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Applying genomic surveillance to understand Leishmania infantum virulence in Brazil

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  • Full or part time
    Dr D Jeffares
    Prof J Mottram
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Leishmania infantum is a globally-distributed parasite that is transmitted via sand
flies. Leishmania infantum and the related Leishmania donovani cause 20,000-
30,000 deaths per year, mainly in Brazil, India, and Africa. This project will use
cutting edge genome sequencing and population genomics methods to uncover
mechanisms of drug resistance. This knowledge will be applied to rapid genomic
typing of parasites in Brazil, enhancing treatments.
The project will start by describing the complex landscape of Leishmania
populations in Brazil. You will uncover new aspects of the history of the parasite
by analysing L. infantum and L. donovani genome data from hundreds of clinical
isolates from Brazil, India and Israel. This exploration will form the background
‘genetic landscape’ to inform the reminder of the project. You will then learn how
to apply genome wide association studies (GWAS) to detect genetic changes
that affect drug resistance and/or parasite virulence. Tests for evolutionary
change will be used detect new genetic changes that are sweeping through the
population, spreading drug resistance. Genetic changes that are particularly
striking will be examined using CRISPR-Cas9 genome editing, to uncover the
mechanistic details underlying the genetics.
Finally, you will develop methods for extremely rapid genome sequencing that
can be performed on-site in Brazil. You will first develop an application of Oxford
Nanopore Technology to collect genome data from Leishmania infantum isolates
and identify the genetic alterations in the genome. This will allow us to use our
genomic knowledge (from GWAS, CRISPR and selection tests) to characterise
drug resistant or virulent strains while the patient is still in treatment. You will
then apply this rapid ‘genomic diagnostics’ in Brazil. This approach has real
potential to save lives.
The project will be supervised by Daniel Jeffares (University of York), with Kai
Zeng (University of Sheffield, population genetics), and Jeremy Mottram
(University of York, Leishmania biology), with researcher/medical collaborators
in Brazil. The student will learn cutting edge genomic methods, population
genomics, CRISPR-Cas9 genome editing and will spend time in Brazil

developing rapid, on-site genome sequencing.

Funding Notes

Funding: This is a 4 year fully-funded studentship part of the BBSRC White Rose Doctoral Training Partnership in Mechanistic Biology. The studentship covers: (i) a tax-free stipend at the standard Research Council rate (around £15,000 per year), (ii) tuition fees at UK/EU rate, (iii) research consumables and training necessary for the project.

Entry requirements: At least an upper second class honours degree, or equivalent in any biological, chemical, and/or physical science. Students with mathematical backgrounds who are interested in using their skills in addressing biological questions are also welcome to apply.


Eligibility: The studentships are available to UK and EU students who meet the UK residency requirements. Students from EU countries who do not meet the residency requirements may still be eligible for a fees-only award. Further information about eligibility for Research Council UK funding

Shortlisting: Applicants will be notified if they have been selected for interview in the week commencing on Monday 28 January 2019.

Interviews: Shortlisted applicants will be invited for an interview to take place in the Department of Biology at the University of York on Wednesday 6 and Thursday 7 February 2019. Prior to the interview candidates will be asked to give a 5 minute presentation on a research project carried out by them.

Related Subjects

How good is research at University of York in Biological Sciences?

FTE Category A staff submitted: 44.37

Research output data provided by the Research Excellence Framework (REF)

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