Bioinformatical assay of human gene morbidity

A Systematic Review of the Human Accelerated Regions in Schizophrenia and Related Disorders: Where the Evolutionary and Neurodevelopmental Hypotheses Converge

Schizophrenia is a psychiatric disorder that results from genetic and environmental factors interacting and disrupting neurodevelopmental trajectories. Human Accelerated Regions (HARs) are evolutionarily conserved genomic regions that have accumulated human-specific sequence changes. Thus, studies on the impact of HARs in the context of neurodevelopment, as well as with respect to adult brain phenotypes, have increased considerably in the last few years. Through a systematic approach, we aim to offer a comprehensive review of HARs' role in terms of human brain development, configuration, and cognitive abilities, as well as whether HARs modulate the susceptibility to neurodevelopmental psychiatric disorders such as schizophrenia. First, the evidence in this review highlights HARs' molecular functions in the context of the neurodevelopmental regulatory genetic machinery. Second, brain phenotypic analyses indicate that HAR genes' expression spatially correlates with the regions that suffered human-specific cortical expansion, as well as with the regional interactions for synergistic information processing. Lastly, studies based on candidate HAR genes and the global "HARome" variability describe the involvement of these regions in the genetic background of schizophrenia, but also in other neurodevelopmental psychiatric disorders. Overall, the data considered in this review emphasise the crucial role of HARs in human-specific neurodevelopment processes and encourage future research on this evolutionary marker for a better understanding of the genetic basis of schizophrenia and other neurodevelopmental-related psychiatric disorders. Accordingly, HARs emerge as interesting genomic regions that require further study in order to bridge the neurodevelopmental and evolutionary hypotheses in schizophrenia and other related disorders and phenotypes.

Low expression of PEBP1P2 promotes metastasis of clear cell renal cell carcinoma by post-transcriptional regulation of PEBP1 and KLF13 mRNA

Background

Pseudogenes play an essential role in tumor occurrence and progression. However, the functions and mechanisms of pseudogenes in clear cell renal cell carcinoma (ccRCC) remain largely elusive.

Methods

We quantified PEBP1P2 expression in ccRCC tissues and cells using fluorescence in situ hybridization and real-time PCR. Besides, we evaluated the role of PEBP1P2 in ccRCC using a lung metastasis model and a transwell assay. Finally, we documented the interactions between PEBP1P2, PEBP1, and KLF13 by performing luciferase, RNA immunoprecipitation, RNA pulldown, and targeted RNA demethylation assays.

Results

Low PEBP1P2 expression correlates significantly with advanced stages and poor prognosis in ccRCC patients. Besides, PEBP1P2 overexpression inhibits ccRCC metastasis formation in vivo and in vitro. Interestingly, PEBP1P2 directly interacted with 5-methylcytosine (m5C)-containing PEBP1 mRNA and recruited the YBX1/ELAVL1 complex, stabilizing PEBP1 mRNA. In addition, PEBP1P2 increased KLF13 mRNA levels by acting as a sponge for miR-296, miR-616, and miR-3194.

Conclusions

PEBP1P2 inhibits ccRCC metastasis formation and regulates both PEBP1 and KLF13. Therefore, molecular therapies targeting PEBP1P2 might be an effective treatment strategy against ccRCC and other cancers with low PEBP1P2 levels.

Potential Pathogenic Genes Prioritization Based on Protein Domain Interaction Network Analysis

Pathogenicity-related studies are of great importance in understanding the pathogenesis of complex diseases and improving the level of clinical medicine. This work proposed a bioinformatics scheme to analyze cancer-related gene mutations, and try to figure out potential genes associated with diseases from the protein domain-domain interaction network. Herein, five measures of the principle of centrality lethality had been adopted to implement potential correlation analysis, and prioritize the significance of genes. This method was further applied to KEGG pathway analysis by taking the malignant melanoma as an example. The experimental results show that 25 domains can be found, and 18 of them have high potential to be pathogenically important related to malignant melanoma. Finally, a web-based tool, named Human Cancer Related Domain Interaction Network Analyzer, is developed for potential pathogenic genes prioritization for 26 types of human cancers, and the analysis results can be visualized and downloaded online.

