Rif1 controls DNA replication by directing Protein Phosphatase 1 to reverse Cdc7-mediated phosphorylation of the MCM complex

Peer reviewedPublisher PD

Robustness and epistasis in mutation-selection models

We investigate the fitness advantage associated with the robustness of a phenotype against deleterious mutations using deterministic mutation-selection models of quasispecies type equipped with a mesa shaped fitness landscape. We obtain analytic results for the robustness effect which become exact in the limit of infinite sequence length. Thereby, we are able to clarify a seeming contradiction between recent rigorous work and an earlier heuristic treatment based on a mapping to a Schr\"odinger equation. We exploit the quantum mechanical analogy to calculate a correction term for finite sequence lengths and verify our analytic results by numerical studies. In addition, we investigate the occurrence of an error threshold for a general class of epistatic landscape and show that diminishing epistasis is a necessary but not sufficient condition for error threshold behavior.Comment: 20 pages, 14 figure

Evolutionary dynamics of the most populated genotype on rugged fitness landscapes

We consider an asexual population evolving on rugged fitness landscapes which are defined on the multi-dimensional genotypic space and have many local optima. We track the most populated genotype as it changes when the population jumps from a fitness peak to a better one during the process of adaptation. This is done using the dynamics of the shell model which is a simplified version of the quasispecies model for infinite populations and standard Wright-Fisher dynamics for large finite populations. We show that the population fraction of a genotype obtained within the quasispecies model and the shell model match for fit genotypes and at short times, but the dynamics of the two models are identical for questions related to the most populated genotype. We calculate exactly several properties of the jumps in infinite populations some of which were obtained numerically in previous works. We also present our preliminary simulation results for finite populations. In particular, we measure the jump distribution in time and find that it decays as $t^{-2}$ as in the quasispecies problem.Comment: Minor changes. To appear in Phys Rev

Records and sequences of records from random variables with a linear trend

We consider records and sequences of records drawn from discrete time series of the form $X_{n}=Y_{n}+cn$, where the $Y_{n}$ are independent and identically distributed random variables and $c$ is a constant drift. For very small and very large drift velocities, we investigate the asymptotic behavior of the probability $p_n(c)$ of a record occurring in the $n$th step and the probability $P_N(c)$ that all $N$ entries are records, i.e. that $X_1 < X_2 < ... < X_N$. Our work is motivated by the analysis of temperature time series in climatology, and by the study of mutational pathways in evolutionary biology.Comment: 21 pages, 7 figure

The Effect of Mobile Element IS10 on Experimental Regulatory Evolution in Escherichia coli

Mobile genetic elements are widespread in bacteria, where they cause several kinds of mutations. Although their effects are on the whole negative, rare beneficial mutations caused by insertion sequence elements are frequently selected in some experimental evolution systems. For example, in earlier work, we found that strains of Escherichia coli that lack the sigma factor RpoS adapt to a high-osmolarity environment by the insertion of element IS10 into the promoter of the otsBA operon, rewiring expression from RpoS dependent to RpoS independent. We wished to determine how the presence of IS10 in the genome of this strain shaped the evolutionary outcome. IS10 could influence the outcome by causing mutations that confer adaptive phenotypes that cannot be achieved by strains without the element. Alternatively, IS10 could influence evolution by increasing the rate of appearance of certain classes of beneficial mutations even if they are no better than those that could be achieved by a strain without the element. We found that populations evolved from an IS10-free strain did not upregulate otsBA. An otsBA-lacZY fusion facilitated the recovery of a number of mutations that upregulate otsB without involving IS10 and found that two caused greater fitness increases than IS10 insertion, implying that evolution could have upregulated otsBA in the IS10-free strain. Finally, we demonstrate that there is epistasis between the IS10 insertion into the otsBA promoter and the other adaptive mutations, implying that introduction of IS10 into the otsBA promoter may alter the trajectory of adaptive evolution. We conclude that IS10 exerts its effect not by creating adaptive phenotypes that could not otherwise occur but by increasing the rate of appearance of certain adaptive mutations

