Protein Condensate Atlas from predictive models of heteromolecular condensate composition

Biomolecular condensates help cells organise their content in space and time. Cells harbour a variety of condensate types with diverse composition and many are likely yet to be discovered. Here, we develop a methodology to predict the composition of biomolecular condensates. We first analyse available proteomics data of cellular condensates and find that the biophysical features that determine protein localisation into condensates differ from known drivers of homotypic phase separation processes, with charge mediated protein-RNA and hydrophobicity mediated protein-protein interactions playing a key role in the former process. We then develop a machine learning model that links protein sequence to its propensity to localise into heteromolecular condensates. We apply the model across the proteome and find many of the top-ranked targets outside the original training data to localise into condensates as confirmed by orthogonal immunohistochemical staining imaging. Finally, we segment the condensation-prone proteome into condensate types based on an overlap with biomolecular interaction profiles to generate a Protein Condensate Atlas. Several condensate clusters within the Atlas closely match the composition of experimentally characterised condensates or regions within them, suggesting that the Atlas can be valuable for identifying additional components within known condensate systems and discovering previously uncharacterised condensates

Targeting the Conserved Stem Loop 2 Motif in the SARS-CoV-2 Genome.

RNA structural elements occur in numerous single-stranded positive-sense RNA viruses. The stem-loop 2 motif (s2m) is one such element with an unusually high degree of sequence conservation, being found in the 3' untranslated region (UTR) in the genomes of many astroviruses, some picornaviruses and noroviruses, and a variety of coronaviruses, including severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2. The evolutionary conservation and its occurrence in all viral subgenomic transcripts imply a key role for s2m in the viral infection cycle. Our findings indicate that the element, while stably folded, can nonetheless be invaded and remodeled spontaneously by antisense oligonucleotides (ASOs) that initiate pairing in exposed loops and trigger efficient sequence-specific RNA cleavage in reporter assays. ASOs also act to inhibit replication in an astrovirus replicon model system in a sequence-specific, dose-dependent manner and inhibit SARS-CoV-2 replication in cell culture. Our results thus permit us to suggest that the s2m element is readily targeted by ASOs, which show promise as antiviral agents. IMPORTANCE The highly conserved stem-loop 2 motif (s2m) is found in the genomes of many RNA viruses, including SARS-CoV-2. Our findings indicate that the s2m element can be targeted by antisense oligonucleotides. The antiviral potential of this element represents a promising start for further research into targeting conserved elements in RNA viruses.ERC, BBSR

A single-molecule view of CTCF-mediated DNA looping

Genome folding is a key aspect of nuclear processes in eukaryotic cells, underlying proper gene expression, orderly DNA replication and genomic stability. CTCF and cohesin have emerged as two key architectural proteins that mediate long-range DNA interactions and are essential for proper genome folding. Both proteins are found enriched at the base of chromatin loops and the borders of self-interacting chromatin domains, which are a major structural and functional feature of genome organisation in interphase. CTCF binding sites at the base of chromatin loops show a strong preference for convergent orientation along the linear DNA sequence. The mechanistic features underlying the role of CTCF in loop formation are incompletely understood. In particular, it is unclear how CTCF binding directionality controls loop formation. There is growing support from computational and experimental studies for the loop extrusion model, which proposes that a cohesin-based complex extrudes DNA loop until it is blocked by encountering convergently oriented CTCF sites. This thesis focuses on investigating molecular features of CTCF-DNA interaction to gain insight into how CTCF executes its functions in genome folding, and touches on the role of cohesin in this context. In particular, we investigated whether CTCF binding induces a bend in DNA, which might provide a mechanism for biasing loop formation or halting loop extrusion. We did not detect bending in single molecule imaging assays. However, we discovered that CTCF protein alone can induce DNA loop formation in vitro. The frequency of these looping events did not depend on the orientation of the CTCF binding sites, suggesting that additional factors are required to impose the preference for convergence in vivo. To investigate this further, we erformed mass spectrometry analysis to identify interaction partners of DNA-bound CTCF, and generated tools to study the particular contribution of cohesin towards CTCF-based DNA looping with single molecule fluorescence microscopy.Open Acces

Protein Condensate Atlas from predictive models of heteromolecular condensate composition

