Genetic profiling of chromosome 1 in breast cancer: mapping of regions of gains and losses and identification of candidate genes on 1q

Chromosome 1 is involved in quantitative anomalies in 50–60% of breast tumours. However, the structure of these anomalies and the identity of the affected genes remain to be determined. To characterise these anomalies and define their consequences on gene expression, we undertook a study combining array-CGH analysis and expression profiling using specialised arrays. Array-CGH data showed that 1p was predominantly involved in losses and 1q almost exclusively in gains. Noticeably, high magnitude amplification was infrequent. In an attempt to fine map regions of copy number changes, we defined 19 shortest regions of overlap (SROs) for gains (one at 1p and 18 at 1q) and of 20 SROs for losses (all at 1p). These SROs, whose sizes ranged from 170 kb to 3.2 Mb, represented the smallest genomic intervals possible based on the resolution of our array. The elevated incidence of gains at 1q, added to the well-established concordance between DNA copy increase and augmented RNA expression, made us focus on gene expression changes at this chromosomal arm. To identify candidate oncogenes, we studied the RNA expression profiles of 307 genes located at 1q using a home-made built cDNA array. We identified 30 candidate genes showing significant overexpression correlated to copy number increase. In order to substantiate their involvement, RNA expression levels of these candidate genes were measured by quantitative (Q)-RT–PCR in a panel of 25 breast cancer cell lines previously typed by array-CGH. Q–PCR showed that 11 genes were significantly overexpressed in the presence of a genomic gain in these cell lines, and 20 overexpressed when compared to normal breast

An Analysis of the Abstracts Presented at the Annual Meetings of the Society for Neuroscience from 2001 to 2006

Annual meeting abstracts published by scientific societies often contain rich arrays of information that can be computationally mined and distilled to elucidate the state and dynamics of the subject field. We extracted and processed abstract data from the Society for Neuroscience (SFN) annual meeting abstracts during the period 2001–2006 in order to gain an objective view of contemporary neuroscience. An important first step in the process was the application of data cleaning and disambiguation methods to construct a unified database, since the data were too noisy to be of full utility in the raw form initially available. Using natural language processing, text mining, and other data analysis techniques, we then examined the demographics and structure of the scientific collaboration network, the dynamics of the field over time, major research trends, and the structure of the sources of research funding. Some interesting findings include a high geographical concentration of neuroscience research in the north eastern United States, a surprisingly large transient population (66% of the authors appear in only one out of the six studied years), the central role played by the study of neurodegenerative disorders in the neuroscience community, and an apparent growth of behavioral/systems neuroscience with a corresponding shrinkage of cellular/molecular neuroscience over the six year period. The results from this work will prove useful for scientists, policy makers, and funding agencies seeking to gain a complete and unbiased picture of the community structure and body of knowledge encapsulated by a specific scientific domain

Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations

Abstract Background In a recent study, two-dimensional (2D) network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D) network layouts, displayed through 2D and 3D viewing methods. Findings The 3D network layout (displayed through the 3D viewing method) revealed that genes implicated in many diseases (non-specific genes) tended to be predominantly down-regulated, whereas genes regulated in a few diseases (disease-specific genes) tended to be up-regulated. This new global relationship was quantitatively validated through comparison to 1000 random permutations of networks of the same size and distribution. Our new finding appeared to be the result of using specific features of the 3D viewing method to analyze the 3D renal network. Conclusions The global relationship between gene regulation and gene specificity is the first clue from human studies that there exist common mechanisms across several renal diseases, which suggest hypotheses for the underlying mechanisms. Furthermore, the study suggests hypotheses for why the 3D visualization helped to make salient a new regularity that was difficult to detect in 2D. Future research that tests these hypotheses should enable a more systematic understanding of when and how to use 3D network visualizations to reveal complex regularities in biological networks.http://deepblue.lib.umich.edu/bitstream/2027.42/112972/1/13104_2010_Article_700.pd

Mesophilic and Thermophilic Conditions Select for Unique but Highly Parallel Microbial Communities to Perform Carboxylate Platform Biomass Conversion

