Post-genomic approaches to understanding interactions between fungi and their environment

Fungi inhabit every natural and anthropogenic environment on Earth. They have highly varied life-styles including saprobes (using only dead biomass as a nutrient source), pathogens (feeding on living biomass), and symbionts (co-existing with other organisms). These distinctions are not absolute as many species employ several life styles (e.g. saprobe and opportunistic pathogen, saprobe and mycorrhiza). To efficiently survive in these different and often changing environments, fungi need to be able to modify their physiology and in some cases will even modify their local environment. Understanding the interaction between fungi and their environments has been a topic of study for many decades. However, recently these studies have reached a new dimension. The availability of fungal genomes and development of post-genomic technologies for fungi, such as transcriptomics, proteomics and metabolomics, have enabled more detailed studies into this topic resulting in new insights. Based on a Special Interest Group session held during IMC9, this paper provides examples of the recent advances in using (post-)genomic approaches to better understand fungal interactions with their environments

A MIQE-Compliant Real-Time PCR Assay for Aspergillus Detection

PMCID: PMC3393739This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Global analyses of TetR family transcriptional regulators in mycobacteria indicates conservation across species and diversity in regulated functions

BACKGROUND: Mycobacteria inhabit diverse niches and display high metabolic versatility. They can colonise both humans and animals and are also able to survive in the environment. In order to succeed, response to environmental cues via transcriptional regulation is required. In this study we focused on the TetR family of transcriptional regulators (TFTRs) in mycobacteria. RESULTS: We used InterPro to classify the entire complement of transcriptional regulators in 10 mycobacterial species and these analyses showed that TFTRs are the most abundant family of regulators in all species. We identified those TFTRs that are conserved across all species analysed and those that are unique to the pathogens included in the analysis. We examined genomic contexts of 663 of the conserved TFTRs and observed that the majority of TFTRs are separated by 200 bp or less from divergently oriented genes. Analyses of divergent genes indicated that the TFTRs control diverse biochemical functions not limited to efflux pumps. TFTRs typically bind to palindromic motifs and we identified 11 highly significant novel motifs in the upstream regions of divergently oriented TFTRs. The C-terminal ligand binding domain from the TFTR complement in M. tuberculosis showed great diversity in amino acid sequence but with an overall architecture common to other TFTRs. CONCLUSION: This study suggests that mycobacteria depend on TFTRs for the transcriptional control of a number of metabolic functions yet the physiological role of the majority of these regulators remain unknown. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-1696-9) contains supplementary material, which is available to authorized users

Conserved Secondary Structures in Aspergillus

Background: Recent evidence suggests that the number and variety of functional RNAs (ncRNAs as well as cis-acting RNA elements within mRNAs) is much higher than previously thought; thus, the ability to computationally predict and analyze RNAs has taken on new importance. We have computationally studied the secondary structures in an alignment of six Aspergillus genomes. Little is known about the RNAs present in this set of fungi, and this diverse set of genomes has an optimal level of sequence conservation for observing the correlated evolution of base-pairs seen in RNAs. Methodology/Principal Findings: We report the results of a whole-genome search for evolutionarily conserved secondary structures, as well as the results of clustering these predicted secondary structures by structural similarity. We find a total of 7450 predicted secondary structures, including a new predicted,60 bp long hairpin motif found primarily inside introns. We find no evidence for microRNAs. Different types of genomic regions are over-represented in different classes of predicted secondary structures. Exons contain the longest motifs (primarily long, branched hairpins), 59 UTRs primarily contain groupings of short hairpins located near the start codon, and 39 UTRs contain very little secondary structure compared to other regions. There is a large concentration of short hairpins just inside the boundaries of exons. The density of predicted intronic RNAs increases with the length of introns, and the density of predicted secondary structures within mRNA coding regions increases with the number of introns in a gene

Neurospora from natural populations: Population genomics insights into the Life history of a model microbial Eukaryote

The ascomycete filamentous fungus Neurospora crassa played a historic role in experimental biology and became a model system for genetic research. Stimulated by a systematic effort to collect wild strains initiated by Stanford geneticist David Perkins, the genus Neurospora has also become a basic model for the study of evolutionary processes, speciation, and population biology. In this chapter, we will first trace the history that brought Neurospora into the era of population genomics. We will then cover the major contributions of population genomic investigations using Neurospora to our understanding of microbial biogeography and speciation, and review recent work using population genomics and genome-wide association mapping that illustrates the unique potential of Neurospora as a model for identifying the genetic basis of (potentially adaptive) phenotypes in filamentous fungi. The advent of population genomics has contributed to firmly establish Neurospora as a complete model system and we hope our review will entice biologists to include Neurospora in their research

