Relocation of genes generates non-conserved chromosomal segments in Fusarium graminearum that show distinct and co-regulated gene expression patterns

Background

Genome comparisons between closely related species often show non-conserved regions across chromosomes. Some of them are located in specific regions of chromosomes and some are even confined to one or more entire chromosomes. The origin and biological relevance of these non-conserved regions are still largely unknown. Here we used the genome of Fusarium graminearum to elucidate the significance of non-conserved regions.

Results

The genome of F. graminearum harbours thirteen non-conserved regions dispersed over all of the four chromosomes. Using RNA-Seq data from the mycelium of F. graminearum, we found weakly expressed regions on all of the four chromosomes that exactly matched with non-conserved regions. Comparison of gene expression between two different developmental stages (conidia and mycelium) showed that the expression of genes in conserved regions is stable, while gene expression in non-conserved regions is much more influenced by developmental stage. In addition, genes involved in the production of secondary metabolites and secreted proteins are enriched in non-conserved regions, suggesting that these regions could also be important for adaptations to new environments, including adaptation to new hosts. Finally, we found evidence that non-conserved regions are generated by sequestration of genes from multiple locations. Gene relocations may lead to clustering of genes with similar expression patterns or similar biological functions, which was clearly exemplified by the PKS2 gene cluster.

Conclusions

Our results showed that chromosomes can be functionally divided into conserved and non-conserved regions, and both could have specific and distinct roles in genome evolution and regulation of gene expression.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-191) contains supplementary material, which is available to authorized users.     

Differential display-mediated rapid identification of different members of a multigene family, HSP16.9 in wheat

Isolation of cDNAs encoding individual members of a gene family is essential for assessing their role in a biological phenomenon. However, this process is often laborious and slow due to highly conserved protein-coding region that interferes with the isolation of the individual members. Identification of gene-specific probes from 3 non-coding regions of different members can assist in the fast retrieval and characterization of individual members of a multigene family. We used the recent technique of differential display for the same purpose. As an example of a multigene family in plants, we selected a heat shock protein gene family, HSP16.9 from wheat, with estimated 12 members. We modified the original differential display technique for selective amplification of the 3 non-coding regions of different wheat HSP16.9 genes by replacing the random 10-mer in the original method with a conserved HSP16.9 gene family-specific primer. Sixteen cDNA fragments from these experiments were sequenced and they represent 8 different members of a 12 member gene family. Our succes can be attributed to shorter 3 non-coding regions that are typical of higher-plant genes and use of highly conserved gene family-specific primer in these experiments. This modified differential display technique can be of general application to other plant systems where cloning of the different members of a gene family is desired.     

Categorizer: a tool to categorize genes into user-defined biological groups based on semantic similarity

Background

Communalities between large sets of genes obtained from high-throughput experiments are often identified by searching for enrichments of genes with the same Gene Ontology (GO) annotations. The GO analysis tools used for these enrichment analyses assume that GO terms are independent and the semantic distances between all parent–child terms are identical, which is not true in a biological sense. In addition these tools output lists of often redundant or too specific GO terms, which are difficult to interpret in the context of the biological question investigated by the user. Therefore, there is a demand for a robust and reliable method for gene categorization and enrichment analysis.

Results

We have developed Categorizer, a tool that classifies genes into user-defined groups (categories) and calculates p-values for the enrichment of the categories. Categorizer identifies the biologically best-fit category for each gene by taking advantage of a specialized semantic similarity measure for GO terms. We demonstrate that Categorizer provides improved categorization and enrichment results of genetic modifiers of Huntington’s disease compared to a classical GO Slim-based approach or categorizations using other semantic similarity measures.

Conclusion

Categorizer enables more accurate categorizations of genes than currently available methods. This new tool will help experimental and computational biologists analyzing genomic and proteomic data according to their specific needs in a more reliable manner.     