The carboxylate platform is a flexible, cost-effective means of converting lignocellulosic materials into chemicals and liquid fuels. Although the platform's chemistry and engineering are well studied, relatively little is known about the mixed microbial communities underlying its conversion processes. In this study, we examined the metagenomes of two actively fermenting platform communities incubated under contrasting temperature conditions (mesophilic 40°C; thermophilic 55°C), but utilizing the same inoculum and lignocellulosic feedstock. Community composition segregated by temperature. The thermophilic community harbored genes affiliated with Clostridia, Bacilli, and a Thermoanaerobacterium sp, whereas the mesophilic community metagenome was composed of genes affiliated with other Clostridia and Bacilli, Bacteriodia, γ-Proteobacteria, and Actinobacteria. Although both communities were able to metabolize cellulosic materials and shared many core functions, significant differences were detected with respect to the abundances of multiple Pfams, COGs, and enzyme families. The mesophilic metagenome was enriched in genes related to the degradation of arabinose and other hemicellulose-derived oligosaccharides, and the production of valerate and caproate. In contrast, the thermophilic community was enriched in genes related to the uptake of cellobiose and the transfer of genetic material. Functions assigned to taxonomic bins indicated that multiple community members at either temperature had the potential to degrade cellulose, cellobiose, or xylose and produce acetate, ethanol, and propionate. The results of this study suggest that both metabolic flexibility and functional redundancy contribute to the platform's ability to process lignocellulosic substrates and are likely to provide a degree of stability to the platform's fermentation processes

Blocking human fear memory with the matrix metalloproteinase inhibitor doxycycline

Learning to predict threat is a fundamental ability of many biological organisms, and a laboratory model for anxiety disorders. Interfering with such memories in humans would be of high clinical relevance. On the basis of studies in cell cultures and slice preparations, it is hypothesised that synaptic remodelling required for threat learning involves the extracellular enzyme matrix metalloproteinase (MMP) 9. However, in vivo evidence for this proposal is lacking. Here we investigate human Pavlovian fear conditioning under the blood-brain barrier crossing MMP inhibitor doxycyline in a pre-registered, randomised, double-blind, placebo-controlled trial. We find that recall of threat memory, measured with fear-potentiated startle 7 days after acquisition, is attenuated by ~60% in individuals who were under doxycycline during acquisition. This threat memory impairment is also reflected in increased behavioural surprise signals to the conditioned stimulus during subsequent re-learning, and already late during initial acquisition. Our findings support an emerging view that extracellular signalling pathways are crucially required for threat memory formation. Furthermore, they suggest novel pharmacological methods for primary prevention and treatment of posttraumatic stress disorder.Molecular Psychiatry advance online publication, 4 April 2017; doi:10.1038/mp.2017.65

Translating microarray data for diagnostic testing in childhood leukaemia

BACKGROUND: Recent findings from microarray studies have raised the prospect of a standardized diagnostic gene expression platform to enhance accurate diagnosis and risk stratification in paediatric acute lymphoblastic leukaemia (ALL). However, the robustness as well as the format for such a diagnostic test remains to be determined. As a step towards clinical application of these findings, we have systematically analyzed a published ALL microarray data set using Robust Multi-array Analysis (RMA) and Random Forest (RF). METHODS: We examined published microarray data from 104 ALL patients specimens, that represent six different subgroups defined by cytogenetic features and immunophenotypes. Using the decision-tree based supervised learning algorithm Random Forest (RF), we determined a small set of genes for optimal subgroup distinction and subsequently validated their predictive power in an independent patient cohort. RESULTS: We achieved very high overall ALL subgroup prediction accuracies of about 98%, and were able to verify the robustness of these genes in an independent panel of 68 specimens obtained from a different institution and processed in a different laboratory. Our study established that the selection of discriminating genes is strongly dependent on the analysis method. This may have profound implications for clinical use, particularly when the classifier is reduced to a small set of genes. We have demonstrated that as few as 26 genes yield accurate class prediction and importantly, almost 70% of these genes have not been previously identified as essential for class distinction of the six ALL subgroups. CONCLUSION: Our finding supports the feasibility of qRT-PCR technology for standardized diagnostic testing in paediatric ALL and should, in conjunction with conventional cytogenetics lead to a more accurate classification of the disease. In addition, we have demonstrated that microarray findings from one study can be confirmed in an independent study, using an entirely independent patient cohort and with microarray experiments being performed by a different research team

Missing value imputation for microarray gene expression data using histone acetylation information

