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(Non)Parallel Evolution 3 4 Daniel I. Bolnick1,2*, Rowan Barrett3, Krista 1 2 3 (Non)Parallel Evolution 4 5 Daniel I. Bolnick1,2*, Rowan Barrett3, Krista Oke3,4 , Diana J. Rennison5 , Yoel E. Stuart1 6 7 1 Department of Integrative Biology, University of Texas at Austin, Austin TX 78712, USA; 8 [email protected]; [email protected] 9 2 Department of Ecology and Evolution, University of Connecticut, Storrs CT 06268, USA (as of 10 07/2018) 11 3 Redpath Museum, McGill University, Montreal, Quebec, Canada; [email protected] 12 4 Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa 13 Cruz, CA, 95060, USA; [email protected] 14 5 Institute of Ecology and Evolution, University of Bern, 3012 Bern, Switzerland; 15 [email protected] 16 17 * Corresponding author: Email: [email protected] Telephone: +011 (512) 471-2824 18 19 20 Running title: “(Non)Parallel Evolution” 21 22 1 23 Abstract 24 Parallel evolution across replicate populations has provided evolutionary biologists with iconic 25 examples of adaptation. When multiple populations colonize seemingly similar habitats, they 26 may evolve similar genes, traits, or functions. Yet, replicated evolution in nature or in the lab 27 often yields inconsistent outcomes: some replicate populations evolve along highly similar 28 trajectories, whereas other replicate populations evolve to different extents or in atypical 29 directions. To understand these heterogeneous outcomes, biologists are increasingly treating 30 parallel evolution not as a binary phenomenon but rather as a quantitative continuum ranging 31 from nonparallel to parallel. By measuring replicate populations’ positions along this 32 “(non)parallel” continuum, we can test hypotheses about evolutionary and ecological factors that 33 influence the likelihood of repeatable evolution. We review evidence regarding the distribution of 34 (non)parallel evolution in the laboratory and in nature and enumerate the many genetic and 35 evolutionary processes that contribute to variation in the extent of parallel evolution. 36 37 Key Words: Adaptation, Convergence, Divergence, Many-to-One Mapping, Nonparallel, 38 Parallel Evolution 2 39 I. INTRODUCTION 40 41 Parallel evolution holds a special place in the annals of evolutionary biology because it provides 42 strong evidence for adaptation. The replicated independent evolution of similar traits leads us to 43 infer that evolution was driven by a deterministic process, most likely natural selection (Harvey 44 & Pagel 1991). Biologists therefore use the repeated, parallel evolution of genes, phenotypes, 45 or ecotypes to infer that (i) similar environments impose similar natural selection, (ii) there exist 46 few solutions to this selection, and (iii) the traits or genes that evolve in parallel are adaptations. 47 These inferences offer the hope that, in some situations, evolution may even be predictable 48 enough that we can anticipate evolution of pests or disease-causing agents, or evolutionary 49 responses to anthropogenic environmental change (Agrawal 2017, Day 2012, de Visser & Krug 50 2014, Langerhans 2017). However, this optimistic goal of predicting future evolution is only 51 plausible if parallel evolution is common and strong. 52 There are many textbook cases of parallel evolution that have rightfully received a lot of 53 attention (e.g., Colosimo et al 2005, Elmer et al 2014, Khaitovich et al 2005, Thompson et al 54 1997). But, are these representative of replicated evolution more generally, or have we given 55 undue attention to a few exceptionally parallel genes, traits, or species? If we objectively 56 surveyed replicate populations in similar habitats, how common and how extensive would 57 parallel evolution be? What fraction of replicate populations would evolve in parallel, for what 58 number of traits? Conversely, how often would replicate populations diverge genetically or 59 phenotypically despite experiencing similar environments? 60 As we describe in this review, there is widespread evidence that replicate populations in 61 similar environments sometimes evolve similar traits (or genes) and sometimes evolve 62 dissimilar traits (or genes). Thus, we argue here that parallel evolution is best viewed as an 63 extreme end of a quantitative continuum of ‘(non)parallel evolution’ (see Fig. 1 for a visual 64 glossary). Section II provides examples of this continuum of (non)parallel evolution, drawn from 65 settings of practical interest (e.g., disease, agriculture) to motivate study of (non)parallelism. 66 After addressing some semantics (Section III), we then describe approaches to quantify 67 (non)parallel evolution (Section IV), what those measures have revealed (Section V), and what 68 we learn about evolutionary biology more generally as a result (Section VI). Throughout this 69 essay, we seek answers to questions such as: What evolutionary forces generate variation in 70 (non)parallelism among replicate populations? What kinds of traits are more or less parallel? 