Teleportation of geometric structures in 3D

Simplest quantum teleportation algorithms can be represented in geometric terms in spaces of dimensions 3 (for real state-vectors) and 4 (for complex state-vectors). The geometric representation is based on geometric-algebra coding, a geometric alternative to the tensor-product coding typical of quantum mechanics. We discuss all the elementary ingredients of the geometric version of the algorithm: Geometric analogs of states and controlled Pauli gates. Fully geometric presentation is possible if one employs a nonstandard representation of directed magnitudes, formulated in terms of colors defined via stereographic projection of a color wheel, and not by means of directed volumes.Comment: typos corrected, one plot remove

Maximally-localized generalized Wannier functions for composite energy bands

We discuss a method for determining the optimally-localized set of generalized Wannier functions associated with a set of Bloch bands in a crystalline solid. By ``generalized Wannier functions'' we mean a set of localized orthonormal orbitals spanning the same space as the specified set of Bloch bands. Although we minimize a functional that represents the total spread sum_n [ _n - _n^2 ] of the Wannier functions in real space, our method proceeds directly from the Bloch functions as represented on a mesh of k-points, and carries out the minimization in a space of unitary matrices U_mn^k describing the rotation among the Bloch bands at each k-point. The method is thus suitable for use in connection with conventional electronic-structure codes. The procedure also returns the total electric polarization as well as the location of each Wannier center. Sample results for Si, GaAs, molecular C2H4, and LiCl will be presented.Comment: 22 pages, two-column style with 4 postscript figures embedded. Uses REVTEX and epsf macros. Also available at http://www.physics.rutgers.edu/~dhv/preprints/index.html#nm_wan

