Ecology

Global genetic structure of the pathogen tracks the historical spread of wheatWe assessed the evolutionary trajectory of the pathogen in conjunction with the history of global wheat cultivation (Fig. 1a). For this, we assembled a worldwide collection of Z. tritici isolates from naturally infected fields (Fig. 1b). We selected isolates covering most wheat production areas, both in the center of origin of the crop (i.e., the Fertile Crescent in the Middle East), and in areas where wheat was introduced during the last millennia (i.e., Europe and North Africa), or last centuries (i.e., the Americas and Oceania; Fig. 1c). We called variants in a set of 1109 high-quality short-read resequencing datasets (Supplementary Data 1, 2) covering 42 countries and a broad range of climates. Using a joint genotyping approach, we produced raw variant calls mapped to the telomere-to-telomere assembled reference genome IPO323. To assess genotyping accuracy, we used eight isolates with replicate sequencing data to analyze discrepancies. We adjusted quality thresholds targeting specifically the type of genotyping errors observed in our data set (Fig. S1). The improved filtering yielded 8,406,818 high-confidence short variants (short indels and SNPs). The final variant set included 5,578,488 biallelic SNPs corresponding to 14.1% of the genome.Fig. 1: Global sampling of the wheat pathogen Zymoseptoria tritici retracing the historical spread of its host.a Schematic representation of the introduction of wheat across continents. b Septoria tritici blotch symptoms caused by Z. tritici on wheat leaves. Pictures taken by B. A. McDonald, ETH Zurich. c Map of the sampling scheme for the global collection of 1109 isolates for whole-genome sequencing.Full size imageWe tested whether global diversity patterns of pathogen populations are likely a consequence of the history of wheat cultivation. We first performed unsupervised clustering of genotypes and identified eleven well-supported clusters (Fig. 2a, Figs. S2,3). Over 90% of the genotypes were clearly assigned to a single cluster (Fig. 2a, Supplementary Data 3). Two clusters were identified among genotypes originating from the pathogen center of origin, distinguishing collections from Iran and Middle Eastern regions. Genotypes from Africa and Europe split into two distinct genetic clusters without any apparent secondary structure within clusters. This lack of any fine-scale structure is remarkable given the extensive geographic sampling of European genotypes and suggests extensive gene flow within the continent. Genotypes from Oceania grouped into three distinct clusters marked by collections from Tasmania, the Australian mainland, and New Zealand. Genotypes from North America formed two clusters along a North-South separation. Finally, South American genotypes formed two clusters split along the Andes (Chile versus Argentina and Uruguay). Some uncertainty exists in the assessment of regional population structure by low coverage of major wheat-producing countries such as Russia and Ukraine. Septoria tritici blotch is only sporadically reported in China. In complementary analyses, we found that a phylogenetic network accounting for the high frequency of recombination consistently reflected the global population structure (Fig. S4). A principal component analysis of all genotypes confirmed the nested genetic structure with differentiation at the continent level, subdivisions within some continents and the existence of admixed genotypes (Fig. 2b, Fig. S5).Fig. 2: Global genetic structure based on 1109 genomes.a Map of the genetic clustering based on a thinned genome-wide SNP dataset using sNMF. Each color represents a different genetic cluster, and the sizes of the slices represent the average attribution to the cluster across the isolates from each location. Fractions representing less than 10% of all genotypes of a location were colored in grey to improve clarity. The large pie chart outside of the map represents the proportion of isolates assigned clearly (≥75%) to a single genetic cluster (pure; in teal) and isolates identified as hybrids (admixed) between clusters (in yellow). Names of the clusters include an abbreviation of continents and a more precise geographical location (MEA: Middle East and Africa; NA: North America; SA: South America; OC: Oceania). b Principal component analysis, showing the first and second component (PCs) based on a subset of variants. Colors and shapes indicate the genomic clusters identified with the sNMF method (with hybrids in grey). The marginal distributions represent the distribution for each PC. PCs 1 to 8 are shown in Fig S4. c Population tree based on Treemix, rooted using two genomes from the sister species Z. passerinii and Z. ardabiliae. The colors are the same as in the previous panels and only samples which were fully assigned to a cluster