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Human genetic variation is the incarnation of diverse evolutionary history, which reflects both selectively advantageous and selectively neutral change. In this study, we catalogue structural and functional features of proteins that restrain genetic variation leading to single amino acid substitutions. Our variation dataset is divided into three categories: i) Mendelian disease-related variants, ii) neutral polymorphisms and iii) cancer somatic mutations. We characterize structural environments of the amino acid variants by the following properties: i) side-chain solvent accessibility, ii) main-chain secondary structure, and iii) hydrogen bonds from a side chain to a main chain or other side chains. To address functional restraints, amino acid substitutions in proteins are examined to see whether they are located at functionally important sites involved in protein-protein interactions, protein-ligand interactions or catalytic activity of enzymes. We also measure the likelihood of amino acid substitutions and the degree of residue conservation where variants occur. We show that various types of variants are under different degrees of structural and functional restraints, which affect their occurrence in human proteome.

Recent analyses of human genome sequences have given rise to impressive advances in identifying non-synonymous single nucleotide polymorphisms (nsSNPs). By contrast, the annotation of nsSNPs and their links to diseases are progressing at a much slower pace. Many of the current approaches to analysing disease-associated nsSNPs use primarily sequence and evolutionary information, while structural information is relatively less exploited. In order to explore the potential of such information, we developed a structure-based approach, Bongo (Bonds ON Graph), to predict structural effects of nsSNPs. Bongo considers protein structures as residue–residue interaction networks and applies graph theoretical measures to identify the residues that are critical for maintaining structural stability by assessing the consequences on the interaction network of single point mutations. Our results show that Bongo is able to identify mutations that cause both local and global structural effects, with a remarkably low false positive rate. Application of the Bongo method to the prediction of 506 disease-associated nsSNPs resulted in a performance (positive predictive value, PPV, 78.5%) similar to that of PolyPhen (PPV, 77.2%) and PANTHER (PPV, 72.2%). As the Bongo method is solely structure-based, our results indicate that the structural changes resulting from nsSNPs are closely associated to their pathological consequences., Author Summary Non-synonymous single nucleotide polymorphisms (nsSNPs) are single base differences between individual genomes that lead to amino acid changes in protein sequences. They may influence an individual's susceptibility to disease or response to drugs through their impacts on a protein's structure and hence cause functional changes. In this paper, we present a new methodology to estimate the impact of nsSNPs on disease susceptibility. This is made possible by characterising the protein structure and the change of structural stability due to nsSNPs. We show that our computer program Bongo, which describes protein structures as interlinked amino acids, can identify conformational changes resulting from nsSNPs that are closely associated with pathological consequences. Bongo requires only structural information to analyze nsSNPs and thus is complementary to methods that use evolutionary information. Bongo helps us investigate the suggestion that most disease-causing mutations disturb structural features of proteins, thus affecting their stability. We anticipate that making Bongo available to the community will facilitate a better understanding of disease-associated nsSNPs and thus benefit personal medicine in the future.

West Nile virus (WNV) has emerged globally as an increasingly important pathogen for humans and domestic animals. Studies of the evolutionary diversity of the virus over its known history will help to elucidate conserved sites, and characterize their correspondence to other pathogens and their relevance to the immune system. We describe a large-scale analysis of the entire WNV proteome, aimed at identifying and characterizing evolutionarily conserved amino acid sequences. This study, which used 2,746 WNV protein sequences collected from the NCBI GenPept database, focused on analysis of peptides of length 9 amino acids or more, which are immunologically relevant as potential T-cell epitopes. Entropy-based analysis of the diversity of WNV sequences, revealed the presence of numerous evolutionarily stable nonamer positions across the proteome (entropy value of < or = 1). The representation (frequency) of nonamers variant to the predominant peptide at these stable positions was, generally, low (< or = 10% of the WNV sequences analyzed). Eighty-eight fragments of length 9-29 amino acids, representing approximately 34% of the WNV polyprotein length, were identified to be identical and evolutionarily stable in all analyzed WNV sequences. Of the 88 completely conserved sequences, 67 are also present in other flaviviruses, and several have been associated with the functional and structural properties of viral proteins. Immunoinformatic analysis revealed that the majority (78/88) of conserved sequences are potentially immunogenic, while 44 contained experimentally confirmed human T-cell epitopes. This study identified a comprehensive catalogue of completely conserved WNV sequences, many of which are shared by other flaviviruses, and majority are potential epitopes. The complete conservation of these immunologically relevant sequences through the entire recorded WNV history suggests they will be valuable as components of peptide-specific vaccines or other therapeutic applications, for sequence-specific diagnosis of a wide-range of Flavivirus infections, and for studies of homologous sequences among other flaviviruses.

Genotype imputation methods are now being widely used in the analysis of genome-wide association studies. Most imputation analyses to date have used the HapMap as a reference dataset, but new reference panels (such as controls genotyped on multiple SNP chips and densely typed samples from the 1,000 Genomes Project) will soon allow a broader range of SNPs to be imputed with higher accuracy, thereby increasing power. We describe a genotype imputation method (IMPUTE version 2) that is designed to address the challenges presented by these new datasets. The main innovation of our approach is a flexible modelling framework that increases accuracy and combines information across multiple reference panels while remaining computationally feasible. We find that IMPUTE v2 attains higher accuracy than other methods when the HapMap provides the sole reference panel, but that the size of the panel constrains the improvements that can be made. We also find that imputation accuracy can be greatly enhanced by expanding the reference panel to contain thousands of chromosomes and that IMPUTE v2 outperforms other methods in this setting at both rare and common SNPs, with overall error rates that are 15%–20% lower than those of the closest competing method. One particularly challenging aspect of next-generation association studies is to integrate information across multiple reference panels genotyped on different sets of SNPs; we show that our approach to this problem has practical advantages over other suggested solutions., Author Summary Large association studies have proven to be effective tools for identifying parts of the genome that influence disease risk and other heritable traits. So-called “genotype imputation” methods form a cornerstone of modern association studies: by extrapolating genetic correlations from a densely characterized reference panel to a sparsely typed study sample, such methods can estimate unobserved genotypes with high accuracy, thereby increasing the chances of finding true associations. To date, most genome-wide imputation analyses have used reference data from the International HapMap Project. While this strategy has been successful, association studies in the near future will also have access to additional reference information, such as control sets genotyped on multiple SNP chips and dense genome-wide haplotypes from the 1,000 Genomes Project. These new reference panels should improve the quality and scope of imputation, but they also present new methodological challenges. We describe a genotype imputation method, IMPUTE version 2, that is designed to address these challenges in next-generation association studies. We show that our method can use a reference panel containing thousands of chromosomes to attain higher accuracy than is possible with the HapMap alone, and that our approach is more accurate than competing methods on both current and next-generation datasets. We also highlight the modeling issues that arise in imputation datasets.

International audience; Background: Array comparative genomic hybridization (aCGH) to detect copy number variants (CNVs) in mammalian genomes has led to a growing awareness of the potential importance of this category of sequence variation as a cause of phenotypic variation. Yet there are large discrepancies between studies, so that the extent of the genome affected by CNVs is unknown. We combined molecular and aCGH analyses of CNVs in inbred mouse strains to investigate this question. Principal Findings: Using a 2.1 million probe array we identified 1,477 deletions and 499 gains in 7 inbred mouse strains. Molecular characterization indicated that approximately one third of the CNVs detected by the array were false positives and we estimate the false negative rate to be more than 50%. We show that low concordance between studies is largely due to the molecular nature of CNVs, many of which consist of a series of smaller deletions and gains interspersed by regions where the DNA copy number is normal. Conclusions: Our results indicate that CNVs detected by arrays may be the coincidental co-localization of smaller CNVs, whose presence is more likely to perturb an aCGH hybridization profile than the effect of an isolated, small, copy number alteration. Our findings help explain the hitherto unexplored discrepancies between array-based studies of copy number variation in the mouse genome.

7 pages, 3 figures, PMID: 20644733 [PubMed]PMCID: PMC2904377 [PubMed Central]Free PMC Article, BACKGROUND: How to extract useful information from complex biological networks is a major goal in many fields, especially in genomics and proteomics. We have shown in several works that iterative hierarchical clustering, as implemented in the UVCluster program, is a powerful tool to analyze many of those networks. However, the amount of computation time required to perform UVCluster analyses imposed significant limitations to its use. METHODOLOGY/PRINCIPAL FINDINGS: We describe the suite Jerarca, designed to efficiently convert networks of interacting units into dendrograms by means of iterative hierarchical clustering. Jerarca is divided into three main sections. First, weighted distances among units are computed using up to three different approaches: a more efficient version of UVCluster and two new, related algorithms called RCluster and SCluster. Second, Jerarca builds dendrograms based on those distances, using well-known phylogenetic algorithms, such as UPGMA or Neighbor-Joining. Finally, Jerarca provides optimal partitions of the trees using statistical criteria based on the distribution of intra- and intercluster connections. Outputs compatible with the phylogenetic software MEGA and the Cytoscape package are generated, allowing the results to be easily visualized. CONCLUSIONS/SIGNIFICANCE: THE FOUR MAIN ADVANTAGES OF JERARCA IN RESPECT TO UVCLUSTER ARE: 1) Improved speed of a novel UVCluster algorithm; 2) Additional, alternative strategies to perform iterative hierarchical clustering; 3) Automatic evaluation of the hierarchical trees to obtain optimal partitions; and, 4) Outputs compatible with popular software such as MEGA and Cytoscape., This research was supported by grant BIO2008-05067 (Programa Nacional de Biotecnologia; Ministerio de Ciencia e Innovacion, Spain).

