 90% (87/97) of the cases, with a concordance of 95% compared with parallel analyses with the full sections. The correlation with other pathological parameters was not significantly different between full-section and array-based results. It is concluded that the constructed tissue microarray with a single core per specimen ensures full biological representativeness to identify the associations between biomarkers and clinicopathological parameters, with no significant associated sampling bias.

Towards accurate estimation of the proportion of true null hypotheses in multiple testing

Background

Biomedical researchers are now often faced with situations where it is necessary to test a large number of hypotheses simultaneously, eg, in comparative gene expression studies using high-throughput microarray technology. To properly control false positive errors the FDR (false discovery rate) approach has become widely used in multiple testing. The accurate estimation of FDR requires the proportion of true null hypotheses being accurately estimated. To date many methods for estimating this quantity have been proposed. Typically when a new method is introduced, some simulations are carried out to show the improved accuracy of the new method. However, the simulations are often very limited to covering only a few points in the parameter space.

Results

Here I have carried out extensive in silico experiments to compare some commonly used methods for estimating the proportion of true null hypotheses. The coverage of these simulations is unprecedented thorough over the parameter space compared to typical simulation studies in the literature. Thus this work enables us to draw conclusions globally as to the performance of these different methods. It was found that a very simple method gives the most accurate estimation in a dominantly large area of the parameter space. Given its simplicity and its overall superior accuracy I recommend its use as the first choice for estimating the proportion of true null hypotheses in multiple testing.