were used. d Diversity estimated with using pi per genetic cluster. The boxplots are ordered according to the tree of panel. c. The lower and upper hinges correspond to the first and third quartiles, the whiskers to the largest value are within 1.5 times the inter-quartile range, and the central horizontal line defines the median. e Linkage disequilibrium (r2) between variants per genetic cluster. Colors are identical among panels.Full size imageWe analyzed the history of population splits and admixture using allele frequency information (Fig. 2c). The analyses largely supported a genetic structure shaped by the introduction of wheat across continents. The historical relationships between clusters show an early divergence of the Middle Eastern and North African clusters matching the early introduction of agriculture in these regions. Populations in Europe and the Americas share a similar time point of divergence consistent with extensive contributions of European genotypes to the Western hemisphere. Oceanian groups have diverged as a single branch from genotypes most closely related to extant European populations. Matching the introduction of wheat to Oceania from the European continent, the Australian and New Zealand pathogen populations share a common origin rooted in European genetic diversity. Populations from Australia show also a striking loss of diversity and higher linkage disequilibrium compared to European diversity consistent with a significant founder effect (Fig. 2d, e). Similarly, populations in South and North America have reduced genetic diversity compared to extant European populations as suggested previously based on Sanger sequencing16. The highest diversity was found in populations from Africa and the Middle East closest to the center of origin. Overall, the global genetic structure of the pathogen reveals multiple founder events associated with the introduction of wheat to new continents.Ongoing gene flow among regions should lead to admixed genotypes. We found that nearly 10% of all analyzed genotypes showed contributions from at least two clusters. The most significant recent gene flow was detected between Middle Eastern/North African clusters and European clusters in North Africa (i.e., Algeria and Tunisia) as well as Southern and Eastern Europe (i.e., France, Italy, Hungary, Ukraine, Portugal, and Spain; Supplementary Data 3). We found a particularly high incidence of recent immigration in a durum wheat population in the south of France. The population consisted only of hybrids or atypical genotypes suggesting either recent migration from North Africa or host specialization on durum wheat varieties. Additionally, we found hybrid genotypes with European ancestry in both North America and in Oceania. The relatively balanced ancestry proportions in these hybrids suggest very recent gene flow dating back to only a few generations. We further investigated past gene flow between clusters by allowing Treemix to infer migration events, thus creating a population network (Fig. S6a–d). Three distinct recent migration events were best explaining the data with specific migration routes from the Middle East/African clusters to North America, from an Australian cluster to South America and between two Oceanian clusters (Fig. S6d). However, the migration events did not affect the overall shape of the inferred population tree (Fig. 2c, Fig. S6b–d). To better understand effects of long-distance gene flow, we investigated the relationship between relatedness among genotypes (i.e., identity-by-state) and geographic distance. At the continent level, we observed a negative relationship between identity-by-state and geographic distance (Fig. S7). The wide distribution of identity-by-state values shows that although closely related isolates tend to be found at closer geographic distance, distantly related isolates can be found at both far and close geographic distances. Long-distance migration events are most likely caused by international trade similar as for other crop pathogens17,18,19. In combination, our findings show an important role of long-distance dispersal impacting the genetic make-up of populations from individual fields to continental scale genetic diversity.Relaxation of genomic defenses against transposable elements concurrent with global spreadTransposable elements (TEs) are drivers of genome evolution. In Z. tritici, TE activity created beneficial mutations for fungicide resistance and virulence on the wheat host20,21. Rapid recent adaptation of the pathogen has benefitted from the activity of TEs with consequences for genome size22. Unchecked transposition of TEs can be deleterious and an array of defenses mechanisms has evolved to counteract their activity both at the genomic and epigenetic level including targeted mutations and silencing23. To analyze the effectiveness of genomic defenses against active TEs, we screened all genomes for evidence of TE insertions. We mapped short-read sequencing data on the reference genome and a species-specific TE sequence library. We classified evidence for TEs in each of the analyzed isolates as reference TEs (i.e. also present in the reference genome) and non-reference TE (i.e. absent). Detected TEs among isolates were binned into loci (width 100 bp) to account for uncertainties about the precise mapping of the insertion point. We found that the frequency spectrum of TE insertions is heavily skewed towards low frequencies with 77% of TE insertions being found in single isolates (~0.1% frequency) and 96% of insertions were found in ten or fewer isolates ( More