Negative selection on human genes underlying inborn errors depends on disease outcome and both the mode and mechanism of inheritance

Genetic variants underlying life-threatening diseases, being unlikely to be transmitted to the next generation, are gradually and selectively eliminated from the population through negative selection. We study the determinants of this evolutionary process in human genes underlying monogenic diseases by comparing various negative selection scores and an integrative approach, CoNeS, at 366 loci underlying inborn errors of immunity (IEI). We find that genes underlying autosomal dominant (AD) or X-linked IEI have stronger negative selection scores than those underlying autosomal recessive (AR) IEI, whose scores are not different from those of genes not known to be disease causing. Nevertheless, genes underlying AR IEI that are lethal before reproductive maturity with complete penetrance have stronger negative selection scores than other genes underlying AR IEI. We also show that genes underlying AD IEI by loss of function have stronger negative selection scores than genes underlying AD IEI by gain of function, while genes underlying AD IEI by haploinsufficiency are under stronger negative selection than other genes underlying AD IEI. These results are replicated in 1,140 genes underlying inborn errors of neurodevelopment. Finally, we propose a supervised classifier, SCoNeS, which predicts better than state-of-the-art approaches whether a gene is more likely to underlie an AD or AR disease. The clinical outcomes of monogenic inborn errors, together with their mode and mechanisms of inheritance, determine the levels of negative selection at their corresponding loci. Integrating scores of negative selection may facilitate the prioritization of candidate genes and variants in patients suspected to carry an inborn error.

The morbid cutaneous anatomy of the human genome revealed by a bioinformatic approach

Computational approaches have been developed to prioritize candidate genes in disease gene identification. They are based on different pieces of evidences associating each gene with the given disease. In this study, 648 genes underlying genodermatoses have been compared to 1808 genes involved in other genetic diseases using a bioinformatic approach. These genes were studied at the structural, evolutionary and functional levels. Results show that genes underlying genodermatoses present longer CDS and have more exons. Significant differences were observed in nucleotide motif and amino-acid compositions. Evolutionary conservation analysis revealed that genodermatoses genes have less paralogs, more orthologs in Mouse and Dog and are less conserved. Functional analysis revealed that genodermatosis genes seem to be involved in immune system and skin layers. The Bayesian network model returned a rate of good classification of around 80%. This computational approach could help investigators working in the field of dermatology by prioritizing positional candidate genes for mutation screening.

Essential Protein Detection by Random Walk on Weighted Protein-Protein Interaction Networks

Essential proteins are critical to the development and survival of cells. Identification of essential proteins is helpful for understanding the minimal set of required genes in a living cell and for designing new drugs. To detect essential proteins, various computational methods have been proposed based on protein-protein interaction (PPI) networks. However, protein interaction data obtained by high-throughput experiments usually contain high false positives, which negatively impacts the accuracy of essential protein detection. Moreover, most existing studies focused on the local information of proteins in PPI networks, while ignoring the influence of indirect protein interactions on essentiality. In this paper, we propose a novel method, called Essentiality Ranking (EssRank in short), to boost the accuracy of essential protein detection. To deal with the inaccuracy of PPI data, confidence scores of interactions are evaluated by integrating various biological information. Weighted edge clustering coefficient (WECC), considering both interaction confidence scores and network topology, is proposed to calculate edge weights in PPI networks. The weight of each node is evaluated by the sum of WECC values of its linking edges. A random walk method, making use of both direct and indirect protein interactions, is then employed to calculate protein essentiality iteratively. Experimental results on the yeast PPI network show that EssRank outperforms most existing methods, including the most commonly-used centrality measures (SC, DC, BC, CC, IC, and EC), topology based methods (DMNC and NC) and the data integrating method IEW.

