Genome-wide prediction of functional gene-gene interactions inferred from patterns of genetic differentiation in mice and men

The human genome encodes a limited number of genes yet contributes to individual differences in a vast array of heritable traits. A possible explanation for the capacity our genome to generate this virtually unlimited range of phenotypic variation in complex traits is to assume functional interactions between genes. Therefore we searched two mammalian genomes to identify potential epistatic interactions by looking for co-adapted genes marked by excess two-locus genetic differentiation between populations/lineages using publicly available SNP genotype data. The practical motivation for this effort is to reduce the number of pair-wise tests that need to be performed in genome-wide association studies aimed at detecting GxG interactions, by focusing on pairs predicted to be more likely to jointly affect variation in complex traits. Hence, this approach generates a list of candidate interactions that can be empirically tested. In both the mouse and human data we observed two-locus genetic differentiation in excess of what can be expected from chance alone based on simulations. In an attempt to validate our hypothesis that pairs of genes showing excess genetic divergence represent potential functional interactions, we selected a small set of gene combinations postulated to be interacting based on our analyses and looked for a combined effect of the selected genes on variation in complex traits in both mice and man. In both cases the individual effect of the genes were not significant, instead we observed marginally significant interaction effects. These results show that genome wide searches for gene-gene interactions based on population genetic data are feasible and can generate interesting candidate gene pairs to be further tested for their contribution to phenotypic variation in complex traits.     

Structure and expression analysis of rice paleo duplications

Having a well-known history of genome duplication, rice is a good model for studying structural and functional evolution of paleo duplications. Improved sequence alignment criteria were used to characterize 10 major chromosome-to-chromosome duplication relationships associated with 1440 paralogous pairs, covering 47.8% of the rice genome, with 12.6% of genes that are conserved within sister blocks. Using a micro-array experiment, a genome-wide expression map has been produced, in which 2382 genes show significant differences of expression in root, leaf and grain. By integrating both structural (1440 paralogous pairs) and functional information (2382 differentially expressed genes), we identified 115 paralogous gene pairs for which at least one copy is differentially expressed in one of the three tissues. A vast majority of the 115 paralogous gene pairs have been neofunctionalized or subfunctionalized as 88%, 89% and 96% of duplicates, respectively, expressed in grain, leaf and root show distinct expression patterns. On the basis of a Gene Ontology analysis, we have identified and characterized the gene families that have been structurally and functionally preferentially retained in the duplication showing that the vast majority (>85%) of duplicated have been either lost or have been subfunctionalized or neofunctionalized during 50–70 million years of evolution.     

Transcriptional divergence of the duplicated oxidative stress-responsive genes in the Arabidopsis genome

Previous studies have indicated that Arabidopsis thaliana experienced a genome-wide duplication event shortly before its divergence from Brassica followed by extensive chromosomal rearrangements and deletions. While a large number of the duplicated genes have significantly diverged or lost their sister genes, we found 4222 pairs that are still highly conserved, and as a result had similar functional assignments during the annotation of the genome sequence. Using whole-genome DNA microarrays, we identified 906 duplicated gene pairs in which at least one member exhibited a significant response to oxidative stress. Among these, only 117 pairs were up- or down-regulated in both pairs and many of these exhibited dissimilar patterns of expression. Examination of the expression patterns of PAL1 and PAL2, ACD1 and ACD2, genes coding for two Hsp20s, various P450s, and electron transfer flavoproteins suggests Arabidopsis evolved a number of distinct oxidative stress response mechanisms using similar gene sets following the duplication of its genome.     

