{ "data": [{ "title": "Detecting and dissecting host-pathogen genetic interactions", "PI": "Dr. Azim Ansari and Dr. Gavin Band", "email": "azim.ansari@ndm.ox.ac.uk", "mix": "0% wet lab, 100% dry lab", "description": "The outcome of infections depends on a complex interplay of interactions between host cells and the pathogens that invade and replicate inside them. Underlying this is natural genetic variation, but little is known about the mutations involved, their biological function or how they interact. In this project, you will work with Dr. Azim Ansari (Medawar) and Dr. Gavin Band (Wellcome Centre for Human Genetics) to develop and apply statistical genetic methods that will improve our understanding of this. There are several key challenges to overcome which could form the source of a rotation project, including\n-- Population genetics and natural selection: infections are a major source of natural selection on the human genome, and human resistance alleles provide a strong selective force on pathogens. But how this plays out at the level of populations is mostly unknown. It could involve stable interactions between segregating mutations, but also selective sweeps or development of complex genetic diversity through diversifying or balancing selection. Understanding this is important because it shapes attempts to discover genetic interactions and, ultimately, to discover new aspects of infection biology. A key way to gain a better understanding of this will be to conduct population-genetic simulations, potentially incorporating host-pathogen interactions, spatial aspects, recombination and mutation, between-pathogen competition and potentially epistasis, to generate realistic population-genetic outcomes. You will then compare that to real population datasets.\n-- Robust methods for genotyping complex variation in human and pathogen genomes are needed. Both Dr. Ansari and Dr. Band are engaged in projects to leverage new developments in long-read sequencing. There is an opportunity for you to contribute by developing targeted assays for genomic regions of interest, apply long-read methods such as Oxford Nanopore Technologies MinION, and conduct in silico work to call and analyse the resulting data, and apply results to existing datasets.\n-- An important feature of infections is that they are often mixed – that is, they consist of more than one genetic type of pathogen at the same time. Current analysis methods deal with this in limited and ad hoc ways, such as simply taking a ‘majority’ or arbitrary genotype call at each locus, but as a result they can lose power. You will develop better methods to handle this in the context of host-pathogen analyses, either by exploiting genome-wide data or panels of known sequences to estimate and exploit the mixture composition of infections, from genome-wide sequence data.\nThis work will be carried out using large datasets on Hepatitis B and C, HIV and malaria infections.", "training": "The student will develop expertise in Statistical genomics, Statistical Modelling, Machine Learning, bioinformatics, infectious diseases, evolution and population genetics. This studentship will be based at the Peter Medawar Building for Pathogen Research (PMB) and the Wellcome Centre for Human Genetics (WHG) at Oxford. The PMB houses around 150 scientists working on HIV, HCV, influenza, TB, malaria, SARS-CoV-2 and dengue and many of the PIs are global leaders in the study of infections.", "background_reading": "-- Band, G, et al. “Malaria protection due to sickle haemoglobin depends on parasite genotype” Nature 602, 106–111 (2022). https://doi.org/10.1038/s41586-021-04288-3\n-- Ansari, M. Azim, et al. 'Genome-to-genome analysis highlights the effect of the human innate and adaptive immune systems on the hepatitis C virus.' Nature genetics 49.5 (2017): 666-673.\n-- Behr, Merle, et al. 'Testing for dependence on tree structures.' Proceedings of the National Academy of Sciences 117.18 (2020): 9787-9792.\n-- Crawford, Lorin, et al. 'Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits.' PLoS genetics 13.7 (2017): e1006869." },{ "title": "Understanding mechanisms of sex disparities in infectious diseases", "PI": "Dr. Azim Ansari", "email": "azim.ansari@ndm.ox.ac.uk", "mix": "0% wet lab, 100% dry lab", "description": "The mortality rate for COVID-19 pandemic has been two to three times higher in men than women. Similar observation extends to susceptibility and outcome of most other infectious diseases. For instance, after initial Hepatitis C Virus infection women are more likely to spontaneously clear the virus without any interventions. The rate of progression to severe liver disease is also slower in women than men and there is some evidence that women respond better to direct-acting antiviral therapies. On the other hand, many auto-immune diseases (such as lupus) have a much higher rate of occurrence in women than men. Despite large evidence for sex differences in autoimmune diseases and susceptibility and outcome of infectious diseases, data addressing the biological mechanism are remarkably scarce.\nIn this short project you will use computational and (and potentially experimental) methods to probe differences in immune system that lead to sex differences in infectious diseases. One hypothesis for the sex differences in immune responses are that in females, due to the random nature of X-chromosome inactivation process, organs are mosaic and consist of two populations of cells with preferential expression of either paternal or maternal X-chromosome. This increased level of genetic heterogeneity at organ level in females relative to males could explain, better immune responses against infections.\nTo test this hypothesis, you will use liver RNA-seq and genomic data from a cohort of 200 patients infected with HCV to estimate what proportion of cells express paternal and maternal X-chromosomes in each patient. We will then test for association between clinical phenotypes (viral load and treatment outcome) and the level of X-chromosome expression heterogeneity.", "training": "The student will develop expertise in Statistical genomics, Statistical Modelling, Machine Learning, bioinformatics, infectious diseases, evolution and population genetics. This studentship will be based at the Peter Medawar Building for Pathogen Research (PMB) and the Wellcome Centre for Human Genetics (WHG) at Oxford. The PMB houses around 150 scientists working on HIV, HCV, influenza, TB, malaria, SARS-CoV-2 and dengue and many of the PIs are global leaders in the study of infections.", "background_reading": "-- Vieira VA, Zuidewind P, Muenchhoff M, Roider J, Millar J, Clapson M, Van Zyl A, Shingadia D, Adland E, Athavale R, Grayson N, Ansari MA, et al. Strong sex bias in elite control of paediatric HIV infection. AIDS (London, England). 2019 Jan 2;33(1):67.\n-- Shvetsova, E, et al. Skewed X-inactivation is common in the general female population, European Journal of Human Genetics, 2019.\n-- Tukiainen, T, et al. Landscape of X chromosome inactivation across human tissues, Nature, 2017.\n-- Oliva, Meritxell, et al. 'The impact of sex on gene expression across human tissues.' Science 369.6509 (2020)." },{ "title": "Resolve T cell receptor diversity using long-read sequencing!", "PI": "Dr. Gavin Band, Prof. Julian Knight and Prof. John Todd", "email": "gavin.band@well.ox.ac.uk", "mix": "0% wet lab, 100% dry lab", "description": "Some of the most complex, diverse and important parts of the human genome are those that encode for adaptive immunity. Among these, the immunoglobulin regions (which code for antibody diversity) and the T cell receptor regions are particularly important, but also particularly hard to study using traditional methods. In this project you will develop new computational methods to try to resolve T cell receptor diversity using long-read sequencing data.\nimage://data/images/band_gavin_01.png\nFigure 1: a schematic of the T cell receptor alpha / delta locus\nThis approach exploits a key feature of T cell development: the T cell receptor regions undergo somatic recombination (so called 'VJ' or 'V(D)J' recombination) to generate a large number of different haplotypes carried by different T cells. These recombinations splice together different gene components to make the functional receptor genes. This is one of nature's most complex and beautiful mechanisms of generating genetic diversity, and is a core component of what makes immunity 'adaptive' in the first place.\nWe recently worked with Pacific Biosciences (PacBio) and with Oxford Nanopore Technologies (ONT) to generated high-coverage long-read sequencing data for a healthy volunteer codenamed 'HV31'. Crucially, because this is long-read data, sequencing reads from recombinant T cells are easily visible because they don't align in one piece to the genome. In other words, the reads capture both the germline structure of the genome and somatically rearranged DNA from T cells.\nYour mission (if you choose to accept) it is to:\n-- Learn to analyse long-read sequencing data from PacBio and ONT.\n-- Find a way to extract out reads in the T cell receptor regions, that come from the somatic recombinant T cells.\n-- Use the reads to estimate the proportion of T cells in the sample.\n-- Estimate the proportion of αβ versus γδ T cells in the sample (which can be distinguished as they undergo different recombination processes).\n-- Identify and determine the different T cell recombinant haplotypes - for example by clustering the reads and aligning them to each other - and predict gene content.\n-- Analyse the T cell receptor diversity in HV31, at least at a broad scale.\n-- Analyse patterns of methylation on these recombinant haplotypes.\nThe work is part of a larger project to assemble and align functional data to the HV31 genome, and there are many opportunities to pursue it further.", "training": "You'll learn to work with highly accurate long read genomic sequencing data, and will develop and apply algorithms to analyse it. You'll also learn about the fundamental mechanisms of receptor diversity underpinning the adaptive immune system. This is a research project", "background_reading": "-- Zhang et al., 'Using de novo assembly to identify structural variation of eight complex immune system gene regions' PLOS Computational Biology (2021) https://doi.org/10.1371/journal.pcbi.1009254\n-- The T cell Receptor Factsbook, https://www.sciencedirect.com/book/9780124413528/the-t-cell-receptor-factsbook" },{ "title": "An atlas of long-range LD in the malaria parasite genome", "PI": "Dr. Gavin Band", "email": "gavin.band@well.ox.ac.uk", "mix": "0% wet lab, 100% dry lab", "description": "Our recent work turned up an extremely puzzling feature of the malaria parasite genome: the presence of long-range linkage disequilbrium (LD) in African parasite genomes. In other words - we found pairs of mutations that are almost always observed together in the same parasites, even though they lie very far apart in the genome (Band et al Nature 2021).\nimage://data/images/band_gavin_02.png\nFigure 1: regions of outlying LD in the malaria parasite genome.\nThis was surprising because malaria parasites undergo meiosis and recombination during transmission through the mosquito - an obligate part of the lifecycle. If long-range LD is present, it suggests selective forces - possibly epistasis - must be operating to maintain it. But right now we don't know the full extent of the loci that are involved or the forces involved.\nIn this project you will shine light on this by exploiting the largest set of malaria parasite genome sequences currently available (the MalariaGEN Pf7 dataset) to estimate long-range LD in global parasite populations, using tools developed by the group. The output will be an 'atlas of long-range LD'. You will aim to replicate previous findings,identify possible new signals of LD that we don't know about yet, and compare LD between populations.", "training": "You'll learn to work with large-scale genotyping datasets, and to build computational pipelines that can make this tractable. You'll develop new methods and visualisations to describe signals of LD across populations, and you'll start to think about the complicated population genetics that must underlie these signals.", "background_reading": "-- Band et al., 'Malaria protection due to sickle haemoglobin depends on parasite genotype' Nature (2021) https://doi.org/10.1038/s41586-021-04288-3" },{ "title": "Investigating the B and T cell mediated pro- and anti-tumour mechanisms in cancers", "PI": "Dr Rachael Bashford-Rogers", "email": "rachael.bashford-rogers@bioch.ox.ac.uk", "mix": "can be wet or dry lab work, or mixture depending on student preference", "description": "B cell infiltration has prognostic significance in solid tumours, and ongoing studies are investigating their phenotypes through single cell transcriptomics and spatial imaging. Characterising the B cell response to tumour cells, particularly their antigenic specificities, will be key to developing more immunologically appropriate cancer therapies. However, currently, the B cell antibody (Ab) specificity cannot be coupled with the Ab sequence, phenotype or transcriptome in a high-throughput manner. Here we propose a DPhil studentship project to develop novel technologies to be able to bridge the gap between B cell populations and antibody reactivity, thus giving a unique perspective on the development of anti-self, anti-tumour and anti-non-self Ab responses. This project will involve the development a novel high-throughput method to probe the antigen specificities of B cells, which will be coupled with single cell resolution of clonal phenotype and single cell transcriptome. This will be used to investigate the development and role of tumour-infiltrating B cells across a range of tumours with varying degrees of immunogenicity. This study will provide a unique platform to understand the probe between tumour neo-antigen, B cell immune-surveillance and specificity, and B cell phenotype, with the overall aim of highlighting new therapeutic options. Furthermore, this method is not just broadly applicable to cancer, but will have wider applications in immunology and biotechnology. This will be achieved through the development and application of novel experimental and computational approaches, working in partnership with a global network of clinicians, immunologists and sample cohorts. This information may be used to develop potential biomarkers of resistance to therapy and to determine potential therapeutic interventions that could be combined with the current standard of care that could target persistent clones in autoimmune diseases.\nThis project will apply novel single-cell genetic technologies, imaging and functional experiments to link the development, regulation and function of B and T cell populations in health and immunological diseases to the underlying host genetics.\nThis project aims to investigate the B and T cell immune response of circulating and tumour-infiltrating B cells across a range of tumours with different levels of immunogenicity and neo-antigen expression, including pancreatic and renal cancers. This will be done within the context of the tumour, stromal and myeloid cell populations to gain a global understanding of key immune cell dependencies and modes of modulation. This will involve the development of a novel platforms and methodologies to answer key questions in the field of tumour immunology including:\n-- What are the key features defining B and T cell infiltration into the tumour and how may this be modulated?\n-- What are the functions and cell-cell interaction effects of tumour infiltrating B and T cells?\n-- What determines the balance of whether B and T cells serve a pro- or an anti-tumourigenic function?\n-- Do tumour-associated B cells produce Abs against tumour cells, and how does tumour cell neo-antigen variation and expression level dictate Ab response? Are tumour-associated Abs cross-reactive to other self or non-self antigen, poly-reactive, or highly specific to tumour cells?\nOverall, this may help shed light on the B cell response to tumour cells, the specificity and breadth of response, and potentially highlight novel therapeutic targets. We envisage that this novel platform may be extended to the other areas of B cell biology, and could be a general tool that could be of great value other researchers.", "training": "The DPhil will gain experience and training in laboratory molecular biology and single cell methods, bioinformatics and immunology. These include:\n-- Single-cell RNA sequencing and analysis of patient samples.\n-- Integration of single-cell RNA sequencing and other “omics” datasets to associate information about the B/T cell receptor with the single-cell transcriptome.\n-- Development of novel functional, imaging and computational analyses to gain an understanding of the role ad communication of immune cells within the contexture of the tumour environment.\n-- Characterisation of B/T cell traits throughout development and tissues.\n-- Validation of associations using a wide range of immunological techniques.\n-- The project will work in partnership with a global network of clinicians, immunologists and sample cohorts.", "background_reading": "-- Double-jeopardy: scRNA-seq doublet/multiplet detection using multi-omic profiling (Cell Reports Methods, 2021).\nBo Sun, Emmanuel Bugarin-Estrada, Lauren E. Overend, Catherine E. Walker, Felicia A. Tucci, Rachael J. M. Bashford-Rogers\n-- Activated regulatory T-cells, dysfunctional and senescent T-cells hinder the immunity in pancreatic cancer (Cancers, 2021) (https://doi.org/10.1101/2020.06.20.163071)\nShivan Sivakumar, Enas Abu-Shah, David Ahern, Edward H Arbe-Barnes, Nagina Mangal, Srikanth Reddy, Aniko Rendek, Alistair Easton, Elke Kurz, Michael Silva, Lara R Heij, Zahir Soonawalla, Rachael Bashford-Rogers, Mark R Middleton, Michael Dustin\n-- B cell receptor repertoire analysis in six immune-mediated diseases (Nature, 2019)\nRJM Bashford-Rogers, L Bergamaschi, EF McKinney, DC Pombal, F Mescia, JC Lee, DC Thomas, SM Flint, P Kellam, DRW Jayne, PA Lyons, KGC Smith" },{ "title": "Tissue-specific regulation of chromatin proteins in neural development", "PI": "Dr. Robert Beagrie", "email": "robert.beagrie@well.ox.ac.uk", "mix": "20% wet lab, 80% dry lab", "description": "Mutations chromatin organising genes cause a group of human syndromes called “chromatinopathies”. Loss of function mutations in these chromatin genes are frequently associated with some degree of impaired neural development. Some of these same genes are also more highly expressed in the brain than they are in other organs. These kinds of tissue-specific gene expression patterns are usually driven by regulatory DNA regions called enhancers. Common genetic variants that are associated with small changes in susceptibility to human disease can affect enhancers. Rare loss of function variants in the genes driven by these enhancers are often associated with a much higher risk of similar diseases. We therefore hypothesise that common human sequence variants affecting neural enhancers of chromatin genes might contribute to common neurological diseases (e.g. mental health issues)\nThe aim of this rotation project would be to identify potential neural enhancers driving brain-specific upregulation of chromatin genes in normal development. This would be done by exploring published datasets of open chromatin regions (bulk and single-cell ATAC-seq), histone modifications (ChIP-seq, single-cell CUT&TAG) and gene expression (bulk and single-cell RNA-seq) for various neural lineages. If we can identify possible enhancers, we would then ask whether any of these regions contain known sequence variants linked to common human disease (e.g. variants identified in Genome Wide Association Studies for mental health conditions).\nThere would also be the opportunity to validate brain specific expression patterns of the genes in question if the student particularly wanted to gain some wet-lab experience as part of this project.", "training": "Students would be trained in analysis and mining of ATAC-seq, ChIP-seq, CUT&TAG, single-cell RNA-seq and Hi-C datasets. Computational analyses and training can be conducted in either R or Python according to the student’s preference. The student could also learn RNA extraction, qPCR and immunofluorescence as wet-lab techniques.", "background_reading": "-- Valencia AM and Pașca SP (2022). Chromatin Dynamics in Human Brain Development and Disease. Trends in Cell Biology. Available at: https://doi.org/10.1016/j.tcb.2021.09.001\n-- Ummi C and van Bokhoven H (2020). The Phenomenal Epigenome in Neurodevelopmental Disorders. Human Molecular Genetics. Available at: https://doi.org/10.1093/hmg/ddaa175\n-- Ziffra RS et al. (2021). Single-Cell Epigenomics Reveals Mechanisms of Human Cortical Development. Nature. Available at: https://doi.org/10.1038/s41586-021-03209-8" },{ "title": "Studying histone modifications in a mouse model of Cornelia de Lange Syndrome", "PI": "Dr. Robert Beagrie", "email": "robert.beagrie@well.ox.ac.uk", "mix": "50% wet lab, 50% dry lab", "description": "DNA loops allow enhancers to contact and activate their target genes in the 3D space of the nucleus. These loops are formed and stabilised by CTCF (a zinc-finger transcription factor) and cohesin (a ring-shaped multi-protein complex). Mutations in components of the cohesin complex cause a rare genetic disease called Cornelia de Lange Syndrome (CdLS) which affects between 1 in 10,000 and 1 in 50,000 individuals. Around 30% of CdLS patients are born with congenital heart defects. This project aims to understand the molecular mechanisms linking disrupted DNA loop formation with heart development.