The causes of epistasis

[EN] Since Bateson's discovery that genes can suppress the phenotypic effects of other genes, gene interactions-called epistasis-have been the topic of a vast research effort. Systems and developmental biologists study epistasis to understand the genotype-phenotype map, whereas evolutionary biologists recognize the fundamental importance of epistasis for evolution. Depending on its form, epistasis may lead to divergence and speciation, provide evolutionary benefits to sex and affect the robustness and evolvability of organisms. That epistasis can itself be shaped by evolution has only recently been realized. Here, we review the empirical pattern of epistasis, and some of the factors that may affect the form and extent of epistasis. Based on their divergent consequences, we distinguish between interactions with or without mean effect, and those affecting the magnitude of fitness effects or their sign. Empirical work has begun to quantify epistasis in multiple dimensions in the context of metabolic and fitness landscape models. We discuss possible proximate causes (such as protein function and metabolic networks) and ultimate factors (including mutation, recombination, and the importance of natural selection and genetic drift). We conclude that, in general, pleiotropy is an important prerequisite for epistasis, and that epistasis may evolve as an adaptive or intrinsic consequence of changes in genetic robustness and evolvability.We thank Fons Debets, Ryszard Korona, Alexey Kondrashov, Joachim Krug, Sijmen Schoustra and an anonymous reviewer for constructive comments, and funds from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement 225167 (eFLUX), a visitor grant from Research School Production Ecology and Resource Conservation for S.F.E., and NSF grant DEB-0844355 for T.F.C.De Visser, JAGM.; Cooper, TF.; Elena Fito, SF. (2011). The causes of epistasis. Proceedings of the Royal Society B: Biological Sciences. 278(1725):3617-3624. https://doi.org/10.1098/rspb.2011.1537S361736242781725Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. D., Sevier, C. S., … Mostafavi, S. (2010). The Genetic Landscape of a Cell. Science, 327(5964), 425-431. doi:10.1126/science.1180823Moore, J. H., & Williams, S. M. (2005). Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. BioEssays, 27(6), 637-646. doi:10.1002/bies.20236Phillips, P. C. (2008). Epistasis — the essential role of gene interactions in the structure and evolution of genetic systems. Nature Reviews Genetics, 9(11), 855-867. doi:10.1038/nrg2452Azevedo, R. B. R., Lohaus, R., Srinivasan, S., Dang, K. K., & Burch, C. L. (2006). Sexual reproduction selects for robustness and negative epistasis in artificial gene networks. Nature, 440(7080), 87-90. doi:10.1038/nature04488Desai, M. M., Weissman, D., & Feldman, M. W. (2007). Evolution Can Favor Antagonistic Epistasis. Genetics, 177(2), 1001-1010. doi:10.1534/genetics.107.075812Gros, P.-A., Le Nagard, H., & Tenaillon, O. (2009). The Evolution of Epistasis and Its Links With Genetic Robustness, Complexity and Drift in a Phenotypic Model of Adaptation. Genetics, 182(1), 277-293. doi:10.1534/genetics.108.099127Liberman, U., & Feldman, M. (2008). On the evolution of epistasis III: The haploid case with mutation. Theoretical Population Biology, 73(2), 307-316. doi:10.1016/j.tpb.2007.11.010Liberman, U., & Feldman, M. W. (2005). On the evolution of epistasis I: diploids under selection. Theoretical Population Biology, 67(3), 141-160. doi:10.1016/j.tpb.2004.11.001Liberman, U., Puniyani, A., & Feldman, M. W. (2007). On the evolution of epistasis II: A generalized Wright–Kimura framework. Theoretical Population Biology, 71(2), 230-238. doi:10.1016/j.tpb.2006.10.002Martin, O. C., & Wagner, A. (2009). Effects of Recombination on Complex Regulatory Circuits. Genetics, 183(2), 673-684. doi:10.1534/genetics.109.104174Misevic, D., Ofria, C., & Lenski, R. E. (2005). Sexual reproduction reshapes the genetic architecture of digital organisms. Proceedings of the Royal Society B: Biological Sciences, 273(1585), 457-464. doi:10.1098/rspb.2005.3338Bateson W. Saunders E. R. Punnett R. C.& Hurst C. C.. 1905 Reports to the Evolution Committee of the Royal Society Report II. London UK: Harrison and Sons.Fisher, R. A. (1919). XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance. Transactions of the Royal Society of Edinburgh, 52(2), 399-433. doi:10.1017/s0080456800012163Kondrashov, F. A., & Kondrashov, A. S. (2001). Multidimensional epistasis and the disadvantage of sex. Proceedings of the National Academy of Sciences, 98(21), 12089-12092. doi:10.1073/pnas.211214298Barton, N. H. (1995). A general model for the evolution of recombination. 