Dr Sandra Beleza

Supervisor Details

Contact Details

Genetics and Genome Biology, University of Leicester

Scientific Background

Research Interests

Dr Beleza’s laboratory uses genomic tools, population genetics and statistical genetics to address questions in human and microbial evolutionary genetics.

The group are focused on the analysis of genetic variation and population history of African and African-descended populations, and in elucidating the genetic architecture and evolution of adaptive and health-related traits in these populations. Some of these traits include phenotypes with anthropological relevance, namely skin and eye colour, and phenotypes with biomedical relevance, such as the large heterogeneity in susceptibility to Helicobacter pylori.

The lab is also particularly interested in understanding the distribution of genetic variation of human-associated microbes across the world, and co-evolution with their host including evolution of antimicrobial drug resistance.

Supervision Style

In three words or phrases: Supportive, collaborative, interested in exploring new ideas.

Provision of Training

You will receive training in bioinformatics, statistics, and laboratory methods from me and other members of the lab in the beginning. I would expect you to become more independent thereafter, but I will always be available for advice and guidance if needed.

Progression Monitoring and Management

I expect to meet you once a week to discuss experimental progress and project direction. However, I will always be available for any problem or idea you would like to discuss. Further weekly or fortnightly lab meetings where you will present your work will allow you to organise you own data and thoughts, get more feedback on your project, as well as gain experience in oral presentation.

Communication

I am available during normal daylight hours (9am - 6pm), always respond to emails and maintain regular meetings with my PhD students.

PhD Students can expect scheduled meetings with me:

In a group meeting

At least once a fortnight

In year 1 of PhD study

At least once a fortnight

In year 2 of PhD study

At least once a week

In year 3 of PhD study

At least once a fortnight

These meetings will preferably be face to face, but we can meet via video chat occasionally. I am usually contactable for an instant response on every working day.

Working Pattern

The timing of work in my lab is completely flexible, and (other than attending pre-arranged meetings), I expect students to manage their own time.

Notice Period for Feedback

I need at least 1 week’s notice to provide feedback on written work of up to 5000 words.

MIBTP Project Details

Current Projects (2025-26)

Primary supervisor for:

Evaluating the epidemiology, genetic basis and molecular evolution of antimicrobial drug resistance in Helicobacter pylori in a populational model

See the PhD Opportunities section to see if this project is currently open for applications via MIBTP.

Please Note: The main page lists projects via BBSRC Research Theme(s) quoted and then relevant Topic(s).

Evaluating the epidemiology, genetic basis and molecular evolution of antimicrobial drug resistance in Helicobacter pylori in a populational model

Secondary Supervisor(s): Dr Katrin Schilcher

University of Registration: University of Leicester

BBSRC Research Themes: Understanding the Rules of Life (Microbiology)

Project Outline

Antimicrobial Drug Resistance (AMR) is a growing and major public health threat facing the worldwide population within the next 30 years, imposing considerable burden on health systems. AMR has been developed against virtually all antibiotics in clinical use. Although clinical and laboratory studies have been using DNA sequencing to identify antibiotic resistance genes (ARGs) and to analyse the transmission of major pathogens and their ARGs between patients, animals and their environment, there are significant gaps in the understanding of the evolutionary emergence and persistence of AMR in these natural reservoirs.

To contribute to bridging this gap, this project will focus on the stomach-infecting bacterium Helicobacter pylori in the African nation of Cabo Verde. H. pylori infection is the most common infection in the world and causes peptic ulcer and gastric cancer; gastric cancer is the second cause of death from malignancy. Preliminary results from our work indicate that the prevalence of infection in the general (asymptomatic) population of Cabo Verde is ~83%, and that the level of AMR of isolated strains to four antibiotics used in H. pylori eradication is high across all subjects’ age groups despite no screening for H. pylori in the country’s health system. We hypothesize that this resistance is due to the continuous antibiotic use for unrelated heath issues, and that this leads to a strong selective pressure for AMR and AMR persistence in the Cabo Verdean community. The features of high frequency of resistance and of correlation with age in healthy subjects will allow to develop evolutionary models to characterise the AMR dynamics of H. pylori ARGs in vivo.

Thus, the main aim of this project is to use Cabo Verde as a population model and apply innovative experimental, computational and genomic approaches to characterise the genetic basis, evolution and persistence of AMR in H. pylori.

Objectives

The objectives are:

To identify new genetic determinants of AMR in H. pylori. Known resistance variants do not fully explain the observed AMR in Cabo Verdean H. pylori. We have a dataset composed by >220 sequenced strains characterized for AMR levels that will allow to perform genome-wide association analyses (GWAS)¹ or employ machine learning algorithms² to prioritise of ARGs in H. pylori. We will then evaluate the evolution of the frequency of newly found resistance variants with time and use natural selection models to calculate fitness costs and the length of persistence in the population.

To use an experimental evolution approach to characterize the evolution of AMR, to complement the GWAS analyses. We will grow a diverse set of H. pylori isolates with sub-lethal antibiotic concentrations. This weak selection pressure has been shown to select for diverse resistance pheno- and genotypes in bacteria³. We will collect samples at extended time points for AMR characterization and whole genome sequencing. The experiments will extend further after the development of resistance, when we will remove the antibiotic to evaluate the reversal of the resistant phenotype and the fitness of resistant and of potential compensatory variants. Questions addressed include: What genomic and phenotypic changes underlie adaptation to antibiotics in bacteria with different genomic backgrounds? Are these changes common to all different bacteria? Do these changes persist after removal of the antibiotic? We will then compare these results with our population dataset analysed in 1.

This project will provide a more complete understanding of AMR in H. pylori and potentially will provide new drug targets and resistance mechanisms. Information about the origin, evolution and persistence of AMR in a major pathogen will inform global health policies on AMR.

Techniques

Techniques that will be undertaken during the project:

The project provides an opportunity to integrate laboratory experiments with computational analyses. The laboratory component includes microbial techniques (bacterial isolation and culture, tests for antimicrobial drug susceptibility), molecular genetics (e.g. DNA extraction, DNA library construction, PCR), and Next-Generation Sequencing using paired-end sequencing. The computational component includes genomics (genome assembly and alignment), evolutionary (testing for selection and applying selection models) and statistical (genetic association, machine learning) analyses.

References

Power et al. (2017). Microbial genome-wide association studies: lessons from human GWAS. Nat Rev Genet 18: 41–50. doi: 10.1038/nrg.2016.132.

Peng et al. (2022). Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming. PLoS Comp Biol 18(3): e1010018. doi: 10.1371/journal.pcbi.1010018.

Sonnenkalb et al. (2023). Bedaquiline and clofazimine resistance in Mycobacterium tuberculosis: an in-vitro and in-silico data analysis. Lancet Microbe 4: e358–e368. doi:10.1016/S2666-5247(23)00002-2.

Co-supervisor on a project with Dr Fabian Freund.