Signatures of reproductive isolation in patterns of single nucleotide diversity across inbred strains of mice

Reproductive isolation is often caused by the disruption of genic interactions that evolve in geographically separate populations. Identifying the genomic regions and genes involved in these interactions, known as "Dobzhansky-Muller incompatibilities," can be challenging but is facilitated by the wealth of genetic markers now available in model systems. In recent years, the complete genome sequence and thousands of single nucleotide polymorphisms (SNPs) from laboratory mice, which are largely genetic hybrids between Mus musculus and M. domesticus, have become available. Here, we use these resources to locate genomic regions that may underlie reproductive isolation between these two species. Using genotypes from 332 SNPs that differ between wild-derived strains of M. musculus and M. domesticus, we identified several physically unlinked SNP pairs that show exceptional gametic disequilibrium across the lab strains. Conspecific alleles were associated in a disproportionate number of these cases, consistent with the action of natural selection against hybrid gene combinations. As predicted by the Dobzhansky-Muller model, this bias was differentially attributable to locus pairs for which one hybrid genotype was missing. We assembled a list of potential Dobzhansky-Muller incompatibilities from locus pairs that showed extreme associations (only three gametic types) among conspecific alleles. Two SNPs in this list map near known hybrid sterility loci on chromosome 17 and the X chromosome, allowing us to nominate partners for disrupted interactions involving these genomic regions for the first time. Together, these results indicate that patterns produced by speciation between M. musculus and M. domesticus are visible in the genomes of lab strains of mice, underscoring the potential of these genetic model organisms for addressing general questions in evolutionary biology.     

Leveraging three-dimensional chromatin architecture for effective reconstruction of enhancer–target gene regulatory interactions

A growing amount of evidence in literature suggests that germline sequence variants and somatic mutations in non-coding distal regulatory elements may be crucial for defining disease risk and prognostic stratification of patients, in genetic disorders as well as in cancer. Their functional interpretation is challenging because genome-wide enhancer–target gene (ETG) pairing is an open problem in genomics. The solutions proposed so far do not account for the hierarchy of structural domains which define chromatin three-dimensional (3D) architecture. Here we introduce a change of perspective based on the definition of multi-scale structural chromatin domains, integrated in a statistical framework to define ETG pairs. In this work (i) we develop a computational and statistical framework to reconstruct a comprehensive map of ETG pairs leveraging functional genomics data; (ii) we demonstrate that the incorporation of chromatin 3D architecture information improves ETG pairing accuracy and (iii) we use multiple experimental datasets to extensively benchmark our method against previous solutions for the genome-wide reconstruction of ETG pairs. This solution will facilitate the annotation and interpretation of sequence variants in distal non-coding regulatory elements. We expect this to be especially helpful in clinically oriented applications of whole genome sequencing in cancer and undiagnosed genetic diseases research.     

Synthetic Lethality between Gene Defects Affecting a Single Non-essential Molecular Pathway with Reversible Steps

Systematic analysis of synthetic lethality (SL) constitutes a critical tool for systems biology to decipher molecular pathways. The most accepted mechanistic explanation of SL is that the two genes function in parallel, mutually compensatory pathways, known as between-pathway SL. However, recent genome-wide analyses in yeast identified a significant number of within-pathway negative genetic interactions. The molecular mechanisms leading to within-pathway SL are not fully understood. Here, we propose a novel mechanism leading to within-pathway SL involving two genes functioning in a single non-essential pathway. This type of SL termed within-reversible-pathway SL involves reversible pathway steps, catalyzed by different enzymes in the forward and backward directions, and kinetic trapping of a potentially toxic intermediate. Experimental data with recombinational DNA repair genes validate the concept. Mathematical modeling recapitulates the possibility of kinetic trapping and revealed the potential contributions of synthetic, dosage-lethal interactions in such a genetic system as well as the possibility of within-pathway positive masking interactions. Analysis of yeast gene interaction and pathway data suggests broad applicability of this novel concept. These observations extend the canonical interpretation of synthetic-lethal or synthetic-sick interactions with direct implications to reconstruct molecular pathways and improve therapeutic approaches to diseases such as cancer.     

A physical genome map of Pseudomonas aeruginosa PAO.