\nOur lab works with mice that have a conditional knockout Nipbl allele as heterozygous loss of function mutations in NIPBL are the most common cause of CdLS. These mice also suffer from heart defects at similar frequencies to human patients (around 30%). In this project you will study histone modifications in early cardiac tissue using CUT&TAG and uncover which regulatory elements (specifically enhancers and promoters) are misregulated in CdLS. The project consists of both lab work and data analysis, and is will hopefully provide new insight in to cardiac-specific gene regulation as well as the role of cohesin in development.", "training": "Students would be trained in preparation of material for CUT&TAG and in analysis of CUT&TAG datasets. There may also be opportunities to incorporate other techniques.", "background_reading": "-- Santos R et al. (2016). Conditional Creation and Rescue of Nipbl-Deficiency in Mice Reveals Multiple Determinants of Risk for Congenital Heart Defects. PLOS Biology. Available at: https://doi.org/10.1371/journal.pbio.2000197\n-- Kawauchi S et al. (2009). Multiple Organ System Defects and Transcriptional Dysregulation in the Nipbl+/− Mouse, a Model of Cornelia de Lange Syndrome. PLoS Genetics. Available at: https://doi.org/10.1371/journal.pgen.1000650\n-- Bartosovic M et al. (2021). Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nature Biotechnology. https://doi.org/10.1038/s41587-021-00869-9" },{ "title": "Identifying the molecular basis of chemokine:receptor binding using phage-display and application to peptide theranostic development’", "PI": "Shoumo Bhattacharya, Graham Davies", "email": "shoumo.bhattacharya@well.ox.ac.uk", "mix": "70% wet lab, 30% dry lab", "description": "The 24 chemokine receptors are GPCRs that either cause leucocyte migration or act as ‘sumps’ to neutralize chemokines (3). The molecular basis of these interactions will be decoded by screening a receptor phage-display library with matrix attached chemokines and analysed by NGS. SLiM-peptides identified will be studied for biochemical (e.g., binding to target by fluorescent polarisation, protein-interaction disruption using bead-capture assays) and biological activity (e.g., receptor dimerization using proximity-ligation assay, cell migration assays).", "training": "Molecular biology, cloning, protein interactions, phage-display, analysis of NGS datasets, R scripting, network analysis, cell biology, chemotaxis, flow cytometry.", "background_reading": "1. Ivarsson, Y., and Jemth, P. (2019). Affinity and specificity of motif-based protein-protein interactions. Curr Opin Struct Biol 54,26-33, 10.1016/j.sbi.2018.09.009\n2. Mclaughlin, M.E., and Sidhu, S.S. (2013). Engineering and analysis of peptide-recognition domain specificities by phage display and deep sequencing. Methods Enzymol 523, 327-349, 10.1016/B978-0-12-394292-0.00015-1\n3. Kufareva, I., Gustavsson, M., Zheng, Y., et al. (2017). What Do Structures Tell Us About Chemokine Receptor Function and Antagonism? Annu Rev Biophys 46, 175-198, 10.1146/annurev-biophys-051013-022942\n4. Bhusal, R.P., Eaton, J.R.O., Chowdhury, S.T., et al. (2020). Evasins: Tick Salivary Proteins that Inhibit Mammalian Chemokines. Trends Biochem Sci 45, 108-122, 10.1016/j.tibs.2019.10.003\n5. Darlot, B., Eaton, J.R.O., Geis-Asteggiante, L., et al. (2020). Engineered anti-inflammatory peptides inspired by mapping an evasin-chemokine interaction. J Biol Chem 295, 10926-10939, 10.1074/jbc.RA120.014103\n6. Von Hundelshausen, P., Agten, S.M., Eckardt, V., et al. (2017). Chemokine interactome mapping enables tailored intervention in acute and chronic inflammation. Sci Transl Med 9, 10.1126/scitranslmed.aah6650" },{ "title": "Identifying the molecular basis of chemokine oligomerization using phage-display and application to peptide theranostic development’", "PI": "Shoumo Bhattacharya, Graham Davies", "email": "shoumo.bhattacharya@well.ox.ac.uk", "mix": "70% wet lab, 30% dry lab", "description": "Chemokine oligomerization is critical for function (6). The molecular basis of these interactions will be decoded by screening a chemokine phage-display library with matrix attached chemokines and analysed by NGS. SLiM-peptides identified will be studied for biochemical (e.g. binding to target by fluorescent polarisation, protein-interaction disruption using bead-capture assays) and biological activity (e.g. receptor dimerization using proximity-ligation assay, cell migration assays).", "training": "Molecular biology, cloning, protein interactions, phage-display, analysis of NGS datasets, R scripting, network analysis, cell biology, chemotaxis, flow cytometry.", "background_reading": "1. Ivarsson, Y., and Jemth, P. (2019). Affinity and specificity of motif-based protein-protein interactions. Curr Opin Struct Biol 54,26-33, 10.1016/j.sbi.2018.09.009\n2. Mclaughlin, M.E., and Sidhu, S.S. (2013). Engineering and analysis of peptide-recognition domain specificities by phage display and deep sequencing. Methods Enzymol 523, 327-349, 10.1016/B978-0-12-394292-0.00015-1\n3. Kufareva, I., Gustavsson, M., Zheng, Y., et al. (2017). What Do Structures Tell Us About Chemokine Receptor Function and Antagonism? Annu Rev Biophys 46, 175-198, 10.1146/annurev-biophys-051013-022942\n4. Bhusal, R.P., Eaton, J.R.O., Chowdhury, S.T., et al. (2020). Evasins: Tick Salivary Proteins that Inhibit Mammalian Chemokines. Trends Biochem Sci 45, 108-122, 10.1016/j.tibs.2019.10.003\n5. Darlot, B., Eaton, J.R.O., Geis-Asteggiante, L., et al. (2020). Engineered anti-inflammatory peptides inspired by mapping an evasin-chemokine interaction. J Biol Chem 295, 10926-10939, 10.1074/jbc.RA120.014103\n6. Von Hundelshausen, P., Agten, S.M., Eckardt, V., et al. (2017). Chemokine interactome mapping enables tailored intervention in acute and chronic inflammation. Sci Transl Med 9, 10.1126/scitranslmed.aah6650" },{ "title": "Defining the function of new causal atherosclerosis genes from coronary artery disease GWAS loci using in vitro and in vivo models", "PI": "Dr Gillian Douglas, Professor Keith Channon", "email": "keith.channon@cardiov.ox.ac.uk", "mix": "70% wet lab, 30% dry lab", "description": "Genome wide association studies have enabled us to identify genes which are associated with cardiovascular disease at the level of the whole genome. These novel genes, which are not associated with traditional risk factors, have the potential to identify novel treatment strategies for coronary artery disease. The work in my lab aims to establish the role of novel candidate genes in cardiovascular disease.\nWorking in close collaboration with bioinformaticians we identify novel candidate genes from GWAS implicated loci. Once candidate genes have been identified we use targeted cardiovascular disease relevant in vitro cell based assays in primary human cells to understand how the candidate gene impacts and cell function. Proteomic and genomic analysis is used to give an unbiased analysis of candidate gene function. This is complimented by advanced cellular imaging as well as molecular biology techniques. The information gained from these In vitro studies is then used in a targeted fashion to investigate the role of the candidate gene in models of In vivo cardiovascular disease, in particular the development and regression of atherosclerosis and models of altered vascular function such as vascular injury and ischaemia models. We also utilize data from local and international biobanks to investigate the role of candidate genes in vascular biology. Doctorial students have the flexibility to focus either on in vitro cell based assays or in vivo models of cardiovascular disease.", "training": "This DPhil will be based in the Division of Cardiovascular Medicine at the Welcome Centre for Human Genetics. We are part of a wider scientific community with expertise in Cardiovascular Disease allowing for collaborative work with other senior scientist. By the end of this project the candidate will have developed a wide range of laboratory skills such as molecular biology techniques (protein and RNA analysis), cell culture techniques and In vivo models of cardiovascular disease. Training in scientific techniques as well as scientific presentation and writing will be given throughout the project.", "background_reading": "-- Douglas G, Mehat V, Al Haj Zen A, Akoumianak I, Goel A, Rashvrook VS, Trelfa L, Donovan L, Drydale E, Chuaiphichai S, Antoniades C, Watkins H, Kyriakou T, Tzima E, Channon KM. A key role for the novel coronary artery disease gene JCAD in atherosclerosis via shear stress mechanotransduction. Cardiovascular Research, doi:10.1093/cvr/cvz263. 2019" },{ "title": "Novel immune escape mutations in mismatch repair deficient cancer", "PI": "David Church, Tim Elliott, Nicola Ternette", "email": "david.church@well.ac.uk", "mix": "75% wet lab, 25% dry lab", "description": "DNA mismatch repair deficiency (MMRd) occurs in many cancer types where it causes hypermutation and instability at DNA microsatellites (MSI). MMRd/MSI tumours have been shown to be especially immunogenic, owing to the enrichment of mutated peptides they present as a conseequence of their mutation burden. Accordingly, these tumours frequently demonstrate loss of function alterations in components of the antigen presentation pathway machinery, the consequence of which is to enable escape from immune surveillance. However, current understanding of these is limited. Our unpublished analysis of >10,000 cancer whole genome sequences (WGS) from the Genomics England 100,000 Genomes Project (GEL 100KGP) has identified two novel genes which commonly subject to loss of function mutations in MMRd/MSI cancer. Both function in class I antigen processing and presentation and thus represent candidate immune escape mutations. This project seeks to perform detailed characterisation of both. Briefly, it will entail:\n-- Definition of genomic, transcriptomic and immunologic correlates of novel immune escape mutations in GEL 100KGP cases and additional clinical trial cohorts\n-- Functional analysis of novel immune escape mutations in model systems including cutting edge methods of immunopeptidomics and protein trafficking\nSupervision will be available for all aspects of the project by postdoctoral scientists. There will be opportunity to travel to collaborators institutes for a period if desired.", "training": "Analysis of whole genome sequence data; analysis of AI based image analysis; general molecular biology techniques; exposure to specialist methods including protein trafficking and immunopeptidomics.", "background_reading": "" },{ "title": "Characterisation of driver mutations in endometrial cancer", "PI": "David Church", "email": "david.church@well.ac.uk", "mix": "75% wet lab, 25% dry lab", "description": "Endometrial cancer is the most common gynaecological malignancy in the developed world, yet has been understudied until the publication of the TCGA molecular analysis in 2013. More recently, our unpublished analysis of the unique cohort of endometrial cancers with whole genome sequencing (WGS) from the Genomics England 100,000 Genomes Project has revealed identified more than 50 driver genes, which vary across molecular subgroups and in combinations. While the function of some is well characterised, for many current understanding is minimal or lacking. This project will address this by genomic and functional analyses. Specifically it will:\n-- Perform detailed analysis of driver mutations in the GEL 100KGP endometrial cancers under the umbrella of the GeCIP\n-- Develop functional models of novel alterations to test their effect on cellular phenotype and therapeutic sensitivity\n-- Examine the basis of mutation cooperativity and antagonism using in selected cases using these systems\nSupervision will be available for all aspects of the project by postdoctoral scientists. There will be opportunity to travel to collaborators institutes for a period if desired.", "training": "Analysis of whole genome sequence data; ; general molecular biology techniques; experience in the development of and analysis of functional model systems.", "background_reading": "" },{ "title": "Using base pair resolution genome architecture to explore enhancer function", "PI": "James Davies", "email": "james.davies@ndcls.ox.ac.uk", "mix": "50% wet lab, 50% dry lab, but will tailor to the candidate", "description": "Careful study of the chromatin architecture and the effects of perturbations of regulatory elements using genome editing at individual genes has led to huge progress in our understanding of gene regulation. However, to date only a handful of genes have had their regulatory landscape defined in detail and the general principles by which genes are controlled are not defined on a molecular scale. In addition, there is very significant variability in chromatin architecture at different loci and there is little understanding of why this has evolved and how it impacts gene expression.\nIn this project we aim to use and adapt the new Micro Capture-C technique that we have developed to explore how enhancers make contact with gene promoters in detail and how this varies between different genes. We have preliminary data which shows how enhancers physically contact the promoter and gene body in far greater detail than has previously been possible. In this project we will use the method to define how enhancer promoter contacts are variable between different genes and how this alters when we make perturbations to the system using genetically engineered cellular models. We would also attempt to explore why the genome architecture is so variable at different sites in the genome using computational methods to interrogate why genes with distal enhancers would provide a selective advantage.", "training": "All basic approaches in molecular and cellular biology (PCR, Cloning, Cell culture etc.). We also offer training in more specialised functional genomics techniques including chromatin immunoprecipitation, chromosome conformation capture, RNA-seq and single cell techniques. We also have expertise in using cutting edge genome editing technology to modify the genome sequence in both cell lines and primary cells. There will be excellent training in the state-of-the-art FACS facility at the WIMM including isolation and purification of populations of haematopoietic cells.\nWe also have expertise in bioinformatics. We are particularly interested in developing novel bioinformatic approaches to analyse next generation sequencing data. In particular, we have recently developed novel methods of analysing chromosome conformation capture datasets, which allow the data to be generated at much higher resolution than was previously possible. There is likely to be the opportunity to undertake the dedicated Centre for Computational Biology training course in bioinformatics.", "background_reading": "-- Hua P, Badat M, Hanssen LLP, Hentges LD, Crump N, Downes DJ, Jeziorska DM, Oudelaar AM, Schwessinger R, Taylor S, Milne TA, Hughes JR, Higgs DR and Davies JO (2021) ‘Defining genome architecture at base pair resolution’ Nature; 595, 125-129" },{ "title": "Investigating properties of de novo duplication or deletion detection using non-invasive prenatal testing data", "PI": "Robert Davies", "email": "robert.davies@stats.ox.ac.uk", "mix": "100% dry lab", "description": "During pregnancy, the blood of the mother contains cell free DNA derived both from maternal cells, and from fetal cells. By sequencing and analyzing this cell free DNA, it is possible to detect abnormal numbers of chromosomes in the fetus (aneuploidies). This process, called non-invasive prenatal testing (NIPT), is now the clinical standard of care for aneupolidy detection, and is in widespread use around the world. However, traditional use of this method cannot detect de novo sub-chromosomal abnormalities very well, as it uses the difference in counts of reads between chromosomes or regions, and these are not very precise due to the low fraction of fetal DNA in the sample and number of reads. Recently, I had a paper accepted on a method (QUILT) for low coverage imputation that can probabilistically assign reads to maternal or paternal origin. In this project, you would study how using the principle of assigning reads to their haplotypic origin (here maternal transmitted, untransmitted or paternally transmitted) and then looking for differences in the levels in between them, can facilitate sub-chromosomal aneuploidy detection. An outline for this project could be as follows. First, to set up a simulation framework, so that for sub-chromosomal event, that simulated sequencing reads could be generated. Second, to assing reads to their haplotypic background, using either truth data or by estimating it programatically (QUILT). Third, to develop a probabilistic model to determine the probability of different mutational events (normal DNA vs duplication vs deletion), conditional on the observed sequencing reads and prior probabilities. Fourth, to evaluate this model, and compare it to one where we don’t estimate what haplotypes reads come from. Time-permitting, this evaluation would be done across a variety of factors, for example different human populations, sequencing depths, different genomic regions, etc. Taken together, this project will help us determine whether assigning sequencing reads to their chromosomal backgrounds can improve de novo subchromosomal variant detection in NIPT.", "training": "In this project you’ll develop skills in methods development, statistics, and whole genome sequencing", "background_reading": "-- Liu Siyang, …, 2018. Genomic Analyses from Non-invasive Prenatal Testing Reveal Genetic Associations, Patterns of Viral Infections, and Chinese Population History. Cell. https://doi.org/10.1016/j.cell.2018.08.016\n-- Davies Robert, …, 2021. Rapid genotype imputation from sequence with reference panels . Nature Genetics. https://www.nature.com/articles/s41588-021-00877-0" },{ "title": "TYK2 as a genetically determined drug target against multiple immune-mediated diseases", "PI": "Dr Calliope Dendrou", "email": "cdendrou@well.ox.ac.uk", "mix": "10% wet lab, 90% dry lab", "description": "Cross-trait genetic associations are common for the immune-mediated diseases (IMDs), indicating a sharing of etiological mechanisms, despite variation in the precise organs affected for each disorder. However, more systematically profiling these associations - not just between IMDs but across the broader disease phenome - is beginning to provide valuable translational insights. Given the hundreds of loci that can be associated with any single IMD, the scale and nature of cross-trait association patterns can help to pinpoint genetic variants and biological pathways to be prioritized for further investigations in a therapeutic context. For example, associations across multiple diseases can reveal targets amenable to drug repositioning approaches, whilst different directions of association for different diseases may suggest trade-offs that can inform patient stratification and that may predict potential side effects as a result of therapeutically manipulating particular biological pathways.\nBased on such cross-trait analyses and investigating genetic variants with the same patterns of association, the TYK2 locus serves as an immunological ‘hub’ around which several other associated loci can be organized, suggesting that TYK2 functions at the centre of several different signalling pathways each implicated in different IMDs. Moreover, TYK2 has emerged as a promising drug target. The project will involve interrogating the relationship between disease-associated genetic variation and the dynamics of TYK2-dependent immune cell signalling, as well as assessing the how the genetic effect relates to the impact of a novel allosteric TYK2 inhibitor. The project will be well suited to a student interested in bioinformatics/data analysis but can also include a wet-lab component depending on interest.", "training": "The proposed project will involve single-cell RNA sequencing, bulk RNA-seq, proteomics and/or imaging data analysis.", "background_reading": "-- Cortes A et al. (2020) Identifying cross-disease components of genetic risk across hospital data in the UK Biobank. Nature Genetics, 52, 126-134.\n-- Cortes A et al. (2017) Bayesian analysis of genetic association across tree-structures routine healthcare data in the UK Biobank. Nature Genetics, 49, 1311-1318.\n-- Dendrou CA et al. (2016) Resolving TYK2 locus genotype-to-phenotype conflict reveals therapeutic optimum for autoimmunity. Science Translational Medicine, 8, 363ra149.\n-- Makin S (2021) Cracking the genetic code of autoimmune disease. Nature, 595, S57-S59." },{ "title": "Spatially resolved molecular and cellular profiling of immunopathological responses", "PI": "Dr Calliope Dendrou", "email": "cdendrou@well.ox.ac.uk", "mix": "100% dry lab", "description": "Molecular profiling techniques are valuable tools for understanding cellular function in health and disease, investigating pathophysiological mechanisms, setting clinical diagnoses, monitoring disease prognosis and identifying drug targets.