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The Number of Mutations Selected During Adaptation in a Laboratory Population of Saccharomyces cerevisiae. Genetics, 169(4), 1825-1831. doi:10.1534/genetics.104.027102Peña, M. de la, Elena, S. F., & Moya, A. (2000). EFFECT OF DELETERIOUS MUTATION-ACCUMULATION ON THE FITNESS OF RNA BACTERIOPHAGE MS2. Evolution, 54(2), 686. doi:10.1554/0014-3820(2000)054[0686:eodmao]2.0.co;2De Visser, J. A. G. M., Hoekstra, R. F., & van den Ende, H. (1997). Test of Interaction Between Genetic Markers That Affect Fitness in Aspergillus niger. Evolution, 51(5), 1499. doi:10.2307/2411202Elena, S. F. (1999). Little Evidence for Synergism Among Deleterious Mutations in a Nonsegmented RNA Virus. Journal of Molecular Evolution, 49(5), 703-707. doi:10.1007/pl00000082Elena, S. F., & Lenski, R. E. (1997). Test of synergistic interactions among deleterious mutations in bacteria. Nature, 390(6658), 395-398. doi:10.1038/37108Hall, D. W., Agan, M., & Pope, S. C. (2010). Fitness Epistasis among 6 Biosynthetic Loci in the Budding Yeast Saccharomyces cerevisiae. Journal of Heredity, 101(Supplement 1), S75-S84. doi:10.1093/jhered/esq007Kelly, J. K. (2005). Epistasis in Monkeyflowers. Genetics, 171(4), 1917-1931. doi:10.1534/genetics.105.041525Segrè, D., DeLuna, A., Church, G. M., & Kishony, R. (2004). Modular epistasis in yeast metabolism. Nature Genetics, 37(1), 77-83. doi:10.1038/ng1489He, X., Qian, W., Wang, Z., Li, Y., & Zhang, J. (2010). Prevalent positive epistasis in Escherichia coli and Saccharomyces cerevisiae metabolic networks. Nature Genetics, 42(3), 272-276. doi:10.1038/ng.524Carneiro, M., & Hartl, D. L. (2009). Adaptive landscapes and protein evolution. Proceedings of the National Academy of Sciences, 107(suppl_1), 1747-1751. doi:10.1073/pnas.0906192106Franke, J., Klözer, A., de Visser, J. A. G. M., & Krug, J. (2011). Evolutionary Accessibility of Mutational Pathways. PLoS Computational Biology, 7(8), e1002134. doi:10.1371/journal.pcbi.1002134Weinreich, D. M. (2006). Darwinian Evolution Can Follow Only Very Few Mutational Paths to Fitter Proteins. Science, 312(5770), 111-114. doi:10.1126/science.1123539Lunzer, M. (2005). The Biochemical Architecture of an Ancient Adaptive Landscape. Science, 310(5747), 499-501. doi:10.1126/science.1115649O’Maille, P. E., Malone, A., Dellas, N., Andes Hess, B., Smentek, L., Sheehan, I., … Noel, J. P. (2008). Quantitative exploration of the catalytic landscape separating divergent plant sesquiterpene synthases. Nature Chemical Biology, 4(10), 617-623. doi:10.1038/nchembio.113Lozovsky, E. R., Chookajorn, T., Brown, K. M., Imwong, M., Shaw, P. J., Kamchonwongpaisan, S., … Hartl, D. L. (2009). Stepwise acquisition of pyrimethamine resistance in the malaria parasite. Proceedings of the National Academy of Sciences, 106(29), 12025-12030. doi:10.1073/pnas.0905922106De Visser, J. A. G. M., Park, S., & Krug, J. (2009). Exploring the Effect of Sex on Empirical Fitness Landscapes. The American Naturalist, 174(S1), S15-S30. doi:10.1086/599081Khan, A. I., Dinh, D. M., Schneider, D., Lenski, R. E., & Cooper, T. F. (2011). Negative Epistasis Between Beneficial Mutations in an Evolving Bacterial Population. Science, 332(6034), 1193-1196. doi:10.1126/science.1203801Chou, H.-H., Chiu, H.-C., Delaney, N. F., Segre, D., & Marx, C. J. (2011). Diminishing Returns Epistasis Among Beneficial Mutations Decelerates Adaptation. Science, 332(6034), 1190-1192. doi:10.1126/science.1203799Da Silva, J., Coetzer, M., Nedellec, R., Pastore, C., & Mosier, D. E. (2010). Fitness Epistasis and Constraints on Adaptation in a Human Immunodeficiency Virus Type 1 Protein Region. Genetics, 185(1), 293-303. doi:10.1534/genetics.109.112458Hinkley, T., Martins, J., Chappey, C., Haddad, M., Stawiski, E., Whitcomb, J. M., … Bonhoeffer, S. (2011). A systems analysis of mutational effects in HIV-1 protease and reverse transcriptase. Nature Genetics, 43(5), 487-489. doi:10.1038/ng.795Kvitek, D. J., & Sherlock, G. (2011). Reciprocal Sign Epistasis between Frequently Experimentally Evolved Adaptive Mutations Causes a Rugged Fitness Landscape. PLoS Genetics, 7(4), e1002056. doi:10.1371/journal.pgen.1002056MacLean, R. C., Perron, G. G., & Gardner, A. (2010). Diminishing Returns From Beneficial Mutations and Pervasive Epistasis Shape the Fitness Landscape for Rifampicin Resistance in Pseudomonas aeruginosa. Genetics, 186(4), 