A complete macrorestriction map of the 5.9 Mb genome of Pseudomonas aeruginosa PAO (DSM 1707) was constructed by the combination of various one- and two-dimensional pulsed field gel electrophoresis techniques. A total of 51 restriction sites (36 SpeI sites, 15 DpnI sites) were placed on the physical map yielding an average resolution of 110 kb. Several genes encoding virulence factors and enzymes of metabolic pathways were located on the anonymous map by Southern hybridization. Distances between the gene loci were similar on the genetic and physical maps, suggesting an even distribution of genome mobility throughout the bacterial chromosome. The four rRNA operons were organized in pairs of inverted repeats. The two-dimensional macro-restriction techniques described herein are generally applicable for the genome mapping of any prokaryote and lower eukaryote which yields resolvable fragment patterns on two-dimensional pulsed field gels.     

Genome-wide characterization of Tetrahymena thermophila chromosome breakage sites. II. Physical and genetic mapping

The chromosomes of the macronuclear (expressed) genome of Tetrahymena thermophila are generated by developmental fragmentation of the five micronuclear (germline) chromosomes. This fragmentation is site specific, directed by a conserved chromosome breakage sequence (Cbs element). An accompanying article in this issue reports the development of a successful scheme for the genome-wide cloning and identification of functional chromosome breakage sites. This article reports the physical and genetic characterization of 30 functional chromosome breakage junctions. Unique sequence tags and physical sizes were obtained for the pair of macronuclear chromosomes generated by fragmentation at each Cbs. Cbs-associated polymorphisms were used to genetically map 11 junctions to micronuclear linkage groups and macronuclear coassortment groups. Two pairs of junctions showed statistically significant similarity of the sequences flanking the Cbs, suggestive of relatively recent duplications of entire Cbs junctions during Tetrahymena genome evolution. Two macronuclear chromosomes that lose at least one end in an age-related manner were also identified. The whole-genome shotgun sequencing of the Tetrahymena macronucleus has recently been completed at The Institute for Genome Research (TIGR). By providing unique sequence from natural ends of macronuclear chromosomes, Cbs junctions will provide useful sequence tags for relating macro- and micronuclear genetic, physical, and whole-genome sequence maps.     

Genomics of Divergence along a Continuum of Parapatric Population Differentiation

The patterns of genomic divergence during ecological speciation are shaped by a combination of evolutionary forces. Processes such as genetic drift, local reduction of gene flow around genes causing reproductive isolation, hitchhiking around selected variants, variation in recombination and mutation rates are all factors that can contribute to the heterogeneity of genomic divergence. On the basis of 60 fully sequenced three-spined stickleback genomes, we explore these different mechanisms explaining the heterogeneity of genomic divergence across five parapatric lake and river population pairs varying in their degree of genetic differentiation. We find that divergent regions of the genome are mostly specific for each population pair, while their size and abundance are not correlated with the extent of genome-wide population differentiation. In each pair-wise comparison, an analysis of allele frequency spectra reveals that 25–55% of the divergent regions are consistent with a local restriction of gene flow. Another large proportion of divergent regions (38–75%) appears to be mainly shaped by hitchhiking effects around positively selected variants. We provide empirical evidence that alternative mechanisms determining the evolution of genomic patterns of divergence are not mutually exclusive, but rather act in concert to shape the genome during population differentiation, a first necessary step towards ecological speciation.     

SLAF-seq: An Efficient Method of Large-Scale De Novo SNP Discovery and Genotyping Using High-Throughput Sequencing

Large-scale genotyping plays an important role in genetic association studies. It has provided new opportunities for gene discovery, especially when combined with high-throughput sequencing technologies. Here, we report an efficient solution for large-scale genotyping. We call it specific-locus amplified fragment sequencing (SLAF-seq). SLAF-seq technology has several distinguishing characteristics: i) deep sequencing to ensure genotyping accuracy; ii) reduced representation strategy to reduce sequencing costs; iii) pre-designed reduced representation scheme to optimize marker efficiency; and iv) double barcode system for large populations. In this study, we tested the efficiency of SLAF-seq on rice and soybean data. Both sets of results showed strong consistency between predicted and practical SLAFs and considerable genotyping accuracy. We also report the highest density genetic map yet created for any organism without a reference genome sequence, common carp in this case, using SLAF-seq data. We detected 50,530 high-quality SLAFs with 13,291 SNPs genotyped in 211 individual carp. The genetic map contained 5,885 markers with 0.68 cM intervals on average. A comparative genomics study between common carp genetic map and zebrafish genome sequence map showed high-quality SLAF-seq genotyping results. SLAF-seq provides a high-resolution strategy for large-scale genotyping and can be generally applicable to various species and populations.     