\nThe project will involve the integration of biochemical signatures derived from Fourier-transform infrared microspectroscopy with gene expression signatures derived from the application of spatial transcriptomics and single-nuclear transcriptomics techniques, using central nervous system tissue from patients with neuroinflammatory disease versus controls. A particular interest is in cross-correlating the biochemical and gene expression signatures and intercellular interactions in the tissue areas around the infiltrating immune cells to assess how the damage spreads from the immune lesions to the adjacent tissue.", "training": "The proposed project will involve: microspectroscopy, single-nuclear RNA sequencing, spatially resolved transcriptomics and spatial proteomics data analysis; depending on interest there may be opportunities for wet lab analyses.", "background_reading": "" },{ "title": "Strategies to facilitate targeted high-efficiency gene editing for treatment of lung diseases", "PI": "Professor Deborah Gill & Dr Altar Munis", "email": "Deborah.gill@ndcls.ox.ac.uk", "mix": "75% wet lab, 25% dry lab", "description": "The clustered regularly interspersed palindromic repeats (CRISPR) system, a powerful genome editing tool, has now been used ubiquitously in medical research including gene therapy. Several proof-of-concept studies have demonstrated the feasibility of in vivo gene editing to correct disease-causing mutations using both Homology Directed Repair (HDR) and Homology-Independent Targeted Integration (HITI) strategies. A successful therapeutic gene editing strategy to tackle lung disorders will require the targeted and highly efficient repair action of the CRISPR-Cas9 system adapted for specific cell types; for example, targeting alveolar type II pneumocytes in the case of interstitial lung diseases, and also ionocytes and ciliated cells for cystic fibrosis. We have developed a novel transgenic reporter mouse model, which ubiquitously expresses the fluorescent TdTomato transgene, to evaluate genome editor tools and delivery methods in vivo. We aim to use this model to explore, validate, and demonstrate the utility of in vivo genome editing strategies for lung disorders.\nThe rotation project, which can serve as a starting point for subsequent DPhil study, focuses on gene editing using a range of delivery vectors, specifically comparing recombinant Lentiviral and AAV vectors and also vectors encoding cell-specific promoters. Gene editing strategies will first be tested in vitro using primary human lung airway cultures and surfactant air-liquid interface (SALI) cultures modelling human lung parenchyma. Off-targets and editing efficiencies will be assessed via droplet-digital PCR (ddPCR) and Nanopore MinION-based third generation next-generation sequencing (NGS) method AFIS-Seq. Utilising in vitro data to optimise experimental design (e.g. gRNA and donor design, Cas9 to donor ratios, etc), targeted, cell-specific, gene editing experiments can be performed in the reporter TdTomato mouse model. In parallel, a (partly) humanised mouse model can be generated for surfactant B deficiency (a lethal rare genetic disorder affecting the lung parenchyma) and those candidates with the ‘best’ combination of vector and cell-specific promoter will be used to demonstrate proof-of-concept. We also have a conditional surfactant protein B knockout mouse model, which can be utilised as proof-of-principle if needed. Demonstrating efficient gene editing of specific and rare disease-causing mutations will support the use of personalised medicine to treat rare lung diseases where no other treatment options exist.", "training": "The proposed project will involve: human cell culture including cell manipulation via vector transfection & transduction; recombinant virus (lentiviral & AAV) vector design, production & titration; general recombinant DNA techniques including plasmid DNA manipulation, DNA & RNA purification, cloning, etc; gene quantification by PCR and droplet digital PCR; flow cytometry; confocal and immunofluorescence microscopy; immunoblotting; CRISPR‐Cas9 genome engineering; Nanopore MinION based, third-generation next generation sequencing & relevant bioinformatics pipeline involving Python3 and R coding/programming; use and design of transgenic mouse models of human disease, including handling, processing, and analysing mouse blood and tissue samples, and training for a Home Office Personal Licence. The student will also receive training in scientific writing, oral presentations and public engagement activities. The project would suit someone who would like to focus on wet lab techniques and contribute to the overall translation of a genetic therapy to the clinic.", "background_reading": "Munis, A. M., Hyde, S. C., & Gill, D. R. (2020). A human surfactant B deficiency air-liquid interface cell culture model suitable for gene therapy applications. Molecular therapy. Methods & clinical development, 20, 237–246. https://doi.org/10.1016/j.omtm.2020.11.013\nvan Haasteren, J., Munis, A. M., Gill, D. R., & Hyde, S. C. (2021). Genome-wide integration site detection using Cas9 enriched amplification-free long-range sequencing. Nucleic acids research, 49(3), e16. https://doi.org/10.1093/nar/gkaa1152\nMiura, H., Imafuku, J., Kurosaki, A., Sato, M., Ma, Y., Zhang, G., Mizutani, A., Kamimura, K., Gurumurthy, C. B., Liu, D., & Ohtsuka, M. (2021). Novel reporter mouse models useful for evaluating in vivo gene editing and for optimization of methods of delivering genome editing tools. Molecular therapy. Nucleic acids, 24, 325–336. https://doi.org/10.1016/j.omtn.2021.03.003\nHu, J., Bourne, R. A., McGrath, B. C., Lin, A., Pei, Z., & Cavener, D. R. (2021). Co-opting regulation bypass repair as a gene correction strategy for monogenic diseases. Molecular therapy: the journal of the American Society of Gene Therapy, S1525-0016(21)00204-5. Advance online publication. https://doi.org/10.1016/j.ymthe.2021.04.017\nKelly, J. J., Saee-Marand, M., Nyström, N. N., Evans, M. M., Chen, Y., Martinez, F. M., Hamilton, A. M., & Ronald, J. A. (2021). Safe harbor-targeted CRISPR-Cas9 homology-independent targeted integration for multimodality reporter gene-based cell tracking. Science advances, 7(4), eabc3791. https://doi.org/10.1126/sciadv.abc3791" },{ "title": "Goriely Laboratory Project: ‘Probing selfish selection of de novo mutations’", "PI": "Prof Anne Goriely", "email": "Anne.Goriely@imm.ox.ac.uk", "mix": "80% wet lab, 20% dry lab", "description": "As mutations are at the origin of all genetic variations, understanding the factors that influence mutational rates and patterns (and the reason for which they occur) is crucial to the study of disease, evolution and genome diversity. It is now well established that ~60 new point mutations are acquired spontaneously at each generation. Although these point mutations initially arise as random miscopying events, preferentially from the paternal germline, we have described a process (selfish selection) by which some pathogenic de novo mutations (DNMs) become progressively enriched in the testis as men age. This project will aim to develop multi-disciplinary strategies to identify new genes/molecular pathways subject to selfish selection and establish the potential impact of this process on human disease and genome heterogeneity.\nBecause selfish variants are selected in ageing testes and are present at elevated levels in the sperm of most men, they are anticipated to recur more frequently as DNMs in patient cohorts. Hence, one of the approaches we will follow consists in mining large DNM datasets derived from analysis of WES/WGS family trios. This analysis will generate a list of candidate variants/genes that are recurrently transmitted as DNMs. We will then assess whether these variants are enriched in human testes or sperm using custom assays for ultra-rare mutation detection (such a ‘RED-PCR’, rhAMPSeq or duplex sequencing).", "training": "This project represents a unique opportunity to gain in-depth training in Human Genetics, germline stem cell biology and the application of Next-generation sequencing technologies for detection of ultra-rare variants. The project can be tailored to suit personal interests and need for training but will typically involve both a wet-lab component and bioinformatic analysis of genomic datasets.", "background_reading": "-- Maher GJ, Ralph HK, Ding Z, Koelling N, Mlcochova H, Giannoulatou E, Dhami P, Paul DS, Stricker SH, Beck S, McVean G, Wilkie AOM & Goriely A, 2018: Selfish mutations dysregulating RAS-MAPK signaling are pervasive in aged human testes, Genome Res. 28(12):1779-1790\n-- Giannoulatou E, McVean GAT, Taylor IB, McGowan SJ, Maher GJ, Iqbal Z, Pfeifer SP, Turner I, Burkitt-Wright EMM, Shorto J, Itani A, Turner K, Gregory L, Buck D, Rajpert-De Meyts E, Looijenga LHJ, Kerr B, Wilkie AOM & Goriely A, 2013: Contributions of intrinsic mutation rate and selfish selection to levels of de novo HRAS mutations in the paternal germline. Proc Natl Acad Sci USA, 110(50):20152-20157.\n-- Goriely A & Wilkie AOM, 2012: Paternal age effect mutations and selfish spermatogonial selection: causes and consequences for human disease. Am J Hum Genet. 90(2):175-200" },{ "title": "Estimating the genome-wide prevalence of mosaicism through analysis of large-scale whole-genome dataset from family trios", "PI": "Dr Nicky Whiffin and Prof Anne Goriely", "email": "Anne.Goriely@imm.ox.ac.uk", "mix": "20% wet lab, 80% dry lab (flexible)", "description": "Although most de novo mutations (DNMs) occur as one-off events during spermatogenesis, they can also originate through a process called ‘mosaicism’. Mutations occurring during early embryonic development result in somatic and/or gonadal mosaicism and can be found at elevated levels across multiple tissues. Mosaicism is increasingly recognised as a significant contributor to spontaneous disease, with recent studies highlighting that ~10% of ‘apparently’ DNMs found in children have actually arisen early during one of the parents’ development. This has important implications for genetic counselling as mosaic DNMs are likely present in multiple eggs or sperm and are associated with an increased recurrence risk (as high as 50%). Moreover, DNMs can also occur during the child’s own development, potentially causing a variable phenotype compared with the equivalent constitutive mutation. However, the contribution of mosaicism has been difficult to establish and is frequently overlooked in the analysis of large datasets because of the technical challenges associated with mosaic variant detection and calling.\nWe propose to use and compare state-of-the-art calling algorithms (e.g. MuTect, Streka, DeepMosaic, MosaicHunter) to systematically analyse the whole-genome sequencing (WGS) data derived from the Genomics England 100K Genomes family trios dataset (~5000 trios). This will allow us to interrogate this dataset and single-out different classes of mosaic variants such as those that:\n1) Are apparently de novo but are present at low levels in one of the parental samples\n2) Are present at levels deviating from the expected 50:50 ratio in the child\n3) Are present in one parent at elevated levels but are in fact mosaic in this individual - this situation is anticipated to lead to missed diagnoses due to exclusion by bioinformatics pipelines used for DNM calling.\nFor cases where we have access to biological samples, we will use ultra-deep NGS techniques to validate candidate mosaic variants derived from this analysis – this may include the analysis of paternal semen samples that provide a direct means to establish the contribution of gonadal mosaicism.", "training": "This project represents a unique opportunity to gain in-depth training in Human Genetics, analysis of large-scale genomics datasets and the application of Next-generation sequencing technologies for detection of rare variants. The project can be tailored to suit personal interests and need for training but will typically involve both bioinformatic and statistical analysis of large-scale genomic datasets combined with a wet-lab component to provide validation of the data generated in silico.", "background_reading": "Bernkopf M, Abdullah UB, et al, under review: The PREGCARE study: Personalized recurrence risk assessment following the birth of a child with a pathogenic de novo mutation https://www.biorxiv.org/content/10.1101/2022.07.26.501520v1" },{ "title": "Determining the grammar and syntax of regulatory elements using laboratory and computational approaches", "PI": "Professor Doug Higgs, Professor Jim Hughes", "email": "doug.higgs@imm.ox.ac.uk", "mix": "~50% wet lab, ~50% dry lab", "description": "A major goal of current biology is to understand how DNA sequences are read by the nuclear machinery to direct normal development and differentiation and how this is perturbed in human disease. Whole genome sequences of a wide range of organisms spanning 500 million years of evolution are now available for detailed analysis. We currently know that there are ~20,000 structural genes in humans but their expression is regulated by as many as 1,000,000 regulatory elements including enhancers, promoters and boundary elements. Of particular interest, enhancers integrate external cell signals with the internal transcriptional and epigenetic programmes and communicate this information to their cognate promoters thereby controlling when and where specific genes are switched on and off. Many single genes are controlled via clusters of enhancers which are each bound by a variety of cell specific and general transcription factors which relay information from enhancers to promoters. Although structural genes are in general well conserved, the positions and DNA sequences of regulatory elements which control their expression change quite rapidly throughout evolution.\nThese observations beg the question of how the precise timing and cell-specific expression of a particular gene is maintained in the face of such dramatic changes in the regulatory elements? This question gets to the heart of our lack of understanding of the mechanism by which enhancers communicate with promoters. If regulatory elements can change so much during evolution how is specific information passing from enhancers to promoters encoded? We will study this key issue by initially studying the well characterised alpha globin locus as a model. The globin genes are expressed in a very similar developmental and tissue-specific manner in diverse species spanning 500 million years of evolution and the structure of the genes encoding the globin proteins is well conserved. By contrast, the cluster of enhancers (so called super-enhancer) controlling their expression varies considerably. Using a combination of established lab-based assays together with extensive computational analysis of the regulatory elements we will initially determine how the positions and sequences of the regulatory elements have evolved in a wide variety of species including fish, amphibians, birds and mammals including a wide range of primates. Based on our consequent understanding of the evolution of globin gene regulation, we will test new hypotheses using synthetic biology as described in Blaney et al (Cell in press) and extend and generalise these observations to other complex enhancers throughout the genome.", "training": "Our laboratory of ~12-14 scientists, includes post-docs, students and research assistants. Students undertaking their studies in the lab have day-to-day supervision from a team of scientists who have considerable experience in all aspects of current genomics including, for example, RNA-seq, ATAC-Seq, ChIP-seq, and various forms of chromosome conformation capture. We also have considerable expertise in cell biology and imaging. A key aspect of our work in collaboration with the WIMM Centre for Computational Biology involves a full range of analytical approaches using computational biology.", "background_reading": "The Tree of Life Project: https://www.sanger.ac.uk/programme/tree-of-life/\nOudelaar AM, Higgs DR. The relationship between genome structure and function. Nat Rev Genet. 2021 Mar;22(3):154-168. doi: 10.1038/s41576-020-00303-x.\nJoseph Blayney, Helena Francis, Brendan Camellato, Leslie Mitchell, Rosa Stolper, Jef Boeke, Douglas Higgs*, Mira Kassouf* Super-enhancers require a combination of classical enhancers and novel facilitator elements to drive high levels of gene expression. bioRxiv 2022.06.20.496856; doi: https://doi.org/10.1101/2022.06.20.496856 (2023 Cell in press).\nBuffry AD, Mendes CC, McGregor AP. The Functionality and Evolution of Eukaryotic Transcriptional Enhancers. Adv Genet. 2016;96:143-206. doi: 10.1016/bs.adgen.2016.08.004." },{ "title": "The landscape of de novo mutations in humans", "PI": "Dr Anjali Hinch", "email": "anjali.hinch@well.ox.ac.uk", "mix": "Flexible", "description": "The chromosomes we inherit from our parents are not exact copies but mosaics of their chromosomes. These mosaics are created during the formation of eggs and sperm when cells cut chromosomes up and re-attach them, sometimes in new combinations (recombination). We have discovered that our cells make an unexpectedly large number of errors in this process leading to changes in DNA (mutations) [Science, In press].\nIn your DPhil, you will address one or more of the following key questions:\n-- Do we vary in our propensity to acquire de novo mutations? If so, why?\n-- How do de novo mutations impact our health?\n-- What are the mechanisms underlying de novo mutation?\nWithin this larger framework, we are offering the following rotation projects:\n-- Mapping genetic determinants of de novo mutation rate in the British population using a genome-wide association study (GWAS) (dry lab)\n-- Identifying de novo mutations using long-read Nanopore and PacBio sequence data in a model for a genetically-modified Breast Cancer gene (BRCA2) (dry lab)\n-- Mapping sites of DNA synthesis following DNA break repair in a genetically-engineered model (wet lab)\nOur approach is data driven. We utilise large-scale genetic and phenotypic datasets in humans as well as performing a range of experimental assays, including CRISPR-Cas9 mediated genome-editing. We then use machine learning and other statistical techniques to characterise their interactions. For further details, please get in touch (anjali.hinch@well.ox.ac.uk).", "training": "We offer computational, statistical and wet-lab projects and the flexibility to combine them for a comprehensive, in-depth and well-rounded training in genomic science. We are based at the internationally recognised multidisciplinary research institute, Wellcome Centre for Human Genetics, with state-of-the-art facilities for genomic research.\nWe are a close-knit and multidisciplinary team with a track record of highly influential work and our students and postdocs regularly present work at international conferences.\nSpecifically, projects in our lab include opportunities to perform\n-- Genome-wide association studies (GWAS)\n-- Machine learning and other statistical methods to decode mutations in the germline and in cancers and de novo mutation discovery\n-- CRISPR/Cas9-mediated genome-engineering in murine models\n-- Single-cell DNA and RNA sequencing\n-- Protein occupancy and interaction assays", "background_reading": "R Hinch, P Donnelly, AG Hinch. Meiotic DNA breaks drive multifaceted mutagenesis in the human germline. Science (In press).\nAG Hinch et al. The Configuration of RPA, RAD51, and DMC1 Binding in Meiosis Reveals the Nature of Critical Recombination Intermediates. Mol Cell, 79 (4), pp. 689-701.e10.\nAG Hinch et al. Factors influencing meiotic recombination revealed by whole-genome sequencing of single sperm. Science, 363 (6433).\nB Davies et al. Re-engineering the zinc fingers of PRDM9 reverses hybrid sterility in mice. Nature, 530 (7589), pp. 171-6.\nAG Hinch et al. The landscape of recombination in African Americans. Nature, 476 (7359), pp. 170-5." },{ "title": "Various computational and bench projects", "PI": "Professor Jim Hughes", "email": "jim.hughes@imm.ox.ac.uk", "mix": "varies", "description": "The group has opportunities for purely computational projects and bench projects although bench scientists are strongly encouraged and trained to do their own bioinformatic analysis and develop their own coding skills.\nComputational projects combine the integration of genomics data, including single cell epigenomic and transcriptomic data, with the development of machine learning based approaches to predict fundamental aspects of gene regulation in the mammalian genome. The ultimate goals of these projects are to use the deep neural network based approaches to understand the basic principles of how cell type specific gene regulation is achieved and to provide predictive platforms to identify casual changes in the non-coding genome and to identify the underlying mechanism and genes linked to human disease.\nRelevant publications.\n-- Schwessinger, R., et al (2017). Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints. Genome Res. 27: 1730-1742.\n-- Schwessinger, R., et al. (2020). DeepC: predicting 3D genome folding using megabase-scale transfer learning. Nat Methods. 17: 1118-1124.\n-- Downes, D.J., et al (2019). An integrated platform to systematically identify causal variants and genes for polygenic human traits. bioRxiv: 813618.