A model-based scan statistic for identifying extreme chromosomal regions of gene expression in human tumors

MOTIVATION: The analysis of gene expression data in its chromosomal context has been a recent development in cancer research. However, currently available methods fail to account for variation in the distance between genes, gene density and genomic features (e.g. GC content) in identifying increased or decreased chromosomal regions of gene expression. RESULTS: We have developed a model-based scan statistic that accounts for these aspects of the complex landscape of the human genome in the identification of extreme chromosomal regions of gene expression. This method may be applied to gene expression data regardless of the microarray platform used to generate it. To demonstrate the accuracy and utility of this method, we applied it to a breast cancer gene expression dataset and tested its ability to predict regions containing medium-to-high level DNA amplification (DNA ratio values >2). A classifier was developed from the scan statistic results that had a 10-fold cross-validated classification rate of 93% and a positive predictive value of 88%. This result strongly suggests that the model-based scan statistic and the expression characteristics of an increased chromosomal region of gene expression can be used to accurately predict chromosomal regions containing amplified genes. AVAILABILITY: Functions in the R-language are available from the author upon request. CONTACT: fcouples@umich.edu.     

What can patterns of differentiation across plant genomes tell us about adaptation and speciation?

Genome scans have become a common approach to identify genomic signatures of natural selection and reproductive isolation, as well as the genomic bases of ecologically relevant phenotypes, based on patterns of polymorphism and differentiation among populations or species. Here, we review the results of studies taking genome scan approaches in plants, consider the patterns of genomic differentiation documented and their possible causes, discuss the results in light of recent models of genomic differentiation during divergent adaptation and speciation, and consider assumptions and caveats in their interpretation. We find that genomic regions of high divergence generally appear quite small in comparisons of both closely and more distantly related populations, and for the most part, these differentiated regions are spread throughout the genome rather than strongly clustered. Thus, the genome scan approach appears well-suited for identifying genomic regions or even candidate genes that underlie adaptive divergence and/or reproductive barriers. We consider other methodologies that may be used in conjunction with genome scan approaches, and suggest further developments that would be valuable. These include broader use of sequence-based markers of known genomic location, greater attention to sampling strategies to make use of parallel environmental or phenotypic transitions, more integration with approaches such as quantitative trait loci mapping and measures of gene flow across the genome, and additional theoretical and simulation work on processes related to divergent adaptation and speciation.     

Gene flow and selection interact to promote adaptive divergence in regions of low recombination

Adaptation to new environments often occurs in the face of gene flow. Under these conditions, gene flow and recombination can impede adaptation by breaking down linkage disequilibrium between locally adapted alleles. Theory predicts that this decay can be halted or slowed if adaptive alleles are tightly linked in regions of low recombination, potentially favouring divergence and adaptive evolution in these regions over others. Here, we compiled a global genomic data set of over 1,300 individual threespine stickleback from 52 populations and compared the tendency for adaptive alleles to occur in regions of low recombination between populations that diverged with or without gene flow. In support of theory, we found that putatively adaptive alleles (F_ST and d_XY outliers) tend to occur more often in regions of low recombination in populations where divergent selection and gene flow have jointly occurred. This result remained significant when we employed different genomic window sizes, controlled for the effects of mutation rate and gene density, controlled for overall genetic differentiation, varied the genetic map used to estimate recombination and used a continuous (rather than discrete) measure of geographic distance as proxy for gene flow/shared ancestry. We argue that our study provides the first statistical evidence that the interaction of gene flow and selection biases divergence toward regions of low recombination.     

Electric imaging through active electrolocation: implication for the analysis of complex scenes

The electric sense of mormyrids is often regarded as an adaptation to conditions unfavourable for vision and in these fish it has become the dominant sense for active orientation and communication tasks. With this sense, fish can detect and distinguish the electrical properties of the close environment, measure distance, perceive the 3-D shape of objects and discriminate objects according to distance or size and shape, irrespective of conductivity, thus showing a degree of abstraction regarding the interpretation of sensory stimuli. The physical properties of images projected on the sensory surface by the fish's own discharge reveal a "Mexican hat" opposing centre-surround profile. It is likely that computation of the image amplitude to slope ratio is used to measure distance, while peak width and slope give measures of shape and contrast. Modelling has been used to explore how the images of multiple objects superimpose in a complex manner. While electric images are by nature distributed, or 'blurred', behavioural strategies orienting sensory surfaces and the neural architecture of sensory processing networks both contribute to resolving potential ambiguities. Rostral amplification is produced by current funnelling in the head and chin appendage regions, where high density electroreceptor distributions constitute foveal regions. Central magnification of electroreceptive pathways from these regions particularly favours the detection of capacitive properties intrinsic to potential living prey. Swimming movements alter the amplitude and contrast of pre-receptor object-images but image modulation is normalised by central gain-control mechanisms that maintain excitatory and inhibitory balance, removing the contrast-ambiguity introduced by self-motion in much the same way that contrast gain-control is achieved in vision.     