The essential and downstream common proteins of amyotrophic lateral sclerosis: A protein-protein interaction network analysis

Amyotrophic Lateral Sclerosis (ALS) is a devastative neurodegenerative disease characterized by selective loss of motoneurons. While several breakthroughs have been made in identifying ALS genetic defects, the detailed molecular mechanisms are still unclear. These genetic defects involve in numerous biological processes, which converge to a common destiny: motoneuron degeneration. In addition, the common comorbid Frontotemporal Dementia (FTD) further complicates the investigation of ALS etiology. In this study, we aimed to explore the protein-protein interaction network built on known ALS-causative genes to identify essential proteins and common downstream proteins between classical ALS and ALS+FTD (classical ALS + ALS/FTD) groups. The results suggest that classical ALS and ALS+FTD share similar essential protein set (VCP, FUS, TDP-43 and hnRNPA1) but have distinctive functional enrichment profiles. Thus, disruptions to these essential proteins might cause motoneuron susceptible to cellular stresses and eventually vulnerable to proteinopathies. Moreover, we identified a common downstream protein, ubiquitin-C, extensively interconnected with ALS-causative proteins (22 out of 24) which was not linked to ALS previously. Our in silico approach provides the computational background for identifying ALS therapeutic targets, and points out the potential downstream common ground of ALS-causative mutations.

Properties of human disease genes and the role of genes linked to Mendelian disorders in complex disease aetiology

Do genes presenting variation that has been linked to human disease have different biological properties than genes that have never been related to disease? What is the relationship between disease and fitness? Are the evolutionary pressures that affect genes linked to Mendelian diseases the same to those acting on genes whose variation contributes to complex disorders? The answers to these questions could shed light on the architecture of human genetic disorders and may have relevant implications when designing mapping strategies in future genetic studies. Here we show that, relative to non-disease genes, human disease (HD) genes have specific evolutionary profiles and protein network properties. Additionally, our results indicate that the mutation-selection balance renders an insufficient account of the evolutionary history of some HD genes and that adaptive selection could also contribute to shape their genetic architecture. Notably, several biological features of HD genes depend on the type of pathology (complex or Mendelian) with which they are related. For example, genes harbouring both causal variants for Mendelian disorders and risk factors for complex disease traits (Complex-Mendelian genes), tend to present higher functional relevance in the protein network and higher expression levels than genes associated only with complex disorders. Moreover, risk variants in Complex-Mendelian genes tend to present higher odds ratios than those on genes associated with the same complex disorders but with no link to Mendelian diseases. Taken together, our results suggest that genetic variation at genes linked to Mendelian disorders plays an important role in driving susceptibility to complex disease.

Template-directed chemical ligation to obtain 3'-3' and 5'-5' phosphodiester DNA linkages

Up to now, the direct ligation of two DNA fragments with opposite directions to obtain 3'-3' or 5'-5' phosphate ester bonds is still challenging. The only way to obtain DNA oligonucleotides containing a 3'-3' or 5'-5' inversion of polarity sites is based on professional DNA chemical synthesis. Herein, we demonstrate a convenient template-directed chemical ligation that enables 3'-3' and 5'-5' linkages of two DNA oligonucleotides. This method is based on the assembly of two oligonucleotides on a template in opposite directions through forming antiparallel and parallel duplexes simultaneously, followed by coupling with N-Cyanoimidazole under mild condition. Moreover, on the basis of DNA oligonucleotides with 5'-5' linkage obtained through our template-directed chemical ligation, we developed a new cDNA display technique for in vitro selection of functional polypeptides.

The role of mutation rate variation and genetic diversity in the architecture of human disease

BACKGROUND

We have investigated the role that the mutation rate and the structure of genetic variation at a locus play in determining whether a gene is involved in disease. We predict that the mutation rate and its genetic diversity should be higher in genes associated with disease, unless all genes that could cause disease have already been identified.

RESULTS

Consistent with our predictions we find that genes associated with Mendelian and complex disease are substantially longer than non-disease genes. However, we find that both Mendelian and complex disease genes are found in regions of the genome with relatively low mutation rates, as inferred from intron divergence between humans and chimpanzees, and they are predicted to have similar rates of non-synonymous mutation as other genes. Finally, we find that disease genes are in regions of significantly elevated genetic diversity, even when variation in the rate of mutation is controlled for. The effect is small nevertheless.

CONCLUSIONS

Our results suggest that gene length contributes to whether a gene is associated with disease. However, the mutation rate and the genetic architecture of the locus appear to play only a minor role in determining whether a gene is associated with disease.