\nBench projects involve using the latest genomics technologies, including those developed by the group to understand the basic regulation of genes and the impact of sequence variation on it. The group is expert in chromosome conformation capture technologies and have developed the suite of Capture-C technolgies (Capture-C, Tiled-C and Tri-C) used to interogate the regulatory landcapes of genes. Projects include using these and high-resolution variants such as Micro Capture-C (with J Davies) in primary cells to understand the effect of human variation on specific genes and en masse to understand basic principles. The group also leaverges large-scale synthetic biology to build regulatory domains from first principles (https://www.thedarkmatterproject.org/main) to discover the principles of how they are built and to provide a practical toolkit to build and exploit functional bespoke gene regulatory domains in the mammalian genome. Projects also exist to develop new methods to fill in our current “blindspots” in our ability to assess activity and function in the genome.\nRelevant publications.\n-- Larke, M.S.C., et al. (2021). Enhancers predominantly regulate gene expression during differentiation via transcription initiation. Mol Cell. 81: 983-997 e7.\n-- Oudelaar, A.M., et al. (2018). Single-allele chromatin interactions identify regulatory hubs in dynamic compartmentalized domains. Nat Genet.\n-- Oudelaar, A.M., and Beagrie, R.A., et al (2020). Dynamics of the 4D genome during in vivo lineage specification and differentiation. Nat Commun. 11: 2722\n-- Hua, P., et al (2021). Defining genome architecture at base-pair resolution. Nature. 595: 125-129.\nPlease contact directly for further information.\nThese pages were reviewed/updated: [23-07-21]", "training": "", "background_reading": "" },{ "title": "Improved inference of genetic ancestry", "PI": "Jerome Kelleher", "email": "jerome.kelleher@bdi.ox.ac.uk", "mix": "0% wet lab, 100% dry lab", "description": "Recent breakthroughs in computational genomics have made it possible to infer genetic ancestry in recombining organisms at scale for the first time, making numerous downstream applications possible. A number of different methods have recently been proposed, providing deep insights into human evolution. However, these methods are in their infancy, and much work remains to be done before they are ready for mainstream genomics. Our group developed the 'tsinfer' method, which is capable of accurately inferring genetic ancestry for millions of whole genomes, based on the 'succinct tree sequence' data structure. This method of encoding genetic ancestry has also lead to performance improvements of multiple orders of magnitude in genome simulation and statistical computation, and has the potential to solve many of the major computational problems facing large scale genomics. For example, the data compression levels achieved by the tree sequence data structure are so high that it is in principle possible to store the ancestral history of 10 billion humans in around 1TB of storage.\nIn this rotation project and potential extension into a DPhil, you will use simulations and human data to investigate areas in which tsinfer's accuracy and computational performance can be improved. You will implement updates to the core algorithms in tsinfer's Python and C codebase as part of an open-source development process. Specific areas for development include:\n-- Better heuristics for ancestral haplotype generation\n-- Improved recombination breakpoint detection\n-- Detailed analysis of patterns of recurrent mutations and their relation to sequencing error\n-- Incorporation of uncertainty via probabilistic ancestor generation and stochastic HMM traceback\n-- Better performance via more fine-grained parallelisation strategies\n-- Better scalability by distribution across multiple machines", "training": "This project will suit a student interested in a DPhil focused on computational statistical genomics, and will include a large software development component.", "background_reading": "-- Kelleher et al. (2019). Inferring whole-genome histories in large population datasets. Nature Genetics. https://doi.org/10.1038/s41588-019-0483-y\n-- Wohns et al. (2021). A unified genealogy of modern and ancient genomes. Preprint. https://doi.org/10.1101/2021.02.16.431497\n-- Project website: https://tskit.dev" },{ "title": "A bioinformatics approach to study the cellular ubiquitin system in health & disease", "PI": "Dr Andreas Damianou, Dr Philip Charles, Prof Benedikt Kessler", "email": "benedikt.kessler@ndm.ox.ac.uk", "mix": "66% wet lab, 33% dry lab", "description": "The Ubiquitin System is indispensable for a human cell as it controls several cellular functions including degradation, autophagy, DNA repair and cell proliferation. This control occurs through the conjugation of ubiquitin to proteins, which can influence multiple aspects of their functionality, such as their activity, localisation or half-life (turnover).\nThe ubiquitination of target proteins is complex, with the formation/removal of various poly-ubiquitin chain linkages being carried out by the conjugating enzymes (E1, E2 and E3) as well as by the erasers deubiquitinating enzymes (DUBs). The balance between ubiquitin conjugation and de-conjugation is crucial and well-regulated in cells. Many studies indicate that a dysfunction of this system could lead to numerous human diseases including cancer and neurodegenerative disorders.\nGlobal approaches to determine the molecular function of components of the ubiquitin system (E3 and DUBs) include deep proteome, ubiquitome and interactome based on mass spectrometry studies, which are applied in our laboratory. The cutting-edge of mass spectrometry technology as well as molecular tools give us the opportunity to gain unprecedented depth and novel molecular insights into these cellular processes. Nevertheless, the data analysis for such complex -omic studies is still challenging and merits the development of more advanced mining tools. Therefore, we are looking for a highly motivated individual with a keen interest in molecular/ cellular biology and experience in programming as well as bioinformatics to i) help in the development of an –omics data analysis workflow and ii) apply this to untangle complex cellular –omics (proteomics / transcriptomics / ubiquitomics) data sets.", "training": "-- Introduction to background biology of the cellular ubiquitin system and its function in normal physiology as well as cancer and neurodegeneration\n-- Training on getting familiar with –omics data, such as transcriptomics, mass spectrometry derived data sets such as proteomics, metabolomics, but also ubiquitomics, interactomics data sets\n-- Introduction to bioinformatics tools to process –omics data, such as R (training courses) and more specialised –omics analysis software including Mascot, MaxQuant, Perseus, SAINT, Progenesis IQ, Proteomics Discoverer, PEAKS, MS Fragger, Fragpipe; possibilities to follow advanced courses on programming (Python, JavaScript, HTML, Elm etc).", "background_reading": "-- Interaction mapping of endoplasmic reticulum ubiquitin ligases identifies modulators of innate immune signalling. Fenech EJ, Lari F, Charles PD, Fischer R, Laétitia-Thézénas M, Bagola K, Paton AW, Paton JC, Gyrd-Hansen M, Kessler BM, Christianson JC. Elife. 2020 Jul 2;9:e57306. doi: 10.7554/eLife.57306.\n-- Comprehensive Landscape of Active Deubiquitinating Enzymes Profiled by Advanced Chemoproteomics. Pinto-Fernández A, Davis S, Schofield AB, Scott HC, Zhang P, Salah E, Mathea S, Charles PD, Damianou A, Bond G, Fischer R, Kessler BM. Front Chem. 2019 Aug 29;7:592. doi: 10.3389/fchem.2019.00592.\n-- Molecular basis of USP7 inhibition by selective small-molecule inhibitors. Turnbull AP, Ioannidis S, Krajewski WW, Pinto-Fernandez A, Heride C, Martin ACL, Tonkin LM, Townsend EC, Buker SM, Lancia DR, Caravella JA, Toms AV, Charlton TM, Lahdenranta J, Wilker E, Follows BC, Evans NJ, Stead L, Alli C, Zarayskiy VV, Talbot AC, Buckmelter AJ, Wang M, McKinnon CL, Saab F, McGouran JF, Century H, Gersch M, Pittman MS, Marshall CG, Raynham TM, Simcox M, Stewart LMD, McLoughlin SB, Escobedo JA, Bair KW, Dinsmore CJ, Hammonds TR, Kim S, Urbé S, Clague MJ, Kessler BM, Komander D. Nature. 2017 Oct 26;550(7677):481-486. doi: 10.1038/nature24451.\nimage://data/images/kessler_benedikt_03.png\n1) Figure/Photo – Cellular Ubiquitin System\nFigure 1: Cellular ubiquitin system – The turn-over of most proteins in cells are controlled by the attachment of ubiquitin, a small protein (black dots), to protein substrates. These are then recognised by the 26S proteasome complex for destruction into peptides and further degraded to amino acids (AAs). AAs are serving as building blocks for protein synthesis. The protein’s life cycle is perturbed in cancer and other human diseases, a trait that is the focus of this bioinformatics based research project." },{ "title": "Validating a genetics-led approach to drug target prioritisation in immune traits", "PI": "Prof Julian Knight", "email": "julian.knight@well.ox.ac.uk", "mix": "50% wet lab, 50% dry lab", "description": "The high attrition rate in late-stage drug development requires new approaches to establish evidence for target validation, the therapeutic hypothesis that perturbing a target will benefit patients with minimal toxicity. Human genetic evidence predicts successful progress along the drug development pipeline but systematic use in drug target validation has not yet been achieved.\nThis project would be an opportunity to join an established research effort within the Knight group to develop approaches to maximise the informativeness of genetics for drug target identification and validation as well as related questions such as prediction of adverse effects, predicting mechanisms of therapeutic modulation and identifying repurposing opportunities.\nThe relative balance of computational and wet lab work in the project will depend on the individual student’s interests. We aim to build and validate computational tools and pipelines, and to generate experimental evidence to support this. You would apply cutting-edge functional genomic approaches and gain significant expertise in bioinformatics. The project would provide relevant training for a laboratory rotation or form the basis for a 3-year doctoral research project.", "training": "You would have the opportunity to gain valuable bioinformatic skills in the analysis of genetic and -omic datasets and more broadly within the drug target prioritisation approaches we are establishing that integrates and leverages information involving common and rare disease alleles, functional genomic and epigenomic annotations, population genetic diversity, systems immunology, connectivity, interactions, model organism phenotypes and ontologies. Examples of experimental approaches that you would use include high throughput CRISPR screens and siRNA knock down together with use of highly selective chemical probes (small molecule inhibitors generated by Structural Genomics Consortium) to determine the consequences of modulating specific targets. You would apply these to patho-physiologically relevant phenotypic readouts for the trait of interest, including using iPSC, primary human cells and where possible patient-derived cellular assays. You would also leverage publicly accessible data, for example from compound screens within the Library of Integrated Network-based Cellular Signatures together with existing genome-wide mutagenesis and CRISPR screens. This work is made tractable by our established collaborations with academia and industry. You will benefit from working within a supportive research group with a strong track record in graduate student training and mentoring. You will have the opportunity to regularly present your work within the group, to your peers within the Centre as well as at international conferences. Students are encouraged and supported to undertake further relevant training courses in Oxford and elsewhere depending on need.", "background_reading": "-- Fang H, Consortium U-D, De Wolf H, Knezevic B, Burnham KL, Osgood J, Sanniti A, Lledo Lara A, Kasela S, De Cesco S, Wegner JK, Handunnetthi L, McCann FE, Chen L, Sekine T, Brennan PE, Marsden BD, Damerell D, O'Callaghan CA, Bountra C, Bowness P, Sundstrom Y, Milani L, Berg L, Gohlmann HW, Peeters PJ, Fairfax BP, Sundstrom M & Knight JC. 2019 A genetics-led approach defines the drug target landscape of 30 immune-related traits. Nat Genet 51:1082-1091\n-- Fang H, Chen L & Knight JC. 2019 From genome-wide association studies to rational drug target prioritisation in inflammatory arthritis. The Lancet Rheumatology 2, 50-62\n-- Zhang P, Amarasinghe HE, Whalley JP, Tay C, Fang H, Migliorini G, Brown AC, Allcock A, Scozzafava G, Rath P, Davies B & Knight JC. 2022 Epigenomic analysis reveals a dynamic and context-specific macrophage enhancer landscape associated with innate immune activation and tolerance. Genome Biology 23, 136" },{ "title": "Resolving heterogeneity in the response to infection using -omics", "PI": "Prof Julian Knight, Dr Alex Mentzer", "email": "julian.knight@well.ox.ac.uk", "mix": "50% wet lab, 50% dry lab", "description": "The dysregulated host response to infection results in organ dysfunction and death, accounting for substantial morbidity and mortality in intensive care but the basis for why this develops in only specific individuals with an infection remains unclear. We are taking novel integrative multi-omics approaches to understand this in sepsis and COVID-19, with a view to developing personalised therapy that is appropriate to the individual patient at a particular stage in their illness.\nThis project would form the basis of a laboratory rotation or a 3-year doctoral research project. In sepsis, we have established one of the largest cohorts of patients for genomic studies worldwide, the UK Genomic Advances in Sepsis (GAinS) Study. With our collaborators we performed the first substantive genome-wide association study for outcome in sepsis and complemented this with functional genomic analysis showing that transcriptomic signatures predict underlying response state, outcome and response to therapy. Moreover, we found that a patient's genetic background influenced this with specific genetic variants associated with differences in gene expression dependent on their immune response state. This was further emphasised by our findings in healthy volunteers of expression quantitative trait loci for the response to bacterial endotoxin.\nThe COVID-19 Multi-omic Blood ATlas (COMBAT) Consortium has performed deep phenotyping of COVID-19 patients in Oxford using multi-omic profiling in blood, combined with knowledge of host genetics, pathogen diversity and immunological response, to allow an integrated systems biology approach to understanding the nature and basis of observed disease heterogeneity and drivers of severe illness. This includes bulk and single-cell transcriptomics, proteomics, repertoire sequencing and epigenomics, plasma profiling using timsTOF mass spectrometry and multiplexed immunoassays, serology, host genetics and viral sequencing.\nThis project will aim to follow up on this work to understand individual variation in the response to infection leveraging ongoing work in sepsis and COVID-19 through the UK GAinS and COMBAT studies, and how this could be used to develop and apply therapy. The work provides the opportunity to define the individual response to infection, the specific modulated genes and pathways that may be important in pathogenesis and potential drug targets, and how to use this knowledge effectively to develop personalised therapy. The project will benefit from access to large genomic and clinical datasets, both publicly available and those generated in house. Depending on the structure and duration of the project this could involve using statistical genetics and epidemiology to fine map genetic associations and establish their functional basis; bioinformatics to leverage genomic and epigenomic data, functionally annotate and integrate with diverse related data types to identify and prioritise potential novel drug targets; systems biology and integrative analysis approaches to maximise the informativeness of complex multidimensional datasets; genome editing to knockdown expression of specific genes or investigate the impact of particular genetic variants to establish mechanism; and application of single cell -omic and immune profiling approaches to further define pathogenesis.", "training": "This project will offer a comprehensive training programme in genomic science together with molecular biology and immunology. As described above, this can include both dry (bioinformatics/statistics/computational science) and wet lab (molecular biology/functional genomics/immunology) work, making it an ideal DPhil project for students wishing to gain skills in both areas. There are established sample and data collections for the proposed work, together with a very strong collaborative research network with other researchers on the GMS programme in this area (including within COMBAT). The required wet lab and bioinformatic approaches are well established with expertise in complex trait genetics, gene expression profiling, next generation sequencing technologies including RNA-seq and ChIP-seq, expression quantitative trait mapping, epigenomic profiling, genome editing, immunological assays and other approaches. Students will benefit from working within a supportive research group with a strong track record in graduate student training and mentoring. You will have the opportunity to regularly present your work within the group, to your peers within the Centre as well as at international conferences. Students are encouraged and supported to undertake further relevant training courses in Oxford and elsewhere depending on need.", "background_reading": "-- Burnham KL, Davenport EE, Radhakrishnan J, Humburg P, Gordon AC, Hutton P, Svoren-Jabalera E, Garrard C, Hill AVS, Hinds CJ, Knight JC. 2017 Shared and Distinct Aspects of the Sepsis Transcriptomic Response to Fecal Peritonitis and Pneumonia. Am J Respir Crit Care Med 196, 328-339.\n-- Davenport EE, Burnham KL, Radhakrishnan J, Humburg P, Hutton P, Mills TC, Rautanen A, Gordon AC, Garrard C, Hill AVS, Hinds CJ & Knight JC. 2016 Genomic landscape of the individual host response and outcomes in sepsis: a prospective cohort study. Lancet Respir Med 4, 259-271.\n-- Fairfax BP, Humburg P, Makino S, Naranbhai V, Wong D, Lau E, Jostins L, Plant K, Andrews R, McGee C & Knight JC. 2014 Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949.\n-- COMBAT Consortium. 2022 A blood atlas of COVID-19 defines hallmarks of disease severity and specificity. Cell 185, 916-938 e958.\n-- Maslove DM, Tang B, Shankar-Hari M, Lawler PR, Angus DC, Baillie JK, Baron RM, Bauer M, Buchman TG, Calfee CS, dos Santos CC, Giamarellos-Bourboulis EJ, Gordon AC, Kellum JA, Knight JC, Leligdowicz A, McAuley DF, McLean AS, Menon DK, Meyer NJ, Moldawer LL, Reddy K, Reilly JP, Russell JA, Sevransky JE, Seymour CW, Shapiro NI, Singer M, Summers C, Sweeney TE, Thompson BT, van der Poll T, Venkatesh B, Walley KR, Walsh TS, Ware LB, Wong HR, Zador ZE and Marshall JC (2022). Redefining critical illness. Nature Medicine 28, 1141-1148." },{ "title": "Methods to decompose various factors that contribute to genotype-phenotype associations", "PI": "Augustine Kong", "email": "augustine.kong@bdi.ox.ac.uk", "mix": "100% dry lab", "description": "Genotype-phenotype associations result from direct and indirect genetic effects and confounding due to population stratification and assortative mating (refs 1, 2, 3). Data of nuclear families and/or first-degree relative pairs can be used to separate out these effects. Methods to do so exist. Our aim is to further develop these methods: (a) to increase statistical power (refs 4, 5), and (b) to incorporate more complex models. For example, with (b), most analyses currently do not take into account ascertainment bias, i.e. the individuals in a study such as the UK Biobank is not a random sample drawn from the population.", "training": "", "background_reading": "1) Kong A, Thorleifsson G, Frigge ML, Vilhjalmasson BJ, Young AI, Thorgeirsson TE, Benonisdottir S, Oddsson A, Halldorsson BV, Masson G, Gudbjartsson DF, Helgason A, Bjornsdottir G, Thorsteinsdottir U, Stefansson K. The nature of nurture: Effects of parental genotypes. Science. 2018, Jan 26, 359(6374):424-428.\n2) Young AI, Benonisdottir S, Przeworski M, Kong A. Deconstructing the sources of genotype-phenotype associations in humans. Science. 2019, Sep 27, 365(6460):1396-1400.\n3) Young AI, Frigge ML, Gudbjartsson DF, Thorleifsson G, Bjornsdottir G, Sulem P, Masson G, Thorsteinsdottir U, Stefansson K, Kong A. Relatedness Disequilibrium Regression estimates heritability without environmental bias. Nat Genet. 2018 Sep, 50(9):1304-1310.\n4) Kong A, Benonisdottir S, Young AI. Family analysis with Mendelian Imputations. bioRxiv. 2020.\n5) Young AI, Nehzati SM, Lee C, Benonisdottir S, Cesarini D, Benjamin D, Turley P, Kong A. Mendelian Imputations of parental genotypes for genome-wide estimation of direct and indirect genetic effects. bioRxiv. 2020.\nhttps://www.nytimes.com/2018/01/25/science/children-parents-genes-education.html" },{ "title": "Using ancient DNA to understand the impact of humans on the immune system of domesticated animals.", "PI": "Professor Greger Larson, Professor Adrian Smith and Dr Laurent Frantz", "email": "greger.larson@arch.ox.ac.uk", "mix": "mixed", "description": "Domestication of animals was a key process in the emergence of modern societies and it is clear that various traits were deliberately selected by early populations including temperament, resilience and production traits. However, by changing the character and environment of animals we also altered the pathogen profiles that affected these animals (and sometimes spilled over to us). Whilst the genetics of some traits has received considerable attention (e.g. behavior and production) others have been neglected, in particular the influence of domestication (and more recently intensification) on the immune system of these animals.