Computational identification of insertional mutagenesis targets for cancer gene discovery

Insertional mutagenesis is a potent forward genetic screening technique used to identify candidate cancer genes in mouse model systems. An important, yet unresolved issue in the analysis of these screens, is the identification of the genes affected by the insertions. To address this, we developed Kernel Convolved Rule Based Mapping (KC-RBM). KC-RBM exploits distance, orientation and insertion density across tumors to automatically map integration sites to target genes. We perform the first genome-wide evaluation of the association of insertion occurrences with aberrant gene expression of the predicted targets in both retroviral and transposon data sets. We demonstrate the efficiency of KC-RBM by showing its superior performance over existing approaches in recovering true positives from a list of independently, manually curated cancer genes. The results of this work will significantly enhance the accuracy and speed of cancer gene discovery in forward genetic screens. KC-RBM is available as R-package.     

Identifying interventional and pathogenic mechanisms by generative inverse modeling of gene expression profiles.

MOTIVATION: The characterization of genetic mechanisms underlying normal cellular function, cancer development, pathogenesis, and the effect of drug treatment is one of the most challenging topics for cancer research and molecular biology. Existing methods for inferring genetic regulatory networks from genome-wide expression profiles provide important information about gene interactions and regulatory relationships. However, these methods do not provide information about the impact of possible interventions or changes on such regulatory networks to study cause-effect relationships at a systems-biology level. RESULTS: We present a data-driven method called generative inverse modeling, which simulates the effect of local genetic changes on the global cellular state, as reflected by an altered genome-wide expression profile. For each genetic change we define a pathogenic score by calculating to what extent it transforms the simulated expression patterns into patterns measured for pathologically altered tissues. The method can be used to estimate the relevance of genes for disease-specific genetic mechanisms, e.g., as presented here for pathogenesis. Generative inverse modeling is based on a Bayesian probability density estimation from a set of measured gene-expression patterns.     

Genome-wide identification studies – A primer to explore new genes in plant species

Genome data have accumulated rapidly in recent years, doubling roughly after every 6 months due to the influx of next-generation sequencing technologies. A plethora of plant genomes are available in comprehensive public databases. This easy access to data provides an opportunity to explore genome datasets and recruit new genes in various plant species not possible a decade ago. In the past few years, many gene families have been published using these public datasets. These genome-wide studies identify and characterize gene members, gene structures, evolutionary relationships, expression patterns, protein interactions and gene ontologies, and predict putative gene functions using various computational tools. Such studies provide meaningful information and an initial framework for further functional elucidation. This review provides a concise layout of approaches used in these gene family studies and demonstrates an outline for employing various plant genome datasets in future studies.     

Updated map of duplicated regions in the yeast genome

We have updated the map of duplicated chromosomal segments in the Saccharomyces cerevisiae genome originally published by Wolfe and Shields in 1997 (Nature 387, 708-713). The new analysis is based on the more sensitive Smith Waterman search method instead of BLAST. The parameters used to identify duplicated chromosomal regions were optimized such as to maximize the amount of the genome placed into paired regions, under the assumption that the hypothesis that the entire genome was duplicated in a single event is correct. The core of the new map, with 52 pairs of regions containing three or more duplicated genes, is largely unchanged from our original map. 39 tRNA gene pairs and one snRNA pair have been added. To find additional pairs of genes that may have been formed by whole genome duplication, we searched through the parts of the genome that are not covered by this core map, looking for putative duplicated chromosomal regions containing only two duplicate genes instead of three, or having lower-scoring gene pairs. This approach identified a further 32 candidate paired regions, bringing the total number of protein-coding genes on the duplication map to 905 (16% of the proteome). The updated map suggests that a second copy of the ribosomal DNA array has been deleted from chromosome IV.     

