A groundbreaking international study led by the Center for Biotechnology and Plant Genomics (CBGP) at the Universidad Politécnica de Madrid (UPM) explores the predictability of evolution, using bacterial mutations as a case study. Published in the journal Science, the research sheds light on the age-old question of whether evolution is entirely random or if it can be anticipated.

The study delves into the evolutionary behavior of bacteria, leveraging current genetic tools to analyze their mutations. Surprisingly, the findings suggest that bacterial evolution can be predictable in the short term, opening avenues for anticipating the evolution of pathogens and pests. The research also hints at potential biotechnological applications for better control.

Researchers aimed to determine if the impact of mutations remains constant throughout adaptation or exhibits significant historical dependence, such as a beneficial mutation in an ancestor becoming detrimental in descendants and vice versa.

To conduct the study, the team employed advanced genetic engineering technology capable of introducing hundreds of thousands of mutations into bacteria. This allowed them to individually study the effects of each mutation across the bacterial genome’s four million letters.

The study focused on the well-known Long-Term Evolution Experiment, spanning over 35 years and involving 12 populations of Escherichia coli bacteria evolving under constant laboratory conditions. Surprisingly, the study revealed that the overall proportion of lethal, harmful, and neutral mutations remained almost constant throughout the evolution of these bacterial lineages, despite the specific identity of mutations showing volatility.

Particularly noteworthy were the findings related to lethal mutations. While many lethal genes in the ancestor ceased to be lethal in evolved strains, a similar fraction of non-lethal mutations in the ancestor turned lethal later on, maintaining a constant fraction of lethal mutations throughout evolution. This challenges existing models of the minimal genome concept and holds practical implications for biotechnology and medicine.

The study also highlighted the dynamic nature of beneficial mutations. While initial major adaptations were found to be predictable, this predictive capacity diminished as evolution progressed. This insight is crucial, as initial adaptations often determine survival or extinction.

The results offer encouragement for predicting the evolution of pathogens, particularly those with antibiotic resistance or emerging pandemic viruses. Additionally, the study suggests that massive genetic engineering techniques could rapidly develop microbes tailored for various demands or applications, such as protecting plants against pathogens or efficiently producing or degrading specific compounds.

Article written by Agencia Sinc| Image by Unsplash



Agencia Sinc