Fungal biology and agriculture: revisiting the field

Biomedical Perspectives of Citronellal: Biological Activities, Toxicological Profile and Molecular Mechanisms

Citronellal, known as rhodinal, is a naturally occurring monoterpenoid aldehyde distinctly found in the distilled oils of Cymbopogon species including C. marginatus, C. citratus, C. validus and C. winterianus family Gramineae. It is also obtained from eucalyptus, mentha, melissa, cinnamomum and allium. It is traditionally used in air freshener, cleaner, floor polishing, deodorants, moisturizing hand/body lotion, perfumes, and adhesives due to its lemon characteristic fragrance and therapeutic benefits. This study aimed to summarize the pharmacological activities and underlying mechanisms of citronellal against different diseases, as well as its toxicological profile. The data was collected from various reliable and authentic literatures by searching different academic search engines, including PubMed, Springer Link, Scopus, Wiley Online, Web of Science, ScienceDirect, and Google Scholar. The findings imply that citronellal demonstrated several pharmacological effects in various preclinical and pharmacological experimental systems. The results indicated that citronellal demonstrated antioxidant, anti-inflammatory, antibacterial, antifungal, anthelminthic, and anticancer effects with beneficial effects in neurological and cardiovascular diseases. Our findings also indicated the toxic level of the phytochemical. In conclusion, it has been proposed that citronellal has the capability to serve as a hopeful therapeutic agent, so further extensive clinical research is necessary to develop it as a reliable drug.

The Sordariomycetes: an expanding resource with Big Data for mining in evolutionary genomics and transcriptomics

Advances in genomics and transcriptomics accompanying the rapid accumulation of omics data have provided new tools that have transformed and expanded the traditional concepts of model fungi. Evolutionary genomics and transcriptomics have flourished with the use of classical and newer fungal models that facilitate the study of diverse topics encompassing fungal biology and development. Technological advances have also created the opportunity to obtain and mine large datasets. One such continuously growing dataset is that of the Sordariomycetes, which exhibit a richness of species, ecological diversity, economic importance, and a profound research history on amenable models. Currently, 3,574 species of this class have been sequenced, comprising nearly one-third of the available ascomycete genomes. Among these genomes, multiple representatives of the model genera Fusarium, Neurospora, and Trichoderma are present. In this review, we examine recently published studies and data on the Sordariomycetes that have contributed novel insights to the field of fungal evolution via integrative analyses of the genetic, pathogenic, and other biological characteristics of the fungi. Some of these studies applied ancestral state analysis of gene expression among divergent lineages to infer regulatory network models, identify key genetic elements in fungal sexual development, and investigate the regulation of conidial germination and secondary metabolism. Such multispecies investigations address challenges in the study of fungal evolutionary genomics derived from studies that are often based on limited model genomes and that primarily focus on the aspects of biology driven by knowledge drawn from a few model species. Rapidly accumulating information and expanding capabilities for systems biological analysis of Big Data are setting the stage for the expansion of the concept of model systems from unitary taxonomic species/genera to inclusive clusters of well-studied models that can facilitate both the in-depth study of specific lineages and also investigation of trait diversity across lineages. The Sordariomycetes class, in particular, offers abundant omics data and a large and active global research community. As such, the Sordariomycetes can form a core omics clade, providing a blueprint for the expansion of our knowledge of evolution at the genomic scale in the exciting era of Big Data and artificial intelligence, and serving as a reference for the future analysis of different taxonomic levels within the fungal kingdom.

Generation of a wheat leaf rust, Puccinia triticina, EST database from stage-specific cDNA libraries

