Project description:Regulation of gene expression is mediated by combinations of DNA binding transcription factors that work in concert to recruit transcriptional machinery. Each cell type expresses hundreds of sequence-specific transcription factors, many of which recognize identical or similar DNA sequences. Such factors can play both redundant and non-redundant roles, but mechanisms determining overlapping or distinct biological outcomes are largely unknown. Here, we implement a machine learning approach to investigate how local combinations of sequence motifs influence the genome wide binding patterns of different members of the AP-1 transcription factor family in macrophages. Significant motifs associated with family member specific binding patterns were validated by assessing effects of motif mutations in different strains of mice. We further confirmed the prediction of PPARg to be preferentially associated with the specific binding pattern of cJun using PPARg knockout macrophages. Together, our results provide evidence that unique binding patterns of AP-1 family members result in part from the corresponding unique ensembles of nearby regulatory elements embedded within enhancers and promoters, and that these elements can be identified by machine learning models trained using genomic sequence.
Project description:Regulation of gene expression is mediated by combinations of DNA binding transcription factors that work in concert to recruit transcriptional machinery. Each cell type expresses hundreds of sequence-specific transcription factors, many of which recognize identical or similar DNA sequences. Such factors can play both redundant and non-redundant roles, but mechanisms determining overlapping or distinct biological outcomes are largely unknown. Here, we implement a machine learning approach to investigate how local combinations of sequence motifs influence the genome wide binding patterns of different members of the AP-1 transcription factor family in macrophages. Significant motifs associated with family member specific binding patterns were validated by assessing effects of motif mutations in different strains of mice. We further confirmed the prediction of PPARg to be preferentially associated with the specific binding pattern of cJun using PPARg knockout macrophages. Together, our results provide evidence that unique binding patterns of AP-1 family members result in part from the corresponding unique ensembles of nearby regulatory elements embedded within enhancers and promoters, and that these elements can be identified by machine learning models trained using genomic sequence.
Project description:This phase I trial is studying the best dose of 3-AP and the side effects of giving 3-AP together with gemcitabine in treating patients with advanced solid tumors or lymphoma. Drugs used in chemotherapy, such as 3-AP and gemcitabine (GEM), work in different ways to stop the growth of cancer cells, either by killing the cells or by stopping them from dividing. 3-AP may help gemcitabine kill more cancer cells by making the cells more sensitive to the drug. 3-AP may also stop the growth of tumor cells by blocking some of the enzymes needed for cell growth.
Project description:Investigation of whole genome gene expression level changes in leaves of apple seedlings (Golden delicious) 3 days after treatment by tomato cutin monomer extract (CME) versus formulation blank (FB). CME is a formulated extract enriched of hydroxy fatty acids from tomato cuticle
Project description:to discover the miRNAs involved in the production of cytokines by RAW264.7 cells treated with LPS and ginsinoside Rd monomer We then performed gene expression profiling analysis using data obtained from RNA-seq of RAW264.7 cells.
Project description:To validate the sequence motifs identified by our multi-task learning model MTtrans, a new 5' UTR library with around 8,000 synthetic 5'UTRs was built to express EGFP. The reads count was used as a proxy of translation rate here to validate the estimated regulatory effect of motifs that we inferred from multiple datasets, proving the robustness of the sequence motifs.