Production of Escovopsis conidia and the potential use of this parasitic fungus as a biological control agent of leaf-cutting ant fungus gardens

This study was carried out to investigate the use of different substrates for the production of Escovopsis conidia and verify the virulence of four different isolates cultured on four types of substrates using a novel bioassay. Escovopsis isolates were molecularly identified, based on Internal Transcribed Spacer (ITS) nucleotide sequences. To evaluate conidial production, suspensions (1 × 106 conidia mL-1) of each Escovopsis isolate were inoculated onto four substrates (parboiled rice, white rice, rolled oats, and corn grits). After 14 days, conidial yields were assessed. The virulence of each isolate cultured on the four substrates was tested against Leucoagaricus fungus garden fragments, by directly applying 500 µL of each conidial suspension (1 × 107 conidia mL-1), and the development of the parasite was monitored daily until it completely colonized the fungus garden. It was observed that rolled oats were the best substrate for conidial production, with a yield of 1.7 × 107 to 2.0 × 108 conidia mL-1. Furthermore, isolate AT-01 produced the highest number of conidia when compared with the other isolates. Regardless of the substrate used to produce AT-01 conidia, this isolate completely colonized the fungus garden 6 days post inoculation (dpi), followed by AT-02, AC-01, and AC-2. High levels of both conidial production and virulence against the leaf-cutting ant fungus garden were observed here.

Light effects on Lasiodiplodia theobromae metabolome cultured in vitro

INTRODUCTION

The present work identified and compared intracellular metabolites and metabolic networks in mycelial cultures of Lasiodiplodia theobromae grown under 12 natural light and 24 hours' dark using a 1 H NMR-based metabolomics approach.

MATERIALS AND METHODS

Fungal cultures were grown in potato dextrose media, and metabolites were extracted by sonication with sodium phosphate-buffered saline (pH = 6.0, 10% D2O, 0.1 mM TSP) from mycelium samples collected every week over four weeks.

RESULTS

Multivariate analyses revealed that the light exposure group showed a positive correlation within beta-hydroxybutyrate, acetoacetate, acetone, betaine, choline, glycerol, and phosphocholine. On the other hand, phenyl acetate, leucine, isoleucine, valine, and tyrosine were positively correlated with dark conditions. Light favored the oxidative degradation of valine, leucine, and isoleucine, leading to the accumulation of choline, phosphocholine, betaine, and ketone bodies (ketogenesis). Ketogenesis, gluconeogenesis, and the biosynthesis of choline, phosphocholine, and betaine, were considered discriminatory routes for light conditions. The light-sensing pathways were interlinked with fungal development, as verified by the increased production of mycelia biomass without fruiting bodies and stress signaling, as demonstrated by the increased production of pigments.

Geminivirus data warehouse: a database enriched with machine learning approaches

BACKGROUND

The Geminiviridae family encompasses a group of single-stranded DNA viruses with twinned and quasi-isometric virions, which infect a wide range of dicotyledonous and monocotyledonous plants and are responsible for significant economic losses worldwide. Geminiviruses are divided into nine genera, according to their insect vector, host range, genome organization, and phylogeny reconstruction. Using rolling-circle amplification approaches along with high-throughput sequencing technologies, thousands of full-length geminivirus and satellite genome sequences were amplified and have become available in public databases. As a consequence, many important challenges have emerged, namely, how to classify, store, and analyze massive datasets as well as how to extract information or new knowledge. Data mining approaches, mainly supported by machine learning (ML) techniques, are a natural means for high-throughput data analysis in the context of genomics, transcriptomics, proteomics, and metabolomics.

RESULTS

Here, we describe the development of a data warehouse enriched with ML approaches, designated geminivirus.org. We implemented search modules, bioinformatics tools, and ML methods to retrieve high precision information, demarcate species, and create classifiers for genera and open reading frames (ORFs) of geminivirus genomes.

CONCLUSIONS

The use of data mining techniques such as ETL (Extract, Transform, Load) to feed our database, as well as algorithms based on machine learning for knowledge extraction, allowed us to obtain a database with quality data and suitable tools for bioinformatics analysis. The Geminivirus Data Warehouse (geminivirus.org) offers a simple and user-friendly environment for information retrieval and knowledge discovery related to geminiviruses.

Synonymous site variation due to recombination explains higher genetic variability in begomovirus populations infecting non-cultivated hosts

Begomoviruses are ssDNA plant viruses that cause serious epidemics in economically important crops worldwide. Non-cultivated plants also harbour many begomoviruses, and it is believed that these hosts may act as reservoirs and as mixing vessels where recombination may occur. Begomoviruses are notoriously recombination-prone, and also display nucleotide substitution rates equivalent to those of RNA viruses. In Brazil, several indigenous begomoviruses have been described infecting tomatoes following the introduction of a novel biotype of the whitefly vector in the mid-1990s. More recently, a number of viruses from non-cultivated hosts have also been described. Previous work has suggested that viruses infecting non-cultivated hosts have a higher degree of genetic variability compared with crop-infecting viruses. We intensively sampled cultivated and non-cultivated plants in similarly sized geographical areas known to harbour either the weed-infecting Macroptilium yellow spot virus (MaYSV) or the crop-infecting Tomato severe rugose virus (ToSRV), and compared the molecular evolution and population genetics of these two distantly related begomoviruses. The results reinforce the assertion that infection of non-cultivated plant species leads to higher levels of standing genetic variability, and indicate that recombination, not adaptive selection, explains the higher begomovirus variability in non-cultivated hosts.