Intrinsically unstructured proteins and their functions

Many gene sequences in eukaryotic genomes encode entire proteins or large segments of proteins that lack a well-structured three-dimensional fold. Disordered regions can be highly conserved between species in both composition and sequence and, contrary to the traditional view that protein function equates with a stable three-dimensional structure, disordered regions are often functional, in ways that we are only beginning to discover. Many disordered segments fold on binding to their biological targets (coupled folding and binding), whereas others constitute flexible linkers that have a role in the assembly of macromolecular arrays.     

Application of MUTIC to the exploration of gene expression data in prostate cancer

We show here an example of the application of a novel method, MUTIC (model utilization-based clustering), used for identifying complex interactions between genes or gene categories based on gene expression data. The method deals with binary categorical data which consist of a set of gene expression profiles divided into two biologically meaningful categories. It does not require data from multiple time points. Gene expression profiles are represented by feature vectors whose component features are either gene expression values, or averaged expression values corresponding to gene ontology or protein information resource categories. A supervised learning algorithm (genetic programming) is used to learn an ensemble of classification models distinguishing the two categories based on the feature vectors corresponding to their members. Each feature is associated with a "model utilization vector", which has an entry for each high-quality classification model found, indicating whether or not the feature was used in that model. These utilization vectors are then clustered using a variant of hierarchical clustering called Omniclust. The result is a set of model utilization-based clusters, in which features are gathered together if they are often considered together by classification models - which may be because they are co-expressed, or may be for subtler reasons involving multi-gene interactions. The MUTIC method is illustrated here by applying it to a dataset regarding gene expression in prostate cancer and control samples. Compared to traditional expression-based clustering, MUTIC yields clusters that have higher mathematical quality (in the sense of homogeneity and separation) and that also yield novel insights into the underlying biological processes.     

SLEPR: a sample-level enrichment-based pathway ranking method -- seeking biological themes through pathway-level consistency

Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. This gene-level paradigm can be limited as a result of valid sample fluctuations and biological complexities. In this report, we describe a novel method, SLEPR, which eliminates this limitation by relying on pathway-level consistencies. Our method first selects the sample-level differentiated genes from each individual sample, capturing genes missed by other analysis methods, ascertains the enrichment levels of associated pathways from each of those lists, and then ranks annotated pathways based on the consistency of enrichment levels of individual samples from both sample classes. As a proof of concept, we have used this method to analyze three public microarray datasets with a direct comparison with the GSEA method, one of the most popular pathway-level analysis methods in the field. We found that our method was able to reproduce the earlier observations with significant improvements in depth of coverage for validated or expected biological themes, but also produced additional insights that make biological sense. This new method extends existing analyses approaches and facilitates integration of different types of HTP data.     

Genetic dissection of intermale aggressive behavior in BALB/cJ and A/J mice

Aggressive behaviors are disabling, treatment refractory, and sometimes lethal symptoms of several neuropsychiatric disorders. However, currently available treatments for patients are inadequate, and the underlying genetics and neurobiology of aggression is only beginning to be elucidated. Inbred mouse strains are useful for identifying genomic regions, and ultimately the relevant gene variants (alleles) in these regions, that affect mammalian aggressive behaviors, which, in turn, may help to identify neurobiological pathways that mediate aggression. The BALB/cJ inbred mouse strain exhibits relatively high levels of intermale aggressive behaviors and shows multiple brain and behavioral phenotypes relevant to neuropsychiatric syndromes associated with aggression. The A/J strain shows very low levels of aggression. We hypothesized that a cross between BALB/cJ and A/J inbred strains would reveal genomic loci that influence the tendency to initiate intermale aggressive behavior. To identify such loci, we conducted a genomewide scan in an F2 population of 660 male mice bred from BALB/cJ and A/J inbred mouse strains. Three significant loci on chromosomes 5, 10 and 15 that influence aggression were identified. The chromosome 5 and 15 loci are completely novel, and the chromosome 10 locus overlaps an aggression locus mapped in our previous study that used NZB/B1NJ and A/J as progenitor strains. Haplotype analysis of BALB/cJ, NZB/B1NJ and A/J strains showed three positional candidate genes in the chromosome 10 locus. Future studies involving fine genetic mapping of these loci as well as additional candidate gene analysis may lead to an improved biological understanding of mammalian aggressive behaviors.