Evolutionary dynamics of genomes, viruses, and microbial populations
- Ferreiro López, Sandra - Student
- López Beltrán, Adrián - Student
- Recuero Martínez, Enrique - Student
Evolutionary theory aims to unify the diversity and complexity observed in nature. And yet, more than 150 years after the Origin of Species, we are only starting to understand some of the key mechanisms and processes through which evolution shapes life. Microbial evolution is one example where evolutionary theory has experienced great progress in recent times, much due to the surge of microbial genomics in the last 2 decades. Unlike animals and plants, which have been the focus of classical evolutionary studies, microbial (specifically prokaryotic) evolution is critically dependent on the exchange of genes through multiple mechanisms collectively known as horizontal gene transfer (HGT). HGT generates diversity in microbial populations and provides ready-made tools that facilitate fast adaptation to environmental challenges in a way that is inaccessible to multicellular eukaryotes. As a result, the sharing of genetic information through HGT underlies much of the adaptability and evolutionary plasticity of prokaryotes. A second driving force of microbial evolution is the arms race between parasites and hosts, where microbes can play both roles. Far from being unrelated phenomena, HGT and parasite-host coevolution are inextricably coupled: the same parasites that microbes “strive” to resist (bacteriophages, conjugative plasmids, and other selfish genetic elements) constitute the main vehicles for HGT or were even domesticated to serve such a purpose. Adding an extra layer of complexity, HGT and parasite-host coevolution occur at timescales that overlap with those of ecological processes. The interplay of HGT, parasite-host coevolution, and microbial population dynamics often leads to eco-evolutionary feedbacks by which ecological interactions determine evolutionary processes and vice versa.
Our ultimate goal is to understand the major evolutionary and ecological processes that occur in complex microbial populations from a systems perspective, with a focus on the interplay of HGT, parasite-host coevolution, and microbiome dynamics. Our research lies at the interface of Biology, Mathematics, and Physics and combines empirical data with tools from computational biology, comparative genomics, statistical mechanics, game theory, and network science.
MAIN RESEARCH LINES
Selection, drift, and HGT in the evolution of microbial genomes. In collaboration with Dr. Eugene Koonin (NCBI, United States), Prof. Mikhail Katsnelson (Radboud University, The Netherlands), Prof. José Cuesta (UC3M, Spain), and Dr. Susanna Manrubia (CNB, Spain), we combine mathematical models and comparative genomics to study the mechanisms that control the size and composition of prokaryotic genomes and determine the evolutionary regimes associated with different classes of genes (Figure 1). This research line is improving our quantitative understanding of microbial genome evolution by disentangling the roles that selection and HGT play in maintaining beneficial as well as burdensome (e.g. selfish) genes (Iranzo et al, PLoS Comput Biol, 2014; Iranzo et al, PNAS, 2017; Iranzo & Koonin, EPL, 2018).
Figure 1: Mathematical and computational study of the forces that drive prokaryotic genome evolution. The effects of selection, HGT, and gene loss on bacterial and archaeal genomes can be captured by a mathematical model (a), whose parameters are optimized to fit the gene copy number distributions found in real genomes (b). The model can be applied to large genomic datasets to determine the long-term adaptive value of genes from different functional categories. Note that genes involved in carbohydrate metabolism do not provide a net benefit in the long term, most likely because the carbon sources that are available to different strains of the same species are highly variable over time and with location. Adapted from Iranzo & Koonin (EPL, 2018) and Iranzo et al. (PNAS, 2017).
Eco-evolutionary forces shaping microbial pangenomes.
The existence of large pangenomes (i.e. the fact that the entire gene repertoire of a given taxonomic group is much larger than the gene repertoire found in any individual genome) is a characteristic feature of prokaryotic genomics that likely results from an eco-evolutionary interplay (Figure 2). However, the relative contribution of ecological (niche-specific) adaptation, gain and loss of genes involved in virus-host arms races, and other non-adaptive evolutionary processes to a species’ pangenome is a matter of debate. To shed light on the eco-evolutionary forces that shape microbial pangenomes, we are developing methods to infer ecological interactions in microbial communities from metagenomic data and reconstruct the evolutionary events that led to pangenome diversification in closely related strains. By correlating the ecological complexity of a species with the size and composition of its accessory genome, we expect to disentangle the roles of niche adaptation, virus-host coevolution, and background gene turnover on the genomic plasticity of prokaryotes.
Figure 2: The structure of a species pangenome and the overlap with the pangenomes of different species (right) result from the interplay of evolutionary and ecological factors (left), such as the ecological niches occupied by distinct subpopulations (circles of the same colors) and the rates of migration and HGT within and across populations of the same or different species.