Current estimates indicate that only about 1.2% of the mammalian genome codes for amino acids in proteins. However, mounting evidence over the past decade has suggested that the vast majority of the genome is transcribed, well beyond the boundaries of known genes, a phenomenon known as pervasive transcription [1]. Challenging this view, an article published in PLoS Biology by van Bakel et al. concluded that “the genome is not as pervasively transcribed as previously reported” [2] and that the majority of the detected low-level transcription is due to technical artefacts and/or background biological noise. These conclusions attracted considerable publicity [3]–[6]. Here, we present an evaluation of the analysis and conclusions of van Bakel et al. compared to those of others and show that (1) the existence of pervasive transcription is supported by multiple independent techniques; (2) re-analysis of the van Bakel et al. tiling arrays shows that their results are atypical compared to those of ENCODE and lack independent validation; and (3) the RNA sequencing dataset used by van Bakel et al. suffered from insufficient sequencing depth and poor transcript assembly, compromising their ability to detect the less abundant transcripts outside of protein-coding genes. We conclude that the totality of the evidence strongly supports pervasive transcription of mammalian genomes, although the biological significance of many novel coding and noncoding transcripts remains to be explored.

The authors propose that short repeat sequences may play an important role in causing the pervasive clustering of mutations across diverse genomes from prokaryotes to humans., The genome-sequencing gold rush has facilitated the use of comparative genomics to uncover patterns of genome evolution, although their causal mechanisms remain elusive. One such trend, ubiquitous to prokarya and eukarya, is the association of insertion/deletion mutations (indels) with increases in the nucleotide substitution rate extending over hundreds of base pairs. The prevailing hypothesis is that indels are themselves mutagenic agents. Here, we employ population genomics data from Escherichia coli, Saccharomyces paradoxus, and Drosophila to provide evidence suggesting that it is not the indels per se but the sequence in which indels occur that causes the accumulation of nucleotide substitutions. We found that about two-thirds of indels are closely associated with repeat sequences and that repeat sequence abundance could be used to identify regions of elevated sequence diversity, independently of indels. Moreover, the mutational signature of indel-proximal nucleotide substitutions matches that of error-prone DNA polymerases. We propose that repeat sequences promote an increased probability of replication fork arrest, causing the persistent recruitment of error-prone DNA polymerases to specific sequence regions over evolutionary time scales. Experimental measures of the mutation rates of engineered DNA sequences and analyses of experimentally obtained collections of spontaneous mutations provide molecular evidence supporting our hypothesis. This study uncovers a new role for repeat sequences in genome evolution and provides an explanation of how fine-scale sequence contextual effects influence mutation rates and thereby evolution., Author Summary An intriguing observation made during the comparison of genomes is that insertion and deletion mutations (indels) cluster together with nucleotide substitutions. Two (not mutually exclusive) hypotheses have been proposed to explain this phenomenon. The first postulates that an indel mutation causes an increase in the likelihood of the surrounding sequence incurring nucleotide substitutions, while the second claims that the region of DNA in which such a cluster is located is more likely to sustain both indels and substitutions. Here, we present evidence suggesting that the region of DNA, and not the indel, is associated with the accumulation of clusters of mutations over evolutionary time scales. We find that repeat sequences are closely associated with a large proportion of indels and that the abundance of repeat sequences is linked with regions of increased nucleotide diversity. By analysing molecular data and measuring the mutation rates of genes engineered to contain repeats, we find that the mutation rate can be manipulated by the insertion of long repeat sequences. On the basis of these results, we propose a model in which repeat sequences are prone to cause stalling of the high-fidelity DNA polymerase, leading to the recruitment of error-prone repair polymerases which then replicate the surrounding sequence with a higher-than-average error rate.

Genome-wide association studies (GWAS) examine a large number of markers across the genome to identify associations between genetic variants and disease. Most published studies examine only single markers, which may be less informative than considering multiple markers and multiple genes jointly because genes may interact with each other to affect disease risk. Much knowledge has been accumulated in the literature on biological pathways and interactions. It is conceivable that appropriate incorporation of such prior knowledge may improve the likelihood of making genuine discoveries. Although a number of methods have been developed recently to prioritize genes using prior biological knowledge, such as pathways, most methods treat genes in a specific pathway as an exchangeable set without considering the topological structure of a pathway. However, how genes are related with each other in a pathway may be very informative to identify association signals. To make use of the connectivity information among genes in a pathway in GWAS analysis, we propose a Markov Random Field (MRF) model to incorporate pathway topology for association analysis. We show that the conditional distribution of our MRF model takes on a simple logistic regression form, and we propose an iterated conditional modes algorithm as well as a decision theoretic approach for statistical inference of each gene's association with disease. Simulation studies show that our proposed framework is more effective to identify genes associated with disease than a single gene–based method. We also illustrate the usefulness of our approach through its applications to a real data example., Author Summary Statistical methods used in most GWAS are based on the analysis of single markers. Prior biological information about markers, genes, and pathways is not commonly incorporated in the detection of associated disease loci. Recently a number of methods have been developed to incorporate such information, and it has been shown that they may make use of prior biological knowledge in association analysis. However, most of these methods ignore the regulatory relationships and functional interactions among genes. In this article, we propose a statistical method that can explicitly model the interactions of genes in a neighborhood defined by the topology of a pathway. Simulation studies and a real data example show that the proposed method can improve the power of identifying associated genes when they are in the neighborhood of other genes whose association has been firmly established in previous studies.

Approximately 80% of human breast carcinomas present as oestrogen receptor α-positive (ER+ve) disease, and ER status is a critical factor in treatment decision-making. Recently, single nucleotide polymorphisms (SNPs) in the region immediately upstream of the ER gene (ESR1) on 6q25.1 have been associated with breast cancer risk. Our investigation of factors associated with the level of expression of ESR1 in ER+ve tumours has revealed unexpected associations between genes in this region and ESR1 expression that are important to consider in studies of the genetic causes of breast cancer risk. RNA from tumour biopsies taken from 104 postmenopausal women before and after 2 weeks treatment with an aromatase (oestrogen synthase) inhibitor was analyzed on Illumina 48K microarrays. Multiple-testing corrected Spearman correlation revealed that three previously uncharacterized open reading frames (ORFs) located immediately upstream of ESR1, C6ORF96, C6ORF97, and C6ORF211 were highly correlated with ESR1 (Rs = 0.67, 0.64, and 0.55 respectively, FDR, Author Summary Recent genome-wide analysis has revealed that the way in which genes are arranged on chromosomes and the conformation of these chromosomes are crucial for the regulation of gene expression. Reflecting this arrangement, clusters of genes which are regulated together have been discovered. We have identified a previously unreported transcriptional activity hub spanning ESR1, the gene encoding the important breast cancer biomarker oestrogen receptor. Genetic variants immediately upstream of ESR1 have recently been linked to breast cancer risk. We found that three open reading frames within this region are tightly co-expressed with ESR1. We investigated the function of these genes and discovered that one of these co-expressed genes, C6ORF211, affects proliferation in cultured cells and is correlated with proliferation in breast tumours. Another of the genes, C6ORF97, is negatively correlated with proliferation in breast tumours and predicts for outcome on the anti-oestrogen drug tamoxifen. These findings suggest that the genes could contribute to the phenotype associated with oestrogen-receptor positivity. In addition, they may be involved in the mechanism by which genetic variation in this region of the genome contributes to breast cancer susceptibility.

In higher eukaryotes, messenger RNAs (mRNAs) are exported from the nucleus to the cytoplasm via factors deposited near the 5′ end of the transcript during splicing. The signal sequence coding region (SSCR) can support an alternative mRNA export (ALREX) pathway that does not require splicing. However, most SSCR–containing genes also have introns, so the interplay between these export mechanisms remains unclear. Here we support a model in which the furthest upstream element in a given transcript, be it an intron or an ALREX–promoting SSCR, dictates the mRNA export pathway used. We also experimentally demonstrate that nuclear-encoded mitochondrial genes can use the ALREX pathway. Thus, ALREX can also be supported by nucleotide signals within mitochondrial-targeting sequence coding regions (MSCRs). Finally, we identified and experimentally verified novel motifs associated with the ALREX pathway that are shared by both SSCRs and MSCRs. Our results show strong correlation between 5′ untranslated region (5′UTR) intron presence/absence and sequence features at the beginning of the coding region. They also suggest that genes encoding secretory and mitochondrial proteins share a common regulatory mechanism at the level of mRNA export., Author Summary The function and evolution of introns have been topics of great interest since introns were discovered in the 1970s. Introns that interrupt protein-coding regions have the most obvious potential to affect coding sequences and their evolution, and they have therefore been studied most intensively. However, about one third of human genes contain introns within 5′ untranslated regions (UTR). Here we observe that certain classes of genes, including those targeted to the endoplasmic reticulum and nuclear-encoded mitochondrial genes, are surprisingly depleted of 5′UTR introns. We offer and support a model that explains this observation and points to a surprising connection between 5′UTR introns and how mRNAs are exported from the nucleus.

The Hox genes are responsible for generating morphological diversity along the anterior-posterior axis during animal development. The Drosophila Hox gene Ultrabithorax (Ubx), for example, is required for specifying the identity of the third thoracic (T3) segment of the adult, which includes the dorsal haltere, an appendage required for flight, and the ventral T3 leg. Ubx mutants show homeotic transformations of the T3 leg towards the identity of the T2 leg and the haltere towards the wing. All Hox genes, including Ubx, encode homeodomain containing transcription factors, raising the question of what target genes Ubx regulates to generate these adult structures. To address this question, we carried out whole genome ChIP-chip studies to identify all of the Ubx bound regions in the haltere and T3 leg imaginal discs, which are the precursors to these adult structures. In addition, we used ChIP-chip to identify the sites bound by the Hox cofactor, Homothorax (Hth). In contrast to previous ChIP-chip studies carried out in Drosophila embryos, these binding studies reveal that there is a remarkable amount of tissue- and transcription factor-specific binding. Analyses of the putative target genes bound and regulated by these factors suggest that Ubx regulates many downstream transcription factors and developmental pathways in the haltere and T3 leg. Finally, we discovered additional DNA sequence motifs that in some cases are specific for individual data sets, arguing that Ubx and/or Hth work together with many regionally expressed transcription factors to execute their functions. Together, these data provide the first whole-genome analysis of the binding sites and target genes regulated by Ubx to specify the morphologies of the adult T3 segment of the fly.