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Theropod dinosaurs such as Tarbosaurus bataar grew large or small in a range of ways.Credit: Marco Ansaloni/SPL

A sweeping analysis of shin bones has given researchers a glimpse into how some dinosaurs evolved into mega-beasts such as Tyrannosaurus, and others into smaller, bird-like creatures. The work, published this week in Science1, reveals that dinosaurs used more than one evolutionary trick to become larger — or smaller — over time.Prevailing wisdom held that large-bodied animals are bigger than their smaller-bodied relatives because they grow faster during their most rapid period of growth. That trend holds true for modern animals including birds and mammals — elephants and ostriches grow faster than chihuahuas and sparrows, for example.It’s not the case for all animals. Crocodiles and alligators, for instance, become large because they grow for a long time. But palaeontologists had assumed that for theropod dinosaurs — a group that includes the iconic T. rex and which spawned modern birds — large species got big through rapid growth spurts. “It’s kind of become the established idea in dinosaurs,” says palaeontologist Michael D’Emic at Adelphi University in Garden City, New York.But that’s not what D’Emic found when he sawed into the bones of Majungasaurus, a 7-metre-long T. rex relative that lived 66 million years ago on what is now Madagascar. The speed of growth in dinosaurs is recorded in rings laid down each year in their bones. Instead of seeing wide rings corresponding to a rapid adolescent growth spurt, D’Emic found lots of narrow growth rings, suggesting that Majungasaurus had become large over a prolonged period.“I was very surprised,” he says. The next dinosaur he examined, a similar-sized beast called Ceratasaurus, was the opposite — a big dinosaur that grew fast during its growth spurt, says D’Emic.Bone growth ringsOver a decade, D’Emic and his colleagues amassed bone growth-ring measurements from 42 theropod species to see which strategies led to large and small bodies. They found that 31% of theropod species were larger than their ancestors because of faster growth and 28% because of prolonged growth. Meanwhile, 21% became smaller than their ancestors by shortening their growth spurts, and 19% by slowing growth.The study covered theropod species that lived between 230 million years ago and the end of the Cretaceous period 66 million years ago, when a mass-extinction event wiped out the non-avian dinosaurs. It’s “a huge evolutionary timescale”, to include in an analysis, says Vera Weisbecker, an evolutionary biologist at Flinders University in Adelaide, Australia. “That is really impressive,” she says. “It’s just fascinating that there are so many developmental ways to become big or small.”Palaeontologist Kevin Padian at the University of California, Berkeley, says the analysis is the kind of work that needs to be done, animal group by animal group, to understand how body size evolves.Drivers of changeBut Meike Köhler, an evolutionary palaeobiologist at the Catalan Institution for Research and Advanced Studies in Barcelona, Spain, says the findings are not surprising because previous work has shown a range of growth strategies across animal species. Köhler would like to see an analysis that considers what ecological circumstances influenced how animals changed in size over time.Weisbecker says that the growth strategy used might be related to evolutionary pressures. “If you looked at all the ones with explosive early growth, you might be able to test if they happen to be the ones that are more likely to be predated on, for example,” she says.For each species, the growth strategy that led to its individual body size probably related to its unique environment, says Padian. “It’s not a one-size-fits-all, which is a good thing for us to learn,” he says. “We might have thought that, but they’ve documented it.”D’Emic says he and his team are conducting similar analyses on other groups, including mammals — a group that contains many more species to sample — to see whether the diversity is found in other branches of the evolutionary tree. More