Networks of gene sharing across the virosphere. In collaboration with Dr. Eugene Koonin (NCBI, United States) and Dr. Mart Krupovic (Institut Pasteur, France) we are pioneering the use of bipartite network analysis to identify the evolutionary connections among viruses from the 3 domains of life and their relation to capsid-less mobile genetic elements (e.g. plasmids and transposable elements)[Figure 3]. Such an application of network analysis to overcome the limitations of classical (tree-based) phylogenetic methods has become a promising research direction for deep viral phylogenomics and has already drawn the attention of multiple research groups. We have applied the method to study deep phylogenetic relations among double-stranded DNA viruses (Iranzo et al, mBio, 2016), archaeal viruses (Iranzo et al, J. Virol, 2016), and RNA viruses (Wolf et al, mBio, 2018). The results of these works have been highlighted in several reviews (e.g. Prangishvili et al, Nat Rev Microbiol, 2017).
Figure 3: Bipartite network of gene sharing across archaeal viruses and related mobile genetic elements. Different colors indicate the modules of the archaeal virus network. Adapted from Krupovic et al. (Virus Res, 2018).
OTHER RESEARCH LINES
Cancer genomics. Leveraging the large amount of cancer sequencing data produced by the TCGA initiative, we are developing advanced analysis tools to extract biologically relevant information about the accumulation of mutational events that drive cancer progression (Figure 4). In a first study, done in collaboration with Dr. Iñigo Martincorena (Sanger Institute, UK), we applied bipartite network analysis to investigate the number and specificity of driver mutations across body sites (Iranzo et al, PNAS, 2018). We are currently adapting methods from evolutionary biology to investigate epistasis among cancer driver mutations.
Figure 4: Bipartite network of cancer somatic mutations in the TCGA dataset. (a) Tumor samples are displayed along the X axis, organized by cancer types; cancer genes are displayed along the Y axis. Links indicate the presence of a mutation affecting a given gene in a given sample. Colors highlight sets of cancer genes mutated in specific cancer types. (b) The same network, represented using a traditional force-directed projection. (c) Degree distribution of sample and gene nodes. (d) Clustering coefficient, as a function of the node degree. Adapted from Iranzo et al. (PNAS, 2018)
Emergence of cooperation from competition among networked systems. In collaboration with Dr. Jacobo Aguirre (CNB, Spain), we study how competition among networked systems, such as professional networks or financial networks, determines the large-scale structure of networks-of-networks in the real world (Figure 5). We have found that, in a broad range of real situations, weak competitors (those with low centrality) have a critical role in promoting a state of global cooperation where not only the weak competitors but also the whole system gets maximum benefit (Iranzo et al, Nat Commun, 2016). Such “power of the weak” is a novel phenomenon with relevant applications in decision-making processes.
Figure 5: Competition for centrality among the loan networks of three rural communities. The small villages (B and C) can choose to establish connections with the large village A (bottom left) or to cooperate with each other (bottom right). The final state largely depends on the actions of the weak communities. Adapted from Iranzo et al. (Nat Commun, 2016).
Iranzo, J., Wolf, Y.I., Koonin, E.V., Sela, I. 2019. Gene gain and loss push prokaryotes beyond the homologous recombination barrier and accelerate genome sequence divergence. Nature Communications 10, 5376. DOI: 10.1038/s41467-019-13429-2
Wolf, Y.I., Kazlauskas, D., Iranzo, J., Lucía-Sanz, A., Kuhn, J.H., Krupovic, M., Dolja, V.V., Koonin, E.V. 2019. Reply to Holmes and Duchêne, “Can Sequence Phylogenies Safely Infer the Origin of the Global Virome?”: Deep Phylogenetic Analysis of RNA Viruses Is Highly Challenging but Not Meaningless. mBio 10, e00542-19. DOI: 10.1128/mBio.00542-19
Wolf, Y.I., Kazlauskas, D., Iranzo, J., Lucía-Sanz, A., Kuhn, J.H., Krupovic, M., Dolja, V.V., Koonin, E.V. 2018. Origins and Evolution of the Global RNA Virome. mBio 9, e02329-18. DOI: 10.1128/mBio.02329-18