In the quest to reconstruct the Tree of Life, researchers have increasingly turned to phylogenomics, the inference of phylogenetic relationships using genome-scale data (Box 1). Mesmerized by the sustained increase in sequencing throughput, many phylogeneticists entertained the hope that the incongruence frequently observed in studies using single or a few genes [1] would come to an end with the generation of large multigene datasets. Yet, as so often happens, reality has turned out to be far more complex, as three recent large-scale analyses, one published in PLoS Biology [2]–[4], make clear. The studies, which deal with the early diversification of animals, produced highly incongruent (Box 2) findings despite the use of considerable sequence data (see Figure 1). Clearly, merely adding more sequences is not enough to resolve the inconsistencies.

Genome-wide interaction-based association (GWIBA) analysis has the potential to identify novel susceptibility loci. These interaction effects could be missed with the prevailing approaches in genome-wide association studies (GWAS). However, no convincing loci have been discovered exclusively from GWIBA methods, and the intensive computation involved is a major barrier for application. Here, we developed a fast, multi-thread/parallel program named “pair-wise interaction-based association mapping” (PIAM) for exhaustive two-locus searches. With this program, we performed a complete GWIBA analysis on seven diseases with stringent control for false positives, and we validated the results for three of these diseases. We identified one pair-wise interaction between a previously identified locus, C1orf106, and one new locus, TEC, that was specific for Crohn's disease, with a Bonferroni corrected P, Author Summary Recent studies on the genetic basis of common diseases have identified many loci that confer disease susceptibility. However, much of the heritability of these diseases remains unexplained. Loci involved in gene–gene interactions are considered cryptic, because they confer susceptibility, but may not generate a detectable signal on their own. These interactions may account for the “missing heritability” of common diseases. Theoretically, these interactions can be identified with the genome-wide interaction-based association analysis. But, in reality, very few gene–gene interactions have been identified with that method, and most were based on prior biological knowledge. Here, we applied a parallel computing technique that facilitated the identification of multiple new cryptic susceptibility loci involved in common diseases. We applied stringent control for false positives, and we validated our findings with independent datasets. This study demonstrated that interactions between gene loci could be successfully identified with the genome-wide interaction-based approach. With this approach, we also identified cryptic loci with moderate single-locus effects. The identified loci and interactions merit further investigations for fine mapping and functional analyses. Our results extend the current knowledge of common diseases for future studies in genetic mapping. This approach is applicable to current and future genome-wide association datasets.

Background Alternative polyadenylation as a mechanism in gene expression regulation has been widely recognized in recent years. Arabidopsis polyadenylation factor PCFS4 was shown to function in leaf development and in flowering time control. The function of PCFS4 in controlling flowering time was correlated with the alternative polyadenylation of FCA, a flowering time regulator. However, genetic evidence suggested additional targets of PCFS4 that may mediate its function in both flowering time and leaf development. Methodology/Principal Findings To identify further targets, we investigated the whole transcriptome of a PCFS4 mutant using Affymetrix Arabidopsis genomic tiling 1.0R array and developed a data analysis pipeline, termed RADPRE (Ratio-based Analysis of Differential mRNA Processing and Expression). In RADPRE, ratios of normalized probe intensities between wild type Columbia and a pcfs4 mutant were first generated. By doing so, one of the major problems of tiling array data—variations caused by differential probe affinity—was significantly alleviated. With the probe ratios as inputs, a hierarchy of statistical tests was carried out to identify differentially processed genes (DPG) and differentially expressed genes (DEG). The false discovery rate (FDR) of this analysis was estimated by using the balanced random combinations of Col/pcfs4 and pcfs4/Col ratios as inputs. Gene Ontology (GO) analysis of the DPGs and DEGs revealed potential new roles of PCFS4 in stress responses besides flowering time regulation. Conclusion/Significance We identified 68 DPGs and 114 DEGs with FDR at 1% and 2%, respectively. Most of the 68 DPGs were subjected to alternative polyadenylation, splicing or transcription initiation. Quantitative PCR analysis of a set of DPGs confirmed that most of these genes were truly differentially processed in pcfs4 mutant plants. The enriched GO term “regulation of flower development” among PCFS4 targets further indicated the efficacy of the RADPRE pipeline. This simple but effective program is available upon request.

We conducted a comprehensive study of copy number variants (CNVs) well-tagged by SNPs (r2≥0.8) by analyzing their effect on gene expression and their association with disease susceptibility and other complex human traits. We tested whether these CNVs were more likely to be functional than frequency-matched SNPs as trait-associated loci or as expression quantitative trait loci (eQTLs) influencing phenotype by altering gene regulation. Our study found that CNV–tagging SNPs are significantly enriched for cis eQTLs; furthermore, we observed that trait associations from the NHGRI catalog show an overrepresentation of SNPs tagging CNVs relative to frequency-matched SNPs. We found that these SNPs tagging CNVs are more likely to affect multiple expression traits than frequency-matched variants. Given these findings on the functional relevance of CNVs, we created an online resource of expression-associated CNVs (eCNVs) using the most comprehensive population-based map of CNVs to inform future studies of complex traits. Although previous studies of common CNVs that can be typed on existing platforms and/or interrogated by SNPs in genome-wide association studies concluded that such CNVs appear unlikely to have a major role in the genetic basis of several complex diseases examined, our findings indicate that it would be premature to dismiss the possibility that even common CNVs may contribute to complex phenotypes and at least some common diseases., Author Summary Despite the large number of SNPs found to be reproducibly associated with complex diseases, they collectively account for only a small proportion of the overall heritability to such traits. CNVs have thus been proposed to explain some of the missing heritability and to alter disease susceptibility. However, a recent study of the genetics of 8 common diseases involving 16,000 cases and 3,000 controls failed to identify any novel CNVs associated with disease and concluded that CNVs are unlikely to play a major role in their etiology. Studies we report here show that we must be careful not to dismiss the possibility that CNVs may indeed underlie some of the observed associations with complex disease. Our findings show that well-tagged CNVs are disproportionately more likely to be eQTLs, as well as cis-eQTLs, than frequency-matched SNPs; furthermore, reproducible trait associations, as represented in the NHGRI catalog, are enriched for well-tagged CNVs than frequency-matched SNPs. Because of these findings on the strong functional relevance of these CNVs, we created a database (available at http://www.scandb.org/) of expression associated CNVs to supplement our earlier studies of SNP eQTLs and to contribute to future studies of the genetics of complex traits.

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., Author Summary Genetic interactions indicate functional dependencies between genes and are a powerful tool to predict gene function. Functionally related genes tend to have similar profiles of genetic interactions. Recently, global scale mapping of quantitative (positive and negative) genetic interactions has been performed. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, such as biological processes or protein complexes, or connections between modules, but it is not yet known how these patterns globally relate to known functional modules. Here we systematically evaluate the monochromatic nature of known biological processes and their connections in the yeast Saccharomyces cerevisiae. We find that 10% of biological processes 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 monochromatic groups. 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.

Saxitoxin and its analogs cause the paralytic shellfish-poisoning syndrome, adversely affecting human health and coastal shellfish industries worldwide. Here we report the isolation, sequencing, annotation, and predicted pathway of the saxitoxin biosynthetic gene cluster in the cyanobacterium Lyngbya wollei. The gene cluster spans 36 kb and encodes enzymes for the biosynthesis and export of the toxins. The Lyngbya wollei saxitoxin gene cluster differs from previously identified saxitoxin clusters as it contains genes that are unique to this cluster, whereby the carbamoyltransferase is truncated and replaced by an acyltransferase, explaining the unique toxin profile presented by Lyngbya wollei. These findings will enable the creation of toxin probes, for water monitoring purposes, as well as proof-of-concept for the combinatorial biosynthesis of these natural occurring alkaloids for the production of novel, biologically active compounds.

We studied the global relationship between gene expression and neuroanatomical connectivity in the adult rodent brain. We utilized a large data set of the rat brain “connectome” from the Brain Architecture Management System (942 brain regions and over 5000 connections) and used statistical approaches to relate the data to the gene expression signatures of 17,530 genes in 142 anatomical regions from the Allen Brain Atlas. Our analysis shows that adult gene expression signatures have a statistically significant relationship to connectivity. In particular, brain regions that have similar expression profiles tend to have similar connectivity profiles, and this effect is not entirely attributable to spatial correlations. In addition, brain regions which are connected have more similar expression patterns. Using a simple optimization approach, we identified a set of genes most correlated with neuroanatomical connectivity, and find that this set is enriched for genes involved in neuronal development and axon guidance. A number of the genes have been implicated in neurodevelopmental disorders such as autistic spectrum disorder. Our results have the potential to shed light on the role of gene expression patterns in influencing neuronal activity and connectivity, with potential applications to our understanding of brain disorders. Supplementary data are available at http://www.chibi.ubc.ca/ABAMS., Author Summary We tested the idea that the “wiring diagram” of the adult brain has a relationship with where genes are expressed. We were inspired by similar work carried out by groups examining the nematode worm Caenorhabditis elegans. By using large-scale databases of brain connectivity and gene expression in rodents, we found that many genes involved in the development of the brain show correlations with anatomical connectivity patterns. Some of the genes we found have been implicated in disorders such as autism, which is suspected to affect brain wiring. While the biological causes of the patterns we found are not yet known, we believe they provide new insight into the patterns of gene expression in the brain and will spur further study of this problem.

