Male infertility is on the rise. While the genetics and molecular mechanisms underlying the phenomenon remain poorly understood, mutations in >75 human protein-coding genes have already been confidently linked to male infertility (Oud et al, 2019). The advent of widespread clinical sequencing provides tremendous potential for improving our ability to identify and predict infertility-causing mutations. This would reduce the need for invasive and extensive experimental testing, and could also help in the identification of protein and enzyme targets that control sperm formation and motility, which could be developed as contraception targets.
While many computational approaches have been developed to identify damaging mutations from sequence, they are still extremely limited in their accuracy and utility: they all result in many false positives and false negatives, and fail to take into consideration inheritance and the fact that different mutations in the same gene often give rise to widely varying phenotypes. Ongoing work from the Marsh and Welburn labs has shown how detailed investigations into individual proteins that incorporate structural, functional, evolutionary and mechanistic information  can vastly outperform generic computational phenotype predictors that have been designed to work on all genes.
Part 1: Computational analysis and prediction of mutations associated with male infertility
The student will first exhaustively search the literature and mutation databases to compile functional and phenotypic data for as many mutations as possible. Next, using methods employed extensively in the Marsh lab, they will analyze mutations at the level of protein sequence, structure, mechanism and evolution, looking for patterns that can distinguish mutations on the basis of phenotype. They will also assess the performance of existing phenotype predictors. This will identify mutations that are poorly predicted and guide the choice of mutations to study experimentally. The student will establish approaches for the best possible prediction of mutations for each gene, either using existing predictors in combination with gene-specific guidelines, or by developing new gene-specific predictors. They will then apply these approaches to mutations observed in the human population (e.g. gnomAD, UK Biobank) in order to identify new candidate mutations for further experimental characterization.
Training outcome: This offers the student the opportunity to learn and develop computational methodology in bioinformatics, protein modelling and machine learning
Part 2: Elucidating the molecular mechanisms underlying infertility-associated mutations in cytoskeletal proteins.
A number of infertility-associated proteins are known to form cytoskeletal complexes, such as the dodecameric katanin, and proteins regulating flagella and cilia . These are proteins for which there are excellent reagents and expertise developed by the Welburn group. The student will dissect the underlying molecular mechanism of known pathogenic and candidate mutants, identified from the above computational analyses, using in vitro reconstitution assays and cell biology to probe their function in a cellular context. They will also have the opportunity to characterize new candidates mechanistically. The results of these experiments will then inform the computational modelling, and lead to even further improved prediction of novel variants.
Training outcome: This offers the student the opportunity to learn state-of-the art biochemical, cell and molecular biology approaches
Part 3: Identification of candidate inhibitors of male infertility-associated proteins
For the final part of the project, the student will use in silico screening approaches used extensively in the Marsh lab (Abrusan and Marsh, 2019) to identify putative small-molecule inhibitors of male infertility-associated proteins, with the idea that these could represent ideal candidates for future contraceptives. The student will then select candidate small molecules for experimental validation using the same experimental systems in which they have already acquired expertise.
Training outcome: This offers the student the opportunity to learn in silico and in vitro drug screening methodologies
This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.
All applications should be made via the University of Edinburgh, irrespective of project location. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow. http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919
Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.
For more information about Precision Medicine visit: http://www.ed.ac.uk/usher/precision-medicine
 Oud, M.S., L. Volozonoka, R.M. Smits, L.E. Vissers, L. Ramos, and J.A. Veltman. 2019. A systematic review and standardized clinical validity assessment of male infertility genes. Human Reprod. 34:932-941
 Bergendahl, L.T., L. Gerasimavicius, J. Miles, L. Macdonald, J.N. Wells, J.P.I. Welburn, and J.A. Marsh. 2019. The role of protein complexes in human genetic disease. Protein Sci. 28:1400-1411.
 Smith, L.B., L. Milne, N. Nelson, S. Eddie, P. Brown, N. Atanassova, M.K. O'Bryan, L. O'Donnell, D. Rhodes, S. Wells, D. Napper, P. Nolan, Z. Lalanne, M. Cheeseman, and J. Peters. 2012. KATNAL1 regulation of sertoli cell microtubule dynamics is essential for spermiogenesis and male fertility. PLoS Genet. 8:e1002697.
 Abrusan, G. and J. Marsh. 2019. Ligands and Receptors with Broad Binding Capabilities Have Common Structural Characteristics: An Antibiotic Design Perspective. J Med Chem. doi: 10.1021/acs.jmedchem.9b00220