1345-1354. doi:10.1534/genetics.110.123083Rokyta, D. R., Joyce, P., Caudle, S. B., Miller, C., Beisel, C. J., & Wichman, H. A. (2011). Epistasis between Beneficial Mutations and the Phenotype-to-Fitness Map for a ssDNA Virus. PLoS Genetics, 7(6), e1002075. doi:10.1371/journal.pgen.1002075Salverda, M. L. M., Dellus, E., Gorter, F. A., Debets, A. J. M., van der Oost, J., Hoekstra, R. F., … de Visser, J. A. G. M. (2011). Initial Mutations Direct Alternative Pathways of Protein Evolution. PLoS Genetics, 7(3), e1001321. doi:10.1371/journal.pgen.1001321Hayashi, Y., Aita, T., Toyota, H., Husimi, Y., Urabe, I., & Yomo, T. (2006). Experimental Rugged Fitness Landscape in Protein Sequence Space. PLoS ONE, 1(1), e96. doi:10.1371/journal.pone.0000096De Visser, J. A. G., & Lenski, R. E. (2002). BMC Evolutionary Biology, 2(1), 19. doi:10.1186/1471-2148-2-19Kryazhimskiy, S., Tkacik, G., & Plotkin, J. B. (2009). The dynamics of adaptation on correlated fitness landscapes. Proceedings of the National Academy of Sciences, 106(44), 18638-18643. doi:10.1073/pnas.0905497106Lehner, B. (2011). Molecular mechanisms of epistasis within and between genes. Trends in Genetics, 27(8), 323-331. doi:10.1016/j.tig.2011.05.007Feist, A. M., Henry, C. S., Reed, J. L., Krummenacker, M., Joyce, A. R., Karp, P. D., … Palsson, B. Ø. (2007). A genome‐scale metabolic reconstruction for Escherichia coli K‐12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Molecular Systems Biology, 3(1), 121. doi:10.1038/msb4100155Szappanos, B., Kovács, K., Szamecz, B., Honti, F., Costanzo, M., Baryshnikova, A., … Papp, B. (2011). An integrated approach to characterize genetic interaction networks in yeast metabolism. Nature Genetics, 43(7), 656-662. doi:10.1038/ng.846Dean, A. M., Dykhuizen, D. E., & Hartl, D. L. (1986). Fitness as a function of β-galactosidase activity in Escherichia coli. Genetical Research, 48(1), 1-8. doi:10.1017/s0016672300024587Trindade, S., Sousa, A., Xavier, K. B., Dionisio, F., Ferreira, M. G., & Gordo, I. (2009). Positive Epistasis Drives the Acquisition of Multidrug Resistance. PLoS Genetics, 5(7), e1000578. doi:10.1371/journal.pgen.1000578Agrawal, A. F., & Whitlock, M. C. (2010). Environmental duress and epistasis: how does stress affect the strength of selection on new mutations? Trends in Ecology & Evolution, 25(8), 450-458. doi:10.1016/j.tree.2010.05.003Bonhoeffer, S. (2004). Evidence for Positive Epistasis in HIV-1. Science, 306(5701), 1547-1550. doi:10.1126/science.1101786Burch, C. L., & Chao, L. (2004). Epistasis and Its Relationship to Canalization in the RNA Virus φ6. Genetics, 167(2), 559-567. doi:10.1534/genetics.103.021196Martin, G., Elena, S. F., & Lenormand, T. (2007). Distributions of epistasis in microbes fit predictions from a fitness landscape model. Nature Genetics, 39(4), 555-560. doi:10.1038/ng1998DePristo, M. A., Weinreich, D. M., & Hartl, D. L. (2005). Missense meanderings in sequence space: a biophysical view of protein evolution. Nature Reviews Genetics, 6(9), 678-687. doi:10.1038/nrg1672Wang, X., Minasov, G., & Shoichet, B. K. (2002). Evolution of an Antibiotic Resistance Enzyme Constrained by Stability and Activity Trade-offs. Journal of Molecular Biology, 320(1), 85-95. doi:10.1016/s0022-2836(02)00400-xBj&ouml;rkman, J. (2000). Effects of Environment on Compensatory Mutations to Ameliorate Costs of Antibiotic Resistance. 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PLoS ONE, 3(3), e1715. doi:10.1371/journal.pone.0001715Kashtan, N., & Alon, U. (2005). Spontaneous evolution of modularity and network motifs. Proceedings of the National Academy of Sciences, 102(39), 13773-13778. doi:10.1073/pnas.0503610102De Visser, J. A. G. M., Hermisson, J., Wagner, G. P., Meyers, L. A., Bagheri-Chaichian, H., Blanchard, J. L., … Whitlock, M. C. (2003). PERSPECTIVE:EVOLUTION AND DETECTION OF GENETIC ROBUSTNESS. Evolution, 57(9), 1959. doi:10.1554/02-750rWilke, C. O., & Christoph, A. (2001). Interaction between directional epistasis and average mutational effects. Proceedings of the Royal Society of London. Series B: Biological Sciences, 268(1475), 1469-1474. doi:10.1098/rspb.2001.1690Sanjuan, R., & Elena, S. F. (2006). Epistasis correlates to genomic complexity. Proceedings of the National Academy of Sciences, 103(39), 14402-14405. doi:10.1073/pnas.0604543103Sanjuán, R., & Nebot, M. R. (2008). 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J., Ferrada, E., & Wagner, A. (2011). Cryptic genetic variation promotes rapid evolutionary adaptation in an RNA enzyme. Nature, 474(7349), 92-95. doi:10.1038/nature1008

