Baltic amber from Northern Europe is an outstanding deposit due to the great number and diversity of preserved organisms. Although it is the richest source of fossils ever, science has consistently failed to define its geological age, despite applying various traditional approaches. It limits the scientific utility of all Baltic amber fossils in evolutionary divergence time estimation. This is very unfortunate, especially because these fossils would be very important for reconstructing evolutionary events in the Eocene, which is known as a deep-time analogue of current climatic changes. The core idea of this project is to apply an innovative and highly promising approach of precise dating for this very important fossil deposit by using DNA, morphology of extinct and extant species, and powerful statistics. I propose to time-calibrate Baltic amber fossils using a dataset from my previous project with the addition of Baltic amber and other, well-dated Cenozoic fossils. With these data, I will apply an existing method for divergence time estimation, the total-evidence dating under the fossilized birth–death process with its extension. The extension of the method will allow for phylogenetic estimation of the age of fossils from Baltic amber, and thus the amber itself. Firmly establishing the age of Baltic amber fossils will be a breakthrough for evolutionary biology, palaeoecology, biogeography, and palaeoclimatology. The project builds on my experience as a palaeoentomologist and evolutionary biologist, and it greatly expands my horizons as a future leader through gaining new insights and learning new facts and analytical methods, working in multidisciplinary academic environments and acquiring additional skills transferrable to new scientific areas. The hosts are the leading specialists in statistical phylogenetics and palaeontological studies, and leaders of dynamic research groups targeting different methods for understanding evolutionary patterns and processes

Memory of gene expression states is essential for development and maintenance of tissues. Chromatin, a protein DNA complex, is implicated in the process. Local, chromatin feedback loops are involved in maintenance of gene silencing, but analogous mechanisms for preservation of active states are unknown. In order to reveal feedback loops for active gene expression states it is crucial to uncouple them from ongoing transcription, as it occurs in the case of maintenance of silent states. Such partition takes place during trained immunity – memory of the innate immune system. A key paradigm is interferon gamma (IFNγ) stimulation. While IFNγ induces many genes, a subset of those is maintained in a poised, inactive state that allows for rapid re-activation at a later time – an event called transcriptional memory. In my ongoing research I have discovered novel genes that show strong transcriptional memory of prior IFNγ activation and have obtained initial mechanistic insights into the role of chromatin in the process. Importantly, I discovered that memory results in a larger proportion of cells expressing the target gene. This forms the basis of a cell sorting, high throughput assay that I have developed. I am now in a unique position to capitalize on those discoveries and tools. I aim to identify novel transcriptional memory maintenance factors in human cells using an unbiased approach based on an efficient cell selection strategy combined with genome wide, CRISPR-Cas9 mutagenesis protocols. Secondly I wish to establish the function of the identified components in the stability of dendritic cells identity after differentiation form monocytes. The multidisciplinary and unique perspective of this project: combining gene expression analysis and selection strategies with immunological expertise will advance our understanding of the mechanisms underlying maintenance of gene expression states and contribute to the development of new immune therapies and vaccines.