BackgroundEndogenous retroviruses (ERVs) are genetic fossils of ancient retroviral integrations that remain in the genome of many organisms. Most loci are rendered non-functional by mutations, but several intact retroviral genes are known in mammalian genomes. Some have been adopted by the host species, while the beneficial roles of others remain unclear. Besides the obvious possible immunogenic impact from transcribing intact viral genes, endogenous retroviruses have also become an interesting and useful tool to study phylogenetic relationships. The determination of the integration time of these viruses has been based upon the assumption that both 5' and 3' Long Terminal Repeats (LTRs) sequences are identical at the time of integration, but evolve separately afterwards. Similar approaches have been using either a constant evolutionary rate or a range of rates for these viral loci, and only single species data. Here we show the advantages of using different approaches.ResultsWe show that there are strong advantages in using multiple species data and state-of-the-art phylogenetic analysis. We incorporate both simple phylogenetic information and Monte Carlo Markov Chain (MCMC) methods to date the integrations of these viruses based on a relaxed molecular clock approach over a Bayesian phylogeny model and applied them to several selected ERV sequences in primates. These methods treat each ERV locus as having a distinct evolutionary rate for each LTR, and make use of consensual speciation time intervals between primates to calibrate the relaxed molecular clocks.ConclusionsThe use of a fixed rate produces results that vary considerably with ERV family and the actual evolutionary rate of the sequence, and should be avoided whenever multi-species phylogenetic data are available. For genome-wide studies, the simple phylogenetic approach constitutes a better alternative, while still being computationally feasible.

The application of deep sequencing to map 5′ capped transcripts has confirmed the existence of at least two distinct promoter classes in metazoans: “focused” promoters with transcription start sites (TSSs) that occur in a narrowly defined genomic span and “dispersed” promoters with TSSs that are spread over a larger window. Previous studies have explored the presence of genomic features, such as CpG islands and sequence motifs, in these promoter classes, but virtually no studies have directly investigated the relationship with chromatin features. Here, we show that promoter classes are significantly differentiated by nucleosome organization and chromatin structure. Dispersed promoters display higher associations with well-positioned nucleosomes downstream of the TSS and a more clearly defined nucleosome free region upstream, while focused promoters have a less organized nucleosome structure, yet higher presence of RNA polymerase II. These differences extend to histone variants (H2A.Z) and marks (H3K4 methylation), as well as insulator binding (such as CTCF), independent of the expression levels of affected genes. Notably, differences are conserved across mammals and flies, and they provide for a clearer separation of promoter architectures than the presence and absence of CpG islands or the occurrence of stalled RNA polymerase. Computational models support the stronger contribution of chromatin features to the definition of dispersed promoters compared to focused start sites. Our results show that promoter classes defined from 5′ capped transcripts not only reflect differences in the initiation process at the core promoter but also are indicative of divergent transcriptional programs established within gene-proximal nucleosome organization., Author Summary How are genes transcribed at the right levels and under the right conditions? Transcription regulation in eukaryotes has long been proposed to work by a division of labor: ubiquitous DNA sequence features in the core promoter region, close to the transcription start site (TSS) of genes, were thought to generically encode information to recruit RNA polymerase to initiate transcription, while specific sequence features, often distal from the genes, were thought to boost expression under the right conditions. Supporting the generic function of core promoters, genome-wide chromatin maps showed a stereotypical arrangement of well-spaced nucleosomes providing access to the TSS. High-throughput sequencing has generated genome-wide TSS maps at high resolution, which show that promoters exhibit different initiation patterns, ranging from focused start sites to dispersed regions. Linking these patterns to chromatin maps, we now find distinct core promoter classes, those in which the TSS location is defined broadly on the chromatin level and those in which the TSS is defined by precisely positioned sequence features. Notably, these architectures are conserved deeply across eukaryotes and are used for different functional classes of genes. Our work adds to the increasing understanding that core promoters contribute significantly to the complexity of eukaryotic gene expression.

High resolution, system-wide characterizations have demonstrated the capacity to identify genomic regions that undergo genomic aberrations. Such research efforts often aim at associating these regions with disease etiology and outcome. Identifying the corresponding biologic processes that are responsible for disease and its outcome remains challenging. Using novel analytic methods that utilize the structure of biologic networks, we are able to identify the specific networks that are highly significantly, nonrandomly altered by regions of copy number amplification observed in a systems-wide analysis. We demonstrate this method in breast cancer, where the state of a subset of the pathways identified through these regions is shown to be highly associated with disease survival and recurrence.

Evolution maintains organismal fitness by preserving genomic information. This is widely assumed to involve conservation of specific genomic loci among species. Many genomic encodings are now recognized to integrate small contributions from multiple genomic positions into quantitative dispersed codes, but the evolutionary dynamics of such codes are still poorly understood. Here we show that in yeast, sequences that quantitatively affect nucleosome occupancy evolve under compensatory dynamics that maintain heterogeneous levels of A+T content through spatially coupled A/T-losing and A/T-gaining substitutions. Evolutionary modeling combined with data on yeast polymorphisms supports the idea that these substitution dynamics are a consequence of weak selection. This shows that compensatory evolution, so far believed to affect specific groups of epistatically linked loci like paired RNA bases, is a widespread phenomenon in the yeast genome, affecting the majority of intergenic sequences in it. The model thus derived suggests that compensation is inevitable when evolution conserves quantitative and dispersed genomic functions., Author Summary Purifying selection is a major force in conserving genomic features. It pushes deleterious mutations to extinction while conserving the specific DNA sequence. Here we show that a large proportion of the yeast genome evolves under compensatory dynamics that conserve genomic properties while modifying the genomic sequence. Such compensatory evolution conserves the local G+C content of the genome, which influences nucleosome organization. Since purifying selection is too weak to eliminate every weakly deleterious mutation in nucleosome bound or unbound sequences, the local G+C content is frequently stabilized by compensatory G+C gaining and G+C losing mutations in proximal loci. Theoretical analysis shows that compensatory evolution is inevitable when natural selection is weak and the genomic feature is distributed over many loci. These results imply that sequence conservation may not always be equated with overall selection. They demonstrate that cycles of weakly deleterious substitutions followed by positive selection for corrective mutations, which were so far studied mostly in RNA coding genes, are observed broadly and profoundly affect genome evolution.

The increasing ability to generate large-scale, quantitative proteomic data has brought with it the challenge of analyzing such data to discover the sequence elements that underlie systems-level protein behavior. Here we show that short, linear protein motifs can be efficiently recovered from proteome-scale datasets such as sub-cellular localization, molecular function, half-life, and protein abundance data using an information theoretic approach. Using this approach, we have identified many known protein motifs, such as phosphorylation sites and localization signals, and discovered a large number of candidate elements. We estimate that ~80% of these are novel predictions in that they do not match a known motif in both sequence and biological context, suggesting that post-translational regulation of protein behavior is still largely unexplored. These predicted motifs, many of which display preferential association with specific biological pathways and non-random positioning in the linear protein sequence, provide focused hypotheses for experimental validation.

Background: Anopheles funestus is one of the primary vectors of human malaria, which causes a million deaths each year in sub-Saharan Africa. Few scientific resources are available to facilitate studies of this mosquito species and relatively little is known about its basic biology and evolution, making development and implementation of novel disease control efforts more difficult. The An. funestus genome has not been sequenced, so in order to facilitate genome-scale experimental biology, we have sequenced the adult female transcriptome of An. funestus from a newly founded colony in Burkina Faso, West Africa, using the Illumina GAIIx next generation sequencing platform. Methodology/Principal Findings: We assembled short Illumina reads de novo using a novel approach involving iterative de novo assemblies and ‘‘target-based’’ contig clustering. We then selected a conservative set of 15,527 contigs through comparisons to four Dipteran transcriptomes as well as multiple functional and conserved protein domain databases. Comparison to the Anopheles gambiae immune system identified 339 contigs as putative immune genes, thus identifying a large portion of the immune system that can form the basis for subsequent studies of this important malaria vector. We identified 5,434 1:1 orthologues between An. funestus and An. gambiae and found that among these 1:1 orthologues, the protein sequence of those with putative immune function were significantly more diverged than the transcriptome as a whole. Short read alignments to the contig set revealed almost 367,000 genetic polymorphisms segregating in the An. funestus colony and demonstrated the utility of the assembled transcriptome for use in RNA-seq based measurements of gene expression. Conclusions/Significance: We developed a pipeline that makes de novo transcriptome sequencing possible in virtually any organism at a very reasonable cost ($6,300 in sequencing costs in our case). We anticipate that our approach could be used to develop genomic resources in a diversity of systems for which full genome sequence is currently unavailable. Our An. funestus contig set and analytical results provide a valuable resource for future studies in this non-model, but epidemiologically critical, vector insect.

Background Microarray technology is a commonly used tool for assessing global gene expression. Many models for estimation of target concentration based on observed microarray signal have been proposed, but, in general, these models have been complex and platform-dependent. Principal Findings We introduce a universal Langmuir model for estimation of absolute target concentration from microarray experiments. We find that this sequence-independent model, characterized by only three free parameters, yields excellent predictions for four microarray platforms, including Affymetrix, Agilent, Illumina and a custom-printed microarray. The model also accurately predicts concentration for the MAQC data sets. This approach significantly reduces the computational complexity of quantitative target concentration estimates. Conclusions Using a simple form of the Langmuir isotherm model, with a minimum of parameters and assumptions, and without explicit modeling of individual probe properties, we were able to recover absolute transcript concentrations with high R2 on four different array platforms. The results obtained here suggest that with a “spiked-in” concentration series targeting as few as 5–10 genes, reliable estimation of target concentration can be achieved for the entire microarray.

Background The brown planthopper (BPH) Nilaparvata lugens (Stal) is one of the most serious insect pests of rice in Asia. However, little is known about the mechanisms responsible for the development, wing dimorphism and sex difference in this species. Genomic information for BPH is currently unavailable, and, therefore, transcriptome and expression profiling data for this species are needed as an important resource to better understand the biological mechanisms of BPH. Methodology/Principal Findings In this study, we performed de novo transcriptome assembly and gene expression analysis using short-read sequencing technology (Illumina) combined with a tag-based digital gene expression (DGE) system. The transcriptome analysis assembles the gene information for different developmental stages, sexes and wing forms of BPH. In addition, we constructed six DGE libraries: eggs, second instar nymphs, fifth instar nymphs, brachypterous female adults, macropterous female adults and macropterous male adults. Illumina sequencing revealed 85,526 unigenes, including 13,102 clusters and 72,424 singletons. Transcriptome sequences larger than 350 bp were subjected to Gene Orthology (GO) and KEGG Orthology (KO) annotations. To analyze the DGE profiling, we mainly compared the gene expression variations between eggs and second instar nymphs; second and fifth instar nymphs; fifth instar nymphs and three types of adults; brachypterous and macropterous female adults as well as macropterous female and male adults. Thousands of genes showed significantly different expression levels based on the various comparisons. And we randomly selected some genes to confirm their altered expression levels by quantitative real-time PCR (qRT-PCR). Conclusions/Significance The obtained BPH transcriptome and DGE profiling data provide comprehensive gene expression information at the transcriptional level that could facilitate our understanding of the molecular mechanisms from various physiological aspects including development, wing dimorphism and sex difference in BPH.