Fusion and Fission of Genes Define a Metric between Fungal Genomes

Gene fusion and fission events are key mechanisms in the evolution of gene architecture, whose effects are visible in protein architecture when they occur in coding sequences. Until now, the detection of fusion and fission events has been performed at the level of protein sequences with a post facto removal of supernumerary links due to paralogy, and often did not include looking for events defined only in single genomes. We propose a method for the detection of these events, defined on groups of paralogs to compensate for the gene redundancy of eukaryotic genomes, and apply it to the proteomes of 12 fungal species. We collected an inventory of 1,680 elementary fusion and fission events. In half the cases, both composite and element genes are found in the same species. Per-species counts of events correlate with the species genome size, suggesting a random mechanism of occurrence. Some biological functions of the genes involved in fusion and fission events are slightly over- or under-represented. As already noted in previous studies, the genes involved in an event tend to belong to the same functional category. We inferred the position of each event in the evolution tree of the 12 fungal species. The event localization counts for all the segments of the tree provide a metric that depicts the “recombinational” phylogeny among fungi. A possible interpretation of this metric as distance in adaptation space is proposed

Characterization of the Temperature-Sensitive Mutations un-7 and png-1 in Neurospora crassa

The model filamentous fungus Neurospora crassa has been studied for over fifty years and many temperature-sensitive mutants have been generated. While most of these have been mapped genetically, many remain anonymous. The mutation in the N. crassa temperature-sensitive lethal mutant un-7 was identified by a complementation based approach as being in the open reading frame designated NCU00651 on linkage group I. Other mutations in this gene have been identified that lead to a temperature-sensitive morphological phenotype called png-1. The mutations underlying un-7 result in a serine to phenylalanine change at position 273 and an isoleucine to valine change at position 390, while the mutation in png-1 was found to result in a serine to leucine change at position 279 although there were other conservative changes in this allele. The overall morphology of the strain carrying the un-7 mutation is compared to strains carrying the png-1 mutation and these mutations are evaluated in the context of other temperature-sensitive mutants in Neurospora

The Escherichia coli transcriptome mostly consists of independently regulated modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome

RIPCAL: a tool for alignment-based analysis of repeat-induced point mutations in fungal genomic sequences

Background Repeat-induced point mutation (RIP) is a fungal-specific genome defence mechanism that alters the sequences of repetitive DNA, thereby inactivating coding genes. Repeated DNA sequences align between mating and meiosis and both sequences undergo C:G to T:A transitions. In most fungi these transitions preferentially affect CpA di-nucleotides thus altering the frequency of certain di-nucleotides in the affected sequences. The majority of previously published in silico analyses were limited to the comparison of ratios of pre- and post-RIP di-nucleotides in putatively RIP-affected sequences – so-called RIP indices. The analysis of RIP is significantly more informative when comparing sequence alignments of repeated sequences. There is, however, a dearth of bioinformatics tools available to the fungal research community for alignment-based RIP analysis of repeat families. Results We present RIPCAL http://www.sourceforge.net/projects/ripcal, a software tool for the automated analysis of RIP in fungal genomic DNA repeats, which performs both RIP index and alignment-based analyses. We demonstrate the ability of RIPCAL to detect RIP within known RIP-affected sequences of Neurospora crassa and other fungi. We also predict and delineate the presence of RIP in the genome of Stagonospora nodorum – a Dothideomycete pathogen of wheat. We show that RIP has affected different members of the S. nodorum rDNA tandem repeat to different extents depending on their genomic contexts. Conclusion The RIPCAL alignment-based method has considerable advantages over RIP indices for the analysis of whole genomes. We demonstrate its application to the recently published genome assembly of S. nodorum

An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data

<p>Abstract</p> <p>Background</p> <p>The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current <it>in silico </it>prediction methods suffer from gene-model errors introduced during genome annotation.</p> <p>Results</p> <p>A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring <it>Aspergillus </it>species was developed to create an improved list of potential signal peptide containing proteins encoded by the <it>Aspergillus niger </it>genome. As a complement to these <it>in silico </it>predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in <it>A. niger </it>were identified.</p> <p>Conclusions</p> <p>We were able to improve the <it>in silico </it>inventory of <it>A. niger </it>secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed <it>in silico </it>predictions.</p