Climate acts as an environmental filter to plant pathogens.

Climate shapes the distribution of plant-associated microbes such as mycorrhizal and endophytic fungi. However, the role of climate in plant pathogen community assembly is less understood. Here, we explored the role of climate in the assembly of Phytophthora communities at >250 sites along a latitudinal gradient from Spain to northern Sweden and an altitudinal gradient from the Spanish Pyrenees to lowland areas. Communities were detected by ITS sequencing of river filtrates. Mediation analysis supported the role of climate in the biogeography of Phytophthora and ruled out other environmental factors such as geography or tree diversity. Comparisons of functional and species diversity showed that environmental filtering dominated over competitive exclusion in Europe. Temperature and precipitation acted as environmental filters at different extremes of the gradients. In northern regions, winter temperatures acted as an environmental filter on Phytophthora community assembly, selecting species adapted to survive low minimum temperatures. In southern latitudes, a hot dry climate was the main environmental filter, resulting in communities dominated by drought-tolerant Phytophthora species with thick oospore walls, a high optimum temperature for growth, and a high maximum temperature limit for growth. By taking a community ecology approach, we show that the establishment of Phytophthora plant pathogens in Europe is mainly restricted by cold temperatures.

Oomycete Soil Diversity Associated with Betula and Alnus in Forests and Urban Settings in the Nordic-Baltic Region.

This study aimed to determine the differences and drivers of oomycete diversity and community composition in alder- and birch-dominated park and natural forest soils of the Fennoscandian and Baltic countries of Estonia, Finland, Lithuania, Norway, and Sweden. For this, we sequenced libraries of PCR products generated from the DNA of 111 soil samples collected across a climate gradient using oomycete-specific primers on a PacBio high-throughput sequencing platform. We found that oomycete communities are most affected by temperature seasonality, annual mean temperature, and mean temperature of the warmest quarter. Differences in composition were partly explained by the higher diversity of Saprolegniales in Sweden and Norway, as both total oomycete and Saprolegniales richness decreased significantly at higher longitudes, potentially indicating the preference of this group of oomycetes for a more temperate maritime climate. None of the evaluated climatic variables significantly affected the richness of Pythiales or Peronosporales. Interestingly, the relative abundance and richness of Pythiales was higher at urban sites compared to forest sites, whereas the opposite was true for Saprolegniales. Additionally, this is the first report of Phytophthora gallica and P. plurivora in Estonia. Our results indicate that the composition of oomycetes in soils is strongly influenced by climatic factors, and, therefore, changes in climate conditions associated with global warming may have the potential to significantly alter the distribution range of these microbes, which comprise many important pathogens of plants.

Method for Classifying Schizophrenia Patients Based on Machine Learning.

Schizophrenia is a chronic and severe mental disorder that affects individuals in various ways, particularly in their ability to perceive, process, and respond to stimuli. This condition has a significant impact on a considerable number of individuals. Consequently, the study, analysis, and characterization of this pathology are of paramount importance. Electroencephalography (EEG) is frequently utilized in the diagnostic assessment of various brain disorders due to its non-intrusiveness, excellent resolution and ease of placement. However, the manual analysis of electroencephalogram (EEG) recordings can be a complex and time-consuming task for healthcare professionals. Therefore, the automated analysis of EEG recordings can help alleviate the burden on doctors and provide valuable insights to support clinical diagnosis. Many studies are working along these lines. In this research paper, the authors propose a machine learning (ML) method based on the eXtreme Gradient Boosting (XGB) algorithm for analyzing EEG signals. The study compares the performance of the proposed XGB-based approach with four other supervised ML systems. According to the results, the proposed XGB-based method demonstrates superior performance, with an AUC value of 0.94 and an accuracy value of 0.94, surpassing the other compared methods. The implemented system exhibits high accuracy and robustness in accurately classifying schizophrenia patients based on EEG recordings. This method holds the potential to be implemented as a valuable complementary tool for clinical use in hospitals, supporting clinicians in their clinical diagnosis of schizophrenia.

Unravelling hybridization in Phytophthora using phylogenomics and genome size estimation.

