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Human activity impacts the evolutionary trajectories of many species worldwide. Global trade of agricultural goods contributes to the dispersal of pathogens reshaping their genetic makeup and providing opportunities for virulence gains. Understanding how pathogens surmount control strategies and cope with new climates is crucial to predicting the future impact of crop pathogens. Here, we address this by assembling a global thousand-genome panel of Zymoseptoria tritici, a major fungal pathogen of wheat reported in all production areas worldwide. We identify the global invasion routes and ongoing genetic exchange of the pathogen among wheat-growing regions. We find that the global expansion was accompanied by increased activity of transposable elements and weakened genomic defenses. Finally, we find significant standing variation for adaptation to new climates encountered during the global spread. Our work shows how large population genomic panels enable deep insights into the evolutionary trajectory of a major crop pathogen.

With >7000 species the order of rust fungi has a disproportionately large impact on agriculture, horticulture, forestry and foreign ecosystems. The infectious spores are typically dikaryotic, a feature unique to fungi in which two haploid nuclei reside in the same cell. A key example is Phakopsora pachyrhizi, the causal agent of Asian soybean rust disease, one of the world's most economically damaging agricultural diseases. Despite P. pachyrhizi's impact, the exceptional size and complexity of its genome prevented generation of an accurate genome assembly. Here, we sequence three independent P. pachyrhizi genomes and uncover a genome up to 1.25 Gb comprising two haplotypes with a transposable element (TE) content of ~93%. We study the incursion and dominant impact of these TEs on the genome and show how they have a key impact on various processes such as host range adaptation, stress responses and genetic plasticity.

Abstract Structural defects control the kinetic, thermodynamic and mechanical properties of glasses. For instance, rare quantum tunneling two-level systems (TLS) govern the physics of glasses at very low temperature. Due to their extremely low density, it is very hard to directly identify them in computer simulations. We introduce a machine learning approach to efficiently explore the potential energy landscape of glass models and identify desired classes of defects. We focus in particular on TLS and we design an algorithm that is able to rapidly predict the quantum splitting between any two amorphous configurations produced by classical simulations. This in turn allows us to shift the computational effort towards the collection and identification of a larger number of TLS, rather than the useless characterization of non-tunneling defects which are much more abundant. Finally, we interpret our machine learning model to understand how TLS are identified and characterized, thus giving direct physical insight into their microscopic nature.

Asian soybean rust caused by Phakopsora pachyrhizi is an important plant pathogen, but an accurate genome assembly for this fungus has been lacking. This study sequenced three independent P. pachyrhizi isolates and generated reference quality assemblies and genome annotations, representing a critical step for further in-depth studies of this pathogen and the development of new methods of control.

Zymoseptoria tritici is an important fungal pathogen of wheat which has spread globally. Here, the authors perform genomic analyses on a collection of ~1100 Z. tritici samples from 42 countries to describe its global spread and elucidate mechanisms of adaptation to different environmental conditions.

Deep learning (DL) can be used to automatically extract complex features from dynamic systems. Here, the authors combine high-content imaging, DL and mechanistic models to extract and explain drug-induced morphological changes in the growth of the fungus responsible for Asian soybean rust.

The nature of the defects in amorphous materials, analogous to vacancies and dislocations in crystals, remains a matter of debate. Scalliet et al. show that localized and extended defects coexist in a wide range of conditions, yet are associated with distinct energy scales in a prototypical glass model.