Uehara, R., Cerritelli, S.M., Hasin, N., Sakhuja, K., London, M., Iranzo, J., Chon, H., Grinberg, A., Crouch, R.J. 2018. Two RNase H2 Mutants with Differential rNMP Processing Activity Reveal a Threshold of Ribonucleotide Tolerance for Embryonic Development. Cell Reports 25, 1135-1145.e5. DOI: 10.1016/j.celrep.2018.10.019
Krupovic, M., Cvirkaite-Krupovic, V., Iranzo, J., Prangishvili, D., Koonin, E.V. 2018. Viruses of archaea: Structural, functional, environmental and evolutionary genomics. Virus Research 244, 181–193. DOI: 10.1016/j.virusres.2017.11.025
Iranzo, J., Martincorena, I., Koonin, E.V. 2018. Cancer-mutation network and the number and specificity of driver mutations. Proceedings of the National Academy of Sciences 115, E6010–E6019. DOI: 10.1073/pnas.1803155115
Iranzo, J., Koonin, E.V. 2018. How genetic parasites persist despite the purge of natural selection. EPL (Europhysics Letters) 122, 58001. DOI: 10.1209/0295-5075/122/58001
Kurth, E.G., Peremyslov, V.V., Turner, H.L., Makarova, K.S., Iranzo, J., Mekhedov, S.L., Koonin, E.V., Dolja, V.V. 2017. Myosin-driven transport network in plants. Proceedings of the National Academy of Sciences of the United States of America 114, E1385–E1394. DOI: 10.1073/pnas.1620577114
Iranzo, J., Krupovic, M., Koonin, E.V. 2017. A network perspective on the virus world. Communicative & Integrative Biology 10, e1296614. DOI: 10.1080/19420889.2017.1296614
Iranzo, J., Cuesta, J.A., Manrubia, S., Katsnelson, M.I., Koonin, E.V. 2017. Disentangling the effects of selection and loss bias on gene dynamics. Proceedings of the National Academy of Sciences 114, E5616–E5624. DOI: 10.1073/pnas.1704925114
Iranzo, J., Puigbò, P., Lobkovsky, A.E., Wolf, Y.I., Koonin, E.V. 2016. Inevitability of Genetic Parasites. Genome Biology and Evolution 8, 2856–2869. DOI: 10.1093/gbe/evw193
Iranzo, J., Krupovic, M., Koonin, E.V. 2016. The double-stranded DNA virosphere as a modular hierarchical network of gene sharing. mBio 7. DOI: 10.1128/mBio.00978-16
Iranzo, J, Koonin, E.V., Prangishvili, D., Krupovic, M. 2016. Bipartite Network Analysis of the Archaeal Virosphere: Evolutionary Connections between Viruses and Capsidless Mobile Elements. Journal of Virology 90, 11043–11055. DOI: 10.1128/JVI.01622-16
Iranzo, J., Buldú, J.M., Aguirre, J. 2016. Competition among networks highlights the power of the weak. Nature Communications 7, 13273. DOI: 10.1038/ncomms13273
Iranzo, J., Lobkovsky, A.E., Wolf, Y.I., Koonin, E.V. 2015. Immunity, suicide or both? Ecological determinants for the combined evolution of anti-pathogen defense systems. BMC Evolutionary Biology 15, 43. DOI: 10.1186/s12862-015-0324-2
Iranzo, J., Villoslada, P. 2014. Autoimmunity and tumor immunology: two facets of a probabilistic immune system. BMC Systems Biology 8, 120. DOI: 10.1186/s12918-014-0120-4
Iranzo, J., Lobkovsky, A.E., Wolf, Y.I., Koonin, E.V. 2014. Virus-host arms race at the joint origin of multicellularity and programmed cell death. Cell Cycle 13, 3083–3088. DOI: 10.4161/15384101.2014.949496
Iranzo, J., Gómez, M.J., Saro, F.J.L. de, Manrubia, S. 2014. Large-Scale Genomic Analysis Suggests a Neutral Punctuated Dynamics of Transposable Elements in Bacterial Genomes. PLOS Computational Biology 10, e1003680. DOI: 10.1371/journal.pcbi.1003680
Iranzo, J., Lobkovsky, A.E., Wolf, Y.I., Koonin, E.V. 2013. Evolutionary Dynamics of the Prokaryotic Adaptive Immunity System CRISPR-Cas in an Explicit Ecological Context. Journal of Bacteriology 195, 3834–3844. DOI: 10.1128/JB.00412-13
Perales, C., Iranzo, J., Manrubia, S.C., Domingo, E. 2012. The impact of quasispecies dynamics on the use of therapeutics. Trends in Microbiology 20, 595–603. DOI: 10.1016/j.tim.2012.08.010
Iranzo, J., Manrubia, S.C. 2012. Evolutionary dynamics of genome segmentation in multipartite viruses. Proceedings of the Royal Society B: Biological Sciences 279, 3812–3819. DOI: 10.1098/rspb.2012.1086
Iranzo, J., Floría, L.M., Moreno, Y., Sánchez, A. 2012. Empathy Emerges Spontaneously in the Ultimatum Game: Small Groups and Networks. PLOS ONE 7, e43781. DOI: 10.1371/journal.pone.0043781
Iranzo, J., Román, J., Sánchez, A. 2011. The spatial Ultimatum game revisited. Journal of Theoretical Biology 278, 1–10. DOI: 10.1016/j.jtbi.2011.02.020
Iranzo, J., Perales, C., Domingo, E., Manrubia, S.C. 2011. Tempo and mode of inhibitor–mutagen antiviral therapies: A multidisciplinary approach. Proceedings of the National Academy of Sciences 108, 16008–16013. DOI: 10.1073/pnas.1110489108