71 Perhaps most fundamentally: when we see deviations from parallel evolution, what are we to 72 conclude about adaptation? Biologists use parallel evolution as evidence of adaptation, but 3 73 when evolution in similar environments falls toward the nonparallel end of the continuum, should 74 we infer there is maladaptation, neutral evolution, or adaptation? 75 76 77 II. INCOMPLETELY PARALLEL EVOLUTION 78 Our first goal for this review is to motivate it. That is, we must establish that evolution is often 79 less parallel than we might have reasonably expected. Intuitively, we expect that initially similar 80 populations that are exposed to similar selection pressures will evolve similar phenotypic 81 adaptations. As we show in this section, however, in many contexts this expectation is only 82 partly true, and the examples of nonparallel evolution described here illustrate the need for 83 quantitative rather than binary approaches to studying parallel evolution. In presenting these 84 cases of (non)parallel evolution, we focus on evolution in highly applied contexts to convey the 85 point that this evolutionary continuum has very practical consequences and should be 86 considered in an interdisciplinary way. 87 88 II.1 (Non)parallelism in cancer 89 Cancer tumors are evolving populations of cells (Burrell et al 2013, Nowell 1976, Shpak & Lu 90 2016, Swanton 2014). Tumors originate when somatic mutations confer an ‘escape’ from 91 normal cell cycle regulation. Growing tumors contain multiple genetically divergent cell lines that 92 differ in their ability to proliferate, evade the immune system, resist chemotherapy, and 93 metastasize. This genetic variation can therefore be subject to strong selection within a tumor. 94 Typically, each cancer patient is an independent, replicated case of one or more oncogenic 95 mutations that initiate a tumor and the subsequent clonal selection on additional mutations. If 96 tumor evolution is highly parallel, then the same mutations in the same genes should evolve 97 repeatedly in most or all patients. It is increasingly clear, however, that ostensibly similar tumors 98 (i.e., same tissue and histology) often comprise fundamentally different mutations across 99 patients. 100 In an experimental evolution study, Tegze et al. (2012) applied identical selection (18 101 months of chemotherapy) to 29 identical artificial tumors that were all derived from one breast 102 cancer cell line. Only 18 of the 29 replicates evolved resistance, and within those resistant 103 replicates, the underlying genetic changes were nonparallel, affecting different cell functions 104 (Tegze et al 2012). This result highlights some key themes: first, even identical starting 105 populations subjected to identical selection can exhibit nonparallel evolutionary responses. 4 106 Second, parallel evolution of resistance (an emergent function) occurred without parallel 107 evolution of the underlying genes. 108 Such evolutionary inconsistency also occurs in real cancer patients. Takahashi et al 109 (2007) compared allele frequency differences between primary versus metastatic lung tumor 110 genomes to find targets of selection during metastasis. Most of these rapidly evolving genes 111 experienced selection in only one or a few patients, and the rest were never shared by more 112 than half the patients (Takahashi et al 2007 ). This (non)parallel evolution is why cancer 113 treatment is increasingly reliant not on tissue type or histological traits but rather on 114 personalized genomics to tailor therapies to the particular causal gene(s) in an individual 115 (Abbosh et al 2017). 116 117 II.2 (Non)parallel evolution in pathogens 118 Like cancer, human pathogens show (non)parallel evolution in response to therapies and host 119 immunity. In HIV patients with low viral load during drug therapy, an interruption to therapy often 120 results in a rapid rebound of viral load. One study of 12 chronic HIV patients revealed that the 121 HIV-1 gp120 gene evolved rapidly in each patient when they experienced this viral rebound 122 (Martinez-Picado et al 2002). If gp120 evolved in parallel following therapy-interruption, we 123 could potentially develop drugs targeting the gp120 variants that facilitate rapid viral rebound. 124 However, for unknown reasons, different mutations contributed to this rebound in each patient, 125 so we cannot develop therapies that anticipate gp120 evolution following treatment interruption. 126 Human macrophages protect against pathogenic strains of Escherichia coli, but this 127 bacterium sometimes
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