Analysis of transcription factor CREM dependent gene expression during mouse spermatogenesis

Computational methods are getting increasingly important for the analysis of large data sets in molecular biology. The data sets analyzed in this thesis are derived from experiments measuring the changes of expression levels in response to the transcription factor CREM (cAMP Responsive Element Modulator) during mouse spermatogenesis. In the course of this analysis new computational methods were developed and used that will also be of value in other projects in Bioinformatics. CREM belongs to a family of cAMP-responsive nuclear factors. The activator splice-isoform CREM is exclusively expressed at high levels in post-meiotic germ cells during mouse spermiogenesis. Mutant male mice lacking CREM expression are sterile due to lack of maturation of the germ cells. In order to find CREM target genes the mRNA expression levels in testes of CREM-deficient mice and wild-type mice were compared using the suppression subtractive hybridization (SSH) technique as well as oligonucleotide DNA microarrays. SSH was used to selectively amplify the differentially expressed genes. 12,000 clones, which contain sequence fragments of genes expressed stronger in wild-type as in the CREM (-/-) mutant, were analyzed by a combination of sequencing and hybridization. Sequence analysis methods were used to characterize 956 unique sequences. Homologies to 158 known mouse genes and 99 known genes from other organisms were detected. 296 sequences show homologies to sequences of expressed sequence tags (ESTs). 199 novel sequences have been found. The sequences not corresponding to full length genes of known function were characterized using publicly available EST data. To make EST databases useful for data analysis all of the publicly available ESTs have been grouped into clusters and methods to analyze and visualize EST data were developed. Nylon cDNA microarrays containing the unique sequences from the CREM SSH library were constructed to determine expression levels of those sequences. Most of the sequences from the CREM SSH library are shown to be expressed in wild-type but are down-regulated in CREM deficient mice. Statistical methods to standardize microarray expression data were developed and software was implemented to perform comparisons. Further CREM dependent genes were detected comparing the mRNA expression levels in testes of CREM deficient mice and wild-type mice using Affymetrix oligonucleotide microarrays containing 10,000 mouse sequences. Comparison of the different techniques (SSH, nylon cDNA arrays and Affymetrix oligonucleotide microarrays) shows that the results are complementing each other. The unique sequences from the CREM SSH library were further analyzed by determining the spermatogenic stage specific expression profiles. cDNA from prepubertal mice at certain stages of spermatogenesis were hybridized on nylon cDNA arrays. Several important functional groups of genes like transcription factors, signal transduction proteins and metabolic enzymes are shown to be coexpressed at the latest stages of spermatogenesis. Expression profiles were arranged to find similar profile shapes and co-regulation of functionally related genes. An algorithm to arrange the profiles in an optimal linear order was developed. The linear order is constructed in a way that similar expression profiles end up close together in the linear order, i.e. the sum over all distances of neighboring profiles is minimized. This corresponds to the solution of a traveling salesman problem (TSP), which is well known in computer science. A fast algorithm that computes a heuristic solution to a TSP was adapted to be used in expression profile analysis

Survival of pneumococcus on hands and fomites

<p>Abstract</p> <p>Background</p> <p>Pneumococcal hand contamination in Indigenous children in remote communities is common (37%). It is not clear whether this requires frequent inoculation, or if pneumococci will survive on hands for long periods of time. Thus the aim of this study was to determine the survival time of pneumococci on hands and fomites.</p> <p>Findings</p> <p>The hands of 3 adult volunteers, a glass plate and plastic ball were inoculated with pneumococci suspended in two different media. Survival at specified time intervals was determined by swabbing and re-culture onto horse blood agar. Pneumococci inoculated onto hands of volunteers were recovered after 3 minutes at 4% to 79% of the initial inoculum. Recovery from one individual was consistently higher. By one hour, only a small number of pneumococci were recovered and this was dependent on the suspension medium used. At subsequent intervals and up to 3 hours after inoculation, < 10 colony forming units were recovered from hands. On a glass plate, pneumococcal numbers dropped an average 70% in the two hours after inoculation. Subsequently, < 100 colony forming units were recovered up to 15 hours after inoculation.</p> <p>Conclusion</p> <p>The poor survival of pneumococci on hands suggests that the high prevalence of pneumococcal hand contamination in some populations is related to frequent inoculation rather than long survival. It is plausible that hand contamination plays a (brief) role in transmission directly, and indirectly through contamination via fomites. Regular hand washing and timely cleansing or removal of contaminated fomites may aid control of pneumococcal transmission via these routes.</p