\nThis project will employ ancient DNA based approaches to identify the effects of humans on the immunogenetic profiles of domesticated animals. Understanding these historical selective events can be used to improve the welfare and resilience of modern domesticated animals and may help to reduce the impact of infectious diseases including those where domesticated animals represent a primary source of zoonotic disease in humans.", "training": "The DPhil will support training in a wide range of molecular biology (aDNA and standard methods) and bioinformatics techniques and these may also extend to testing the function of selected variants of immune genes in relevant cellular assays.", "background_reading": "-- Flammer PG, Dellicour S, Preston SG, Rieger D, Warren S, Tan CKW, Nicholson R, Přichystalová R, Bleicher N, Wahl J, Faria NR, Pybus OG, Pollard M and Smith AL. (2018) Molecular archaeoparasitology identifies cultural changes in the Medieval Hanseatic trading centre of Lübeck. Proc. R. Soc. B.285:20180991. http://doi.org/10.1098/rspb.2018.0991\n-- Flammer PG, Ryan H, Preston SG, Warren S, Přichystalová R, Rainer Weiss, Valerie Palmowski, Sonja Boschert, Katarina Fellgiebel, Isabelle Jasch-Boley, Madita-Sophie Kairies, Ernst Rümmele, Dirk Rieger, Beate Schmid, Ben Reeves, Rebecca Nicholson, Louise Loe, Christopher Guy, Tony Waldron, Jiří Macháček, Joachim Wahl, Mark Pollard, Greger Larson and Adrian L. Smith (2020) Epidemiological insights from a large-scale investigation of intestinal helminths in Medieval Europe. PLOS Neglected Tropical Diseases 14(8): e0008600. https://doi.org/10.1371/journal.pntd.0008600" },{ "title": "Stem cells and adaptive molecular phenotype in colorectal cancer (STAMP-CRC)", "PI": "Professor Simon Leedham", "email": "Simonl@well.ox.ac.uk", "mix": "can be wet or dry lab work, or mixture depending on student preference", "description": "Tumour heterogeneity plays a key role in cancer adaption and resistance to therapies, but understanding genetic heterogeneity alone cannot paint a complete picture. The forces of natural (and therapeutic) selection act upon phenotypic characteristics, and phenotype is a function of both the genotype and the microenvironment. The capacity to measure and understand relevant cancer cell phenotypic variation is key to monitoring neoplasia evolutionary trajectory. We believe that cancer stem cell molecular phenotype is an informative readout of dynamic evolutionary change within a tumour and is an important, and currently unmeasured metric that can improve prediction of tumour response to treatments, biologically inform existing therapy scheduling and drive the development of cancer cell adaption drug targets. Here we will assess demonstrable cross-species stem cell phenotypic heterogeneity in intestinal tumours, investigate the driving co-evolutionary interaction between the mutant epithelium and surrounding stromal/immune cell compartments, and assess the spatio-temporal impact of therapeutic selective pressures.\nimage://data/images/leedham_simon_04.png", "training": "This would suit a student from any of the eligible pathways with an interest in understanding cancer heterogeneity and tracking tumour evolution and adaption. The project will involve mouse modelling and preclinical drug testing but requires no previous experience. Wet and dry lab training opportunities exist and can be discussed to tailor needs for students", "background_reading": "" },{ "title": "Accelerate the discovery of causal variant(s) associated with fat distribution and central obesity", "PI": "Prof Cecilia Lindren", "email": "celi@well.ox.ac.uk", "mix": "100% dry lab", "description": "Background\nOur research focuses on the integration of large-scale data sets of genomic sequence variation and transcriptional regulation (genetics and genomics) with phenotypic data to advance the understanding of the molecular pathogenesis of obesity related traits. We have been playing a key role in the collaborative work that have brought forward over 7,000 loci associated with various obesity traits (body mass index, BMI, waist:hip ratio, WHR, fat% etc.) (Ref below, and GiANT – unpublished data).\nMy team is dedicated to translating genetic associations into functional and pathophysiological mechanisms, and establishing how this can improve our understanding of the physiology and biology underlying obesity traits.\nThis project seeks to expand our previous efforts on using large-scale genomic approaches to identify causal genetic variants influencing fat distribution. It builds on the global collaboration I lead through the GiANT consortium and the International Common Disease Alliance, aimed at identifying the genetic determinants of obesity and fat distribution.\nDescription of the work\nThe first strand of genetic analysis in this project will identify and fine-map common/low frequency associations to fat distribution using large scale meta-analysis as well as exome sequencing analysis from UKBB.\nIn the second strand of this project, we will explore novel strategies to further accelerate the discovery of novel genetic loci for fat distribution (collaboration with the Neale lab – Broad Institute, and Kong lab – Big Data Institute).\nLastly, we will systematically identify which molecular, cellular, and physiological processes are relevant to genetic risk for central obesity and fat distribution (collaboration with Ebener group- TDI, Finucane lab – Broad Institute and Pers lab – Copenhagen university).\nWe aim to answer the following questions:\n-- What additional phenotypes can be derived out of electronic health records and images that paired with genetics can advance our understanding of the mechanisms underlying obesity, and its different facets?\n-- What are the causal variants in loci associated in genome wide association studies with these obesity traits?\n-- Which are the low-frequency and rare variants not picked up in genome wide association studies of obesity traits and how do we best detect them?\n-- Can we annotate these associated variants for fat distribution with rich regulatory information to elucidate likely effector genes (these will then be followed up functionally, both in our own budding wet lab as well as in large scale international collaborations, particularly with Claussnitzer lab – Broad Institute)?", "training": "The candidate will have ample in-house support for any necessary statistical, bioinformatics package/pipelines. The candidate will have the ability to go to relevant summer schools in the fields of (but not limited to): genetics, bioinformatics, statistical genetics and machine learning. The candidate will have the opportunity to present work in both national and internationally renowned conferences. The candidate will be exposed to a vast network of local, national and international collaborators across a range of areas and disciplines.", "background_reading": "-- [Glastonbury CA, et al. Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits. PLoS Comput Biol. 2020 Aug 14;16(8):e1008044.\n-- Censin JC, et al. Causal relationships between obesity and the leading causes of death in women and men. PLoS Genet. 2019 Oct 24;15(10):e1008405.\n-- Justice AE, et al. Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution. Nat Genet. 2019 Mar;51(3):452-469.\n-- Turcot V, et al. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat Genet. 2018 Jan;50(1):26-41.\n-- Pulit SL, Stoneman C, Morris AP, Wood AR, Glastonbury CA, Tyrrell J, Yengo L, Ferreira T, Marouli E, Ji Y, Yang J, Jones S, Beaumont R, Croteau-Chonka DC, Winkler TW; GIANT Consortium, Hattersley AT, Loos RJF, Hirschhorn JN, Visscher PM, Frayling TM, Yaghootkar H, Lindgren CM. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet. 2019 Jan 1;28(1):166-174.\n-- Claussnitzer, Melina, et al. 'FTO obesity variant circuitry and adipocyte browning in humans.' New Engl J Med 2015.373 (2015): 895-907.\n-- Locke, Adam E., et al. 'Genetic studies of body mass index yield new insights for obesity biology.' Nature 518.7538 (2015): 197.\n-- Shungin, Dmitry, et al. 'New genetic loci link adipose and insulin biology to body fat distribution.' Nature 518.7538 (2015): 187.\n-- Heid IM, et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet. 2010 Nov;42(11):949-960.\n-- Loos RJ, Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet. 2008 Jun;40(6):768-75.\n--" },{ "title": "Ethical legal social aspects (ELSA) of genomic medicine", "PI": "Anneke Lucassen or senior CELS team members", "email": "anneke.lucassen@well.ox.ac.uk", "mix": "100% dry", "description": "To follow if appropriate to offer ELSA type projects on this programme", "training": "To follow if appropriate to offer ELSA type projects on this programme", "background_reading": "To follow if appropriate to offer ELSA type projects on this programme" },{ "title": "Identifying the influence of HLA genetic variation on protein expression", "PI": "Yang Luo", "email": "yang.luo@kennedy.ox.ac.uk", "mix": "100% dry lab", "description": "Variations of human leukocyte antigen (HLA) genes in the major histocompatibility complex region(MHC) play a vital role in our immune system and are of great importance to many areas of medicine,from organ transplantation and disease diagnosis to cancer immunotherapy. In autoimmune diseases,for example, the HLA-DRB1 allele is highly associated with seropositive rheumatoid arthritis, whileindividuals with HLA-B*27 alleles are 100 times more likely to develop active ankylosing spondylitis.Despite considerable advances in identifying genetic associations between HLA and a vast number ofimmune-mediated traits, the underlying molecular mechanisms behind these associations remainlargely unknown. In particular, proteins have key roles in various biological processes, includingsignalling, growth, repair and defence against infection. Proteins can serve as biomarkers for diseaserisk and clinical outcomes, and represent the targets of most approved drugs. Recent advances inscalable affinity-based proteomic techniques allow us to measure thousands of protein targetssimultaneously within an individual. Thus opens the opportunity for us to improve our understandingon how HLA variations influence genome-wide protein expression level. This project will focus onidentifying statistical associations between HLA alleles and protein expression (HLA-pQTL) in large-scale cohort data, including STEpUP OA, the UK Biobank and the China Kandoori Biobanks. We will conduct a systematic investigation of how HLA variants affect protein expression, and thus identifyingimprove our interpretation of the association between HLA and diseases.", "training": "The successful candidate will be benefit from supervision by a team of scientists with key expertise instatistical genetics, immunology and clinical science. You will be based in the Kennedy Institute ofRheumatology, a world-leading centre in genomics and inflammatory biology. Training will be providedin data science techniques including statistical data analysis and visualisation with R, the writing ofcomputational pipelines with Python/Nextflow, and the use of high-performance compute clusters.The student will gain expertise in analysing cutting-edge datasets including genotyping and proteomicsequencing. The primary supervisor, Dr. Yang Luo, is an expert in statistical genetics, and her labdevelops statistical methods and computational software to better understand the geneticcontributions to immune-mediated traits. The successful candidate is expected to meet her on aminimum weekly basis. The secondary supervisor, Professor Tonia Vincent, directs the Centre for OAPathogenesis, and has led the STEpUP OA consortium, the largest international effort to date to definemolecular endotypes within osteoarthritis. The STEpUP OA consortium measures protein signatures inthe synovial fluid of more than 2000 OA patients. The successful candidate is expected to meetProfessor Tonia on a monthly basis. The Kennedy Institute is a world-renowned research centre andhas a vibrant PhD program with weekly journal club, seminars, student symposia, weekly internalinstitute presentations and training. A core curriculum of lectures will provide a solid foundation of abroad range of subjects including data analysis, statistical methods and immunology summer school.Students will also have the opportunity to work closely with both computational and experimentalscientists.", "background_reading": "Please include references as desired. Suggested format:\n-- [Surname] [Firstname], [other authors]… (year in bold) . [Title]. [Journal name], [other details]. Available at: [link]" },{ "title": "Applying deep-learning to determine the factors explaining the within-host genetic diversity of SARS-CoV-2", "PI": "Dr Katrina Lythgoe and Professor David Clifton", "email": "katrina.lythgoe@bdi.ox.ac.uk", "mix": "100% dry lab", "description": "Over the last three years a huge amount SARS-CoV-2 genomic data has been collected, enabling the global community to track the emergence and spread of new variants at an unprecedented scale. For new variants to spread locally and globally, they must first emerge within an infected individual, with the leading hypothesis that the major variants emerged in long term chronically infected individuals. The hope is that through an understanding of the evolutionary process within infected individuals we can better predict key features of new major variants in the short to medium term, which would be helpful for vaccine design, and potentially prevent new variants from emerging in the first place through effective identification and treatment of individuals with persistent infections.\nHowever, determining the factors affecting within-host viral genetic diversity, the building blocks for evolution, is a complex problem. First, not all observed within-host variants, or intra-host single nucleotide variants (iSNVs), represent genuine within-host genetic diversity, but is artefactual. Second, the abundance and frequency of genuine iSNVs likely changes during the course of infections, which can range from days to many months. Third, a large number of other factors may influence which iSNVs are present, including the genetic background of the infecting viral variant(s), the infection and vaccination history of the infected individual, and other host characteristics such as age and gender, and co-morbidities.\nOverarching aim:\n1) Identify variant, platform, and sequencing centre specific artefactual iSNVs, enabling these positions to be masked as appropriate by myself and other researchers working on SARS-CoV-2 within-host sequencing data.\n2) Determine time-since-infection based on the abundance, identity and frequency of iSNVs within a sequenced sample even if other information is not available, such as time since onset of symptoms.\n3) Provide a framework for identifying the factors explaining the patterns of iSNVs observed among individuals, such as infecting variant, time-since-infection, vaccination and infection history, and other host factors.\nWe are part of the Office of National Statistics Covid-19 infection survey, and this project will give you access to >125,000 sequenced samples plus metadata.\nDuring the rotation project you will:\n1. Perform a literature review.\n2. Develop a machine learning model to estimate time-since-infection based on viral genetic factors.\n3. Write up.\nThis could form the foundation of a DPhil, which could help inform, for example, future vaccine development and roll-out.", "training": "-- During this DPhil you will learn how to analyse next-generation viral whole-genome sequencing (WGS) data\n-- Develop machine learning models to analyse complex data\n-- You will be given the opportunity to attend a specialised course on viral phylogenetics, and to audit the Health Data Science CDT lectures, particularly those on the dynamics and evolution of infectious disease.", "background_reading": "-- Lythgoe & Hall et al., Science 2021, “SARS-CoV-2 within-host diversity and transmission”\n-- Golubchik et al. 2022 https://www.medrxiv.org/content/10.1101/2022.05.15.22275117v1\n-- Ghafari et al. 2023 https://www.medrxiv.org/content/10.1101/2023.01.29.23285160v1\n-- Lythgoe & Golubchik 2023 https://royalsocietypublishing.org/doi/10.1098/rspb.2023.1284\n-- Antia et al., Nature 2003, “The role of evolution in the emergence of infectious diseases”" },{ "title": "Characterising the within-host compartmentalisation of Hepatitis C Virus", "PI": "Dr Katrina Lythgoe, Professor Oliver Pybus and Professor Jane A. McKeating", "email": "katrina.lythgoe@bdi.ox.ac.uk", "mix": "100% dry lab", "description": "Hepatitis C virus (HCV) is characterized by high mutation and evolutionary rates, often with long durations of infection between transmission events. Fundamentally, the evolution and epidemiology of this virus at the epidemiological scale cannot be properly elucidated without a detailed understanding of the link between within-host evolutionary dynamics and transmission. Mathematical modeling and evolutionary analysis of genetic data, particularly HIV, has helped answer key questions, including whether HIV continues to replicate whilst on treatment, how many HIV variants are transmitted among individuals, and how HIV spreads within and among population. However, similar work on HCV is in its infancy.\nOur previous work shows HCV infection is characterised by different subpopulations which are only intermittently observed in plasma. Consequently, virus sequenced from a single time point is unlikely to be representative of the viral diversity present in that individual, resulting in the potential to incorrectly infer transmission. However, poor resolution of phylogenetic trees using short/moderate reads make it difficult to test which processes generated these patterns.\nWe have developed a method to generate long-read within-host whole-genome sequence data in order to generate high-resolution next generation sequencing data from a large number individuals sampled over many years. Using this data you will ascertain whether multiple long-lived lineages of virus exist, and whether they are consistent across the whole genome. Further, you will test whether population subdivision is due to selective or non-selective processes, and whether it differs for cirrhotic and non-cirrhotic individuals. Finally, you will use this data to determine the consequences for determine who-infected-whom using genetic data, and time-since-infection from genomic data. This is a crucial question for public health, as it will help determine where intervention efforts should be focussed.", "training": "-- During this DPhil you will learn how to analyse next-generation viral whole-genome sequencing (WGS) data\n-- Develop and use phylogenic models, including structured coalescent models, to determine the extent and tempo of within-host population turnover\n-- Develop viral transmission models to determine the effect of within-host population structure on between-host evolutionary dynamics\n-- You will be given the opportunity to attend a specialised course on viral phylogenetics, and to audit the Health Data Science CDT lectures, particularly those on the dynamics and evolution of infectious disease.", "background_reading": "-- Pybus, O.G. & Rambaut, A. Evolutionary Analysis of the Dynamics of Viral Infectious Disease. Nature Reviews Genetics 2009. Doi:10.1038/nrg2583\n-- Raghwani J, Wu C, Ho CKY, Jong M De, Molenkamp R, Schinkel J, et al. High-Resolution Evolutionary Analysis of Within-Host Hepatitis C Virus Infection. 2019; 1–8. doi:10.1093/infdis/jiy747\n-- Rose R, Rodriguez C, Dollar JJ, Lamers SL, Massaccesi G, Osburn W, et al. Inconsistent temporal patterns of genetic variation of HCV among high-risk subjects may impact inference of transmission networks. Infect Genet Evol. Elsevier; 2019;71: 1–6. doi:10.1016/j.meegid.2019.02.025" },{ "title": "Determining the role of Hepatitis B virus cccDNA transcriptional activity in increasing the lifespan of episomal cccDNA", "PI": "Dr Katrina Lythgoe and Professor Jane A. McKeating", "email": "katrina.lythgoe@bdi.ox.ac.uk", "mix": "100% dry lab", "description": "Hepatitis B virus (HBV) infection is a major global health problem with over 240 million infected individuals at risk of developing progressive liver disease and hepatocellular carcinoma. HBV is an enveloped DNA virus that establishes its genome as an episomal, covalently closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Currently available standard-of-care treatments for chronic hepatitis B (CHB) include nucleos(t)ide analogues (NA) that suppress HBV replication but do not target the cccDNA and hence rarely cure infection. There is considerable interest in determining the lifespan of cccDNA molecules, and the mechanisms resulting in its longevity, to design and evaluate new curative treatments.