Looking for BRPF1 Presence in Differentiating Osteoclast Cells

Through this semester-long experiment, professor Birnbaum and I attempted to identify the presence of BRPF1 (Bromodomain And PHD Finger Containing 1) in harvested RAW osteoclast cells. This was done through a series of steps, beginning with growing RAW osteoclasts, differentiating them, harvesting them and finally running a Western Blot test to look for our target protein. Unfortunately, no major discoveries were made with this project, but there is still reason to be optimistic about the presence of other members of the BRPF family (BRPF3, BRPF4). With some knowledge on our procedure, future researchers should be able to note specific points that could be tweaked for improvement to yield better results

Effect of plant root symbionts on performance of native woody species in competition with an invasive grass in multispecies microcosms

The majority of terrestrial plants form mutualistic associations with arbuscular mycorrhizal fungi (AMF) and rhizobia (i.e., nitrogen-fixing bacteria). Understanding these associations has important implications for ecological theory and for restoration practice. Here, we tested whether the presence of AMF and rhizobia influences the performance of native woody plants invaded by a non-native grass in experimental microcosms. We planted eight plant species (i.e., Acacia acuminata, A. microbotrya, Eucalyptus loxophleba subsp. loxophleba, E. astringens, Calothamnus quadrifidus, Callistemon phoeniceus, Hakea lissocarpha and H. prostrata) in microcosms of field-conditioned soil with and without addition of AMF and rhizobia in a fully factorial experimental design. After seedling establishment, we seeded half the microcosms with an invasive grass Bromus diandrus. We measured shoot and root biomass of native plants and Bromus, and on roots, the percentage colonization by AMF, number of rhizobia-forming nodules and number of proteaceous root clusters. We found no effect of plant root symbionts or Bromus addition on performance of myrtaceous, and as predicted, proteaceous species as they rely little or not at all on AMF and rhizobia. Soil treatments with AMF and rhizobia had a strong positive effect (i.e., larger biomass) on native legumes (A. microbotrya and A. acuminata). However, the beneficial effect of root symbionts on legumes became negative (i.e., lower biomass and less nodules) if Bromus was present, especially for one legume, i.e., A. acuminata, suggesting a disruptive effect of the invader on the mutualism. We also found a stimulating effect of Bromus on root nodule production in A. microbotrya and AMF colonization in A. acuminata which could be indicative of legumes’ increased resource acquisition requirement, i.e., for nitrogen and phosphorus, respectively, in response to the Bromus addition. We have demonstrated the importance of measuring belowground effects because the aboveground effects gave limited indication of the effects occurring belowground

Selective Targeting of Bromodomains of the Bromodomain-PHD Fingers Family Impairs Osteoclast Differentiation

Histone acetyltransferases of the MYST family are recruited to chromatin by BRPF scaffolding proteins. We explored functional consequences and the therapeutic potential of inhibitors targeting acetyl-lysine dependent protein interaction domains (bromodomains) present in BRPF1-3 in bone maintenance. We report three potent and selective inhibitors: one (PFI-4) with high selectivity for the BRPF1B isoform and two pan-BRPF bromodomain inhibitors (OF-1, NI-57). The developed inhibitors displaced BRPF bromodomains from chromatin and did not inhibit cell growth and proliferation. Intriguingly, the inhibitors impaired RANKL-induced differentiation of primary murine bone marrow cells and human primary monocytes into bone resorbing osteoclasts by specifically repressing transcriptional programs required for osteoclastogenesis. The data suggest a key role of BRPF in regulating gene expression during osteoclastogenesis, and the excellent druggability of these bromodomains may lead to new treatment strategies for patients suffering from bone loss or osteolytic malignant bone lesions

Clonal Deletion Prunes but Does Not Eliminate Self-Specific αβ CD8+ T Lymphocytes

SummaryIt has long been thought that clonal deletion efficiently removes almost all self-specific T cells from the peripheral repertoire. We found that self-peptide MHC-specific CD8+ T cells in the blood of healthy humans were present in frequencies similar to those specific for non-self antigens. For the Y chromosome-encoded SMCY antigen, self-specific T cells exhibited only a 3-fold lower average frequency in males versus females and were anergic with respect to peptide activation, although this inhibition could be overcome by a stronger stimulus. We conclude that clonal deletion prunes but does not eliminate self-specific T cells and suggest that to do so would create holes in the repertoire that pathogens could readily exploit. In support of this hypothesis, we detected T cells specific for all 20 amino acid variants at the p5 position of a hepatitis C virus epitope in a random group of blood donors

Multicolour-banding fluorescence in situ hybridisation (mbanding-FISH) to identify recurrent chromosomal alterations in breast tumour cell lines

Recurrent chromosome breakpoints in tumour cells may point to cancer genes, but not many have been molecularly characterised. We have used multicolour-banding fluorescence in situ hybridisation (mbanding-FISH) on breast tumour cell lines to identify regions of chromosome break created by inversions, duplications, insertions and translocations on chromosomes 1, 5, 8, 12 and 17. We delineate a total of 136 regions of break, some of them occurring with high frequency. We further describe two examples of dual-colour FISH characterisation of breakpoints, which target the 1p36 and 5p11–12 regions. Both breaks involve genes whose function is unknown to date. The mbanding-FISH strategy constitutes an efficient first step in the search for potential cancer genes

Machine learning for regulatory analysis and transcription factor target prediction in yeast

High throughput technologies, including array-based chromatin immunoprecipitation, have rapidly increased our knowledge of transcriptional maps—the identity and location of regulatory binding sites within genomes. Still, the full identification of sites, even in lower eukaryotes, remains largely incomplete. In this paper we develop a supervised learning approach to site identification using support vector machines (SVMs) to combine 26 different data types. A comparison with the standard approach to site identification using position specific scoring matrices (PSSMs) for a set of 104 Saccharomyces cerevisiae regulators indicates that our SVM-based target classification is more sensitive (73 vs. 20%) when specificity and positive predictive value are the same. We have applied our SVM classifier for each transcriptional regulator to all promoters in the yeast genome to obtain thousands of new targets, which are currently being analyzed and refined to limit the risk of classifier over-fitting. For the purpose of illustration we discuss several results, including biochemical pathway predictions for Gcn4 and Rap1. For both transcription factors SVM predictions match well with the known biology of control mechanisms, and possible new roles for these factors are suggested, such as a function for Rap1 in regulating fermentative growth. We also examine the promoter melting temperature curves for the targets of YJR060W, and show that targets of this TF have potentially unique physical properties which distinguish them from other genes. The SVM output automatically provides the means to rank dataset features to identify important biological elements. We use this property to rank classifying k-mers, thereby reconstructing known binding sites for several TFs, and to rank expression experiments, determining the conditions under which Fhl1, the factor responsible for expression of ribosomal protein genes, is active. We can see that targets of Fhl1 are differentially expressed in the chosen conditions as compared to the expression of average and negative set genes. SVM-based classifiers provide a robust framework for analysis of regulatory networks. Processing of classifier outputs can provide high quality predictions and biological insight into functions of particular transcription factors. Future work on this method will focus on increasing the accuracy and quality of predictions using feature reduction and clustering strategies. Since predictions have been made on only 104 TFs in yeast, new classifiers will be built for the remaining 100 factors which have available binding data