Extending pathways based on gene lists using InterPro domain signatures

<p>Abstract</p> <p>Background</p> <p>High-throughput technologies like functional screens and gene expression analysis produce extended lists of candidate genes. Gene-Set Enrichment Analysis is a commonly used and well established technique to test for the statistically significant over-representation of particular pathways. A shortcoming of this method is however, that most genes that are investigated in the experiments have very sparse functional or pathway annotation and therefore cannot be the target of such an analysis. The approach presented here aims to assign lists of genes with limited annotation to previously described functional gene collections or pathways. This works by comparing InterPro domain signatures of the candidate gene lists with domain signatures of gene sets derived from known classifications, e.g. KEGG pathways.</p> <p>Results</p> <p>In order to validate our approach, we designed a simulation study. Based on all pathways available in the KEGG database, we create test gene lists by randomly selecting pathway genes, removing these genes from the known pathways and adding variable amounts of noise in the form of genes not annotated to the pathway. We show that we can recover pathway memberships based on the simulated gene lists with high accuracy. We further demonstrate the applicability of our approach on a biological example.</p> <p>Conclusion</p> <p>Results based on simulation and data analysis show that domain based pathway enrichment analysis is a very sensitive method to test for enrichment of pathways in sparsely annotated lists of genes. An R based software package <it>domainsignatures</it>, to routinely perform this analysis on the results of high-throughput screening, is available via Bioconductor.</p

Consensus and meta-analysis regulatory networks for combining multiple microarray gene expression datasets

Microarray data is a key source of experimental data for modelling gene regulatory interactions from expression levels. With the rapid increase of publicly available microarray data comes the opportunity to produce regulatory network models based on multiple datasets. Such models are potentially more robust with greater confidence, and place less reliance on a single dataset. However, combining datasets directly can be difficult as experiments are often conducted on different microarray platforms, and in different laboratories leading to inherent biases in the data that are not always removed through pre-processing such as normalisation. In this paper we compare two frameworks for combining microarray datasets to model regulatory networks: pre- and post-learning aggregation. In pre-learning approaches, such as using simple scale-normalisation prior to the concatenation of datasets, a model is learnt from a combined dataset, whilst in post-learning aggregation individual models are learnt from each dataset and the models are combined. We present two novel approaches for post-learning aggregation, each based on aggregating high-level features of Bayesian network models that have been generated from different microarray expression datasets. Meta-analysis Bayesian networks are based on combining statistical confidences attached to network edges whilst Consensus Bayesian networks identify consistent network features across all datasets. We apply both approaches to multiple datasets from synthetic and real (Escherichia coli and yeast) networks and demonstrate that both methods can improve on networks learnt from a single dataset or an aggregated dataset formed using a standard scale-normalisation

Estimating large-scale signaling networks through nested effect models with intervention effects from microarray data

Motivation: Targeted interventions using RNA interference in combination with the measurement of secondary effects with DNA microarrays can be used to computationally reverse engineer features of upstream non-transcriptional signaling cascades based on the nested structure of effects

Increasing the sensitivity of reverse phase protein arrays by antibody-mediated signal amplification

<p>Abstract</p> <p>Background</p> <p>Reverse phase protein arrays (RPPA) emerged as a useful experimental platform to analyze biological samples in a high-throughput format. Different signal detection methods have been described to generate a quantitative readout on RPPA including the use of fluorescently labeled antibodies. Increasing the sensitivity of RPPA approaches is important since many signaling proteins or posttranslational modifications are present at a low level.</p> <p>Results</p> <p>A new antibody-mediated signal amplification (AMSA) strategy relying on sequential incubation steps with fluorescently-labeled secondary antibodies reactive against each other is introduced here. The signal quantification is performed in the near-infrared range. The RPPA-based analysis of 14 endogenous proteins in seven different cell lines demonstrated a strong correlation (r = 0.89) between AMSA and standard NIR detection. Probing serial dilutions of human cancer cell lines with different primary antibodies demonstrated that the new amplification approach improved the limit of detection especially for low abundant target proteins.</p> <p>Conclusions</p> <p>Antibody-mediated signal amplification is a convenient and cost-effective approach for the robust and specific quantification of low abundant proteins on RPPAs. Contrasting other amplification approaches it allows target protein detection over a large linear range.</p

A new analysis approach of epidermal growth factor receptor pathway activation patterns provides insights into cetuximab resistance mechanisms in head and neck cancer

The pathways downstream of the epidermal growth factor receptor (EGFR) have often been implicated to play crucial roles in the development and progression of various cancer types. Different authors have proposed models in cell lines in which they study the modes of pathway activities after perturbation experiments. It is prudent to believe that a better understanding of these pathway activation patterns might lead to novel treatment concepts for cancer patients or at least allow a better stratification of patient collectives into different risk groups or into groups that might respond to different treatments. Traditionally, such analyses focused on the individual players of the pathways. More recently in the field of systems biology, a plethora of approaches that take a more holistic view on the signaling pathways and their downstream transcriptional targets has been developed. Fertig et al. have recently developed a new method to identify patterns and biological process activity from transcriptomics data, and they demonstrate the utility of this methodology to analyze gene expression activity downstream of the EGFR in head and neck squamous cell carcinoma to study cetuximab resistance. Please see related article: http://www.biomedcentral.com/1471-2164/13/16