The genomic and functional characteristics of disease genes

Increasing evidence indicates that genes containing disease causal variation have distinct functional and genomic properties. The importance of understanding these properties is highlighted by efforts to filter lists of variants from next-generation sequencing studies, where the number of potentially deleterious variants, which are in fact unrelated to disease, may be large. Available evidence indicates that the majority of disease genes are 'non-essential' and their products occupy functionally peripheral positions in protein networks. They tend to be intermediate between genes that have core biological functions, particularly low mutation rates and low haplotype diversity, and genes for which high haplotype diversity and high mutation rates are advantageous (such as those involved in sensory perception and some immune system functions). Evidence presented here supports these conclusions through analysis of integrated data sets incorporating the latest mutational profiles, linkage disequilibrium structure and other genomic properties of individual genes. The analysis highlights the contrasting functions of genes predicted as least and most likely to contain disease variation and provides a basis for filtering gene variant lists to exclude the least plausible disease candidates.

The relationship between gene isoform multiplicity, number of exons and protein divergence

At present we know that phenotypic differences between organisms arise from a variety of sources, like protein sequence divergence, regulatory sequence divergence, alternative splicing, etc. However, we do not have yet a complete view of how these sources are related. Here we address this problem, studying the relationship between protein divergence and the ability of genes to express multiple isoforms. We used three genome-wide datasets of human-mouse orthologs to study the relationship between isoform multiplicity co-occurrence between orthologs (the fact that two orthologs have more than one isoform) and protein divergence. In all cases our results showed that there was a monotonic dependence between these two properties. We could explain this relationship in terms of a more fundamental one, between exon number of the largest isoform and protein divergence. We found that this last relationship was present, although with variations, in other species (chimpanzee, cow, rat, chicken, zebrafish and fruit fly). In summary, we have identified a relationship between protein divergence and isoform multiplicity co-occurrence and explained its origin in terms of a simple gene-level property. Finally, we discuss the biological implications of these findings for our understanding of inter-species phenotypic differences.

Proteins and domains vary in their tolerance of non-synonymous single nucleotide polymorphisms (nsSNPs)

The widespread application of whole-genome sequencing is identifying numerous non-synonymous single nucleotide polymorphisms (nsSNPs), many of which are associated with disease. We analyzed nsSNPs from Humsavar and the 1000 Genomes Project to investigate why some proteins and domains are more tolerant of mutations than others. We identified 311 proteins and 112 Pfam families, corresponding to 2910 domains, as diseasesusceptible and 32 proteins and 67 Pfam families (10,783 domains) as diseaseresistant based on the relative numbers of disease-associated and neutral polymorphisms. Proteins with no significant difference from expected numbers of disease and polymorphism nsSNPs are classified as other. This classification takes into account the phenotypes of all known mutations in the protein or domain rather than simply classifying based on the presence or absence of disease nsSNPs. Of the two hypotheses suggested, our results support the model that disease-resistant domains and proteins are more able to tolerate mutations rather than having more lethal mutations that are not observed. Disease-resistant proteins and domains show significantly higher mutation rates and lower sequence conservation than disease-susceptible proteins and domains. Disease-susceptible proteins are more likely to be encoded by essential genes, are more central in protein-protein interaction networks and are less likely to contain loss-of-function mutations in healthy individuals. We use this classification for nsSNP phenotype prediction, predicting nsSNPs in disease-susceptible domains to be disease and those in disease-resistant domains to be polymorphism. In this way, we achieve higher accuracy than SIFT, a state-of-the-art algorithm.

A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data

BACKGROUND

Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins' essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value.

RESULTS

In this paper, we propose a new centrality measure, named PeC, based on the integration of protein-protein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed centrality measures: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Bottle Neck (BN), Density of Maximum Neighborhood Component (DMNC), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC), Range-Limited Centrality (RL), L-index (LI), Leader Rank (LR), Normalized α-Centrality (NC), and Moduland-Centrality (MC). Especially, the improvement of PeC over the classic centrality measures (BC, CC, SC, EC, and BN) is more than 50% when predicting no more than 500 proteins.