BackgroundLate Onset Alzheimer's disease (LOAD) is the leading cause of dementia. Recent large genome-wide association studies (GWAS) identified the first strongly supported LOAD susceptibility genes since the discovery of the involvement of APOE in the early 1990s. We have now exploited these GWAS datasets to uncover key LOAD pathophysiological processes.MethodologyWe applied a recently developed tool for mining GWAS data for biologically meaningful information to a LOAD GWAS dataset. The principal findings were then tested in an independent GWAS dataset.Principal FindingsWe found a significant overrepresentation of association signals in pathways related to cholesterol metabolism and the immune response in both of the two largest genome-wide association studies for LOAD.SignificanceProcesses related to cholesterol metabolism and the innate immune response have previously been implicated by pathological and epidemiological studies of Alzheimer's disease, but it has been unclear whether those findings reflected primary aetiological events or consequences of the disease process. Our independent evidence from two large studies now demonstrates that these processes are aetiologically relevant, and suggests that they may be suitable targets for novel and existing therapeutic approaches.

The phylogeny and taxonomy of mammalian species were originally based upon shared or derived morphological characteristics. However, genetic analyses have more recently played an increasingly important role in confirming existing or establishing often radically different mammalian groupings and phylogenies. The two most commonly used genetic loci in species identification are the cytochrome oxidase I gene (COI) and the cytochrome b gene (cyt b). For the first time this study provides a detailed comparison of the effectiveness of these two loci in reconstructing the phylogeny of mammals at different levels of the taxonomic hierarchy in order to provide a basis for standardizing methodologies in the future. Interspecific and intraspecific variation is assessed and for the first time, to our knowledge, statistical confidence is applied to sequence comparisons. Comparison of the DNA sequences of 217 mammalian species reveals that cyt b more accurately reconstructs their phylogeny and known relationships between species based on other molecular and morphological analyses at Super Order, Order, Family and generic levels. Cyt b correctly assigned 95.85% of mammal species to Super Order, 94.31% to Order and 98.16% to Family compared to 78.34%, 93.36% and 96.93% respectively for COI. Cyt b also gives better resolution when separating species based on sequence data. Using a Kimura 2-parameter p-distance (x100) threshold of 1.5–2.5, cyt b gives a better resolution for separating species with a lower false positive rate and higher positive predictive value than those of COI.

Gene expression responds to changes in conditions but also stochastically among individuals. In budding yeast, both expression responsiveness across conditions (“plasticity”) and cell-to-cell variation (“noise”) have been quantified for thousands of genes and found to correlate across genes. It has been argued therefore that noise and plasticity may be strongly coupled and mechanistically linked. This is consistent with some theoretical ideas, but a strong coupling between noise and plasticity also has the potential to introduce cost–benefit conflicts during evolution. For example, if high plasticity is beneficial (genes need to respond to the environment), but noise is detrimental (fluctuations are harmful), then strong coupling should be disfavored. Here, evidence is presented that cost–benefit conflicts do occur and that they constrain the evolution of gene expression and promoter usage. In contrast to recent assertions, coupling between noise and plasticity is not a general property, but one associated with particular mechanisms of transcription initiation. Further, promoter architectures associated with coupling are avoided when noise is most likely to be detrimental, and noise and plasticity are largely independent traits for core cellular components. In contrast, when genes are duplicated noise–plasticity coupling increases, consistent with reduced detrimental affects of expression variation. Noise–plasticity coupling is, therefore, an evolvable trait that may constrain the emergence of highly responsive gene expression and be selected against during evolution. Further, the global quantitative data in yeast suggest that one mechanism that relieves the constraints imposed by noise–plasticity coupling is gene duplication, providing an example of how duplication can facilitate escape from adaptive conflicts., Author Summary Gene expression needs to respond to changes in conditions, but also varies stochastically among cells in a homogenous environment. It has been argued that these two levels of expression variation may be coupled, relating to the same underlying molecular mechanisms. However, such a strong coupling between expression “plasticity” and expression “noise” may introduce cost–benefit conflicts during evolution. For example, if plasticity is beneficial, but noise is detrimental, then coupling will be disfavored. In this work, evidence is presented that such cost–benefit conflicts do occur and that they constrain the evolution of gene expression in yeast. In contrast to recent conclusions, it is shown that noise–plasticity coupling is not a general result, but rather one associated with particular mechanisms of transcription initiation. Promoter architectures associated with coupling are avoided when noise is detrimental, and noise and plasticity are not coupled for core cellular components. Noise–plasticity coupling is therefore not a general property of gene expression, but an evolvable trait that may constrain the evolution of gene expression and be selected against during evolution. Further, gene duplication may facilitate escape from the adaptive conflict imposed by coupling.

BackgroundAlthough transcription in mammalian genomes can initiate from various genomic positions (e.g., 3'UTR, coding exons, etc.), most locations on genomes are not prone to transcription initiation. It is of practical and theoretical interest to be able to estimate such collections of non-TSS locations (NTLs). The identification of large portions of NTLs can contribute to better focusing the search for TSS locations and thus contribute to promoter and gene finding. It can help in the assessment of 5' completeness of expressed sequences, contribute to more successful experimental designs, as well as more accurate gene annotation.MethodologyUsing comprehensive collections of Cap Analysis of Gene Expression (CAGE) and other transcript data from mouse and human genomes, we developed a methodology that allows us, by performing computational TSS prediction with very high sensitivity, to annotate, with a high accuracy in a strand specific manner, locations of mammalian genomes that are highly unlikely to harbor transcription start sites (TSSs). The properties of the immediate genomic neighborhood of 98,682 accurately determined mouse and 113,814 human TSSs are used to determine features that distinguish genomic transcription initiation locations from those that are not likely to initiate transcription. In our algorithm we utilize various constraining properties of features identified in the upstream and downstream regions around TSSs, as well as statistical analyses of these surrounding regions.ConclusionsOur analysis of human chromosomes 4, 21 and 22 estimates ∼46%, ∼41% and ∼27% of these chromosomes, respectively, as being NTLs. This suggests that on average more than 40% of the human genome can be expected to be highly unlikely to initiate transcription. Our method represents the first one that utilizes high-sensitivity TSS prediction to identify, with high accuracy, large portions of mammalian genomes as NTLs. The server with our algorithm implemented is available at http://cbrc.kaust.edu.sa/ddm/.

The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays.

To understand the genetic mechanisms leading to phenotypic differentiation, it is important to identify genomic regions under selection. We scanned the genome of two chicken lines from a single trait selection experiment, where 50 generations of selection have resulted in a 9-fold difference in body weight. Analyses of nearly 60,000 SNP markers showed that the effects of selection on the genome are dramatic. The lines were fixed for alternative alleles in more than 50 regions as a result of selection. Another 10 regions displayed strong evidence for ongoing differentiation during the last 10 generations. Many more regions across the genome showed large differences in allele frequency between the lines, indicating that the phenotypic evolution in the lines in 50 generations is the result of an exploitation of standing genetic variation at 100s of loci across the genome., Author Summary Evolution is the process of change in response to selection. Typically, this results in more or less obvious changes to the appearance and physical properties—the phenotype—of an organism. However, these changes reflect underlying changes to the genome of that organism—the genotype. We examine the genomes of two lines of chickens that share a very recent ancestry but have been subjected to 50 generations of selection for high or low body weight, respectively. The effect of selection on the phenotype was dramatic, where on average high line birds are nine times heavier than low line ones. The effect on the genotype was equally dramatic, with a large number of changes distributed all across the genome. We observed more than 100 regions where different genetic variants were established in the two lines of chickens, which is considerable given the number of generations involved.

Background African trypanosomes are protozoan parasites that cause “sleeping sickness” in humans and a similar disease in livestock. Trypanosomes also infect laboratory mice and three major quantitative trait loci (QTL) that regulate survival time after infection with T. congolense have been identified in two independent crosses between susceptible A/J and BALB/c mice, and the resistant C57BL/6. These were designated Tir1, Tir2 and Tir3 for Trypanosoma infection response, and range in size from 0.9–12 cM. Principal Findings Mapping loci regulating survival time after T. congolense infection in an additional cross revealed that susceptible C3H/HeJ mice have alleles that reduce survival time after infection at Tir1 and Tir3 QTL, but not at Tir2. Next-generation resequencing of a 6.2 Mbp region of mouse chromosome 17, which includes Tir1, identified 1,632 common single nucleotide polymorphisms (SNP) including a probably damaging non-synonymous SNP in Pram1 (PML-RAR alpha-regulated adaptor molecule 1), which was the most plausible candidate QTL gene in Tir1. Genome-wide comparative genomic hybridisation identified 12 loci with copy number variants (CNV) that correlate with differential gene expression, including Cd244 (natural killer cell receptor 2B4), which lies close to the peak of Tir3c and has gene expression that correlates with CNV and phenotype, making it a strong candidate QTL gene at this locus. Conclusions By systematically combining next-generation DNA capture and sequencing, array-based comparative genomic hybridisation (aCGH), gene expression data and SNP annotation we have developed a strategy that can generate a short list of polymorphisms in candidate QTL genes that can be functionally tested., Author Summary About one-third of cattle in sub-Saharan Africa are at risk of contracting “Nagana”—a disease caused by Trypanosoma parasites similar to those that cause human “Sleeping Sickness.” Laboratory mice can also be infected by trypanosomes, and different mouse breeds show varying levels of susceptibility to infection, similar to what is seen between different breeds of cattle. Survival time after infection is controlled by the underlying genetics of the mouse breed, and previous studies have localised three genomic regions that regulate this trait. These three “Quantitative Trait Loci” (QTL), which have been called Tir1, Tir2 and Tir3 (for Trypanosoma Infection Response 1–3) are well defined, but nevertheless still contain over one thousand genes, any number of which may be influencing survival. This study has aimed to identify the specific differences associated with genes that are controlling mouse survival after T. congolense infection. We have applied a series of analyses to existing datasets, and combined them with novel sequencing, and other genetic data to create short lists of genes that share polymorphisms across susceptible mouse breeds, including two promising “candidate genes”: Pram1 at Tir1 and Cd244 at Tir3. These genes can now be tested to confirm their effect on response to trypanosome infection.

