EVOLUTIONARY SYSTEMS GENETICS OF MICROBES
- Chihoub, Doha - PhD Student
- Dabos, Laura - Postdoctoral Marie Curie UPM
- Devin Altès, Guillem August - Technician
- Sánchez Maroto Capilla, Laura - PhD Student
- Zahia Inssaf, Nedjari - PhD Student
“We seek to understand the mechanistic forces that drive genetic adaptation, not only because of their fundamental character, but also to help controlling the evolution of microbes in clinical, industrial, and agricultural settings”
Our research activities deal with understanding the fundamental principles that guide the adaptation of microbes through different evolutionary paths (see Figure 1). To this end, we employ a combination of computational (e.g., simulations, comparative genomics) and experimental methods (e.g., multiplexed genome engineering, high-throughput DNA sequencing, experimental evolution) (see Figure 2). An important aspect of our work involves running adaptation experiments in the lab to watch evolution unfold in real time. Using next-generation sequencing, we monitor genome changes in the experimental lines over hundreds of generations. The results from these experiments and analyses are then compared with predictions from computational models, which in turn inform the design of further experiments. While our research is primarily inspired by fundamental questions, we employ a variety of microbial systems (human and plant pathogens, biotechnologically relevant strains) with the ultimate goal of producing insights and resources to help controlling the evolution of microbes in clinical, industrial, and agricultural settings.
Figure 1. Can we predict evolution? Several mutational paths are often available for populations to adapt to novel environments. The choice among different routes has traditionally been understood as the result of the balance between selection and random drift. Recent work, however, has emphasized the role of mutational biases, among other factors, in favouring particular routes (coloured arrows). In the lab, a central topic we seek to understand is how the complex interplay between mutation biases and genetic and environmental interactions determine the outcome of evolution.
Figure 2. A multi-faceted approach to complex questions. We use a combination of computer modeling, bioinformatics and experiments to gain insight into the molecular and population processes that shape microbial evolution. Examples include: a) computer simulations on the invasion dynamics of strains with different mutation rates, b) genome-wide predictions of the fitness effects of novel mutations in a panel of key microbial pathogens covering a range of GC contents (from 33% to 67%, left to right), and c) empirical estimates of the essentiality of genes in E. coli, obtained via transposon insertional mutagenesis coupled with massively parallel sequencing. Taken from Couce 2019 Nat Comm (a, b) and Couce 2017 PNAS (c).
Is evolution predictable, or do chance events make it essentially irreproducible? An important insight gained over the last years is that several factors can reduce the number of potentially adaptive routes that are effectively realised by selection. These limiting factors include strong mutational biases and the constraints that emerge from non-lineal interactions among mutations (i.e., epistasis). In the lab, we explore this topic by studying the frequency with which mutations arise, both spontaneously and induced by environmental conditions; and the effects novel mutations have on the fitness of organisms, both individually and cumulatively. An important model system in the lab are bacterial mutators – strains with highly-elevated and highly-biased mutation rates due to defects in the DNA Repair machinery. Mutators are quite common among human pathogens, being considered a major risk factor for antibiotic resistance evolution, and we suspect that they may play similar roles in plant pathogens. In addition, given the central role that mutation rate plays in evolution, they represent an ideal case study to investigate many long-standing questions in evolutionary genetics, such as the maintenance of genetic diversity, the extinction of small populations or the evolution of sex. Apart from mutational forces, we are also interested in the role of external factors in adaptation; in particular in how the ecological interactions that bacteria experience in natural communities (e.g., phage predation, cross-feeding partnerships) can ultimately guide their evolution.
Motivated and curious about any of these topics? We are expanding and welcome applications from driven and talented students and postdocs who may wish to join the lab. Informal inquiries are encouraged and can be made by email to the address above.
Couce, A., Tenaillon, O. 2019. Mutation bias and GC content shape antimutator invasions. Nature Communications 10, 3114. DOI: 10.1038/s41467-019-11217-6
Couce, A., Viraphong Caudwell, L., Feinauer, C., Hindré, T., Feugeas, J.-P., Weigt, M., Lenski, R.E., Schneider, D., Tenaillon, O. 2017. Mutator genomes decay, despite sustained fitness gains, in a long-term experiment with bacteria. Proceedings of the National Academy of Sciences 114, E9026–E9035. DOI: 10.1073/pnas.1705887114
Couce, A., Alonso-Rodriguez, N., Costas, C., Oliver, A., Blázquez, J. 2016. Intrapopulation variability in mutator prevalence among urinary tract infection isolates of Escherichia coli. Clinical Microbiology and Infection 22, 566.e1-566.e7. DOI: 10.1016/j.cmi.2016.03.008
Couce, A., Rodríguez-Rojas, A., Blázquez, J. 2016. Determinants of Genetic Diversity of Spontaneous Drug Resistance in Bacteria. Genetics 203, 1369–1380. DOI: 10.1534/genetics.115.185355
Couce, A., Rodríguez-Rojas, A., Blázquez, J. 2015. Bypass of genetic constraints during mutator evolution to antibiotic resistance. Proceedings of the Royal Society B: Biological Sciences 282, 20142698. DOI: 10.1098/rspb.2014.2698
Couce, A., Tenaillon, O.A. 2015. The rule of declining adaptability in microbial evolution experiments. Frontiers in Genetics 6, 99. DOI: 10.3389/fgene.2015.00099
Couce, A., Guelfo, J.R., Blázquez, J. 2013. Mutational Spectrum Drives the Rise of Mutator Bacteria. PLOS Genetics 9, e1003167. DOI: 10.1371/journal.pgen.1003167
Couce, A., Briales, A., Rodríguez-Rojas, A., Costas, C., Pascual, Á., Blázquez, J. 2012. Genomewide Overexpression Screen for Fosfomycin Resistance in Escherichia coli: MurA Confers Clinical Resistance at Low Fitness Cost. Antimicrobial Agents and Chemotherapy 56, 2767–2769. DOI: 10.1128/AAC.06122-11
Couce, A., Blázquez, J. 2011. Estimating mutation rates in low-replication experiments. Mutation Research 714, 26–32. DOI: 10.1016/j.mrfmmm.2011.06.005
Candida Monteiro (postdoc)
Javier Guerrero (MSc student)
Adrian Gonzalez (MSc student)
Ignacio Moro (BSc student)
Kartik Dattani (BSc student)
Lorena Pastor (technician)