Using multi-way admixture mapping to elucidate TB susceptibility in the South African Coloured population

Background

The admixed South African Coloured population is ideally suited to the discovery of tuberculosis susceptibility genetic variants and their probable ethnic origins, but previous attempts at finding such variants using genome-wide admixture mapping were hampered by the inaccuracy of local ancestry inference. In this study, we infer local ancestry using the novel algorithm implemented in RFMix, with the emphasis on identifying regions of excess San or Bantu ancestry, which we hypothesize may harbour TB susceptibility genes.

Results

Using simulated data, we demonstrate reasonable accuracy of local ancestry inference by RFMix, with a tendency towards miss-calling San ancestry as Bantu. Regions with either excess San ancestry or excess African (San or Bantu) ancestry are less likely to be affected by this bias, and we therefore proceeded to identify such regions, found in cases but not in controls (642 cases and 91 controls). A number of promising regions were found (overall p-values of 7.19×10^-5 for San ancestry and <2.00×10^-16 for African ancestry), including chromosomes 15q15 and 17q22, which are close to genomic regions previously implicated in TB. Promising immune-related susceptibility genes such as the GADD45A, OSM and B7-H5 genes are also harboured in the identified regions.

Conclusion

Admixture mapping is feasible in the South African Coloured population and a number of novel TB susceptibility genomic regions were uncovered.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1021) contains supplementary material, which is available to authorized users.     

Using Evolutionary Conserved Modules in Gene Networks as a Strategy to Leverage High Throughput Gene Expression Queries

Background

Large-scale gene expression studies have not yielded the expected insight into genetic networks that control complex processes. These anticipated discoveries have been limited not by technology, but by a lack of effective strategies to investigate the data in a manageable and meaningful way. Previous work suggests that using a pre-determined seed-network of gene relationships to query large-scale expression datasets is an effective way to generate candidate genes for further study and network expansion or enrichment. Based on the evolutionary conservation of gene relationships, we test the hypothesis that a seed network derived from studies of retinal cell determination in the fly, Drosophila melanogaster, will be an effective way to identify novel candidate genes for their role in mouse retinal development.

Methodology/Principal Findings

Our results demonstrate that a number of gene relationships regulating retinal cell differentiation in the fly are identifiable as pairwise correlations between genes from developing mouse retina. In addition, we demonstrate that our extracted seed-network of correlated mouse genes is an effective tool for querying datasets and provides a context to generate hypotheses. Our query identified 46 genes correlated with our extracted seed-network members. Approximately 54% of these candidates had been previously linked to the developing brain and 33% had been previously linked to the developing retina. Five of six candidate genes investigated further were validated by experiments examining spatial and temporal protein expression in the developing retina.

Conclusions/Significance

We present an effective strategy for pursuing a systems biology approach that utilizes an evolutionary comparative framework between two model organisms, fly and mouse. Future implementation of this strategy will be useful to determine the extent of network conservation, not just gene conservation, between species and will facilitate the use of prior biological knowledge to develop rational systems-based hypotheses.     

Extensive intra-phylotype diversity in lactobacilli and bifidobacteria from the honeybee gut

Background

In the honeybee Apis mellifera, the bacterial gut community is consistently colonized by eight distinct phylotypes of bacteria. Managed bee colonies are of considerable economic interest and it is therefore important to elucidate the diversity and role of this microbiota in the honeybee. In this study, we have sequenced the genomes of eleven strains of lactobacilli and bifidobacteria isolated from the honey crop of the honeybee A. mellifera.

Results

Single gene phylogenies confirmed that the isolated strains represent the diversity of lactobacilli and bifidobacteria in the gut, as previously identified by 16S rRNA gene sequencing. Core genome phylogenies of the lactobacilli and bifidobacteria further indicated extensive divergence between strains classified as the same phylotype. Phylotype-specific protein families included unique surface proteins. Within phylotypes, we found a remarkably high level of gene content diversity. Carbohydrate metabolism and transport functions contributed up to 45% of the accessory genes, with some genomes having a higher content of genes encoding phosphotransferase systems for the uptake of carbohydrates than any previously sequenced genome. These genes were often located in highly variable genomic segments that also contained genes for enzymes involved in the degradation and modification of sugar residues. Strain-specific gene clusters for the biosynthesis of exopolysaccharides were identified in two phylotypes. The dynamics of these segments contrasted with low recombination frequencies and conserved gene order structures for the core genes. Hits for CRISPR spacers were almost exclusively found within phylotypes, suggesting that the phylotypes are associated with distinct phage populations.

Conclusions

The honeybee gut microbiota has been described as consisting of a modest number of phylotypes; however, the genomes sequenced in the current study demonstrated a very high level of gene content diversity within all three described phylotypes of lactobacilli and bifidobacteria, particularly in terms of metabolic functions and surface structures, where many features were strain-specific. Together, these results indicate niche differentiation within phylotypes, suggesting that the honeybee gut microbiota is more complex than previously thought.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1476-6) contains supplementary material, which is available to authorized users.     

The Chironomus tentans genome sequence and the organization of the Balbiani ring genes

Background

The polytene nuclei of the dipteran Chironomus tentans (Ch. tentans) with their Balbiani ring (BR) genes constitute an exceptional model system for studies of the expression of endogenous eukaryotic genes. Here, we report the first draft genome of Ch. tentans and characterize its gene expression machineries and genomic architecture of the BR genes.

Results

The genome of Ch. tentans is approximately 200 Mb in size, and has a low GC content (31%) and a low repeat fraction (15%) compared to other Dipteran species. Phylogenetic inference revealed that Ch. tentans is a sister clade to mosquitoes, with a split 150–250 million years ago. To characterize the Ch. tentans gene expression machineries, we identified potential orthologus sequences to more than 600 Drosophila melanogaster (D. melanogaster) proteins involved in the expression of protein-coding genes. We report novel data on the organization of the BR gene loci, including a novel putative BR gene, and we present a model for the organization of chromatin bundles in the BR2 puff based on genic and intergenic in situ hybridizations.