\nWe previously reported an evolutionary mathematical model of cccDNA persistence (Lythgoe et al., 2021), based on within-host next-generation sequencing to data, to estimate cccDNA lifespan. However, our estimated values cannot explain the long-term persistence of cccDNA in most patients, suggesting that key processes are missing from our understanding. Working closely with the McKeating group, you will incorporate new data to refine these models, and to better understand the mechanisms regulating cccDNA half-life. This will include both the replenishment of cccDNA via pgRNA-rcDNA, where encapsidated genomes recycle to the nucleus, and the variation in cccDNA transcriptional activity over the course of infection. Specifically, you will test the hypothesis that HBV cccDNA transcriptional activity increases the lifespan of episomal genomes.", "training": "-- Development and analyse of evolutionary models of viral infection\n-- You will be given the opportunity to attend a specialised course on modelling of infectious disease, and to audit the Health Data Science CDT lectures, particularly those on the dynamics and evolution of infectious disease.\n-- Experience working in a collaborative team environment and presenting data at internal lab meetings, journal clubs and seminars.\n-- Contribute data towards publication in peer-peer reviewed journals", "background_reading": "-- Pybus, O.G. & Rambaut, A. Evolutionary Analysis of the Dynamics of Viral Infectious Disease. Nature Reviews Genetics 2009. Doi:10.1038/nrg2583\n-- Lythgoe et al. Estimating hepatitis B virus cccDNA persistence in chronic infection. Virus Evolution 2021. Doi:10.1093/ve/veaa063\n-- Testoni et al. Serum hepatitis B core-related antigen (HBcrAg) correlates with covalently closed circular DNA transcriptional activity in chronic hepatitis B patients. J Hepatol 2019. Doi:10.1016/j.hep.2018.11.030\n-- Bimodal distribution and set point HBV DNA viral loads in chronic infection: retrospective analysis of cohorts from the UK and South Africa. Wellcome Open Research 2020. Doi:10.12688/wellcomeopenres.15941.2" },{ "title": "The functional consequences of human genetic variation on response to infectious disease", "PI": "Dr Alexander Mentzer, Professor Adrian Hill", "email": "alexander.mentzer@ndm.ox.ac.uk", "mix": "can be varied but may be 20-50% wet lab, 50-80% dry lab", "description": "In close collaboration with Professor Adrian Hill at the Jenner Institute and other collaborators in the Wellcome Centre, UK Biobank and other institutes around the world we have generated multiple independent datasets looking at how human genetic diversity affects response to natural infection and vaccination. We have found that variation at multiple loci across the human genome contribute to immune responses against infectious antigens and, in some cases, protection against disease. An overall goal of this project is to combine these datasets to understand how much of this variation is infection- or population-specific, and then to use downstream approaches to understand the impacts of these changes to a molecular level but there are multiple complex regions and diseases that can be studied allowing for a programme tailored to any interested student. The genomic regions of particular interest include the major histocompatibility complex and immunoglobulin loci with diseases including viral infections (such as herpes, hepatitis, HIV and coronavirus) and bacterial diseases that may cause sepsis.\nAny sub-project under this proposal would be designed in 2 stages; the first being a meta-analysis of relevant datasets central to the core objectives of the Mentzer group, and the second phase being an opportunity to follow up these findings using a combination of approaches dependent on the earlier findings. The first stage would be well suited to a laboratory rotation giving an excellent introduction to computational or dry lab skills whereas both could form the basis of a 3-year doctoral project giving the opportunity for a combined dry and wet-lab experience. At the time of writing, specific sub-projects include fine-mapping and molecular characterisation of MHC association signals with multiple infections, long-range sequencing characterisation and mapping of RNA-Seq reads for Immunoglobulin loci, and metagenomic approaches for pathogen detection in whole blood RNA. There may also be increasing opportunities to look at the effects of human genetics on responses to novel agents of interest such as SARS-CoV-2, the cause of COVID-19. Our group has a strong emphasis on tailoring the project to the individual and supporting the individual to achieve outstanding scientific output.", "training": "This project is designed to give an excellent introduction to computer science with rapid availability of datasets and tailored pipelines for data integration and analysis under close supervision that would provide necessary skills for interpretation and hypothesis testing. There is flexibility in the project and connections with multiple labs within the University to enable a diverse and complete follow-up of findings tailored to the interests of the candidate that can provide exposure and comprehensive training in molecular biology, immunology and functional genomics using experimental analysis or direct wet-lab exposure to methods such as flow cytometry, antigen peptide processing, binding and presentation, gene expression analysis, ELISA and other immunoassays. There will be regular opportunities to present work to laboratory colleagues and internationally at conferences and meetings.", "background_reading": "-- Mentzer AJ*, Brenner N* et al 2022; A scalable 20-agent Multiplex Serology platform applied to UK Biobank to define host-pathogen-environment relationships and disease susceptibility; Nature Communications; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8983701/\n-- COMBAT Consortium 2022; A blood atlas of COVID-19 defines hallmarks of disease severity and specificity; Cell https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8776501/\n-- Antibody evasion by the P. 1 strain of SARS-CoV-2; Dejnirattisai W*, Zhou D*, Supasa P*, Liu C*, Mentzer AJ* et al 2021; Cell. 184 (11) 2939\n-- Broad and strong memory CD4+ and CD8+ T cells induced by SARS-CoV-2 in UK convalescent COVID-19 patients 2020; Peng Y*, Mentzer AJ*, Liu G*, Yao Z*, Dejnirattisai W* et al; Nat. Imm 21, 1336 (2020)\n-- Mentzer AJ*, Muruiki JJ*, Band G, et al. 2019; The ferroportin Q248H mutation protects from anemia, but not malaria or bacteremia; Science Advances; 4;5(9):eaaw010\n-- Dilthey AT, Mentzer AJ, Carapito R et al. 2018; HLA*PRG:LA - HLA typing from linearly projected graph alignments; Bioinformatics; pii: btz235\n-- Brenner N, Mentzer AJ, Butt J, Braband KL, Michel A, Jeffery K, Klenerman P, Gärtner B, Schnitzler P, Hill A, Taylor G, Demontis MA, Guy E, Hadfield SJ, Almond R, Allen N, Pawlita M, Waterboer T 2019; Validation of Multiplex Serology for human hepatitis viruses B and C, human T-lymphotropic virus 1 and Toxoplasma gondii; PLoS One:14(1):e0210407\n-- Brenner N, Mentzer AJ, Butt J, Michel A, Prager K, Brozy J, Weißbrich B, Aiello AE, Meier HCS, Breuer J, Almond R, Allen N, Pawlita M, Waterboer T 2018; Validation of Multiplex Serology detecting human herpesviruses 1-5; PLoS One: 13(12):e0209379" },{ "title": "How do transcription factors create new enhancers?", "PI": "Prof Thomas Milne", "email": "thomas.milne@imm.ox.ac.uk", "mix": "50% wet lab, 50% dry lab", "description": "Enhancers are key regulatory elements that control gene expression and function by acting as docking sites for transcription factors. Most work on enhancers (including our own) has concentrated on methods of removing specific factors to determine their effect on endogenous enhancer function. This is essentially a loss of function approach and provides useful information on what factors are necessary for enhancer function. However, to really understand what each factor contributes to enhancer behaviour, gain of function approaches are required to test for sufficiency of a factor. To accomplish this, we used a TetO array inserted into a gene desert region in mouse ES cells. By fusing a protein of interest to the TetR DNA binding domain, it is possible to anchor a protein or domain of interest at this gene desert region and determine whether it can recruit specific activities de novo. Our preliminary findings demonstrate that anchoring the MYB transactivation (TA) domain is sufficient to initiate transcription from regions more than 50kb distal to the TetO locus, and this is associated with increases in H3K27ac and increased DNA interactions as measure by chromosome conformation capture techniques. Conversely, we have found that other major transcription factors such as RUNX1 lack this intrinsic ability to create enhancers de novo. This suggests that transcription factors have varying abilities to impact enhancer function.\nThe goal of this project will be to screen key transcription factors from haematopoietic stem cells for their ability to create novel enhancers de novo, in order to better understand the relationship between transcription factor function and enhancer activity. This project will involve interactions with other labs in the WIMM (such as the Hughes, Davies and Wilkinson labs) and will use a broad range of cutting-edge technologies.", "training": "This project will use a broad range of cutting-edge technologies including state of the art techniques for the analysis of gene regulation on a genome-wide level (single cell ATAC-seq, ChIP-seq, Capture C, nascent RNA-seq, Micro Capture-C), and genome editing (e.g. CRISPR/CAS9) approaches. Training will be specifically provided in these molecular biology techniques as well as in bioinformatics, including machine learning approaches where appropriate.", "background_reading": "-- Harman, J.R. etal (2021). A KMT2A-AFF1 gene regulatory network highlights the role of core transcription factors and reveals the regulatory logic of key downstream target genes. Genome Res. DOI: 10.1101/gr.268490.120\n-- Crump, N.T. et al (2021). Chromatin accessibility governs the differential response of cancer and T cells to arginine starvation. Cell Rep. 35: 109101. DOI: 10.1016/j.celrep.2021.109101\n-- Crump, N.T. etal (2021). BET inhibition disrupts transcription but retains enhancer-promoter contact. Nat Commun. 12: 223. DOI: 10.1038/s41467-020-20400-z" },{ "title": "Endocytosis deficiency as a trigger of cellular senescenceAmisyn at the crossing of modulated neurotransmission and brain pathologies", "PI": "Ira Milosevic, Thibaud MartialIra Milosevic, Meenakshi Bhardwaj", "email": "imilose@well.ox.ac.uk", "mix": "about 75% wet lab, 25% dry lababout 75% wet lab, 25% dry lab", "description": "Several endocytic proteins, including endophilin-A, amphiphysin, dynamin-1, synaptojanin-1, auxilin and intersectin-1, have been linked to neurodegeneration. One family of abovementioned proteins, endophilin A1, A2, and A3 (henceforth endophilin 1, 2, and 3) seems central in this role. Endophilins belong to a protein superfamily containing BAR-domains, which are known to be responsible for sensing and generating membrane curvature, and for recruiting the relevant endocytic factors from the cytosol to the membrane. Our previous work on endophilin-A clarified its role in clathrin-mediated endocytosis, and established that endophilin recruits the phospholipid phosphatase synaptojanin-1 to the bud necks prior to fission by the GTPase dynamin, which also interacts with endophilin.\nSenescence or biological aging is the gradual deterioration of functional characteristics in living cells and organisms. Cellular senescence is a phenomenon characterized by the permanent cell growth arrest in normal and altered physiological processes. Many factors stimulate the appearance of this phenomenon, such as aging, tissue repair, tumor treatments, etc. Senescent cells remain viable, but show metabolic alterations and undergo dramatic changes in gene expression developing a complex senescence-associated secretory phenotype that contributes to senescence spreading to other cells and tissues. Cellular senescence can also compromise tissue repair and regeneration, thereby contributing toward aging.\nHere, we will test if endocytosis deficiency acts as a trigger of cellular senescence by focusing on the functions of two endocytic protein, the key endocytic adaptor endophilin-A that belongs to the family of BAR-domain proteins and synaptojanin-1, a lipid phosphatase. You will be joining our efforts to understand how lack of endophilin or synaptojanin-1 triggers cellular senescence, and consequently neurodegeneration and shorter lifespan. We will capitalize on the cells with partial or complete lack of endophilin or synaptojanin-1 to explore the link between proteins and senescence. We use a multi-disciplinary approach that combines genomics, cell biology, physiology and live imaging.\nKeywords: ageing, geroscience, neuroscience, neurodegenerative diseases, cell biology, live imagingThe human brain is astonishing: it is the source of our thoughts, actions, memories, perceptions and emotions. It confers on us the abilities that make us human, while simultaneously making each of us unique. Through deepened knowledge and understanding of how human brain works, we will comprehend ourselves better and treat brain diseases more incisively. Over recent years, neuroscience has advanced to the level that we can envision spanning molecules, cells and neuronal circuits in action. In particular, there is an emerging view that subtle aspects of presynaptic dysfunction are implicated in an increasing number of brain disorders.\nWe are particularly interested in exocytosis, a process of vital importance for neuronal cells that is controlled by a set of both positive and negative regulators. While promotors of exocytosis are well studied, negative regulators are poorly understood. We discovered that a small SNARE protein amisyn (STXBP6) acts as a vertebrate-specific competitor of synaptobrevin-2, a key player in exocytosis. Amisyn contains an N-terminal pleckstrin homology domain that mediates its transient association with the plasma membrane by binding to phospholipid PI(4,5)P2. Both the pleckstrin homology and SNARE domains are needed to inhibit exocytosis. Of note, amisyn is poorly studied despite several studies have emphasized its importance for exocytosis and reported the occurrence of amisyn mutations in autism-spectrum disorders and diabetes.\nThis project aims to analyse transcriptome and proteome of transgenic mouse model without amisyn already generated for these studies (the model is not yet unpublished). The candidate will then use transgenic mice tissue, as well as human and rodent cell lines, to verify own findings. If time allows, the studies will extend to amisyn patient mutants, and how lack or impaired function of amisyn modulates exocytosis.", "training": "Brain dissection. Transcriptome and proteome analyses of amisyn mutant tissue. Western blotting. Culturing human and/or rodent clonal cells. Immunocytochemistry. Live cell imaging using custom-made fast spinning-disk confocal microscope.", "background_reading": "1. Revisiting the Role of Clathrin-Mediated Endoytosis in Synaptic Vesicle Recycling.\nMilosevic I. Front Cell Neurosci. 2018 Feb 6;12:27. doi: 10.3389/fncel.2018.00027. eCollection 2018. PMID: 29467622\n2. Endophilin-A coordinates priming and fusion of neurosecretory vesicles via intersectin.\nGowrisankaran S, Houy S, Del Castillo JGP, Steubler V, Gelker M, Kroll J, Pinheiro PS, Schwitters D, Halbsgut N, Pechstein A, van Weering JRT, Maritzen T, Haucke V, Raimundo N, Sørensen JB, Milosevic I.\nNat Commun. 2020 Mar 9;11(1):1266. doi: 10.1038/s41467-020-14993-8. PMID: 32152276\n3. PI3K/AKT/MTOR and ERK1/2-MAPK signaling pathways are involved in autophagy stimulation induced by caloric restriction or caloric restriction mimetics in cortical neurons.\nFerreira-Marques M, Carvalho A, Cavadas C, Aveleira CA.\nAging (Albany NY). 2021 Mar 14;13(6):7872-7882. doi: 10.18632/aging.202805. Epub 2021 Mar 14.\nPMID: 33714946\n4. Endophilin-A regulates presynaptic Ca2+ influx and synaptic vesicle recycling in auditory hair cells.\nKroll J, Jaime Tobón LM, Vogl C, Neef J, Kondratiuk I, König M, Strenzke N, Wichmann C, Milosevic I, Moser T.EMBO J. 2019 Mar 1;38(5):e100116. doi: 10.15252/embj.2018100116. Epub 2019 Feb 7. PMID: 30733243\n5. Endophilin-A Deficiency Induces the Foxo3a-Fbxo32 Network in the Brain and Causes Dysregulation of Autophagy and the Ubiquitin-Proteasome System.\nMurdoch JD, Rostosky CM, Gowrisankaran S, Arora AS, Soukup SF, Vidal R, Capece V, Freytag S, Fischer A, Verstreken P, Bonn S, Raimundo N, Milosevic I.Cell Rep. 2016 Oct 18;17(4):1071-1086. doi: 10.1016/j.celrep.2016.09.058. Epub 2016 Oct 6. PMID: 27720640\nOPTIONAL: Recruitment of endophilin to clathrin-coated pit necks is required for efficient vesicle uncoating after fission.\nMilosevic I, Giovedi S, Lou X, Raimondi A, Collesi C, Shen H, Paradise S, O'Toole E, Ferguson S, Cremona O, De Camilli P.Neuron. 2011 Nov 17;72(4):587-601. doi: 10.1016/j.neuron.2011.08.029. PMID: 22099461Kondratiuk I, Jakhanwal S, Jin J, Narayanan U, Kroppen B, Krisko A, Meinecke M, Asheri U, Jahn R, D. Fasshauer, Milosevic I@ (2020) PI(4,5)P2-dependent regulation of exocytosis by amisyn, a vertebrate-specific competitor of synaptobrevin 2. PNAS USA, 117(24):13468-79" },{ "title": "Genealogical analysis of complex traits", "PI": "Prof Pier Palamara", "email": "palamara@stats.ox.ac.uk", "mix": "0% wet lab, 100% dry lab", "description": "Human genomes are connected through complex networks of genealogical relationships, which are shaped by their demographic and evolutionary history. We have developed several statistical and computational approaches to infer details of these genealogical connections [1-5]. We have used these genealogical structures to study natural selection [1,2,5,6] and fine-scale population structure [2], and developed strategies that use inferred genome-wide genealogies to perform analyses of heritable traits such as estimating trait heritability, performing polygenic prediction, and detecting association [3,4]. These strategies use an inferred graph, called ancestral recombination graph (ARG), that compactly represents the evolutionary history of a set of analyzed genomes. An inferred ARG can be used to detect the presence of genomic variants that are missing in reference imputation panels, which are not available for all populations. This framework can be extended in several directions, with the goal of improving complex trait heritability estimation, polygenic prediction, or association. This rotation and a subsequent DPhil project will be focused on the development of these extensions.", "training": "Depending on the specific direction of the project, the student will develop expertise in statistical genetics, population genetics, machine learning, programming in high-level (e.g. Python) and compiled (e.g. C++) languages, and working with very large genomic data sets.", "background_reading": "[1] Palamara et al. High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability. Nature Genetics, 2018.\n[2] Nait Saada et al. Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations. Nature Communications, 2020.\n[3] Zhang et al. Biobank-scale inference of ancestral recombination graphs enables genealogy-based mixed model association of complex traits. Nature Genetics, 2023.\n[4] Harris, K. Using enormous genealogies to map causal variants in space and time. Nature Genetics, 2023.\n[5] Nait Saada et al. Inference of coalescence times and variant ages using convolutional neural networks. Molecular Biology and Evolution, 2023.\n[6] Yasumizu et al. Genome-Wide Natural Selection Signatures Are Linked to Genetic Risk of Modern Phenotypes in the Japanese Population. Molecular Biology and Evolution, 2020." },{ "title": "Projects in Infectious and autoimmune-related disease", "PI": "Stephen Sansom, Paul Bowness", "email": "stephen.sansom@kennedy.ox.ac.uk", "mix": "20% wet lab, 80% dry lab", "description": "Our research involves the generation and analysis of bulk, multi-modal single-cell and spatial genomics datasets from human patients. For data generation, the group has the 10x Chromium and BD rhapsody single cell and the nanoString GeoMx spatial transcriptomics platforms. We are expecting delivery of the higher-resolution nanoString CosMx spatial transcriptomics platform later this year.\nCurrently, we have opportunities for computational projects in two areas:\n1) Network analysis of cellular pathotypes in inflammatory bowel disease. In our initial studies we applied weighted gene co-expression (WGCNA) to bulk RNA-seq profiles from ~100 patients. In this project, you will use more advanced kernel clustering, modularity optimisation and random walk-based algorithms to perform network module identification using a larger RNA-seq dataset from ~1000 patients (IBD Plexus cohorts). The initial goal of the project will be to compare the ability of the algorithms to recover known heterogeneity. The best approach(es) will then be taken forward for novel pathotype discovery. The project will also involve the analysis of single-cell datasets for cell type deconvolution. Discovered pathotypes will be further investigated using spatial transcriptomics and experimental approaches in mouse models. The project will be performed in close collaboration with the group of Professor Fiona Powrie and the wider MRC project team.