CONCLUSIONS

We demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new centrality measure, PeC, is an effective essential protein discovery method.

Defining the role of essential genes in human disease

A greater understanding of the causes of human disease can come from identifying characteristics that are specific to disease genes. However, a full understanding of the contribution of essential genes to human disease is lacking, due to the premise that these genes tend to cause developmental abnormalities rather than adult disease. We tested the hypothesis that human orthologs of mouse essential genes are associated with a variety of human diseases, rather than only those related to miscarriage and birth defects. We segregated human disease genes according to whether the knockout phenotype of their mouse ortholog was lethal or viable, defining those with orthologs producing lethal knockouts as essential disease genes. We show that the human orthologs of mouse essential genes are associated with a wide spectrum of diseases affecting diverse physiological systems. Notably, human disease genes with essential mouse orthologs are over-represented among disease genes associated with cancer, suggesting links between adult cellular abnormalities and developmental functions. The proteins encoded by essential genes are highly connected in protein-protein interaction networks, which we find correlates with an over-representation of nuclear proteins amongst essential disease genes. Disease genes associated with essential orthologs also are more likely than those with non-essential orthologs to contribute to disease through an autosomal dominant inheritance pattern, suggesting that these diseases may actually result from semi-dominant mutant alleles. Overall, we have described attributes found in disease genes according to the essentiality status of their mouse orthologs. These findings demonstrate that disease genes do occupy highly connected positions in protein-protein interaction networks, and that due to the complexity of disease-associated alleles, essential genes cannot be ignored as candidates for causing diverse human diseases.

The interplay between evolution, regulation and tissue specificity in the Human Hereditary Diseasome

Background

Human disease genes can be distinguished from essential (embryonically lethal) and non-disease genes using gene attributes. Such attributes include gene age, tissue specificity of expression, regulatory capacity, sequence length, rate of sequence variation and capacity for interaction. The resulting information has been used to inform data mining approaches seeking to identify novel disease genes. Given the dynamic nature of this field and the rapid rise in relevant information, we have chosen to perform a single integrated mining approach to explore relationships among gene attributes and thereby characterise evolutionary trends associated with disease genes.

Results

All against all cross comparison of 2,522 disease gene attributes revealed significant relationships existed between the age, disease-association and expression pattern of genes and the tissues within which they are expressed. We found that the over-representation of disease genes among old genes holds for tissue-specific genes, but the correlation between age and disease association vanished when conditioning on tissue-specificity. Of the 32 tissues studied, the genes expressed in pancreas are on average older than the genes expressed in any other tissue, while the testis expressed the lowest proportion of old genes. Following a focussed analysis on the impact of regulatory apparatus on evolution of disease genes, we show that regulators, comprising transcription factors and post-translation modified proteins, are over-represented among ancient disease genes. In addition, we show that the proportion of regulator genes is affected by gene age among disease genes and by tissue-specificity among non-disease genes. Finally, using 55,606 true positive gene interaction data, we find that old disease genes interacts with other old disease genes and interacting new genes interacts with genes originating from higher phylostrata.

Conclusion

This study supports the non-random nature of the human diseasome. We have identified a variety of distinct features and correlations to other molecular attributes that can be used to distinguish the set of disease causing genes. This was achieved by harnessing the power of mining large scale datasets from OMIM and other databases. Ultimately such knowledge may contribute to the identification of novel human disease genes and an enhanced understanding of human biology.

Chromosomal distribution of disease genes in the human genome

Genes are nonrandomly distributed in the human genome, both within and between chromosomes. Thus, genes of similar function and common evolutionary origin are often clustered, as are genes with similar expression profiles. We now report that the >2400 genes known to underlie human monogenic inherited disease are non-randomly distributed in the genome over and above the general nonrandomness evident in the distribution of human genes. Further, a subset of 315 inherited disease genes subject to gross deletion was found to exhibit a degree of clustering that was twice that manifested by disease genes in general. The clustering of human disease genes is likely to have important implications for understanding the genotype-phenotype relationship in contiguous gene syndromes as well as those conditions characterized by multigene deletions or complex chromosomal rearrangements.

