Extreme overall mushroom genome expansion in Mycena s.s. irrespective of plant hosts or substrate specializations.

Mycena s.s. is a ubiquitous mushroom genus whose members degrade multiple dead plant substrates and opportunistically invade living plant roots. Having sequenced the nuclear genomes of 24 Mycena species, we find them to defy the expected patterns for fungi based on both their traditionally perceived saprotrophic ecology and substrate specializations. Mycena displayed massive genome expansions overall affecting all gene families, driven by novel gene family emergence, gene duplications, enlarged secretomes encoding polysaccharide degradation enzymes, transposable element (TE) proliferation, and horizontal gene transfers. Mainly due to TE proliferation, Arctic Mycena species display genomes of up to 502 Mbp (2-8× the temperate Mycena), the largest among mushroom-forming Agaricomycetes, indicating a possible evolutionary convergence to genomic expansions sometimes seen in Arctic plants. Overall, Mycena show highly unusual, varied mosaic-like genomic structures adaptable to multiple lifestyles, providing genomic illustration for the growing realization that fungal niche adaptations can be far more fluid than traditionally believed.

Establishment and validation of an electron inelastic mean free path database for narrow bandgap inorganic compounds with a machine learning approach.

Narrow bandgap inorganic compounds are extremely important in many areas of physics. However, their basic parameter database for surface analysis is incomplete. Electron inelastic mean free paths (IMFPs) are important parameters in surface analysis methods, such as electron spectroscopy and electron microscopy. Our previous research has presented a machine learning (ML) method to describe and predict IMFPs from calculated IMFPs for 41 elemental solids. This paper extends the use of the same machine learning method to 42 inorganic compounds based on the experience in predicting elemental electron IMFPs. The in-depth discussion extends to including material dependence discussion and parameter value selections. After robust validation of the ML method, we have produced an extensive IMFP database for 12 039 narrow bandgap inorganic compounds. Our findings suggest that ML is very efficient and powerful for IMFP description and database completion for various materials and has many advantages, including stability and convenience, over traditional methods.

Introgression between highly divergent fungal sister species.

To understand how species evolve and adapt to changing environments, it is important to study gene flow and introgression due to their influence on speciation and radiation events. Here, we apply a novel experimental system for investigating these mechanisms using natural populations. The system is based on two fungal sister species with morphological and ecological similarities occurring in overlapping habitats. We examined introgression between these species by conducting whole genome sequencing of individuals from populations in North America and Europe. We assessed genome-wide nucleotide divergence and performed crossing experiments to study reproductive barriers. We further used ABBA-BABA statistics together with a network analysis to investigate introgression, and conducted demographic modelling to gain insight into divergence times and introgression events. The results revealed that the species are highly divergent and incompatible in vitro. Despite this, small regions of introgression were scattered throughout the genomes and one introgression event likely involves a ghost population (extant or extinct). This study demonstrates that introgression can be found among divergent species and that population histories can be studied without collections of all the populations involved. Moreover, the experimental system is shown to be a useful tool for research on reproductive isolation in natural populations.

Large-scale fungal strain sequencing unravels the molecular diversity in mating loci maintained by long-term balancing selection.

Balancing selection, an evolutionary force that retains genetic diversity, has been detected in multiple genes and organisms, such as the sexual mating loci in fungi. However, to quantify the strength of balancing selection and define the mating-related genes require a large number of strains. In tetrapolar basidiomycete fungi, sexual type is determined by two unlinked loci, MATA and MATB. Genes in both loci define mating type identity, control successful mating and completion of the life cycle. These loci are usually highly diverse. Previous studies have speculated, based on culture crosses, that species of the non-model genus Trichaptum (Hymenochaetales, Basidiomycota) possess a tetrapolar mating system, with multiple alleles. Here, we sequenced a hundred and eighty strains of three Trichaptum species. We characterized the chromosomal location of MATA and MATB, the molecular structure of MAT regions and their allelic richness. The sequencing effort was sufficient to molecularly characterize multiple MAT alleles segregating before the speciation event of Trichaptum species. Analyses suggested that long-term balancing selection has generated trans-species polymorphisms. Mating sequences were classified in different allelic classes based on an amino acid identity (AAI) threshold supported by phylogenetics. 17,550 mating types were predicted based on the allelic classes. In vitro crosses allowed us to support the degree of allelic divergence needed for successful mating. Even with the high amount of divergence, key amino acids in functional domains are conserved. We conclude that the genetic diversity of mating loci in Trichaptum is due to long-term balancing selection, with limited recombination and duplication activity. The large number of sequenced strains highlighted the importance of sequencing multiple individuals from different species to detect the mating-related genes, the mechanisms generating diversity and the evolutionary forces maintaining them.

Mechanomics Biomarker for Cancer Cells Unidentifiable through Morphology and Elastic Modulus.

Cellular mechanical properties are potential cancer biomarkers used for objective cytology to replace the current subjective method relying on cytomorphology. However, heterogeneity among intra/intercellular mechanics and the interplay between cytoskeletal prestress and elastic modulus obscured the difference detectable between malignant and benign cells. In this work, we collected high density nanoscale prestress and elastic modulus data from a single cell by AFM indentation to generate a cellular mechanome. Such high dimensional mechanome data was used to train a malignancy classifier through machine learning. The classifier was tested on 340 single cells of various origins, malignancy, and degrees of similarity in morphology and elastic modulus. The classifier showed instrument-independent robustness and classification accuracy of 89% with an AUC-ROC value of 93%. A signal-to-noise ratio 8 times that of the human-cytologist-based morphological method was also demonstrated, in differentiating precancerous hyperplasia cells from normal cells derived from the same lung cancer patient.

Unveiling the principle descriptor for predicting the electron inelastic mean free path based on a machine learning framework.

The TPP-2M formula is the most popular empirical formula for the estimation of the electron inelastic mean free paths (IMFPs) in solids from several simple material parameters. The TPP-2M formula, however, poorly describes several materials because it relies heavily on the traditional least-squares analysis. Herein, we propose a new framework based on machine learning to overcome the weakness. This framework allows a selection from an enormous number of combined terms (descriptors) to build a new formula that describes the electron IMFPs. The resulting framework not only provides higher average accuracy and stability but also reveals the physics meanings of several newly found descriptors. Using the identified principle descriptors, a complete physics picture of electron IMFPs is obtained, including both single and collective electron behaviors of inelastic scattering. Our findings suggest that machine learning is robust and efficient to predict the IMFP and has great potential in building a regression framework for data-driven problems. Furthermore, this method could be applicable to find empirical formula for given experimental data using a series of parameters given a priori, holds potential to find a deeper connection between experimental data and a priori parameters.