Palaeohexaploid ancestry for Caryophyllales inferred from extensive gene-based physical and genetic mapping of the sugar beet genome (Beta vulgaris)

Sugar beet (Beta vulgaris) is an important crop plant that accounts for 30% of the world's sugar production annually. The genus Beta is a distant relative of currently sequenced taxa within the core eudicotyledons; the genomic characterization of sugar beet is essential to make its genome accessible to molecular dissection. Here, we present comprehensive genomic information in genetic and physical maps that cover all nine chromosomes. Based on this information we identified the proposed ancestral linkage groups of rosids and asterids within the sugar beet genome. We generated an extended genetic map that comprises 1127 single nucleotide polymorphism markers prepared from expressed sequence tags and bacterial artificial chromosome (BAC) end sequences. To construct a genome-wide physical map, we hybridized gene-derived oligomer probes against two BAC libraries with 9.5-fold cumulative coverage of the 758 Mbp genome. More than 2500 probes and clones were integrated both in genetic maps and the physical data. The final physical map encompasses 535 chromosomally anchored contigs that contains 8361 probes and 22 815 BAC clones. By using the gene order established with the physical map, we detected regions of synteny between sugar beet (order Caryophyllales) and rosid species that involves 1400-2700 genes in the sequenced genomes of Arabidopsis, poplar, grapevine, and cacao. The data suggest that Caryophyllales share the palaeohexaploid ancestor proposed for rosids and asterids. Taken together, we here provide extensive molecular resources for sugar beet and enable future high-resolution trait mapping, gene identification, and cross-referencing to regions sequenced in other plant species.     

Graphical representation of hydrogen bonding patterns in proteins

A graphical representation of the intramolecular hydrogen bonding in a protein is described, which provides a direct and easily interpretable display of its secondary and tertiary structural elements. The representation is constructed by scanning the coordinate list for all potential proton donor (PD)--proton acceptor (PA) pairs, and any pair which satisfies certain preset distance and angle criteria is classified as being H-bonded. The resulting list of H-bonds is mapped onto an N x N matrix, where N is the number of residues in the protein, by assigning an element ij of the matrix to all the PA-PD pairs between atoms of residues i and j. Subsequently graphical objects are generated for all elements which are labeled as representing one or more H-bonds, and which can then be plotted or displayed in a way analogous to the graphical representation of the distance matrix (DM). In contrast to the DM, the hydrogen bonding matrix (HBM) is sparse, which allows the patterns representing secondary and tertiary structural motifs to be quickly and clearly recognized. In addition, changes in structure are easily identifiable from changes in the H-bonding patterns. The analysis and interpretation of the HBM is discussed using aspartate amino-transferase and calmodulin as examples.     

Protein complexes are central in the yeast genetic landscape

If perturbing two genes together has a stronger or weaker effect than expected, they are said to genetically interact. Genetic interactions are important because they help map gene function, and functionally related genes have similar genetic interaction patterns. Mapping quantitative (positive and negative) genetic interactions on a global scale has recently become possible. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, like biological processes or complexes, or connections between modules. However it is not yet known how these patterns globally relate to known functional modules. Here we systematically study the monochromatic nature of known biological processes using the largest quantitative genetic interaction data set available, which includes fitness measurements for ～5.4 million gene pairs in the yeast Saccharomyces cerevisiae. We find that only 10% of biological processes, as defined by Gene Ontology annotations, and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these patterns. This suggests that complexes play a central role in shaping the monochromatic landscape of biological processes. Altogether this work shows that both positive and negative monochromatic patterns are found in known biological processes and in their connections and that protein complexes play an important role in these patterns. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms. Furthermore the analysis methods we develop are applicable to other species for which genetic interactions will progressively become more available.