Advances in translational bioinformatics: computational approaches for the hunting of disease genes

Over a 100 years ago, William Bateson provided, through his observations of the transmission of alkaptonuria in first cousin offspring, evidence of the application of Mendelian genetics to certain human traits and diseases. His work was corroborated by Archibald Garrod (Archibald AE. The incidence of alkaptonuria: a study in chemical individuality. Lancert 1902;ii:1616-20) and William Farabee (Farabee WC. Inheritance of digital malformations in man. In: Papers of the Peabody Museum of American Archaeology and Ethnology. Cambridge, Mass: Harvard University, 1905; 65-78), who recorded the familial tendencies of inheritance of malformations of human hands and feet. These were the pioneers of the hunt for disease genes that would continue through the century and result in the discovery of hundreds of genes that can be associated with different diseases. Despite many ground-breaking discoveries during the last century, we are far from having a complete understanding of the intricate network of molecular processes involved in diseases, and we are still searching for the cures for most complex diseases. In the last few years, new genome sequencing and other high-throughput experimental techniques have generated vast amounts of molecular and clinical data that contain crucial information with the potential of leading to the next major biomedical discoveries. The need to mine, visualize and integrate these data has motivated the development of several informatics approaches that can broadly be grouped in the research area of 'translational bioinformatics'. This review highlights the latest advances in the field of translational bioinformatics, focusing on the advances of computational techniques to search for and classify disease genes.

Correlation of microsynteny conservation and disease gene distribution in mammalian genomes

Background

With the completion of the whole genome sequence for many organisms, investigations into genomic structure have revealed that gene distribution is variable, and that genes with similar function or expression are located within clusters. This clustering suggests that there are evolutionary constraints that determine genome architecture. However, as most of the evidence for constraints on genome evolution comes from studies on yeast, it is unclear how much of this prior work can be extrapolated to mammalian genomes. Therefore, in this work we wished to examine the constraints on regions of the mammalian genome containing conserved gene clusters.

Results

We first identified regions of the mouse genome with microsynteny conservation by comparing gene arrangement in the mouse genome to the human, rat, and dog genomes. We then asked if any particular gene types were found preferentially in conserved regions. We found a significant correlation between conserved microsynteny and the density of mouse orthologs of human disease genes, suggesting that disease genes are clustered in genomic regions of increased microsynteny conservation.

Conclusion

The correlation between microsynteny conservation and disease gene locations indicates that regions of the mouse genome with microsynteny conservation may contain undiscovered human disease genes. This study not only demonstrates that gene function constrains mammalian genome organization, but also identifies regions of the mouse genome that can be experimentally examined to produce mouse models of human disease.

Similarly strong purifying selection acts on human disease genes of all evolutionary ages

A number of studies have showed that recently created genes differ from the genes created in deep evolutionary past in many aspects. Here, we determined the age of emergence and propensity for gene loss (PGL) of all human protein–coding genes and compared disease genes with non-disease genes in terms of their evolutionary rate, strength of purifying selection, mRNA expression, and genetic redundancy. The older and the less prone to loss, non-disease genes have been evolving 1.5- to 3-fold slower between humans and chimps than young non-disease genes, whereas Mendelian disease genes have been evolving very slowly regardless of their ages and PGL. Complex disease genes showed an intermediate pattern. Disease genes also have higher mRNA expression heterogeneity across multiple tissues than non-disease genes regardless of age and PGL. Young and middle-aged disease genes have fewer similar paralogs as non-disease genes of the same age. We reasoned that genes were more likely to be involved in human disease if they were under a strong functional constraint, expressed heterogeneously across tissues, and lacked genetic redundancy. Young human genes that have been evolving under strong constraint between humans and chimps might also be enriched for genes that encode important primate or even human-specific functions.