The genus Phytophthora comprises many economically and ecologically important plant pathogens. Hybrid species have previously been identified in at least six of the 12 phylogenetic clades. These hybrids can potentially infect a wider host range and display enhanced vigour compared to their progenitors. Phytophthora hybrids therefore pose a serious threat to agriculture as well as to natural ecosystems. Early and correct identification of hybrids is therefore essential for adequate plant protection but this is hampered by the limitations of morphological and traditional molecular methods. Identification of hybrids is also important in evolutionary studies as the positioning of hybrids in a phylogenetic tree can lead to suboptimal topologies. To improve the identification of hybrids we have combined genotyping-by-sequencing (GBS) and genome size estimation on a genus-wide collection of 614 Phytophthora isolates. Analyses based on locus- and allele counts and especially on the combination of species-specific loci and genome size estimations allowed us to confirm and characterize 27 previously described hybrid species and discover 16 new hybrid species. Our method was also valuable for species identification at an unprecedented resolution and further allowed correct naming of misidentified isolates. We used both a concatenation- and a coalescent-based phylogenomic method to construct a reliable phylogeny using the GBS data of 140 non-hybrid Phytophthora isolates. Hybrid species were subsequently connected to their progenitors in this phylogenetic tree. In this study we demonstrate the application of two validated techniques (GBS and flow cytometry) for relatively low cost but high resolution identification of hybrids and their phylogenetic relations.

Patterns of Genetic Diversification in the Invasive Hybrid Plant Pathogen Phytophthora × alni and Its Parental Species P. uniformis.

In pathogenic fungi and oomycetes, interspecific hybridization may lead to the formation of new species having a greater impact on natural ecosystems than the parental species. From the early 1990s, a severe alder (Alnus spp.) decline due to an unknown Phytophthora species was observed in several European countries. Genetic analyses revealed that the disease was caused by the triploid hybrid P. × alni, which originated in Europe from the hybridization of P. uniformis and P. × multiformis. Here, we investigated the population structure of P. × alni (158 isolates) and P. uniformis (85 isolates) in several European countries using microsatellite markers. Our analyses confirmed the genetic structure previously observed in other European populations, with P. uniformis populations consisting of at most two multilocus genotypes (MLGs) and P. × alni populations dominated by MLG Pxa-1. The genetic structure of P. × alni populations in the Czech Republic, Hungary and Sweden seemed to reflect the physical isolation of river systems. Most rare P. × alni MLGs showed a loss of heterozygosity (LOH) at one or a few microsatellite loci compared with other MLGs. This LOH may allow a stabilization within the P. × alni genome or a rapid adaptation to stress situations. Alternatively, alleles may be lost because of random genetic drift in small, isolated populations, with no effect on fitness of P. × alni. Additional studies would be necessary to confirm these patterns of population diversification and to better understand the factors driving it.

Functional Ecology of Forest Disease.

Global change is pressing forest pathologists to solve increasingly complex problems. We argue that understanding interactive effects between forest pathogens and global warming, globalization, and land-use changes may benefit from a functional ecology mindset. Traits can be more informative about ecological functions than species inventories and may deliver a more mechanistic description of forest disease. Myriad microbes with pathogenic potential interact with forest ecosystems at different organizational levels. Elucidation of functional traits may enable the microbial complexity to be reduced into manageable categories with predictive power. In this review, we propose guidelines that allow the research community to develop a functional forest pathology approach. We suggest new angles by which functional questions can be used to resolve burning issues on tree disease. Building up functional databases for pathogenicity is key to implementing these approaches.

Drought and Phytophthora Are Associated With the Decline of Oak Species in Southern Italy.

Forest decline induced by climate change is a global phenomenon that affects many tree species, mainly in drought-prone areas as the Mediterranean region. In southern Italy, several oak species have shown decline symptoms and elevated mortality since the 2000s due to drought stress. However, it remains to be answered whether decline occurred alone or whether a pathogen was also involved. To this aim, we compared two coexisting oak species in a forest located in southern Italy which are assumed to be less (Quercus cerris) and more tolerant to drought (Quercus pubescens). We sampled fifteen couples of neighboring declining (D) and non-declining (ND) trees of both species. Wood cores were taken from all trees to perform dendrochronological analyses to detect the decline onset and link it to potential climatic drivers. Carbon isotope ratios (d13C) were analyzed in wood of the two vigor classes to compare their water-use efficiency. Phytophthora presence was also assessed in soil samples from ten D-ND couples of trees per species. The oak species most affected by drought-induced decline in terms of leaf shedding and mortality was Q. cerris, i.e., the least tolerant to drought. In both species, the D trees showed a reduced growth rate compared with ND trees from 2000 onward when drought and warming intensified. Q. pubescens showed higher growth sensitivity to precipitation, temperature and drought than Q. cerris. This sensitivity to climate was magnified in D trees whose growth decreased in response to warm and dry conditions during the prior winter and the late summer. The Q. pubescens D trees were more efficient in their water use than ND trees before the growth divergence between D and ND trees amplified. In the studied area, Phytophthora quercina was isolated from 40% of the sampled trees, and tended to be more frequent amongst ND than amongst D trees. Our data suggests that droughts and warm summer conditions triggered oak decline. The high prevalence of P. quercina in the studied area warrants further study as a potential predisposing factor.