Conclusions

We show that the molecular machineries operating in gene expression are largely conserved between Ch. tentans and D. melanogaster, and we provide enhanced insight into the organization and expression of the BR genes. Our data strengthen the generality of the BR genes as a unique model system and provide essential background for in-depth studies of the biogenesis of messenger ribonucleoprotein complexes.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-819) contains supplementary material, which is available to authorized users.     

Predicting disease associations via biological network analysis

Background

Understanding the relationship between diseases based on the underlying biological mechanisms is one of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using system-level biological data is expected to improve our current knowledge of disease relationships, which may lead to further improvements in disease diagnosis, prognosis and treatment.

Results

We took advantage of diverse biological data including disease-gene associations and a large-scale molecular network to gain novel insights into disease relationships. We analysed and compared four publicly available disease-gene association datasets, then applied three disease similarity measures, namely annotation-based measure, function-based measure and topology-based measure, to estimate the similarity scores between diseases. We systematically evaluated disease associations obtained by these measures against a statistical measure of comorbidity which was derived from a large number of medical patient records. Our results show that the correlation between our similarity measures and comorbidity scores is substantially higher than expected at random, confirming that our similarity measures are able to recover comorbidity associations. We also demonstrated that our predicted disease associations correlated with disease associations generated from genome-wide association studies significantly higher than expected at random. Furthermore, we evaluated our predicted disease associations via mining the literature on PubMed, and presented case studies to demonstrate how these novel disease associations can be used to enhance our current knowledge of disease relationships.

Conclusions

We present three similarity measures for predicting disease associations. The strong correlation between our predictions and known disease associations demonstrates the ability of our measures to provide novel insights into disease relationships.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-304) contains supplementary material, which is available to authorized users.     

Conserved syntenic clusters of protein coding genes are missing in birds

Background

Birds are one of the most highly successful and diverse groups of vertebrates, having evolved a number of distinct characteristics, including feathers and wings, a sturdy lightweight skeleton and unique respiratory and urinary/excretion systems. However, the genetic basis of these traits is poorly understood.

Results

Using comparative genomics based on extensive searches of 60 avian genomes, we have found that birds lack approximately 274 protein coding genes that are present in the genomes of most vertebrate lineages and are for the most part organized in conserved syntenic clusters in non-avian sauropsids and in humans. These genes are located in regions associated with chromosomal rearrangements, and are largely present in crocodiles, suggesting that their loss occurred subsequent to the split of dinosaurs/birds from crocodilians. Many of these genes are associated with lethality in rodents, human genetic disorders, or biological functions targeting various tissues. Functional enrichment analysis combined with orthogroup analysis and paralog searches revealed enrichments that were shared by non-avian species, present only in birds, or shared between all species.

Conclusions

Together these results provide a clearer definition of the genetic background of extant birds, extend the findings of previous studies on missing avian genes, and provide clues about molecular events that shaped avian evolution. They also have implications for fields that largely benefit from avian studies, including development, immune system, oncogenesis, and brain function and cognition. With regards to the missing genes, birds can be considered ‘natural knockouts’ that may become invaluable model organisms for several human diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0565-1) contains supplementary material, which is available to authorized users.     

Characterization of the MLO gene family in Rosaceae and gene expression analysis in Malus domestica

Background

Powdery mildew (PM) is a major fungal disease of thousands of plant species, including many cultivated Rosaceae. PM pathogenesis is associated with up-regulation of MLO genes during early stages of infection, causing down-regulation of plant defense pathways. Specific members of the MLO gene family act as PM-susceptibility genes, as their loss-of-function mutations grant durable and broad-spectrum resistance.

Results

We carried out a genome-wide characterization of the MLO gene family in apple, peach and strawberry, and we isolated apricot MLO homologs through a PCR-approach. Evolutionary relationships between MLO homologs were studied and syntenic blocks constructed. Homologs that are candidates for being PM susceptibility genes were inferred by phylogenetic relationships with functionally characterized MLO genes and, in apple, by monitoring their expression following inoculation with the PM causal pathogen Podosphaera leucotricha.

Conclusions

Genomic tools available for Rosaceae were exploited in order to characterize the MLO gene family. Candidate MLO susceptibility genes were identified. In follow-up studies it can be investigated whether silencing or a loss-of-function mutations in one or more of these candidate genes leads to PM resistance.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-618) contains supplementary material, which is available to authorized users.     

OrthoClust: an orthology-based network framework for clustering data across multiple species

Increasingly, high-dimensional genomics data are becoming available for many organisms.Here, we develop OrthoClust for simultaneously clustering data across multiple species. OrthoClust is a computational framework that integrates the co-association networks of individual species by utilizing the orthology relationships of genes between species. It outputs optimized modules that are fundamentally cross-species, which can either be conserved or species-specific. We demonstrate the application of OrthoClust using the RNA-Seq expression profiles of Caenorhabditis elegans and Drosophila melanogaster from the modENCODE consortium. A potential application of cross-species modules is to infer putative analogous functions of uncharacterized elements like non-coding RNAs based on guilt-by-association.

Electronic supplementary material

The online version of this article (doi:10.1186/gb-2014-15-8-r100) contains supplementary material, which is available to authorized users.     

CompareSVM: supervised,Support Vector Machine (SVM) inference of gene regularity networks

Background

Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size.

Results

We developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network.

Conclusions

For network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0395-x) contains supplementary material, which is available to authorized users.