<p>Abstract</p> <p>Background</p> <p>It is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis in bioinformatics. Although several methods have been suggested, their performances are not satisfactory for datasets with high missing percentages.</p> <p>Results</p> <p>The paper explores the feasibility of doing missing value imputation with the help of gene regulatory mechanism. An imputation framework called histone acetylation information aided imputation method (HAIimpute method) is presented. It incorporates the histone acetylation information into the conventional KNN(<it>k</it>-nearest neighbor) and LLS(local least square) imputation algorithms for final prediction of the missing values. The experimental results indicated that the use of acetylation information can provide significant improvements in microarray imputation accuracy. The HAIimpute methods consistently improve the widely used methods such as KNN and LLS in terms of normalized root mean squared error (NRMSE). Meanwhile, the genes imputed by HAIimpute methods are more correlated with the original complete genes in terms of Pearson correlation coefficients. Furthermore, the proposed methods also outperform GOimpute, which is one of the existing related methods that use the functional similarity as the external information.</p> <p>Conclusion</p> <p>We demonstrated that the using of histone acetylation information could greatly improve the performance of the imputation especially at high missing percentages. This idea can be generalized to various imputation methods to facilitate the performance. Moreover, with more knowledge accumulated on gene regulatory mechanism in addition to histone acetylation, the performance of our approach can be further improved and verified.</p

Preference for novel faces in male infant monkeys predicts cerebrospinal fluid oxytocin concentrations later in life

The ability to recognize individuals is a critical skill acquired early in life for group living species. In primates, individual recognition occurs predominantly through face discrimination. Despite the essential adaptive value of this ability, robust individual differences in conspecific face recognition exist, yet its associated biology remains unknown. Although pharmacological administration of oxytocin has implicated this neuropeptide in face perception and social memory, no prior research has tested the relationship between individual differences in face recognition and endogenous oxytocin concentrations. Here we show in a male rhesus monkey cohort (N = 60) that infant performance in a task used to determine face recognition ability (specifically, the ability of animals to show a preference for a novel face) robustly predicts cerebrospinal fluid, but not blood, oxytocin concentrations up to five years after behavioural assessment. These results argue that central oxytocin biology may be related to individual face perceptual abilities necessary for group living, and that these differences are stable traits

Intensive Case Finding and Isoniazid Preventative Therapy in HIV Infected Individuals in Africa: Economic Model and Value of Information Analysis

Background: Tuberculosis (TB) accounts of much of the morbidity and mortality associated with HIV. We evaluate the costeffectiveness of different strategies to actively screen for TB disease in HIV positive individuals, where isoniazid preventative therapy (IPT) is given to those screening negative, and use value of information analysis (VOI) to identify future research priorities. Methodology / Principal Findings: We built an individual sampling model to investigate the costs (2010 US Dollars) and consequences of screening for TB, and providing TB treatment or IPT in adults testing HIV positive in Sub-Saharan Africa. A systematic review and meta-analysis was conducted to assess performance of the nine different TB screening strategies evaluated. Probabilistic sensitivity analysis was conducted to incorporate decision uncertainty, and expected value of perfect information for the entire model and for groups of parameters was calculated. Screening all HIV infected individuals with sputum microscopy was the least costly strategy, with other strategies not cost-effective at WHO recommended thresholds. Screening those with TB symptoms with sputum microscopy and CXR would be cost-effective at a threshold ICER of $7,800 per quality-adjusted life year (QALY), but associated with significant uncertainty. VOI analysis suggests further information would be of value. Conclusions / Significance: Resource-constrained countries in sub-Saharan Africa wishing to scale up TB preventativ

Gene Circuit Analysis of the Terminal Gap Gene huckebein

The early embryo of Drosophila melanogaster provides a powerful model system to study the role of genes in pattern formation. The gap gene network constitutes the first zygotic regulatory tier in the hierarchy of the segmentation genes involved in specifying the position of body segments. Here, we use an integrative, systems-level approach to investigate the regulatory effect of the terminal gap gene huckebein (hkb) on gap gene expression. We present quantitative expression data for the Hkb protein, which enable us to include hkb in gap gene circuit models. Gap gene circuits are mathematical models of gene networks used as computational tools to extract regulatory information from spatial expression data. This is achieved by fitting the model to gap gene expression patterns, in order to obtain estimates for regulatory parameters which predict a specific network topology. We show how considering variability in the data combined with analysis of parameter determinability significantly improves the biological relevance and consistency of the approach. Our models are in agreement with earlier results, which they extend in two important respects: First, we show that Hkb is involved in the regulation of the posterior hunchback (hb) domain, but does not have any other essential function. Specifically, Hkb is required for the anterior shift in the posterior border of this domain, which is now reproduced correctly in our models. Second, gap gene circuits presented here are able to reproduce mutants of terminal gap genes, while previously published models were unable to reproduce any null mutants correctly. As a consequence, our models now capture the expression dynamics of all posterior gap genes and some variational properties of the system correctly. This is an important step towards a better, quantitative understanding of the developmental and evolutionary dynamics of the gap gene network