\n2) Investigating the role of HLA-B*27 in ankylosing spondylitis. Ankylosing spondylitis is a common form of arthritis for which the cellular causes remain mysterious despite a remarkably strong genetic association with HLA-B*27 (odds ratio=131). Projects in this area will use single-cell and spatial genomics data from human patients to evaluate three competing biological hypotheses of how HLA-B*27 might act to initiate disease (the arthritogenic peptide hypothesis, the ER stress hypothesis and the free-heavy chain hypothesis). This work will involve the modelling of cell-cell interactions and the integration of spatial and genetic data. It will be co-supervised by Prof Paul Bowness.", "training": "You will learn how to use network approaches to model large transcriptomics dataset and to analyse and interpret single-cell genomics data. This will involve writing bioinformatics pipelines in Python, performing statistical analysis and data visualisation in R and the use of high-performance compute clusters. You will have the opportunity to work closely with wet-lab and clinical colleagues.", "background_reading": "-- IL-1-driven stromal-neutrophil interaction in deep ulcers defines a pathotype of therapy non-responsive inflammatory bowel disease. Friedrich M. et al. Nature Medicine. 2021\n-- Deconvolution of monocyte responses in inflammatory bowel disease reveals an IL-1 cytokine network that regulates IL-23 in genetic and acquired IL-10 resistance. Aschenbrenner D et al. Gut, 2020.\n-- IRF5 guides monocytes toward an inflammatory CD11c+ macrophage phenotype and promotes intestinal inflammation. Alastair L Corbin, et. al. Science Immunology, 2020.\n-- Distinct fibroblast subsets drive inflammation and damage in arthritis. Adam P. Croft, et. al. Nature, 2019\n-- Progress in our understanding of the pathogenesis of ankylosing spondylitis. Simone D, Al Mossawi and Bowness P. Rheumatology (Oxford), 2018" },{ "title": "How do histone post-translational modifications affect cellular metabolism in cancer?", "PI": "Peter Sarkies", "email": "peter.sarkies@bioch.ox.ac.uk", "mix": "80% dry lab, 20% wet lab", "description": "Histone post-translational modifications are ubiquitous in eukaryotic genomes. Different histone modifications show strong associations with specific transcriptional states, and in some cases changes in histone modifications can bring about changes in transcription. However, modifications of histones are also enzymatic processes that consume core metabolites, therefore have the potential to affect cellular metabolic regulation. The effects of the enzymatic processes of histone modification on metabolism are still largely unknown. In previous work, we discovered using computational analysis across cancer cells and healthy tissues that histone methylation on several different residues on the histone tail, affects S-adenosyl-methionine (SAM) metabolism through the use of SAM as a cofactor for the methylation reaction. Importantly, changes in histone methylation levels affect SAM metabolism without affecting transcription, suggesting that this is an important function of histone methylation beyond gene regulation (Perez and Sarkies, BiorXiv 2023, see reference list). Remarkably, we also showed that the tumour suppressor protein Rb is responsible for controlling total histone methylation levels, affecting many types of histone methylation simultaneously. In this project we aim to follow up these results in two directions. First, using analysis of data across cancers, and particularly focussing on single cell sequencing data, we will investigate further links between Rb, histone methylation, and SAM metabolism to explore the functional consequences of this regulatory axis and how this might contribute to the cancer predisposition caused by loss of the Rb protein. Second, we will extend our computational approach to further histone modifications, in particular histone acetylation, to identify further links between histone modifications and metabolism. Finally, we will test whether perturbing histone modifications experimentally can lead to changes in metabolism directly in human cells.", "training": "The project will be predominantly computational, using correlation analysis to identify links between expression of histone modifying enzymes, the genome-wide levels of the modifications themselves, metabolite levels, and metabolic pathway activity. We will use machine learning to identify the strongest links between metabolism and histone modifications. Moreover, we will test whether the associations apply within individual cells and how cell type affects the relationships using analysis of single cell RNA sequencing data from a variety of different cancers. This will provide training in data science, machine learning/AI methods for computational biology and sophisticated statistical methods to identify causative relationships from the data. Wet lab experiments may be performed towards the end of the project and will involve cell culture and CRISPR-cas9 genome editing of human cells, however, these will be optional depending on the interest of the student.", "background_reading": "Please include references as desired. Suggested format:\n-- Perez, M and Sarkies, P https://www.biorxiv.org/content/10.1101/2023.04.22.537846v1\n-- Findlay, L What is Cancer Metabolism? 2023 Cell, 186: 1670-1688 https://doi.org/10.1016/j.cell.2023.01.038\n-- Morgan, M and Shilatifard, A Reevalulating the role of histone modifying enzymes and their associated chromatin modifications in transcription (2020) Nature Genetics, 52, 1271-1281. https://doi.org/10.1038/s41588-020-00736-4" },{ "title": "Molecular archaeoparasitology approaches to interrogate past populations", "PI": "Professor Adrian Smith, Professor Greger Larson and Dr Patrik Flammer", "email": "adrian.smith@zoo.ox.ac.uk", "mix": "mixed", "description": "Humans can be infected by a wide range of intestinal parasites including helminths and protozoa and many of these parasites are important in large parts of the world, particularly low and middle income countries. Many of these parasites were much more widespread in past populations. Indeed, our recent work demonstrates that a range of helminths were highly prevalent in Medieval Europe (Flammer et al., 2020). As well as being important for human health the diverse life histories of these parasites offer much more information on other aspects of life including sanitation, hygiene, diet and culinary practices (Flammer et al., 2018). The transmission stages of enteric parasites are incredibly robust preserving ancient DNA. Using a combination of parasitological and aDNA approaches we can interrogate many aspects of life in past populations and can also impact on the approaches used in modern control programmes.\nThe project: Molecular archeaoparasitology is an emerging area of research that combines both parasitological and aDNA methods to interrogate infection biology and life of past populations. This project will continue to develop these powerful approaches exploring how pathogen genetics cab be used to identify links between human populations. We anticipate that the project will extend the aDNA approaches including developing baiting based aDNA technologies as well as broadening the array of target parasites to include protozoa as well as helminths.", "training": "The DPhil will support training in a wide range of parasitological and molecular approaches, in particular those employing ancient DNA. The project will also involve extensive bioinformatics training and will provide an appreciation of how we can use aDNA approaches to understand the past and influence the present.", "background_reading": "-- Flammer PG, Dellicour S, Preston SG, Rieger D, Warren S, Tan CKW, Nicholson R, Přichystalová R, Bleicher N, Wahl J, Faria NR, Pybus OG, Pollard M and Smith AL. (2018) Molecular archaeoparasitology identifies cultural changes in the Medieval Hanseatic trading centre of Lübeck. Proc. R. Soc. B.285:20180991. http://doi.org/10.1098/rspb.2018.0991\n-- Flammer PG, Ryan H, Preston SG, Warren S, Přichystalová R, Rainer Weiss, Valerie Palmowski, Sonja Boschert, Katarina Fellgiebel, Isabelle Jasch-Boley, Madita-Sophie Kairies, Ernst Rümmele, Dirk Rieger, Beate Schmid, Ben Reeves, Rebecca Nicholson, Louise Loe, Christopher Guy, Tony Waldron, Jiří Macháček, Joachim Wahl, Mark Pollard, Greger Larson and Adrian L. Smith (2020) Epidemiological insights from a large-scale investigation of intestinal helminths in Medieval Europe. PLOS Neglected Tropical Diseases 14(8): e0008600. https://doi.org/10.1371/journal.pntd.0008600" },{ "title": "Identifying novel rare disease genes and variants from clinical whole genome sequencing data", "PI": "Prof. Jenny Taylor", "email": "jenny.taylor@well.ox.ac.uk", "mix": "100% dry lab", "description": "Rare genetic diseases are collectively common, even if individually rare, since they affect 1:17 people. Reaching a genetic diagnosis for patients and their families not only resolves what is very often a lengthy diagnostic odyssey, sometimes lasting decades, but is often important in selecting the appropriate clinical management and treatment. Historically, genetic testing encompassed targeted or exome sequencing, but the advent of affordable whole genome sequencing (WGS) has revolutionised the field and enabled genetic diagnostics to become mainstream across a range of medical specialties.\nThe Genomics England 100,000 Genomes Project has sequenced the genomes from >75,000 rare disease patients and their family members and the WGS data are available to researchers in a secure Research Environment (RE). The diagnostic yield for 100,000 Genomes Project to date, based on pilot data, is 25%, which means there is a wealth of data still to interrogate.\nThis project will investigate the WGS data in 100KGP to identify novel disease genes and variants. A particular focus will be on investigating structural and splice site variants using algorithms and pipelines developed by our group, including SVRare and AltSplicePredictor and GREEN-VARAN for structural, splicing and regulatory variants, respectively, as well as those available in the RE (eg SpliceAI). The focus will be on musculoskeletal and neurological disorders in 100KGP in first instance but the analysis approaches will be relevant to all rare diseases.", "training": "This project is designed to give an introduction to analysis of whole genome sequencing data using both well-established pipelines and bespoke tools developed in our lab. Training will be with post- doc geneticists and bioinformaticians with extensive experience of these analyses. Findings can be validated using orthogonal approaches such as long read sequencing for structural variants (PacBIo, Nanopore, BioNano), RNA sequencing for splicing variants and other experimental assays for further functional validation. The group collaborates closely with a network of clinicians so there is an opportunity to understand the clinical consequences of the variants identified as well as identifying novel disease genes and variants.", "background_reading": "1) Taylor JC et al Factors influencing success of clinical genome sequencing across a broad spectrum of disorders. Nat Genet. 2015;47(7):717-726. doi: 10.1038/ng.3304. PMID: 25985138;\n2) 100,000 Genomes Project Pilot Investigators et al. 100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care - Preliminary Report. N Engl J Med. 2021;385(20):1868-1880. doi: 10.1056/NEJMoa2035790. PMID: 34758253;\n3) Lin YC et al; SCUBE3 loss-of-function causes a recognizable recessive developmental disorder due to defective bone morphogenetic protein signaling. Am J Hum Genet. 2021 Jan 7;108(1):115-133. doi: 10.1016/j.ajhg.2020.11.015. PMID: 33308444;\n4) Giacopuzzi E, Popitsch N, Taylor JC. GREEN-DB: a framework for the annotation and prioritization of non-coding regulatory variants from whole-genome sequencing data. Nucleic Acids Res. 2022;50(5):2522-2535. doi: 10.1093/nar/gkac130. PMID: 35234913;\n5) Yu et al: SVRare: discovering disease-causing structural variants in the 100K Genomes Project MedRxiv 2021; https://www.medrxiv.org/content/10.1101/2021.10.15.21265069v1" },{ "title": "Genetics of type 1 diabetes", "PI": "Dr Dan Crouch & Prof John Todd", "email": "PA: ailsa@well.ox.ac.uk", "mix": "100% dry", "description": "Using latest genetic information for T1D to study geographical variation across the UK and to use Mendelian Randomisation (MR) to determine causal factors.", "training": "Genetics, coding, MR", "background_reading": "Disentangling the direct and indirect effects of childhood adiposity on type 1 diabetes and immune-associated diseases: a multivariable Mendelian randomization study\nTom G Richardson, Daniel J M Crouch, Grace M Power, Fernanda Morales Berstein, Emma Hazelwood, Si Fang, Yoonsu Cho, Jamie R J Inshaw, Catherine C Robertson, Carlo Sidore, Francesco Cucca, Steven S Rich, John A Todd, George Davey Smith\nmedRxiv 2021.04.19.21255222; doi: https://doi.org/10.1101/2021.04.19.21255222" },{ "title": "Single-cell dynamics of the immune system and its evolutionary partner, the microbiome", "PI": "Dr Ricardo Ferreria, Dr Dominik Dominik Trzupek, Dr Marcin Pekalski & Prof John Todd", "email": "PA: ailsa@well.ox.ac.uk", "mix": "50% dry", "description": "Deep dive into the immune system using latest methods", "training": "Genetics, single-cell omics, immunology, T cell receptor and metagenomic sequencing", "background_reading": "Disentangling the direct and indirect effects of childhood adiposity on type 1 diabetes and immune-associated diseases: a multivariable Mendelian randomization study\nTom G Richardson, Daniel J M Crouch, Grace M Power, Fernanda Morales Berstein, Emma Hazelwood, Si Fang, Yoonsu Cho, Jamie R J Inshaw, Catherine C Robertson, Carlo Sidore, Francesco Cucca, Steven S Rich, John A Todd, George Davey Smith\nmedRxiv 2021.04.19.21255222; doi: https://doi.org/10.1101/2021.04.19.21255222\nPeripheral tolerance to insulin is encoded by mimicry in the microbiome\nArcadio Rubio García, Athina Paterou, Mercede Lee, Hubert Sławiński, Linda S. Wicker, John A. Todd, Marcin Ł. Pękalski\nbioRxiv 2019.12.18.881433; doi: https://doi.org/10.1101/2019.12.18.881433\nSingle-cell multi-omics analysis reveals IFN-driven alterations in T lymphocytes and natural killer cells in systemic lupus erythematosus\nDominik Trzupek, Mercede Lee, Fiona Hamey, Linda S. Wicker, John A. Todd, Ricardo C. Ferreira\nmedRxiv 2021.04.27.21256106; doi: https://doi.org/10.1101/2021.04.27.21256106" },{ "title": "The role of Tau in pancreatic beta cells", "PI": "Dr Irina Stefana & Prof John Todd", "email": "PA: ailsa@well.ox.ac.uk", "mix": "80% wet", "description": "Tau is expressed in neurons but also in the insulin-producing beta cells. It can be expressed in the nucleus and is under cell cycle control.", "training": "Cell biology, microscopy", "background_reading": "" },{ "title": "How do cells respond to mechanical forces?", "PI": "Professor Ellie Tzima, Professor John Reader, Dr Vedanta Mehta", "email": "ellie@well.ox.ac.uk", "mix": "80% wet/ 20% dry", "description": "Cells are constantly exposed to forces that dictate their function. In blood vessels, endothelial cells that line arteries are exposed to forces due to flowing blood; these forces are critical determinants of their physiologial functions but can also instigate development of diseases, such as atherosclerotic plaques and cardiovascular disease. The mechanisms by which cells sense and respond to mechanical forces is a mystery in biology and has implications in a number of diseases, including cancer, immune cell dysfunctions and neurobiology. We have bespoke in vitro systems that allow us to apply different forces on cultured cells and complement these studies with transgenic mouse models in vivo. We generate large datasets from RNA sequencing and proteomics with the ultimate goal of understanding at the genetic, molecular and physiological level how cells respond to forces.", "training": "The DPhil will be based at the Wellcome Centre for Human Genetics. The student will get to experience and learn a wide array of in vitro and in vivo techniques. These include cell culture, transfections, western blotting, co-immunoprecipitation, qPCR, dissection of animal tissue, staining, confocal microscopy and analysis of RNA sequencing and mass spec proteomic data. Training in scientific writing and presentation skills will be provided, and writing of reviews and presentation at conferences will be strongly encouraged.", "background_reading": "Please include references as desired. Suggested format:\n-- 1 Mehta, V. et al. Mechanical forces regulate endothelial-to-mesenchymal transition and atherosclerosis via an Alk5-Shc mechanotransduction pathway. Sci Adv 7, doi:10.1126/sciadv.abg5060 (2021).\n-- 2 Sweet, D. T. et al. Endothelial Shc regulates arteriogenesis through dual control of arterial specification and inflammation via the notch and nuclear factor-kappa-light-chain-enhancer of activated B-cell pathways. Circ Res 113, 32-39, doi:10.1161/CIRCRESAHA.113.301407 (2013).\n-- 3 Liu, Y., Sweet, D. T., Irani-Tehrani, M., Maeda, N. & Tzima, E. Shc coordinates signals from intercellular junctions and integrins to regulate flow-induced inflammation. J Cell Biol 182, 185-196, doi:10.1083/jcb.200709176 (2008).\n--" },{ "title": "Transcriptional blueprint of neutrophil development during homeostasis and disease", "PI": "Professor Irina Udalova, Dr Abhinandan Devaprasad, Prof Helen Byrne", "email": "Irina.udalova@kennedy.ox.ac.uk", "mix": "30% wet lab, 70% dry lab", "description": "Neutrophils represent a major arm of the innate immune defence system, with a long- held view of them being transcriptionally inactive, fast responders, mobilised in response to microbial and tissue insults. Recent developments in the field have changed this perception and firmly positioned neutrophils as transcriptionally active cells with the ability to adapt their transcriptional program. Our recent findings demonstrate that, despite limited residence times in tissues, neutrophils can tailor their properties to support organ homeostasis and mount tissue specific and transcriptionally regulated inflammatory response1,2. Importantly, in inflammation neutrophils are presented as functionally, morphologically, and behaviourally heterogeneous cells in circulation and tissue3.\nimage://data/images/udalova_irina_05.png\nFigure: Model of transcriptional regulation of neutrophils during inflammation. Adopted from Chevre and Soehnlein, Nature Immunology News and Views on Ref 2, 2021. In the process of differentiation in bone marrow, lineage-determining transcriptional factors, including RUNX1, KLF6, CEBPE, and PU.1, are highly expressed and ensure gene expression programmes that promote proper neutrophil maturation. During the mobilization from the bone marrow into the blood, RFX2, RELB, IRF5 and JUNB become upregulated and transcriptionally accessible to support neutrophil cell survival and establish their effector function repertoire, whereas RUNX1 and KLF6 expression are silenced. Upon inflammation, circulating neutrophils migrate into the inflammatory sites, where they are exposed to inflammation-derived signals and become activated. Neutrophil activation leads to the activation of TFs, including RELB, IRF5 and JUNB, and subsequent TF binding to already accessible binding sites, thereby resulting in diverse TFs genomic occupancy and distinct transcriptional outputs (see Ref 2).\nThe goal of this project is to reveal transcriptional circuits that control neutrophil differentiation and function in a signal-driven microenvironment. We aim to discover what transcriptional regulators control stage-specific expression of (1) cytoskeletal genes, that establish structural function and transcriptional regulation in the cell nucleus and distinct morphological features; (2) leukocyte migration and cell-cell interaction genes, responsible for distinct behaviour of neutrophils in the vasculature and tissue and (3) inflammatory response genes. This will be done by using a combination of cutting-edge imaging, genomic and spatial single cell transcriptomic approaches, as well as advanced immunological techniques2. Mathematical modelling of transcriptional circuits will be applied to understand the interaction between neutrophil development and activation. The project will also explore the spatial interactions of neutrophils with other immune cells in tissue using spatial transcriptomic and multimodal imaging data4. It will benefit from the already generated by us multiple genomic datasets and unique tools, such as genetically modified in vitro and in vivo models based on the recently discovered new key regulators2.\nThe outcomes of this study are expected to progress fundamental biology of neutrophils and increase our understanding of neutrophil subsets in disease. This will ultimately lead to the development of a new class of therapeutic strategies, based on selective modulation of neutrophil biology, for therapeutic interventions in inflammatory disorders5.", "training": "The Kennedy Institute is a world-renowned research centre and is housed in a state-of-the-art research facility. Training will be provided in a wide range of functional genomics approaches (e.g. RNA-Seq, ATAC-Seq, ChIP-Seq etc), immunological (cell isolation, tissue culture, FACS), and imaging (immunofluorescence on tissue sections) approaches, as well as cutting edge single cell platforms (10x, Nanostring GeoMx, Nanostring CosMx) and computational pipelines. Recently developed novel in vivo models of inflammatory diseases will be extensively used and new models will be generated. A core curriculum of lectures will be taken in the first term to provide a strong foundation across a broad range of subjects, including musculoskeletal biology, inflammation, epigenetics, translational immunology and data analysis. The student will attend weekly seminars within the department and those relevant in the wider University. They will present their research regularly to the department and the Genomics of Inflammation group, and at the Computational Genomics Forum. They will also attend external conferences at which they will present their research to a global audience. The student will also have the opportunity to work closely with members of the Wolfson Centre for Mathematical Biology at the Mathematical Institute, University of Oxford, and to further broaden their theoretical knowledge by attending lecture courses in mathematical biology, statistics and related subjects.", "background_reading": "(1) Ballesteros I, Rubio-Ponce A, Genua M, Lusito E, Kwok I, Fernández-Calvo G, Khoyratty TE, van Grinsven E, González-Hernández S, Nicolás-Ávila JÁ, Vicanolo T, Maccataio A, Benguría A, Li JL, Adrover JM, Aroca-Crevillen A, Quintana JA, Martín-Salamanca S, Mayo F, Ascher S, Barbiera G, Soehnlein O, Gunzer M, Ginhoux F, Sánchez-Cabo F, Nistal-Villán E, Schulz C, Dopazo A, Reinhardt C, Udalova IA, Ng LG, Ostuni R, Hidalgo A. Co-option of Neutrophil Fates by Tissue Environments. Cell. 2020 Nov 25;183(5):1282-1297.e18.