Biomolecular condensates help cells organise their content in space and time. Cells harbour a variety of condensate types with diverse composition and many are likely yet to be discovered. Here, we develop a methodology to predict the composition of biomolecular condensates. We first analyse available proteomics data of cellular condensates and find that the biophysical features that determine protein localisation into condensates differ from known drivers of homotypic phase separation processes, with charge mediated protein-RNA and hydrophobicity mediated protein-protein interactions playing a key role in the former process. We then develop a machine learning model that links protein sequence to its propensity to localise into heteromolecular condensates. We apply the model across the proteome and find many of the top-ranked targets outside the original training data to localise into condensates as confirmed by orthogonal immunohistochemical staining imaging. Finally, we segment the condensation-prone proteome into condensate types based on an overlap with biomolecular interaction profiles to generate a Protein Condensate Atlas. Several condensate clusters within the Atlas closely match the composition of experimentally characterised condensates or regions within them, suggesting that the Atlas can be valuable for identifying additional components within known condensate systems and discovering previously uncharacterised condensates

Protein Condensate Atlas from predictive models of heteromolecular condensate composition

Acknowledgements: We would like to acknowledge the Schmidt Science Fellowship in partnership with the Rhodes Trust (K.L.S.), St. John’s College Research Fellowship (K.L.S.), the National Institutes of Health Oxford-Cambridge Scholars Programme (L.L.G.), the Cambridge Trust’s Cambridge International Scholarship (L.L.G.), the Intramural Research Programme of the National Institute of Diabetes and Digestive and Kidney Diseases at the National Institutes of Health (L.L.G.), and the European Research Council (T.P.J.K.). The authors gratefully acknowledge funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program through the ERC grant DiProPhys (agreement ID 101001615).Funder: European Research CouncilAbstractBiomolecular condensates help cells organise their content in space and time. Cells harbour a variety of condensate types with diverse composition and many are likely yet to be discovered. Here, we develop a methodology to predict the composition of biomolecular condensates. We first analyse available proteomics data of cellular condensates and find that the biophysical features that determine protein localisation into condensates differ from known drivers of homotypic phase separation processes, with charge mediated protein-RNA and hydrophobicity mediated protein-protein interactions playing a key role in the former process. We then develop a machine learning model that links protein sequence to its propensity to localise into heteromolecular condensates. We apply the model across the proteome and find many of the top-ranked targets outside the original training data to localise into condensates as confirmed by orthogonal immunohistochemical staining imaging. Finally, we segment the condensation-prone proteome into condensate types based on an overlap with biomolecular interaction profiles to generate a Protein Condensate Atlas. Several condensate clusters within the Atlas closely match the composition of experimentally characterised condensates or regions within them, suggesting that the Atlas can be valuable for identifying additional components within known condensate systems and discovering previously uncharacterised condensates.</jats:p

Targeting the Conserved Stem Loop 2 Motif in the SARS-CoV-2 Genome

RNA structural elements occur in numerous single-stranded positive-sense RNA viruses. The stem-loop 2 motif (s2m) is one such element with an unusually high degree of sequence conservation, being found in the 3' untranslated region (UTR) in the genomes of many astroviruses, some picornaviruses and noroviruses, and a variety of coronaviruses, including severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2. The evolutionary conservation and its occurrence in all viral subgenomic transcripts imply a key role for s2m in the viral infection cycle. Our findings indicate that the element, while stably folded, can nonetheless be invaded and remodeled spontaneously by antisense oligonucleotides (ASOs) that initiate pairing in exposed loops and trigger efficient sequence-specific RNA cleavage in reporter assays. ASOs also act to inhibit replication in an astrovirus replicon model system in a sequence-specific, dose-dependent manner and inhibit SARS-CoV-2 replication in cell culture. Our results thus permit us to suggest that the s2m element is readily targeted by ASOs, which show promise as antiviral agents. IMPORTANCE The highly conserved stem-loop 2 motif (s2m) is found in the genomes of many RNA viruses, including SARS-CoV-2. Our findings indicate that the s2m element can be targeted by antisense oligonucleotides. The antiviral potential of this element represents a promising start for further research into targeting conserved elements in RNA viruses