Project description:The importance of the niche to provide regulatory inputs to balance stem cell self-renewal and differentiation has become clear. However, the regulatory interplay between stem cells and their niche at the whole genome level is still poorly understood. To elucidate the mechanisms controlling stem cells and their progenies as they progress through development at the transcriptional level, we recorded the regulatory program of two independent cell lineages in the Drosophila testis. We identified genes active in the soma or germline as well as genome-wide binding profiles of two transcription factors, Zfh-1 and Abd-A, expressed in somatic support cells and crucial for fate acquisition of both cell lineages. In order to uncover gene activities in the testis soma, we first determined the transcriptome of the somatic and germline lineages by RNA polymerase II Targeted DamID (TaDa) (Southall et al., 2013), followed by the identification of genes bound by two regulators active in somatic sub-populations and controlling their development using regular DNA adenine methyltransferase identification (DamID) (Van Steensel et al., 2001). For identifying abd-A and Zfh1 binding regions in the Drosophila testis the fusion protein was expressed from the uninduced minimal Hsp70 promoter of the UAS vector pUAST. As a control for nonspecific Dam activity, transgenic flies expressing the Dam alone were used (Choksi et al., 2006). To express the AbdA-Dam fusion protein, we first generated a pNDam-Myc-abdA construct by cloning abdA in the pNDam-Myc vector (van Steensel et al., 2001) and then subcloned the NDam-Myc-abdA fragment into the pUAST-attB and to express the Zfh1-Dam fusion protein, we first generated a pNDam-Myc-zfh1 construct by cloning zfh1 in the pNDam-Myc vector (van Steensel et al., 2001) and then subcloned the NDam-Myc-zfh1 fragment into the pUAST-attB. For targeted DamID (TaDa) in Drosophila 3rd instar testes cyst and germline cells. Cell-type specific DamID was performed in cyst cells (CySCs and SCCs) and early germline of 3rd instar larval testes for profiling RNA Pol II occupancy in these cells by crossing UAS-LT3-Dam-Pol II and UAS-LT3-Dam control flies to c587-GAL4 (somatic lineage) or Nanos-GAL4 (early germline) drivers. For Dam-ID two individual replicates of Dam-abd-A, Dam-Zfh1 and Dam alone have been generated whereas for TaDa, two individual replicates of c587>UAS-LT3-Dam-PolII, Nanos>UAS-LT3-Dam-Pol II and c587>UAS-LT3-Dam (control), Nanos>UAS-LT3-Dam (control) have been used. Following a methylation-sensitive DNA digestion and PCR amplification, DNA fragments from the above samples were labeled and hybridized to genomic Affymetrix arrays in duplicates (Protocol available at “www.flychip.org.uk”).
Project description:In order to generate data suitable to decipher cis-regulatory logic, we generated ~100 million synthetic promoters (in yeast) comprised of random DNA and measured their expression by FACS (sorting into 18 bins).
Project description:Increasingly, experimental data on biological systems are obtained from several sources and computational approaches are required to integrate this information and derive models for the function of the system. Here, we demonstrate the power of a logic-based machine learning approach to propose hypotheses for gene function integrating information from two diverse experimental approaches. Specifically, we use inductive logic programming that automatically proposes hypotheses explaining the empirical data with respect to logically encoded background knowledge. We study the capsular polysaccharide biosynthetic pathway of the major human gastrointestinal pathogen Campylobacter jejuni. We consider several key steps in the formation of capsular polysaccharide consisting of 15 genes of which 8 have assigned function, and we explore the extent to which functions can be hypothesised for the remaining 7. Two sources of experimental data provide the information for learning-the results of knockout experiments on the genes involved in capsule formation and the absence/presence of capsule genes in a multitude of strains of different serotypes. The machine learning uses the pathway structure as background knowledge. We propose assignments of specific genes to five previously unassigned reaction steps. For four of these steps, there was an unambiguous optimal assignment of gene to reaction, and to the fifth, there were three candidate genes. Several of these assignments were consistent with additional experimental results. We therefore show that the logic-based methodology provides a robust strategy to integrate results from different experimental approaches and propose hypotheses for the behaviour of a biological system. [Data is also available from http://bugs.sgul.ac.uk/E-BUGS-132]
Project description:Pulsed SILAC approaches allow measurement of protein dynamics, including protein translation and degradation. However, its use in quantifying acute changes has been limited due the low labeled peptide stoichiometry. Here, we describe the use of instrument logic to select peaks of interest via targeted mass differences (TMD) for overcoming this limitation. Comparing peptides artificially mixed at low heavy-to-light stoichiometry measured using standard data dependent acquisition with or without TMD revealed 2-3 fold increases in identification without significant loss in quantification precision for both MS2 and MS3 methods. Our benchmarked method approach increases throughput by reducing the necessary machine time. We anticipate that all pulsed SILAC measurements, if combined with TMT or not, would greatly benefit from instrument logic based approaches.