\n(2) Khoyratty T*, Ai Z*, Ballesteros I, Mathie S, Eames HL, Martín-Salamanca S, Wang L, Hemmings A, Willemsen N, von Werz V, Zehrer A, Walzog B, van Grinsven E, Hidalgo A, Udalova IA. Distinct transcription factor networks control neutrophil-driven inflammation. Nature Immunology, 2021 Sep;22(9):1093-1106.\n(3) Wang L, Luqmani R, Udalova IA. The role of neutrophils in rheumatic disease-associated vascular inflammation. Nature Reviews Rheumatology. 2022 Mar;18(3):158-170.\n(4) O Vipond, JA Bull, PS Macklin, U Tillman, CW Pugh, HM Byrne, HA Harrington (2021). Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumours. PNAS 118 (41): e2102166118.\n(5) Devaprasad A, Radstake TRDJ, Pandit A. Integration of Immunome With Disease-Gene Network Reveals Common Cellular Mechanisms Between IMIDs and Drug Repurposing Strategies. Frontiers in Immunology. 2021 May 24;12:669400." },{ "title": "Granulopoiesis: chromatin topology and associated functions", "PI": "Professor Irina Udalova, Dr Ananda Mukherjee, Prof Jim Hughes", "email": "Irina.udalova@kennedy.ox.ac.uk", "mix": "50% wet lab, 50% dry lab", "description": "Granulopoiesis, the generation of new neutrophilic granulocytes, is crucial to health. During healthy neutrophil development its nucleus goes through dramatic morphological changes, from a simple round nucleus to a multi-segmented, lobulated nucleus. Indeed, the nuclear morphology is the main parameter used by pathologists to define the states of human neutrophil development. However, the molecular mechanisms controlling nuclear segmentation and the functional impacts of it are not understood.\nChromatin architecture is what connects global nuclear shape and local regulation of gene expression. Chromatin is organized into distinct compartments within the nucleus and further segregated into spatially distinct regions. While neutrophils develop from progenitor cells to their multi-lobed form, long-range interactions are induced, leading to the chromatin contraction that may facilitate the folding of the neutrophil genome into the confined geometry of a toroid and segmented nucleus [1].\nimage://data/images/udalova_irina_06.png\nOur recent work and results of others clearly demonstrate that neutrophils acquire different functions, such as production of reactive oxygen species, secretion of inflammatory molecules, formation of neutrophil extracellular traps, phagocytosis, bacterial killing etc, at different stages of their differentiation [2,3]. Moreover, we have identified and validated several key transcriptional regulators of neutrophil morphological development and/or functional responses [4]. This indicates that the transcriptional programming of morphological and functional maturation may be partially intertwined and are likely to be connected via changes in chromatin architecture.\nHere we will apply cutting edge chromatin conformation assays and gene expression analysis to correlate dynamic changes in the chromatin organization of the neutrophil genome to changes in gene expression and acquisition of specific functions during neutrophil maturation. Specific objectives:\n1. To capture global chromatin topology changes during neutrophil differentiation using various cutting edge chromatin conformation capture (3-C) analyses of neutrophils throughout the differentiation trajectory [5]. Established computational pipelines would be used for visualization of contact maps, chromatin compartment analysis, chromatin compaction analysis, and finding differentially enriched topologically associating domains. The experimental setup would be used to assess changes to chromatin topology when key transcription factors for neutrophil development are depleted.\n2. To identify regions of local ‘topology dependent’ open and closed chromatin using ATAC-seq analysis to classify regions that show chromatinization changes (open/closed) in regions that gain/loss chromatin interactions. Single-cell ATAC-seq would be used for accessing heterogeneity in local chromatin conformation.\n3. To identify chromatin topology-dependent gene expression alterations using RNA-seq analysis to pin point genes that show topology and chromatin changes in their regulatory promoter/enhancer regions along the differentiation trajectory. The identified gene list will be checked for motifs for the previously identified key transcriptional regulators and validated in neutrophils with specific knock-out of these factors.\nThe outcomes of this study are expected to unravel the regulation and functional consequences of the fundamental biological process, such as segmentation of neutrophil nucleus during the differentiation. This will lead to setting up a framework for further analysis of selective perturbations to this process during immunopathologies.", "training": "The Kennedy Institute is a world-renowned research centre and is housed in a state-of-the-art research facility. Training will be provided in a wide range of functional genomics approaches (e.g. RNA-Seq, ATAC-Seq, ChIP-Seq etc), immunological (cell isolation, tissue culture, FACS), and imaging (immunofluorescence on tissue sections) approaches, as well as cutting edge single cell platforms (10x, Nanostring GeoMx, Nanostring CosMx) and computational pipelines. Recently developed novel in vivo models of inflammatory diseases will be extensively used and new models will be generated. A core curriculum of lectures will be taken in the first term to provide a strong foundation across a broad range of subjects, including musculoskeletal biology, inflammation, epigenetics, translational immunology and data analysis. The student will attend weekly seminars within the department and those relevant in the wider University. They will present their research regularly to the department and the Genomics of Inflammation group, and at the Computational Genomics Forum. They will also attend external conferences at which they will present their research to a global audience. The student will also have the opportunity to work closely with members of the Genome Biology laboratory, MRC Weatherall Institute of Molecular Medicine, University of Oxford, and to further broaden their experimental expertise and theoretical knowledge of the chromatin organisation in health and disease.", "background_reading": "(1) Zhu, Y. … Murre C. Comprehensive characterization of neutrophil genome topology. Genes Dev 2017 Jan 15;31(2):141-153\n(2) Ballesteros I, … Udalova IA, Ng LG, Ostuni R, Hidalgo A. Co-option of Neutrophil Fates by Tissue Environments. Cell. 2020 Nov 25;183(5):1282-1297.e18.\n(3) Wang L, Luqmani R, Udalova IA. The role of neutrophils in rheumatic disease-associated vascular inflammation. Nature Reviews Rheumatology. 2022 Mar;18(3):158-170.\n(4) Khoyratty T*, Ai Z*, …, Udalova IA. Distinct transcription factor networks control neutrophil-driven inflammation. Nature Immunology, 2021 Sep;22(9):1093-1106.\n(5) Oudelaar AM, …, Hughes JR. Dynamics of the 4D genome during in vivo lineage specification and differentiation. Nature Communications. 2020 Jun 1;11(1):2722." },{ "title": "Developing analysis methods to detect human-pathogen interactions", "PI": "Associate Professor Daniel Wilson", "email": "daniel.wilson@bdi.ox.ac.uk", "mix": "100% dry lab", "description": "Infection is fundamentally an interaction between the human and pathogen. Consequently there is long-standing interest in the role of human-pathogen genetic interactions in infection traits, including disease severity and clinical outcome. While the biological questions are readily framed, logistic and analytic challenges have held back progress in this area. The chief difficulties are genotyping and whole genome sequencing large cohorts of humans and pathogens, and devising sensitive analyses that do not generate large numbers of false positives. Research groups around the world are now pursuing such cohorts in earnest. The focus of this project therefore is on the development and application of tools for performing trillions of tests of association between the millions of human genetic variants and millions of pathogen genetic variants, while controlling the false positive rate without loss of statistical power. This is critical for the emerging field of human-pathogen genome-wide association studies. Focusing on published data or new data generated in-house (subject to the project's progress by the commencement of this internship), we will develop and apply the harmonic mean p-value method to this problem. Depending on the student, there will be opportunity to focus more on the applied or theoretical side of the project, and to apply the approach to related problems such as epistasis. Subject to satisfactory progress, there may be opportunity to publish the results of the internship.", "training": "The students will learn about genome-wide association studies and statistical genetics with the help of the supervisor and other group members. Students are welcome to attend other training courses at the university or elsewhere.", "background_reading": "-- The COVID-19 Host Genetics Initiative (2021)\nMapping the human genetic architecture of COVID-19\nNature doi:10.1038/s41586-021-03767-x (abstract pdf)\n-- D. J. Wilson (2019)\nThe harmonic mean p-value for combining dependent tests.\nProceedings of the National Academy of Sciences USA 116: 1195-1200. (abstract pdf)\n-- Young, B. C., et al. (2019)\nPanton-Valentine leukocidin is the key determinant of Staphylococcus aureus pyomyositis in a bacterial genome-wide association study.\neLife 8: e42486 (abstract preprint pdf)\nSee www.danielwilson.me.uk for further group information." },{ "title": "Does endometriosis lead to an increased cardiovascular disease risk and (how) is this link genetically/biologically mediated?", "PI": "Dr Nilufer Rahmioglu, Prof Krina Zondervan, and others", "email": "krinaz@well.ox.ac.uk", "mix": "100% dry lab", "description": "Endometriosis is a common chronic inflammatory disease, causing pelvic pain and reduced fertility in an estimated 5‐10% of pre‐menopausal women (190 million worldwide). It features the presence of tissue that resembles endometrium (the lining of the uterus) outside the uterus, mainly on pelvic organs, but causes remain largely unknown. Diagnosis is often delayed for years as it requires surgery, while treatments are limited to surgery and/or hormonal drugs with many side effects. The chronic inflammatory pelvic environment present in women with endometriosis leads to the question whether the disease is associated with more systemic inflammation-association morbidity. Limited data has emerged that there is an elevated post-menopausal risk of cardiovascular disease among women diagnosed with endometriosis pre-menopausally. However, confirmation and specification of this association is required, along with investigation of the biological mechanisms through which the association may act. This project will leverage data from the UK Biobank, which contains data from more than 8,000 women diagnosed with endometriosis to investigate longitudinal cardiovascular (and potential other inflammatory) outcomes and the genetic vs. causal basis for associations. The analysis will include integrated analysis of clinical phenotypic, (gen)omic, inflammatory biomarker and other available data, as well as the analysis of publicly available data resources.", "training": "Training will involve genetic epidemiological research methods including (bivariate) GWAS and LD score regression, Mendelian randomisation and SMR, eQTL and integrated omics analyses, and functional pathway analyses. Opportunities to work with AI/machine learning methodology in analysing multidimensional data and link with other groups working in this methodological space. In addition to a base in WCHG, students will be part of the Oxford Endometriosis CaRe centre, that focuses on the integration of clinical diagnosis, care and treatment of the disease with clinical and basic research. The group benefits from a strong network of national and international collaborators in the fields of endometriosis, statistical genetics, genomics, bioinformatics, and functional biology. Students will be strongly encouraged to publish their work, participate and lead in outreach activities, present at international conferences, attend bi‐weekly group meetings, journal clubs, as well as departmental seminars and training courses.", "background_reading": "-- Zondervan KT, Becker CM, Missmer SA. Endometriosis. N Engl J Med. 2020;382:1244-1256\n-- Gallagher CS, Mäkinen N, Harris HR, Rahmioglu N, [....] Chasman DI, Missmer SA, Zondervan KT*, Morton CC. Genome-wide association and epidemiological analyses reveal common genetic origins between uterine leiomyomata and endometriosis. Nat Commun. 2019; 10: 4857.\n-- Nilufer Rahmioglu, Karina Banasik, […] Piraye Yurttas Beim, Stacey A Missmer, Grant W Montgomery, Andrew P Morris, Krina T Zondervan. Large-scale genome-wide association meta-analysis of endometriosis reveals 13 novel loci and genetically associated comorbidity with other pain conditions. BioRxiv pre-release, Aug 2018. Under review, Jul 2021.\n-- Zondervan KT, Becker CM, Koga K, Missmer SA, Taylor RN, Viganò P. Endometriosis. Nat Rev Dis Primers 2018 Jul 19;4(1):9" },{ "title": "Early detection of liver cancer using cell-free DNA and other measurements", "PI": "Benjamin Schuster-Böckler, Ellie Barnes", "email": "benjamin.schuster-boeckler@ludwig.ox.ac.uk", "mix": "100% dry lab", "description": "Liver cancer is common amongst patients who have been diagnosed with liver cirrhosis, but so far it is not possible to reliably predict which cirrhosis patient will develop cancer. This makes it costly and difficult to screen the large at-risk population.\nMy group is participating in a large trial funded by CRUK to address this critical clinical need. There are two cohorts, a retrospective and a prospective cohort. For both groups, a number of measurements are collected at different time-points: ultrasound imaging, protein biomarkers, metabolomics data from the blood, and cell-free DNA methylation.\nIf you choose this project for a rotation, you will first work on the cell-free DNA data. We will try to determine liver-cancer specific features in the methylation patterns and the read-density from cancer and control patients. We will also search for known liver-cancer mutations in the cfDNA.\nIf you continue on to a DPhil, the aim will be to fully develop the cell-free DNA analysis into an assay. This will involve developing a robust classifier, quantifying the accuracy and variability of the classifier, estimating the positive predictive value, and determining the minimal set of genomic regions that are informative, in order to reduce the sequencing cost. The next step will be to integrate the predictions from cell-free DNA with the information from the other measurements, to determine whether the accuracy of the test can be further improved by incorporating other data.", "training": "This project would suit a statistically minded person who would like to work on a real-world problem using genomic data. You would learn basic computational genomics techniques and get expose to biomarker discovery and evaluation in the setting of a large collaborative project.", "background_reading": "-- Liu, Y. et al. Bisulfite-free direct detection of 5-methylcytosine and 5-hydroxymethylcytosine at base resolution. Nat Biotech 37, 424-429 (2019).\n-- https://www.oxcode.ox.ac.uk/research-showcase/liver-cancer\n-- Siejka-Zielińska, P. et al. Cell-free DNA TAPS provides multimodal information for early cancer detection. Sci Adv 7, eabh0534 (2021)." },{ "title": "The epigenetic landscape of the human body", "PI": "Benjamin Schuster-Böckler, Chunxiao Song", "email": "benjamin.schuster-boeckler@ludwig.ox.ac.uk", "mix": "100% dry lab", "description": "DNA methylation is an important determinator of cell fate by “switching off” certain genes that are not needed in more differentiated cells. Methylation - and its further modified form of “hydroxymethylation” - also impact on the activity of retroviruses, transposable elements and non-coding RNA, they are involved in DNA repair processes, and might affect a range of other biological processes, some of which are not yet fully understood.\nThe Song lab developed methods to measure methylation and hydroxymethylation using much milder chemistry compared to existing approaches. They now created an atlas of cell- or tissue-type specific methylation and hydroxymethylation from dozens of different sites in the body, as well as various cancer types. This unique resource of nearly 200 whole-genome epigenetic maps can be used to address a whole range of biological questions regarding the function and impact of DNA modifications.\nFor the rotation phase of the project, you would investigate basic covariates in the data, such as the relationship between epigenetic patterns within the same tissue depending on age or gender of the donor, and whether there are tissue independent features that consistently change with age or gender.\nFor the continuation into a DPhil, you could e.g. investigate tissue-dependent methylation and hydroxymethylation patterns inside and outside of regulatory elements, to deepen our understanding of the biological role of these marks. We would also aim to use these maps to explore the relationship between methylation and chromatin structure and develop ways to apply this knowledge for early disease detection, for example using cell-free DNA or other biopsies.", "training": "This project would suit anyone with an interest in genome biology who is keen to perform statistical analysis on large data. You would learn basic computational genomics techniques and gain a lot of knowledge on the role and function of epigenetic regulation in health and disease.\nBackground Reading/References\n-- Greenberg, M. V. C. & Bourc’his, D. The diverse roles of DNA methylation in mammalian development and disease. Nat Rev Mol Cell Biol 20, 590-607 (2019).\n-- Liu, Y. et al. Bisulfite-free direct detection of 5-methylcytosine and 5-hydroxymethylcytosine at base resolution. Nat Biotech 37, 424-429 (2019).\n-- Liu, Y. et al. Subtraction-free and bisulfite-free specific sequencing of 5-methylcytosine and its oxidized derivatives at base resolution. Nat Commun 12, 618 (2021).", "background_reading": "" },{ "title": "Therapeutic genomics: Integrating whole-genome sequencing and functional genomics data to identify loci for genome-targeted therapies in rare disorders", "PI": "Dr. Stephan Sanders", "email": "stephan.sanders@idrm.ox.ac.uk", "mix": "100% dry lab (collaboration with neighbouring labs to develop therapies)", "description": "The past decade has seen tremendous progress in identifying genes underlying rare single-gene disorders, including hundreds of genes that lead to neurodevelopmental delay (NDD, e.g., seizures, cognitive impairment, autism spectrum disorder). The most common mechanism of NDD is the disruption of one of the two copies of a gene (haploinsufficiency), often by germline de novo mutations. For haploinsufficient disorders, upregulating the unaffected copy could act as a therapy, for example, with an antisense oligonucleotide (ASO). By using whole-genome sequencing data of thousands of cases and large-scale bulk and single-cell functional genomic data (RNA, ATAC, ChIP, reporter assays), we aim to identify regulatory processes of genes that can be modified to act as a therapy in specific patients.", "training": "Analysis of whole-genome sequence data to identify and interpret rare genetic variants; analysis of single-cell RNA-seq and ATAC-seq data from the human brain; analysis of RNA-seq data to identify patterns of splicing; genotype-phenotype analysis of rare genetic variants; AI-based methods for identifying noncoding regulatory sequences; participation in a large-scale international collaboration that includes Boston (Harvard), Berkeley (UC Berkeley), and San Francisco (UCSF).", "background_reading": "• Fu et al. 2022. Rare coding variation provides insight into the genetic architecture and phenotypic context of autism. Nature Genetics. PMID: 35982160. https://pubmed.ncbi.nlm.nih.gov/35982160/\n• Jaganathan et al. 2019. Predicting Splicing from Primary Sequence with Deep Learning. Cell. PMID: 30661751. https://pubmed.ncbi.nlm.nih.gov/30661751/\n• An et al. 2018. Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder. Science. PMID: 30545852. https://pubmed.ncbi.nlm.nih.gov/30545852/\n• Sanders et al. 2018. Progress in Understanding and Treating SCN2A-Mediated Disorders. Trends in Neuroscience. PMID: 29691040. https://pubmed.ncbi.nlm.nih.gov/29691040/" }]}