The combination of sucrose and starch in the presence of surface-adsorbed salivary α-amylase and bacterial glucosyltransferases increase the formation of a structurally and metabolically distinctive biofilm by Streptococcus mutans. This host-pathogen-diet interaction may modulate the formation of pathogenic biofilms related to dental caries disease. We conducted a comprehensive study to further investigate the influence of the dietary carbohydrates on S. mutans-transcriptome at distinct stages of biofilm development using whole genomic profiling with a new computational tool (MDV) for data mining. S. mutans UA159 biofilms were formed on amylase-active saliva coated hydroxyapatite discs in the presence of various concentrations of sucrose alone (ranging from 0.25 to 5% w/v) or in combination with starch (0.5 to 1% w/v). Overall, the presence of sucrose and starch (suc+st) influenced the dynamics of S. mutans transcriptome (vs. sucrose alone), which may be associated with gradual digestion of starch by surface-adsorbed amylase. At 21 h of biofilm formation, most of the differentially expressed genes were related to sugar metabolism, such as upregulation of genes involved in maltose/maltotriose uptake and glycogen synthesis. In addition, the groEL/groES chaperones were induced in the suc+st-biofilm, indicating that presence of starch hydrolysates may cause environmental stress. In contrast, at 30 h of biofilm development, multiple genes associated with sugar uptake/transport (e.g. maltose), two-component systems, fermentation/glycolysis and iron transport were differentially expressed in suc+st-biofilms (vs. sucrose-biofilms). Interestingly, lytT (bacteria autolysis) was upregulated, which was correlated with presence of extracellular DNA in the matrix of suc+st-biofilms. Specific genes related to carbohydrate uptake and glycogen metabolism were detected in suc+st-biofilms in more than one time point, indicating an association between presence of starch hydrolysates and intracellular polysaccharide storage. Our data show complex remodeling of S. mutans-transcriptome in response to changing environmental conditions in situ, which could modulate the dynamics of biofilm development and pathogenicity.

From the ENCODE project, it is realized that almost every base of the entire human genome is transcribed. One class of transcripts resulting from this arises from the conjoined gene, which is formed by combining the exons of two or more distinct (parent) genes lying on the same strand of a chromosome. Only a very limited number of such genes are known, and the definition and terminologies used for them are highly variable in the public databases. In this work, we have computationally identified and manually curated 751 conjoined genes (CGs) in the human genome that are supported by at least one mRNA or EST sequence available in the NCBI database. 353 representative CGs, of which 291 (82%) could be confirmed, were subjected to experimental validation using RT-PCR and sequencing methods. We speculate that these genes are arising out of novel functional requirements and are not merely artifacts of transcription, since more than 70% of them are conserved in other vertebrate genomes. The unique splicing patterns exhibited by CGs reveal their possible roles in protein evolution or gene regulation. Novel CGs, for which no transcript is available, could be identified in 80% of randomly selected potential CG forming regions, indicating that their formation is a routine process. Formation of CGs is not only limited to human, as we have also identified 270 CGs in mouse and 227 in drosophila using our approach. Additionally, we propose a novel mechanism for the formation of CGs. Finally, we developed a database, ConjoinG, which contains detailed information about all the CGs (800 in total) identified in the human genome. In summary, our findings reveal new insights about the functionality of CGs in terms of another possible mechanism for gene regulation and genomic evolution and the mechanism leading to their formation.

BackgroundThe isochore, a large DNA sequence with relatively small GC variance, is one of the most important structures in eukaryotic genomes. Although the isochore has been widely studied in humans and other species, little is known about its distribution in pigs.Principal findingsIn this paper, we construct a map of long homogeneous genome regions (LHGRs), i.e., isochores and isochore-like regions, in pigs to provide an intuitive version of GC heterogeneity in each chromosome. The LHGR pattern study not only quantifies heterogeneities, but also reveals some primary characteristics of the chromatin organization, including the followings: (1) the majority of LHGRs belong to GC-poor families and are in long length; (2) a high gene density tends to occur with the appearance of GC-rich LHGRs; and (3) the density of LINE repeats decreases with an increase in the GC content of LHGRs. Furthermore, a portion of LHGRs with particular GC ranges (50%-51% and 54%-55%) tend to have abnormally high gene densities, suggesting that biased gene conversion (BGC), as well as time- and energy-saving principles, could be of importance to the formation of genome organization.ConclusionThis study significantly improves our knowledge of chromatin organization in the pig genome. Correlations between the different biological features (e.g., gene density and repeat density) and GC content of LHGRs provide a unique glimpse of in silico gene and repeats prediction.

Genome wide association (GWA) studies, which test for association between common genetic markers and a disease phenotype, have shown varying degrees of success. While many factors could potentially confound GWA studies, we focus on the possibility that multiple, rare variants (RVs) may act in concert to influence disease etiology. Here, we describe an algorithm for RV analysis, RARECOVER. The algorithm combines a disparate collection of RVs with low effect and modest penetrance. Further, it does not require the rare variants be adjacent in location. Extensive simulations over a range of assumed penetrance and population attributable risk (PAR) values illustrate the power of our approach over other published methods, including the collapsing and weighted-collapsing strategies. To showcase the method, we apply RARECOVER to re-sequencing data from a cohort of 289 individuals at the extremes of Body Mass Index distribution (NCT00263042). Individual samples were re-sequenced at two genes, FAAH and MGLL, known to be involved in endocannabinoid metabolism (187Kbp for 148 obese and 150 controls). The RARECOVER analysis identifies exactly one significantly associated region in each gene, each about 5 Kbp in the upstream regulatory regions. The data suggests that the RVs help disrupt the expression of the two genes, leading to lowered metabolism of the corresponding cannabinoids. Overall, our results point to the power of including RVs in measuring genetic associations., National Science Foundation (U.S.) (grant (IIS-0810905), National Institutes of Health (U.S.) (U19 AG023122-05), National Institutes of Health (U.S.) (R01 MH078151-03), Louis & Harold Price Foundation, National Institutes of Health (U.S.) (N01 MH22005), National Institutes of Health (U.S.) (U01-DA024417-01), National Institutes of Health (U.S.) (P50 MH081755-01), National Institutes of Health (U.S.) (R01 AG030474-02), National Institutes of Health (U.S.) (N01 MH022005), National Institutes of Health (U.S.) (R01 HL089655-02), National Institutes of Health (U.S.) (R01 MH080134-03), National Institutes of Health (U.S.) (U54 CA143906-01), National Institutes of Health (U.S.) (UL1 RR025774-03), Scripps Genomic Medicine Program, National Human Genome Research Institute (U.S.) (Grant Number T32 HG002295 )

Background The glycolytic phosphoglycerate mutases exist as non-homologous isofunctional enzymes (NISE) having independent evolutionary origins and no similarity in primary sequence, 3D structure, or catalytic mechanism. Cofactor-dependent PGM (dPGM) requires 2,3-bisphosphoglycerate for activity; cofactor-independent PGM (iPGM) does not. The PGM profile of any given bacterium is unpredictable and some organisms such as Escherichia coli encode both forms. Methods/Principal Findings To examine the distribution of PGM NISE throughout the Bacteria, and gain insight into the evolutionary processes that shape their phyletic profiles, we searched bacterial genome sequences for the presence of dPGM and iPGM. Both forms exhibited patchy distributions throughout the bacterial domain. Species within the same genus, or even strains of the same species, frequently differ in their PGM repertoire. The distribution is further complicated by the common occurrence of dPGM paralogs, while iPGM paralogs are rare. Larger genomes are more likely to accommodate PGM paralogs or both NISE forms. Lateral gene transfers have shaped the PGM profiles with intradomain and interdomain transfers apparent. Archaeal-type iPGM was identified in many bacteria, often as the sole PGM. To address the function of PGM NISE in an organism encoding both forms, we analyzed recombinant enzymes from E. coli. Both NISE were active mutases, but the specific activity of dPGM greatly exceeded that of iPGM, which showed highest activity in the presence of manganese. We created PGM null mutants in E. coli and discovered the ΔdPGM mutant grew slowly due to a delay in exiting stationary phase. Overexpression of dPGM or iPGM overcame this defect. Conclusions/Significance Our biochemical and genetic analyses in E. coli firmly establish dPGM and iPGM as NISE. Metabolic redundancy is indicated since only larger genomes encode both forms. Non-orthologous gene displacement can fully account for the non-uniform PGM distribution we report across the bacterial domain.

