The response of the same genes to cold stress often varies between maize and sorghum, but the set of genes with conserved patterns of regulation show greater ev

Premise: Maize yields have significantly increased over the past half‐century owing to advances in breeding and agronomic practices. Plants have been grown in increasingly higher densities due to changes in plant architecture resulting in plants with more upright leaves, which allows more efficient light interception for photosynthesis. Natural variation for leaf angle has been identified in maize and sorghum using multiple mapping populations. However, conventional phenotyping techniques for leaf angle are low throughput and labor intensive, and therefore hinder a mechanistic understanding of how the leaf angle of individual leaves changes over time in response to the environment.Methods: High‐throughput time series image data from water‐deprived maize (Zea mays subsp. mays) and sorghum (Sorghum bicolor) were obtained using battery‐powered time‐lapse cameras. A MATLAB‐based image processing framework, Leaf Angle eXtractor (LAX), was developed to extract and quantify leaf angles from images of maize and sorghum plants under drought conditions.Results: Leaf angle measurements showed differences in leaf responses to drought in maize and sorghum. Tracking leaf angle changes at intervals as short as one minute enabled distinguishing leaves that showed signs of wilting under water deprivation from other leaves on the same plant that did not show wilting during the same time period.Discussion: Automating leaf angle measurements using LAX makes it feasible to perform large‐scale experiments to evaluate, understand, and exploit the spatial and temporal variations in plant response to water limitations.

Gibellula (Cordycipitaceae, Hypocreales) is frequently observed growing on spiders, but little is known about their host range. One of the greatest challenges in describing these interactions is identifying the host, since the fungus often rapidly consumes the parasitised spiders and destroys important diagnostic taxonomic traits. Additionally, the global diversity of Gibellula remains unclear, as does the natural history and phylogenetic relationships of most of the species. Herein, we performed an extensive investigation on the species of Gibellula, reconstructed the most complete molecular phylogeny of the genus in the context of Cordycipitaceae, and performed a systematic review in order to provide the foundations towards a better understanding of the genus. Therefore, we have performed an integrative study to investigate the life history of the genus and to disentangle the questionable number of valid species proposed over time. We provided novel molecular data for published species that had not been sequenced before, such as G. mirabilis and G. mainsii, and evaluated all the original and modern morphological descriptions. In addition, we presented its global known distribution and compiled all available molecular data. We suggested a set of terms and morphological traits that should be considered in future descriptions of the genus and that a total of 31 species should be considered as accepted.

Mitochondria originated endosymbiotically from an Alphaproteobacteria-like ancestor. However, it is still uncertain which extant group of Alphaproteobacteria is phylogenetically closer to the mitochondrial ancestor. The proposed groups comprise the order Rickettsiales, the family Rhodospirillaceae, and the genus Rickettsia. In this study, we apply a new complex network approach to investigate the evolutionary origins of mitochondria, analyzing protein sequences modules in a critical network obtained through a critical similarity threshold between the studied sequences. The dataset included three ATP synthase subunits (4, 6, and 9) and its alphaproteobacterial homologs (b, a, and c). In all the subunits, the results gave no support to the hypothesis that Rickettsiales are closely related to the mitochondrial ancestor. Our findings support the hypothesis that mitochondria share a common ancestor with a clade containing all Alphaproteobacteria orders, except Rickettsiales.

The rubber tree, Hevea brasiliensis, is a neotropical Amazonian species. Despite its high economic value and fungi associated with native individuals, in its original area in Brazil, it has been scarcely investigated and only using culture-dependent methods. Herein, we integrated in silico approaches with novel field/experimental approaches and a case study of shotgun metagenomics and small RNA metatranscriptomics of an adult individual. Scientific literature, host fungus, and DNA databases are biased to fungal taxa, and are mainly related to rubber tree diseases and in non-native ecosystems. Metabarcoding retrieved specific phyllospheric core fungal communities of all individuals, adults, plantlets, and leaves of the same plant, unravelling hierarchical structured core mycobiomes. Basidiomycotan yeast-like fungi that display the potential to produce antifungal compounds and a complex of non-invasive ectophytic parasites (Sooty Blotch and Flyspeck fungi) co-occurred in all samples, encompassing the strictest core mycobiome. The case study of the same adult tree (previously studied using culture-dependent approach) analyzed by amplicon, shotgun metagenomics, and small RNA transcriptomics revealed a high relative abundance of insect parasite-pathogens, anaerobic fungi and a high expression of Trichoderma (a fungal genus long reported as dominant in healthy wild rubber trees), respectively. Altogether, our study unravels new and intriguing information/hypotheses of the foliar mycobiome of native H. brasiliensis, which may also occur in other native Amazonian trees.

Fungal infections vary from superficial to invasive and can be life-threatening in immunocompromised and healthy individuals. Antifungal resistance is one of the main reasons for an increasing concern about fungal infections as they become more complex and harder to treat. The fungal “omics” databases help us find drug resistance genes, which is of great importance and extremely necessary. With that in mind, we built a new platform for drug resistance genes. We added seven drug classes of resistance genes to our database: azoles (without specifying which drug), fluconazole, voriconazole, itraconazole, flucytosine, micafungin, and caspofungin. Species with known resistance genes were used to validate the results from our database. This study describes a list of 261 candidate genes related to antifungal resistance, with several genes displaying transport functions involved in azole resistance. Over 65% of the candidate genes found were related to at least one type of azole. Overall, the candidate genes found have functional annotations consistent with genes or enzymes that have been linked to antifungal resistance in previous studies. Also, candidate antifungal resistance genes found exhibit functional annotations consistent with previously described resistance mechanisms. The existence of an HMM profile focusing on antifungal resistance genes allows in silico searches for candidate genes, helping future wet lab experiments, and hence, reducing costs when studying candidate antifungal genes without prior knowledge of the species or genes. Finally, ResFungi has proven to be a powerful tool to narrow down candidate antifungal-related genes and unravel mechanisms related to resistance to help in the design of experiments focusing on the genetic basis of antifungal resistance.