MSCA ELITE-S Fellow
Bruno Gabriel Nascimento Andrade
Cork Institute of Technology
Since 2017 I have been working as a post-doctoral fellowship sponsored by FAPESP, the most prestigious grant agency in Brazil, to study the rumen and fecal microbiomes of beef cattle and its influence on the host’s phenotypes.
This research line proved to be a successful one, allowing us to approve projects in different grant agencies and publish studies regarding different layers of genomic information of beef cattle, such as genomics, metagenomics, and metabolomics. In 2019 I was granted an international joint Post-doctoral fellowship to apply Machine learning approaches to analyze microbiome and cattle data at Cork Institute of Technology, Ireland.
Since November 2020, I took over an EliteS Post-doctoral fellowship position at Cork Institute of Technology/AdaptCentre (Marie Curie funded), under the supervision of Prof. Haithem Afli. I’m currently studying the fecal microbiome diversity of the USA Angus breed, reconstructing Metagenome Assembled Genomes (MAGs), bacterial genes and functions, and understanding how these microorganisms could influence weight gain and other growth phenotypes. I am also involved in several projects with international collaborators, such as the antibiotic resistome in oceans (ResistomeDB), the human microbiome (work in progress), different projects regarding Brazilian beef cattle, and the influence of the nasopharyngeal microbiome in the severity of COVID-19.
To be part of the ELITE-S Programme is to reach a new stage of my scientific career. Such an opportunity will allow me to make scientific advances and contributions to the Agritech field and be a leading expert in this dynamic and competitive field. My project within the ELITE-S Programme has the potential to help farmers and industries to increase cattle efficiency, reducing costs, and its environmental footprint.
Prediction of host phenotypic outcomes with Machine learning
To reduce the environmental impact and costs for meat production and maintain the feeding standards of an ever-growing human population are some of the major concerns of the beef cattle industry worldwide. While several improvement efforts have been performed to tackle this problem, including genomic selection, it was not until recently that the microbiome started to be considered as an important source of phenotypic variation in ruminants.
The increasing evidence of the microbiome role in health, development, and environmental impact of beef cattle, leverages the microbiome as an interesting research object for data analysis and Machine Learning since the identification and manipulation of patterns of microorganisms in farm animals could help mitigate costs and increase their overall efficiency.
The microbiome field relies on approaches that randomly sequence the genetic material (DNA or RNA), generating massive, sparse, and sometimes compositional data, being this field an example of big data exploration in biological sciences. The search for patterns that are associated with environmental features and can be used to predict phenotypic outcomes is challenging and could be benefited from data-driven investigations, such as Machine Learning and Deep learning approaches.
This research aims to explore the faecal microbiome of 313 Angus Calves and to identify the relation between microbiome features and host’s phenotypic outcomes, like growth and health status. Altogether, the results generated by this project have the potential to significantly increase knowledge related to beef cattle, paving the way for a new layer of information to be considered in animal breeding and production. It also has the potential to develop and validate methods that might be useful for other biological models.