Thirteen cDNA libraries constructed from small amounts of leaf rust mRNA using optimized methods served as the source for the generation of 25 558 high-quality DNA sequence reads. Five life-cycle stages were sampled: resting urediniospores, urediniospores germinated over water or plant extract, compatible, interactive stages during appressorium or haustorium formation just before sporulation, and an incompatible interaction. mRNA populations were subjected to treatments such as full-length cDNA production, subtractive and normalizing hybridizations, and size selection methods combined with PCR amplification. Pathogen and host sequences from interactive libraries were differentiated in silico using cereal and fungal sequences, codon usage analyses, and by means of a partial prototype cDNA microarray hybridized with genomic DNAs. This yielded a non-redundant unigene set of 9760 putative fungal sequences consisting of 6616 singlets and 3144 contigs, representing 4.7 Mbp. At an E-value 10(-5), 3670 unigenes (38%) matched sequences in various databases and collections but only 694 unigenes (7%) were similar to genes with known functions. In total, 296 unigenes were identified as most probably wheat and ten as rRNA sequences. Annotation rates were low for germinated urediniospores (4%) and appressoria (2%). Gene sets obtained from the various life-cycle stages appear to be remarkably different, suggesting drastic reprogramming of the transcriptome during these major differentiation processes. Redundancy within contigs yielded information about possible expression levels of certain genes among stages. Many sequences were similar to genes from other rusts such as Uromyces and Melampsora species; some of these genes have been implicated in pathogenicity and virulence.

Separation of sequences from host-pathogen interface using triplet nucleotide frequencies

The identification of genes involved in host-pathogen interactions is important for the elucidation of mechanisms of disease resistance and host susceptibility. A traditional way to classify the origin of genes sampled from a pool of mixed cDNA is through sequence similarity to known genes from either the pathogen or host organism or other closely related species. This approach does not work when the identified sequence has no close homologues in the sequence databases. In our previous studies, we classified genes using their codon frequencies. This method, however, explicitly required the prediction of CDS regions and thus could not be applied to sequences composed from the non-coding regions of genes. In this study, we show that the use of sliding-window triplet frequencies extends the application of the algorithm to both coding and non-coding sequences and also increases the prediction accuracy of a Support Vector Machine classifier from 95.6+/-0.3 to 96.5+/-0.2. Thus the use of the triplet frequencies increased the prediction accuracy of the new method by more than 20% compared to our previous approach. A functional analysis of sequences detected gene families having significantly higher or lower probability to be correctly classified compared to the average accuracy of the method is described. The server to perform classification of EST sequences using triplet frequencies is available at (URL: http://mips.gsf.de/proj/est3).

Lessons from the genome sequence of Neurospora crassa: tracing the path from genomic blueprint to multicellular organism

We present an analysis of over 1,100 of the approximately 10,000 predicted proteins encoded by the genome sequence of the filamentous fungus Neurospora crassa. Seven major areas of Neurospora genomics and biology are covered. First, the basic features of the genome, including the automated assembly, gene calls, and global gene analyses are summarized. The second section covers components of the centromere and kinetochore complexes, chromatin assembly and modification, and transcription and translation initiation factors. The third area discusses genome defense mechanisms, including repeat induced point mutation, quelling and meiotic silencing, and DNA repair and recombination. In the fourth section, topics relevant to metabolism and transport include extracellular digestion; membrane transporters; aspects of carbon, sulfur, nitrogen, and lipid metabolism; the mitochondrion and energy metabolism; the proteasome; and protein glycosylation, secretion, and endocytosis. Environmental sensing is the focus of the fifth section with a treatment of two-component systems; GTP-binding proteins; mitogen-activated protein, p21-activated, and germinal center kinases; calcium signaling; protein phosphatases; photobiology; circadian rhythms; and heat shock and stress responses. The sixth area of analysis is growth and development; it encompasses cell wall synthesis, proteins important for hyphal polarity, cytoskeletal components, the cyclin/cyclin-dependent kinase machinery, macroconidiation, meiosis, and the sexual cycle. The seventh section covers topics relevant to animal and plant pathogenesis and human disease. The results demonstrate that a large proportion of Neurospora genes do not have homologues in the yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe. The group of unshared genes includes potential new targets for antifungals as well as loci implicated in human and plant physiology and disease.

The path in fungal plant pathogenicity: many opportunities to outwit the intruders?

The number of genes implicated in the infection and disease processes of phytopathogenic fungi is increasing rapidly. Forward genetic approaches have identified mutated genes that affect pathogenicity, host range, virulence and general fitness. Likewise, candidate gene approaches have been used to identify genes of interest based on homology and recently through 'comparative genomic approaches' through analysis of large EST databases and whole genome sequences. It is becoming clear that many genes of the fungal genome will be involved in the pathogen-host interaction in its broadest sense, affecting pathogenicity and the disease process in planta. By utilizing the information obtained through these studies, plants may be bred or engineered for effective disease resistance. That is, by trying to disable pathogens by hitting them where it counts.