Canalization of the evolutionary trajectory of the human influenza virus

Since its emergence in 1968, influenza A (H3N2) has evolved extensively in genotype and antigenic phenotype. Antigenic evolution occurs in the context of a two-dimensional 'antigenic map', while genetic evolution shows a characteristic ladder-like genealogical tree. Here, we use a large-scale individual-based model to show that evolution in a Euclidean antigenic space provides a remarkable correspondence between model behavior and the epidemiological, antigenic, genealogical and geographic patterns observed in influenza virus. We find that evolution away from existing human immunity results in rapid population turnover in the influenza virus and that this population turnover occurs primarily along a single antigenic axis. Thus, selective dynamics induce a canalized evolutionary trajectory, in which the evolutionary fate of the influenza population is surprisingly repeatable and hence, in theory, predictable.Comment: 29 pages, 5 figures, 10 supporting figure

Altered thymic differentiation and modulation of arthritis by invariant NKT cells expressing mutant ZAP70

Various subsets of invariant natural killer T (iNKT) cells with different cytokine productions develop in the mouse thymus, but the factors driving their differentiation remain unclear. Here we show that hypomorphic alleles of Zap70 or chemical inhibition of Zap70 catalysis leads to an increase of IFN-gamma-producing iNKT cells (NKT1 cells), suggesting that NKT1 cells may require a lower TCR signal threshold. Zap70 mutant mice develop IL-17-dependent arthritis. In a mouse experimental arthritis model, NKT17 cells are increased as the disease progresses, while NKT1 numbers negatively correlates with disease severity, with this protective effect of NKT1 linked to their IFN-gamma expression. NKT1 cells are also present in the synovial fluid of arthritis patients. Our data therefore suggest that TCR signal strength during thymic differentiation may influence not only IFN-gamma production, but also the protective function of iNKT cells in arthritis