The Bloomsbury Colleges | PhD Studentships | Studentships 2020 | Veterinary paraclinical metagenomics to prevent bovine respiratory disease and antimicrobial use in calves
Document Actions

Veterinary paraclinical metagenomics to prevent bovine respiratory disease and antimicrobial use in calves

Principal supervisor: Luca Guardabassi (RVC)

Co-Supervisor: Richard Stabler and Kat Holt (LSHTM), Debby Bogaert (Edinburg University)

Award includes tuition fees and a stipend of £17,009 including London Weighting (at 2019/20 rates, so slightly higher for 2020 entry)

100% FTE for 3 years, from September 2020.

Project Description:

Bovine Respiratory Disease (BRD) is a major cause for loss of productivity and one of the most common reasons for antimicrobial use in cattle farming worldwide [1]. The incidence of BRD in pre-weaned calves in the UK has been estimated to be approximately 46% [2]. Notably, some calves develop disease whereas others remain healthy within the same pen, indicating that host-specific factors play an essential role in the clinical manifestation of the disease. In humans, it is now widely recognized that the lung microbiome varies from person-to-person, influencing how individuals respond to a variety of infectious and non-infectious agents. Unfortunately, respiratory microbiome research in animals has lagged behind human research but initial data indicate that the nasopharyngeal microbiota of calves affected by BRD differs from that in healthy controls and contains higher proportions of bacterial genera associated with this disease [3].

Clinical metagenomics is a new discipline based on next generation sequencing that has received increasing attention in human medicine. It refers to the sequencing of all nucleic acid material present within a clinical specimen with the intent to recover clinically relevant microbial information [4]. By this PhD project, we intend to establish a derivative discipline named veterinary paraclinical metagenomics that is not limited to clinical specimens but includes samples from healthy animals to predict occurrence of disease. The aim of the PhD is to investigate how the lung microbiome of calves that are going to develop BRD differ from that of calves that remain healthy, and how this information can be used for early disease detection and prevention.

A pilot study will be conducted in a single farm to standardize sampling techniques and to generate an initial dataset that will be used for training the PhD fellow in bioinformatics and study design. Year 1 will be used for this training as well as for recruiting 5-10 farms with high (≥40%) prevalence of BRD and excellent veterinary care and record keeping in liaison with our veterinary partner Synergy Farm Health Ltd. During year 2, the PhD fellow will monitor lung microbiome development in 10-20 ear-tagged calves per farm until the age of 12 weeks since most BRD cases are likely to occur during this time slot. For each calf, broncho-alveolar lavage (BAL) samples will be collected at weeks 3 (immediately after arrival to the farm), 7 (before weaning) and 12 (end of calf phase). Additional BAL samples will be collected if the tagged calves manifest symptoms of BRD and matched with an equivalent number of samples from healthy calves of the same age. For all calves colostrum status will be assessed via total protein measures taken in the first week of life and ultrasound scanning will be performed to assess lung health prior to each sampling. This work will be incorporated in final year student teaching sessions to reduce the project costs and to allow training of students in these important diagnostic techniques. Antimicrobial exposure (type of antibiotic, dosage, administration, etc.), growth (daily gain weight, girth width and wither height) and clinical conditions (occurrence and duration of disease, symptoms, etc.) will be recorded for each animal by a trained veterinarian during the entire study. Data on antibiotic use, housing quality, vaccination and management practices will be recorded at each farm via structured interview. During year 3, all data will be integrated in a big dataset to identify hidden patterns and unknown correlations between each variable included.

We estimate to collect approximately 380 BAL samples. Total DNA extracted from the pellet of centrifuged BAL will be used sequenced using scalable throughput methods. All samples will be analysed by 16s rRNA gene sequencing [5]. Bacterial DNA will be amplified using standard primers targeting the V3-V4 regions of the 16s rRNA gene, followed by MiSeq (Illumina) sequencing using standard primers and barcodes. Raw data will be processed using QIIME software and sequences will be annotated using the SILVA database. Principal Component Analysis and k-means analysis will be used to establish distribution of bacterial taxa across samples. Based on this preliminary analysis, 80 samples from 10 diseased and 10 non-diseased calves (4 sample per calf) will be selected for metagenomics sequencing [6]. Raw sequences generated by NovaSeq6000 (Illumina) will be filtered for quality and host, and a catalogue of non-redundant genes will be generated using a metagenomics assembly and profiling pipeline (MOCAT). Functional and phylogenetic annotation will be performed to visualize phylogenetic composition (iTOL) and metabolic pathways (iPATH). The EMBL pipeline SIAMCAT will be used for statistical inference of associations between microbial communities and host phenotypes.

The impact of different covariates on the microbial community composition will be analysed using Generalized Linear Models (GLM) and Generalized Linear Mixed Models (GLMM). Key analyses will be: (1) defining the trajectory of microbiome development in healthy calves who do not manifest respiratory infection symptoms; (2) investigating the effects of respiratory illness episodes, associated antimicrobial exposures and other factors on deviation from healthy microbiome development; (3) identifying microbiome signatures at weeks 1, 7 and 12 that are predictive of increased risk of infection during infancy, alone or in combination with other measured factors. Such big data analysis will lead to identification of specific microbiome structures associated with diseased calves that can be exploited as biomarkers for early detection of BRD and selective breeding of calves that are resistant to the disease. Understanding of how specific components of the respiratory microbiome interact with disease/health will also enhance future development of respiratory prebiotics, probiotics or other interventions to improve calf health and productivity.

A veterinary practice, Synergy Farm Health Ltd, has manifested great interest for this project (see attached letter). They will facilitate selection and access to farms, participate in project meetings and contribute to results dissemination and knowledge exchange (KE) with industry and farmer associations by different routes (discussion groups, industry events, etc.). Cattle farmers and veterinarians will be reached by non-scientific publications in UK journals such as Cow Mangement and Farmers Weekly. The scientific community will be involved though peer-reviewed publications and oral or poster contribution to veterinary (e.g. BSAVA) and non-veterinary (e.g. ECCMID) conferences

Subject Areas/Keywords:

Cattle, bovine respiratory disease, disease prevention, antimicrobial use, lung microbiome, metagenomics.

Key References:

  1. 1. Guardabassi L et al. Optimization of antimicrobial treatment to minimize resistance selection. Microbiology Spectrum 2018, 6.
  2. 2. Johnson et al. Prospective cohort study to assess rates of contagious disease in pre-weaned UK dairy heifers: management practices, passive transfer of immunity and associated calf health. Veterinary Record Open 2017, 4.4
  3. 3. Zeineldin M et al. Contribution of the Mucosal Microbiota to Bovine Respiratory Health. Trends Microbiol. 2019, 27:753-70.
  4. 4. Chiu CYMiller SA. Clinical metagenomics. Nat Rev Genet. 2019, 20:341-355.
  5. 5. Espinosa-Gongora C, … Guardabassi L. Differential analysis of the nasal microbiome of pig carriers or non-carriers of Staphylococcus aureus. PLoS One 2016; 11: e0160331.
  6. 6. Leo et al. Detection of Bacterial Pathogens from Broncho-Alveolar Lavage by Next-Generation Sequencing. Int J Mol Sci. 2017, 18: 2011.Candidate requirements (if appropriate):

Further details about the project may be obtained from:

Principal Supervisor: Luca Guardabassi (

Co-Supervisors: Richard Stabler (

Debby Bogaert (

Kathryn Holt (

Further information about PhDs at RVC is available from:

Application forms and details about how to apply are available from:

If you encounter any problems please email

Closing date for applications is: 9th February 2020