ESR1: What are IUGR pigs? – phenotypic and metabolic differentiation

Background

The selection for hyperprolific sows led to an inadequacy between the number of fetus and the uterine capacity. This phenomenon results in impaired placental development and therefore impaired development of some fetuses. The so-called Intra Uterine Growth Retarded (IUGR) piglets display greater early postnatal mortality and morbidity risks, impaired postnatal growth, impaired carcass and meat quality traits. From an animal welfare as well as from a production efficiency point of view IUGR piglets are problematic.

Objectives

  1. Characterise phenotypically and metabolically the IUGR subpopulation and identify their specific nutritional needs in early life.
  2. Define a machine-learning model to describe the IUGR subpopulation thanks to the analysis of piglets' pictures.
  3. Identify IUGR specific physiological traits using a metabolomic based approach.

Methods

  • Computed tomography (CT) - scan imaging will be used to non-invasively assess the piglets’ brain: liver w/w ratio, as the gold standard to identify IUGR.
  • 3D-pictures of newborn piglets will be taken to measure morphological parameters. A machine-learning model will be developed to correlate the brain: liver weight ratio to visual characteristics of head shape, body form and other possible traits.
  • The postnatal characteristics of the piglets (body composition, metabolome, and microbiome) will be investigated.

Expected results

  1. Phenotypic cut-off values for IUGR pigs defined (D1.2).
  2. Differentiation based on the metabolomics profile of IUGR and ́normal ́ pigs established (D2.14).

Planned secondments

  • At: CSEM (3 mo); build a Machine Learning Model based to diagnose IUGR at birth;
  • At: UFA Bühl (3 mo); test and validate the Machine Learning Model;
  • At: UNIBO (3 mo); metabolome analysis and bioinformatics of blood samples collected in the experiments.

Enrolment in Doctoral degree:

ESR1 will be enrolled at the Department of Agricultural and Food Science (DISTAL), University of Bologna.

Supervisors

Catherine Ollagnier (Agroscope), Giuseppe Bee (Agroscope), Paolo Trevisi (University of Bologna)

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