Background To date, there are no highly sensitive and specific minimally invasive biomarkers for detection of breast cancer at an early stage. The occurrence of circulating microRNAs (miRNAs) in blood components (including serum and plasma) has been repeatedly observed in cancer patients as well as healthy controls. Because of the significance of miRNA in carcinogenesis, circulating miRNAs in blood may be unique biomarkers for early and minimally invasive diagnosis of human cancers. The objective of this pilot study was to discover a panel of circulating miRNAs as potential novel breast cancer biomarkers. Methodology/Principal Findings Using microarray-based expression profiling followed by Real-Time quantitative Polymerase Cycle Reaction (RT-qPCR) validation, we compared the levels of circulating miRNAs in plasma samples from 20 women with early stage breast cancer (10 Caucasian American (CA) and 10 African American (AA)) and 20 matched healthy controls (10 CAs and 10 AAs). Using the significance level of p

Background The identification and characterization of antigens expressed in Trypanosoma cruzi stages that parasitize mammals are essential steps for the development of new vaccines and diagnostics. Genes that are preferentially expressed in trypomastigotes may be involved in key processes that define the biology of trypomastigotes, like cell invasion and immune system evasion. Methodology/Principal Findings With the initial aim of identifying trypomastigote-specific expressed tags, we constructed and sequenced an epimastigote-subtracted trypomastigote cDNA library (library TcT-E). More than 45% of the sequenced clones of the library could not be mapped to previously annotated mRNAs or proteins. We validated the presence of these transcripts by reverse northern blot and northern blot experiments, therefore providing novel information about the mRNA expression of these genes in trypomastigotes. A 280-bp consensus element (TcT-E element, TcT-Eelem) located at the 3′ untranslated region (3′ UTR) of many different open reading frames (ORFs) was identified after clustering the TcT-E dataset. Using an RT-PCR approach, we were able to amplify different mature mRNAs containing the same TcT-Eelem in the 3′ UTR. The proteins encoded by these ORFs are members of a novel surface protein family in T. cruzi, (which we named TcTASV for T. cruzi Trypomastigote, Alanine, Serine and Valine rich proteins). All members of the TcTASV family have conserved coding amino- and carboxy-termini, and a central variable core that allows partitioning of TcTASV proteins into three subfamilies. Analysis of the T. cruzi genome database resulted in the identification of 38 genes/ORFs for the whole TcTASV family in the reference CL-Brener strain (lineage II). Because this protein family was not found in other trypanosomatids, we also looked for the presence of TcTASV genes in other evolutionary lineages of T. cruzi, sequencing 48 and 28 TcTASVs members from the RA (lineage II) and Dm28 (lineage I) T. cruzi strains respectively. Detailed phylogenetic analyses of TcTASV gene products show that this gene family is different from previously characterized mucin (TcMUCII), mucin-like, and MASP protein families. Conclusions/Significance We identified TcTASV, a new gene family of surface proteins in T. cruzi., Author Summary Chagas' disease, caused by the kinetoplastid protozoan parasite Trypanosoma cruzi, is endemic in Latin America. At present there are neither vaccines for prevention nor totally effective drugs for the treatment of the disease. T. cruzi has a complex life cycle alternating between a reduviid insect (the vector) and a mammalian host, where different parasite stages are found. Differentially expressed genes are the hallmark of the specialized biology of each life cycle stage. The aim of this work was to identify genes expressed in the trypomastigote stage (a blood-circulating stage that invades new cells and spreads the infection in different organs of the mammalian host) that could be used to develop new vaccines or diagnostics. An initial screening of trypomastigote transcripts was performed by sequencing of an epimastigote-subtracted trypomastigote cDNA library. Besides identifying a large proportion of differentially expressed mRNAs, we discovered a novel protein family, which we denominated TcTASV.

Variation in gene expression has been observed in natural populations and associated with complex traits or phenotypes such as disease susceptibility and drug response. Gene expression itself is controlled by various genetic and non-genetic factors. The binding of a class of small RNA molecules, microRNAs (miRNAs), to mRNA transcript targets has recently been demonstrated to be an important mechanism of gene regulation. Because individual miRNAs may regulate the expression of multiple gene targets, a comprehensive and reliable catalogue of miRNA-regulated targets is critical to understanding gene regulatory networks. Though experimental approaches have been used to identify many miRNA targets, due to cost and efficiency, current miRNA target identification still relies largely on computational algorithms that aim to take advantage of different biochemical/thermodynamic properties of the sequences of miRNAs and their gene targets. A novel approach, ExprTarget, therefore, is proposed here to integrate some of the most frequently invoked methods (miRanda, PicTar, TargetScan) as well as the genome-wide HapMap miRNA and mRNA expression datasets generated in our laboratory. To our knowledge, this dataset constitutes the first miRNA expression profiling in the HapMap lymphoblastoid cell lines. We conducted diagnostic tests of the existing computational solutions using the experimentally supported targets in TarBase as gold standard. To gain insight into the biases that arise from such an analysis, we investigated the effect of the choice of gold standard on the evaluation of the various computational tools. We analyzed the performance of ExprTarget using both ROC curve analysis and cross-validation. We show that ExprTarget greatly improves miRNA target prediction relative to the individual prediction algorithms in terms of sensitivity and specificity. We also developed an online database, ExprTargetDB, of human miRNA targets predicted by our approach that integrates gene expression profiling into a broader framework involving important features of miRNA target site predictions.

A remarkable characteristic of the human major histocompatibility complex (MHC) is its extreme genetic diversity, which is maintained by balancing selection. In fact, the MHC complex remains one of the best-known examples of natural selection in humans, with well-established genetic signatures and biological mechanisms for the action of selection. Here, we present genetic and functional evidence that another gene with a fundamental role in MHC class I presentation, endoplasmic reticulum aminopeptidase 2 (ERAP2), has also evolved under balancing selection and contains a variant that affects antigen presentation. Specifically, genetic analyses of six human populations revealed strong and consistent signatures of balancing selection affecting ERAP2. This selection maintains two highly differentiated haplotypes (Haplotype A and Haplotype B), with frequencies 0.44 and 0.56, respectively. We found that ERAP2 expressed from Haplotype B undergoes differential splicing and encodes a truncated protein, leading to nonsense-mediated decay of the mRNA. To investigate the consequences of ERAP2 deficiency on MHC presentation, we correlated surface MHC class I expression with ERAP2 genotypes in primary lymphocytes. Haplotype B homozygotes had lower levels of MHC class I expressed on the surface of B cells, suggesting that naturally occurring ERAP2 deficiency affects MHC presentation and immune response. Interestingly, an ERAP2 paralog, endoplasmic reticulum aminopeptidase 1 (ERAP1), also shows genetic signatures of balancing selection. Together, our findings link the genetic signatures of selection with an effect on splicing and a cellular phenotype. Although the precise selective pressure that maintains polymorphism is unknown, the demonstrated differences between the ERAP2 splice forms provide important insights into the potential mechanism for the action of selection., Author Summary It has long been known that the extremely high levels of genetic diversity present in the major histocompatibility locus (MHC) are due to balancing selection, a type of natural selection that maintains advantageous genetic diversity in populations. The MHC encodes for molecules required for a type of antigen presentation that mediates detection of infected and cancerous cells by the immune system; the genetic diversity of the MHC thus ensures an adequate response to the wide variety of pathogens that humans encounter. Here, we show that other genes involved in the same antigen-presentation pathway are also subject to balancing selection in humans. Specifically, we show that balancing selection acts to maintain two forms of the endoplasmic reticulum aminopeptidase 2 gene (ERAP2), which encodes a protein also involved in antigen presentation. Although the two ERAP2 forms are present in a similar frequency (close to 0.5), they are associated with differences with respect to the levels of MHC molecules on the cell surface of immune cells. In summary, our findings show that natural selection maintains variants of ERAP2 that affect immune surveillance; they also establish ERAP2 as one of the few examples of balancing selection in humans where the selected variant, its functional consequences, and its influence in interpersonal diversity are known.

BackgroundIn spite of extensive research on the effect of mutation and selection on codon usage, a general model of codon usage bias due to mutational bias has been lacking. Because most amino acids allow synonymous GC content changing substitutions in the third codon position, the overall GC bias of a genome or genomic region is highly correlated with GC3, a measure of third position GC content. For individual amino acids as well, G/C ending codons usage generally increases with increasing GC bias and decreases with increasing AT bias. Arginine and leucine, amino acids that allow GC-changing synonymous substitutions in the first and third codon positions, have codons which may be expected to show different usage patterns.Principal findingsIn analyzing codon usage bias in hundreds of prokaryotic and plant genomes and in human genes, we find that two G-ending codons, AGG (arginine) and TTG (leucine), unlike all other G/C-ending codons, show overall usage that decreases with increasing GC bias, contrary to the usual expectation that G/C-ending codon usage should increase with increasing genomic GC bias. Moreover, the usage of some codons appears nonlinear, even nonmonotone, as a function of GC bias. To explain these observations, we propose a continuous-time Markov chain model of GC-biased synonymous substitution. This model correctly predicts the qualitative usage patterns of all codons, including nonlinear codon usage in isoleucine, arginine and leucine. The model accounts for 72%, 64% and 52% of the observed variability of codon usage in prokaryotes, plants and human respectively. When codons are grouped based on common GC content, 87%, 80% and 68% of the variation in usage is explained for prokaryotes, plants and human respectively.ConclusionsThe model clarifies the sometimes-counterintuitive effects that GC mutational bias can have on codon usage, quantifies the influence of GC mutational bias and provides a natural null model relative to which other influences on codon bias may be measured.

