Transcription factors find their targets by reading the epigenetic code. This project aims to elucidate how transcription factors, proteins that regulate gene expression, find their target genes. The hypothesis is that non-DNA binding domains play an essential role in this process. This project expects to transform our understanding of transcription factor families, and how factors in families with the same DNA-binding domain manage to regulate different genes. Expected outcomes of this project ....Transcription factors find their targets by reading the epigenetic code. This project aims to elucidate how transcription factors, proteins that regulate gene expression, find their target genes. The hypothesis is that non-DNA binding domains play an essential role in this process. This project expects to transform our understanding of transcription factor families, and how factors in families with the same DNA-binding domain manage to regulate different genes. Expected outcomes of this project include revealing how accessory proteins help transcription factors identify their targets in the genome by reading epigenetic marks. This should provide significant benefits including improved design of artificial transcription factors to up- or down-regulate specific genes in research and agriculture.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210101669
Funder
Australian Research Council
Funding Amount
$430,485.00
Summary
Polycomb Group Proteins - Shaping Chromatin Architecture to Silence Genes . This project aims to address the fundamental question of how genes are switched off by studying a group of molecular off-switches, the polycomb group proteins. The project is expected to generate new knowledge in the area of gene regulation and epigenetics by combining innovative methods of structural biology and cell biology in an interdisciplinary way. The expected outcomes include a more complete picture of the molecu ....Polycomb Group Proteins - Shaping Chromatin Architecture to Silence Genes . This project aims to address the fundamental question of how genes are switched off by studying a group of molecular off-switches, the polycomb group proteins. The project is expected to generate new knowledge in the area of gene regulation and epigenetics by combining innovative methods of structural biology and cell biology in an interdisciplinary way. The expected outcomes include a more complete picture of the molecular mechanisms that regulate gene expression and the development of novel methods to image the genome. This should provide significant benefits, such as facilitated development of gene editing tools and regulatory circuits for synthetic biology, as well as novel capabilities to image the genome at high resolution Read moreRead less
Genetic architecture and evolution of complex traits across populations. Most human traits have a genetic component and display substantial diversity within and among populations. How natural selection changes and maintains genetic variation in human traits is a long-standing question in evolution that the proposed project aims to answer. Using innovative statistical methods and largest genomic “big” datasets ever across populations of different ancestral backgrounds, this project expects to gen ....Genetic architecture and evolution of complex traits across populations. Most human traits have a genetic component and display substantial diversity within and among populations. How natural selection changes and maintains genetic variation in human traits is a long-standing question in evolution that the proposed project aims to answer. Using innovative statistical methods and largest genomic “big” datasets ever across populations of different ancestral backgrounds, this project expects to generate new knowledge on the roles of natural selection in shaping the genetic variation in traits and identify key factors that drive the differentiation of human populations. These outcomes will significantly improve our understanding on the evolution of human traits and adaptation of populations to changing environments.Read moreRead less
The transgenerational effect of thermosensing in plants. This project aims to understand how thermosensing mechanisms in plants result in transgenerational change, and potentially adaptation to climate. Exploiting the recent discovery of the thermosensor phytochrome B, this project will decipher the molecular cascade which, either through long-distance communication or through persistence of an epigenetic state in the cell lineage, could lead to a trans generational memory in plants helping with ....The transgenerational effect of thermosensing in plants. This project aims to understand how thermosensing mechanisms in plants result in transgenerational change, and potentially adaptation to climate. Exploiting the recent discovery of the thermosensor phytochrome B, this project will decipher the molecular cascade which, either through long-distance communication or through persistence of an epigenetic state in the cell lineage, could lead to a trans generational memory in plants helping with climate adaptation. This project will unravel novel molecular mechanisms, which have the potential to pave the way for designing new climate-proofing solutions to cope with temperature uncertainty.Read moreRead less
Prediction of phenotype for multiple traits from multi-omic data. This project aims to develop better methods for predicting traits in an individual based on their genome sequence. This method will be tested in agricultural animals and plants and in humans. The prediction formula is derived from a training dataset that has information on the traits and genome sequence of a sample of individuals. The prediction formula can then be applied to predict the trait in individuals where the trait is un ....Prediction of phenotype for multiple traits from multi-omic data. This project aims to develop better methods for predicting traits in an individual based on their genome sequence. This method will be tested in agricultural animals and plants and in humans. The prediction formula is derived from a training dataset that has information on the traits and genome sequence of a sample of individuals. The prediction formula can then be applied to predict the trait in individuals where the trait is unknown. This is useful for selecting the best parents for breeding in agriculture and for predicting the future phenotype of animals, crops and people. The proposed method uses data on very many traits to identify sequence variants that have a function and to predict the traits affected by each variant.Read moreRead less