How Can We Advance Integrative Biology Research in Animal Science in 21st Century? Experience at University of Ljubljana from 2002 to 2022.

In this perspective analysis, we strive to answer the following question: how can we advance integrative biology research in the 21st century with lessons from animal science? At the University of Ljubljana, Biotechnical Faculty, Department of Animal Science, we share here our three lessons learned in the two decades from 2002 to 2022 that we believe could inform integrative biology, systems science, and animal science scholarship in other countries and geographies. Cultivating multiomics knowledge through a conceptual lens of integrative biology is crucial for life sciences research that can stand the test of diverse biological, clinical, and ecological contexts. Moreover, in an era of the current COVID-19 pandemic, animal nutrition and animal science, and the study of their interactions with human health (and vice versa) through integrative biology approaches hold enormous prospects and significance for systems medicine and ecosystem health.

Catalog of genetic variants within mature microRNA seed regions in chicken.

MicroRNA (miRNA) is a class of noncoding RNA important in posttranscriptional regulation of target genes. The regulation mechanism requires complementarity between target mRNA and the miRNA region responsible for their recognition and binding, also called the seed region. It has been estimated that each miRNA targets approximately 200 genes and genetic variability of miRNA genes has been associated with phenotypic variation and disease susceptibility in humans, livestock species, and model organisms. Polymorphisms in miRNA genes especially within the seed region could therefore represent biomarkers for phenotypic traits important in livestock animals. Using the updated Version 5.0 of our previously developed bioinformatics tool miRNA SNiPer we assembled polymorphic miRNA genes in chicken. Out of 740 miRNA genes 263 were polymorphic, among them 77 had SNPs located within the mature region, and 29 of them within the miRNA seed region. Because several polymorphisms in databases result from sequencing errors, we performed experimental validation of polymorphisms located within 4 selected miRNA genes in chicken (gga-mir-1614, -1644, -1648, and -1657). We confirmed the presence of nine polymorphisms and identified 3 additional novel polymorphisms within primary miRNA regions in chicken representing 3 layer-type breeds, one layer-type hybrid, and one meat-type intercrossed population. The developed catalog of mir-SNPs in chicken can serve researchers as a starting point for association studies dealing with poultry production traits and designing functional experiments.

Thirty-four generations of divergent selection for 8-week body weight in chickens.

Chickens of the Slovenian commercial Prelux-bro line were divergently selected over 34 generations for high and low BW at 8 wk of age. The aim of the study was to estimate responses to selection with a nonlinear model. Estimates of BW for each generation were provided by the mixed model. For fitting generation means against generation or cumulative selection differential, an exponential model was used. Estimates of realized heritability over generations were derived from regression of the response on cumulative selection differential. After 34 generations, the lines differed by approximately 2,220 g for males and 1,860 g for females. Estimates for a selection limit in the high line were 2,598.4 and 2,144.1 g, for males and females, respectively. A selection limit was not reached in the low line. Half of the selection response was obtained after approximately 6 to 8 generations in the high line and 20 to 28 generations in the low line. Estimated realized heritability decreased over generations. Heritability was larger for females than males and reduction of heritability was more rapid in the high line than in the low line. Genetic SD decreased over generations. Phenotypic SD increased over generations in the high line, but was constant in the low line in the initial 22 generations and decreased thereafter. According to the good fit of the nonlinear model and informative parameter estimates, the results confirmed the usefulness of the nonlinear model for analyzing responses to long-term selection.