Supervisors: 

Main supervisor: Dr Androniki Psifidi (apsifidi@rvc.ac.uk)

Co-supervisors: Dr Dong Xia (dxia@rvc.ac.uk), Prof Dirk Werling (dwerling@rvc.ac.uk), Dr Debbie Guest (djguest@rvc.ac.uk),

Dr Ankit Hinsu (ahinsu@rvc.ac.uk) (day to day supervisor)

Department: Clinical Science and Services

Project Detail:

Bovine Digital Dermatitis (BDD) is one of the most significant infectious diseases affecting cattle, causing painful digital skin lesions, lameness, and major welfare and economic losses. Although Treponema species are central to disease development, BDD is far more complex than a simple bacterial infection. Evidence points to a multifactorial process shaped by host genetic susceptibility, epigenetic regulation, dysregulated immune responses, and profound changes in skin cellular composition. Traditional single‑omic approaches only capture fragments of this complexity, whereas a multi‑omics strategy offers a powerful way to uncover how genomic variants, gene expression, epigenetic marks, and cell‑type heterogeneity interact to drive disease. Through a large BBSRC-funded programme, we have generated an exceptional multi‑omics resource from healthy, chronic, and healed BDD skin, including whole‑genome variant data, bulk RNA‑seq, long non‑coding RNA and small RNA profiles, and single‑cell and single nuclei transcriptomes. This project will harness these datasets to build an integrated molecular view of BDD pathogenesis.

The project aims to uncover the interconnected molecular events that underpin susceptibility and chronic disease progression by applying state‑of‑the‑art computational integration frameworks. Unsupervised machine learning  approaches such as the Multi‑Omics Factor Analysis (MOFA) will be used to learn latent factors that capture shared and modality‑specific biological variation across datasets, while supervised machine learning approaches such as DIABLO will identify coordinated multi‑omic signatures associated with disease status. After rigorous quality control and preprocessing, these models will be used to pinpoint the genes, regulatory elements, and genomic variants that drive key molecular patterns. Functional enrichment, pathway analysis, and integration with single‑cell data will help interpret these factors in terms of disrupted biological processes, immune pathways, and cell‑type dynamics in diseased skin. The most compelling candidate determinants of BDD will then be validated experimentally using qRT‑PCR and western blotting, providing a direct link between computational predictions and biological reality.

By combining deep computational integration with targeted wet‑lab validation, this project offers the opportunity to generate genuinely novel insights into BDD pathogenesis. The work has the potential to reveal biomarkers of susceptibility, identify molecular signatures of chronic infection, and highlight targets for therapeutics, vaccines, or precision breeding considerably improving animal health and welfare and supporting the sustainability of the livestock sector and food production. The successful MRes student will gain hands‑on experience with multi‑omics analysis, machine learning, and wet-lab techniques, developing a versatile skill set that is highly transferable across modern bioscience research. The work is expected to lead to a peer‑reviewed publication at a scientific journal of broad outreach. 

References:

1. Anagnostopoulos, A., et al. "Association between a genetic index for digital dermatitis resistance and the presence of digital dermatitis, heel horn erosion, and interdigital hyperplasia in Holstein cows." Journal of dairy science 107(7):4915-4925 (2024).

2. Tarsani, E., et al. “Genome-wide association studies of dairy cattle resistance to digital dermatitis recorded at four distinct lactation stages” Scientific Reports 15:8922 (2025).

3. Attree, E., et al. “Multi-omics data integration towards sustainable bovine production, health and welfare: the case of painful foot lesions” PREPRINT available at Research Square. [https://doi.org/10.21203/rs.3.rs-6000979/v1] (2025).

4. Liu, P, et al. “Multi-omics analysis reveals regime shifts in the gastrointestinal ecosystem in chickens following anticoccidial vaccination and Eimeria tenella challenge” mSystems 9:e00947-24 (2024). 

Requirements

Essential

Must meet our standard MRes entry requirements

Applicants should have a genuine interest in computational and data‑driven approaches, as the project involves working with bioinformatics tools and analytical workflows. An in‑depth background in programming or advanced computation is not necessary; enthusiasm for learning and engaging with these methods is far more important

Desirable

It is helpful if applicants have some prior wet lab experience and/or exposure to data analysis, or working in a Linux environment, although these are not essentials.

This can be taken full-time project commencing in October 2026, based at RVC's Hawkshead campus.No animal work is required for this project

Funding

Partially funded: The MRes student will be expected to meet their course fees, living expenses and if they wish to attend a conference the conference fees. All key project costs will be met by the supervisor through existing grants.  

Please note that EU/EEA and Swiss national students may no longer be eligible for the “Home” rate of tuition fees, dependent on personal circumstances (including immigration status and residence history in the UK) and UK government rules which are currently being developed. For up-to-date information on fees for EU/EEA and Swiss national students following Brexit please see our fees and funding page.

How to apply

Deadline: 8th May 2026

For more information on the application process and English Language requirements see How to Apply.Interviews will take place remotely (Teams, Zoom etc) within 4 weeks of the closing date.We welcome informal enquiries - these should be directed to apsifidi@rvc.ac.uk