Genome-wide association studies (GWAS) often identify disease-associated mutations in intergenic and non-coding regions of the genome. Given the high percentage of the human genome that is transcribed, we postulate that for some observed associations the disease phenotype is caused by a structural rearrangement in a regulatory region of the RNA transcript. To identify such mutations, we have performed a genome-wide analysis of all known disease-associated Single Nucleotide Polymorphisms (SNPs) from the Human Gene Mutation Database (HGMD) that map to the untranslated regions (UTRs) of a gene. Rather than using minimum free energy approaches (e.g. mFold), we use a partition function calculation that takes into consideration the ensemble of possible RNA conformations for a given sequence. We identified in the human genome disease-associated SNPs that significantly alter the global conformation of the UTR to which they map. For six disease-states (Hyperferritinemia Cataract Syndrome, β-Thalassemia, Cartilage-Hair Hypoplasia, Retinoblastoma, Chronic Obstructive Pulmonary Disease (COPD), and Hypertension), we identified multiple SNPs in UTRs that alter the mRNA structural ensemble of the associated genes. Using a Boltzmann sampling procedure for sub-optimal RNA structures, we are able to characterize and visualize the nature of the conformational changes induced by the disease-associated mutations in the structural ensemble. We observe in several cases (specifically the 5′ UTRs of FTL and RB1) SNP–induced conformational changes analogous to those observed in bacterial regulatory Riboswitches when specific ligands bind. We propose that the UTR and SNP combinations we identify constitute a “RiboSNitch,” that is a regulatory RNA in which a specific SNP has a structural consequence that results in a disease phenotype. Our SNPfold algorithm can help identify RiboSNitches by leveraging GWAS data and an analysis of the mRNA structural ensemble., Author Summary Genome-wide association studies identify mutations in the human genome that correlate with a particular disease. It is common to find mutations associated with disease in the non-coding region of the genome. These non-coding mutations are more difficult to interpret at a molecular level, because they do not affect the protein sequence. In this study, we analyze disease-associated mutations in non-coding regions of our genome in the context of their structural effect on the message of genetic information in our cells, Ribonucleic Acid (RNA). We focus in particular on the regulatory parts of our genes known as untranslated regions. We find that certain disease-associated mutations in these regulatory untranslated regions have a significant effect on the structure of the RNA message. We call these elements “RiboSNitches,” because they act like switches turning on and off genes, but are caused by Single Nucleotide Polymorphisms (SNPs), which are single point mutations in our genome. The RiboSNitches we identify are potentially a new class of pharmaceutical targets, as it is possible to change the structure of RNA with small drug-like molecules.

Accurate estimates of mutation rates provide critical information to analyze genome evolution and organism fitness. We used whole-genome DNA sequencing, pulse-field gel electrophoresis, and comparative genome hybridization to determine mutation rates in diploid vegetative and meiotic mutation accumulation lines of Saccharomyces cerevisiae. The vegetative lines underwent only mitotic divisions while the meiotic lines underwent a meiotic cycle every ∼20 vegetative divisions. Similar base substitution rates were estimated for both lines. Given our experimental design, these measures indicated that the meiotic mutation rate is within the range of being equal to zero to being 55-fold higher than the vegetative rate. Mutations detected in vegetative lines were all heterozygous while those in meiotic lines were homozygous. A quantitative analysis of intra-tetrad mating events in the meiotic lines showed that inter-spore mating is primarily responsible for rapidly fixing mutations to homozygosity as well as for removing mutations. We did not observe 1–2 nt insertion/deletion (in-del) mutations in any of the sequenced lines and only one structural variant in a non-telomeric location was found. However, a large number of structural variations in subtelomeric sequences were seen in both vegetative and meiotic lines that did not affect viability. Our results indicate that the diploid yeast nuclear genome is remarkably stable during the vegetative and meiotic cell cycles and support the hypothesis that peripheral regions of chromosomes are more dynamic than gene-rich central sections where structural rearrangements could be deleterious. This work also provides an improved estimate for the mutational load carried by diploid organisms., Author Summary Mutations result from errors that occur during DNA metabolism. They provide the raw materials for evolution, can affect organism fitness, and have been shown to accumulate in organisms during asexual growth. During a sexual life cycle, mutations can be removed by recombination and mating. While such removal is thought to provide a fitness advantage, studies have shown that recombination itself is mutagenic. To examine if the mutation rate in an organism differs during asexual and sexual cycles, we sequenced the entire nuclear genome of lines of diploid baker's yeast that underwent only asexual growth, or alternating cycles of asexual and sexual growth. The estimated rate of base substitutions in the vegetative lines was extremely low (2.9×10−10 base substitutions per base per cell generation) and the meiotic mutation rate is within the range of being equal to zero to being 55 times higher than the vegetative rate. Interestingly, we observed a large number of changes in the ends of chromosomes in the asexual and sexual cycles that did not affect fitness; changes at other locations were very rare, suggesting a remarkable genome stability of diploid baker's yeast.

While recent scans for genetic variation associated with human disease have been immensely successful in uncovering large numbers of loci, far fewer studies have focused on the underlying pathways of disease pathogenesis. Many loci which are associated with disease and complex phenotypes map to non-coding, regulatory regions of the genome, indicating that modulation of gene transcription plays a key role. Thus, this study generated genome-wide profiles of both genetic and transcriptional variation from the total blood extracts of over 500 randomly-selected, unrelated individuals. Using measurements of blood lipids, key players in the progression of atherosclerosis, three levels of biological information are integrated in order to investigate the interactions between circulating leukocytes and proximal lipid compounds. Pair-wise correlations between gene expression and lipid concentration indicate a prominent role for basophil granulocytes and mast cells, cell types central to powerful allergic and inflammatory responses. Network analysis of gene co-expression showed that the top associations function as part of a single, previously unknown gene module, the Lipid Leukocyte (LL) module. This module replicated in T cells from an independent cohort while also displaying potential tissue specificity. Further, genetic variation driving LL module expression included the single nucleotide polymorphism (SNP) most strongly associated with serum immunoglobulin E (IgE) levels, a key antibody in allergy. Structural Equation Modeling (SEM) indicated that LL module is at least partially reactive to blood lipid levels. Taken together, this study uncovers a gene network linking blood lipids and circulating cell types and offers insight into the hypothesis that the inflammatory response plays a prominent role in metabolism and the potential control of atherogenesis., Wellcome Trust (London, England) (WT089061), Wellcome Trust (London, England) (WT089062), Academy of Finland (129680), Academy of Finland (213506)

Herbivores can gain indirect access to recalcitrant carbon present in plant cell walls through symbiotic associations with lignocellulolytic microbes. A paradigmatic example is the leaf-cutter ant (Tribe: Attini), which uses fresh leaves to cultivate a fungus for food in specialized gardens. Using a combination of sugar composition analyses, metagenomics, and whole-genome sequencing, we reveal that the fungus garden microbiome of leaf-cutter ants is composed of a diverse community of bacteria with high plant biomass-degrading capacity. Comparison of this microbiome's predicted carbohydrate-degrading enzyme profile with other metagenomes shows closest similarity to the bovine rumen, indicating evolutionary convergence of plant biomass degrading potential between two important herbivorous animals. Genomic and physiological characterization of two dominant bacteria in the fungus garden microbiome provides evidence of their capacity to degrade cellulose. Given the recent interest in cellulosic biofuels, understanding how large-scale and rapid plant biomass degradation occurs in a highly evolved insect herbivore is of particular relevance for bioenergy., Author Summary Leaf-cutter ants form massive subterranean colonies containing millions of workers that harvest hundreds of kilograms of leaves each year. They use these leaves to grow a mutualistic fungus that serves as the colony's primary food source. By farming fungus in specialized garden chambers, these dominant Neotropical herbivores facilitate rapid large-scale plant biomass conversion. Our understanding of this degradation process, and the responsible microbial community, is limited. In this study, we track the degradation of plant polymers in leaf-cutter ant fungus gardens and characterize the microbial community potentially mediating this process. We show that cellulose and hemicelluloses are degraded in the fungus gardens and that a previously unknown microbial community containing a diversity of bacteria is present. Metagenomic analysis of this community's genetic content revealed many genes predicted to encode enzymes capable of degrading plant cell walls. The ability of leaf-cutter ants to maintain an external microbial community with high plant biomass-degrading capacity likely represents a key step in the establishment of these ants as widespread, dominant insect herbivores in the Neotropics. This system is an important model for understanding how microbial communities degrade plant biomass in natural systems and has direct relevancy for bioenergy, given recent interest in cellulosic biofuels.

The relative contribution of genetic risk factors to the progression of subclinical atherosclerosis is poorly understood. It is likely that multiple variants are implicated in the development of atherosclerosis, but the subtle genotypic and phenotypic differences are beyond the reach of the conventional case-control designs and the statistical significance testing procedures being used in most association studies. Our objective here was to investigate whether an alternative approach—in which common disorders are treated as quantitative phenotypes that are continuously distributed over a population—can reveal predictive insights into the early atherosclerosis, as assessed using ultrasound imaging-based quantitative measurement of carotid artery intima-media thickness (IMT). Using our population-based follow-up study of atherosclerosis precursors as a basis for sampling subjects with gradually increasing IMT levels, we searched for such subsets of genetic variants and their interactions that are the most predictive of the various risk classes, rather than using exclusively those variants meeting a stringent level of statistical significance. The area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive value of the variants, and cross-validation was used to assess how well the predictive models will generalize to other subsets of subjects. By means of our predictive modeling framework with machine learning-based SNP selection, we could improve the prediction of the extreme classes of atherosclerosis risk and progression over a 6-year period (average AUC 0.844 and 0.761), compared to that of using conventional cardiovascular risk factors alone (average AUC 0.741 and 0.629), or when combined with the statistically significant variants (average AUC 0.762 and 0.651). The predictive accuracy remained relatively high in an independent validation set of subjects (average decrease of 0.043). These results demonstrate that the modeling framework can utilize the “gray zone” of genetic variation in the classification of subjects with different degrees of risk of developing atherosclerosis., Author Summary Although cardiovascular events, such as myocardial infarction and stroke, usually occur at later ages, it is known that the atherogenic process begins much earlier in life. Detection of subclinical atherosclerosis would therefore offer the means to identify individuals who are at increased risk of developing cardiovascular events. What remains unclear is the relative contribution of genetic variation to the development of the early stages of atherosclerosis. To address this question, we searched for combinations of both genetic and clinical determinants that are the most predictive of the progression of subclinical carotid atherosclerosis in a sample of 1,027 young adults, aged between 24–39 years, from the Finnish general population (The Cardiovascular Risk in Young Finns Study). We demonstrate here, for the first time in a population-based follow-up study, a predictive relationship between individual's genotypic variation and early signs of atherosclerosis, which cannot be explained by conventional cardiovascular risk factors, such as obesity and elevated blood pressure levels. The predictive modeling framework facilitates the usability of genetic information by identifying informative panels of variants, along with conventional risk factors, which may prove to be useful in early detection and management of atherosclerosis. The clinical implications of these findings remain to be studied.

Background The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today. Methodology/Results We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets. Conclusions Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.