Quantifying dominance and deleterious effect on human disease genes

Human genes responsible for inherited diseases are important for the understanding of human disease. We investigated the degree of polymorphism and divergence in the human disease genes to elucidate the effect of natural selection on human disease genes. In particular, the effect of disease dominance was incorporated into the analysis. Both dominant disease genes (DDG) and recessive disease genes (RDG) had a higher mutation rate per site and encoded longer proteins than the nondisease genes, which exposed the disease genes to a faster flux of new mutations. Using an unbiased polymorphism dataset, we found that, proportionally, RDG harbor more nonsynonymous polymorphisms compared with DDG. We estimated the selection intensity on the disease genes using polymorphism and divergence data and determined whether the different patterns of polymorphism and divergence between DDG and RDG could be explained by the difference in only dominance. Even after the dominance effect was considered, the selection intensity on RDG was significantly different from DDG, suggesting that the deleterious effect of the dominant and recessive disease mutations are fundamentally different.

An ancient evolutionary origin of genes associated with human genetic diseases

Several thousand genes in the human genome have been linked to a heritable genetic disease. The majority of these appear to be nonessential genes (i.e., are not embryonically lethal when inactivated), and one could therefore speculate that they are late additions in the evolutionary lineage toward humans. Contrary to this expectation, we find that they are in fact significantly overrepresented among the genes that have emerged during the early evolution of the metazoa. Using a phylostratigraphic approach, we have studied the evolutionary emergence of such genes at 19 phylogenetic levels. The majority of disease genes was already present in the eukaryotic ancestor, and the second largest number has arisen around the time of evolution of multicellularity. Conversely, genes specific to the mammalian lineage are highly underrepresented. Hence, genes involved in genetic diseases are not simply a random subset of all genes in the genome but are biased toward ancient genes.

Natural selection on genes that underlie human disease susceptibility

What evolutionary forces shape genes that contribute to the risk of human disease? Do similar selective pressures act on alleles that underlie simple versus complex disorders [1-3]? Answers to these questions will shed light onto the origin of human disorders (e.g., [4]) and help to predict the population frequencies of alleles that contribute to disease risk, with important implications for the efficient design of mapping studies [5-7]. As a first step toward addressing these questions, we created a hand-curated version of the Mendelian Inheritance in Man database (OMIM). We then examined selective pressures on Mendelian-disease genes, genes that contribute to complex-disease risk, and genes known to be essential in mouse by analyzing patterns of human polymorphism and of divergence between human and rhesus macaque. We found that Mendelian-disease genes appear to be under widespread purifying selection, especially when the disease mutations are dominant (rather than recessive). In contrast, the class of genes that influence complex-disease risk shows little signs of evolutionary conservation, possibly because this category includes targets of both purifying and positive selection.

Natural selection of protein structural and functional properties: a single nucleotide polymorphism perspective

BACKGROUND

The rates of molecular evolution for protein-coding genes depend on the stringency of functional or structural constraints. The Ka/Ks ratio has been commonly used as an indicator of selective constraints and is typically calculated from interspecies alignments. Recent accumulation of single nucleotide polymorphism (SNP) data has enabled the derivation of Ka/Ks ratios for polymorphism (SNP A/S ratios).

RESULTS

Using data from the dbSNP database, we conducted the first large-scale survey of SNP A/S ratios for different structural and functional properties. We confirmed that the SNP A/S ratio is largely correlated with Ka/Ks for divergence. We observed stronger selective constraints for proteins that have high mRNA expression levels or broad expression patterns, have no paralogs, arose earlier in evolution, have natively disordered regions, are located in cytoplasm and nucleus, or are related to human diseases. On the residue level, we found higher degrees of variation for residues that are exposed to solvent, are in a loop conformation, natively disordered regions or low complexity regions, or are in the signal peptides of secreted proteins. Our analysis also revealed that histones and protein kinases are among the protein families that are under the strongest selective constraints, whereas olfactory and taste receptors are among the most variable groups.

CONCLUSION

Our study suggests that the SNP A/S ratio is a robust measure for selective constraints. The correlations between SNP A/S ratios and other variables provide valuable insights into the natural selection of various structural or functional properties, particularly for human-specific genes and constraints within the human lineage.

Natural selection of protein structural and functional properties: a single nucleotide polymorphism perspective

A large-scale survey using single nucleotide polymorphism data from dbSNP provides insights into the evolutionary selection constraints on human proteins of different structural and functional categories.

Background

The rates of molecular evolution for protein-coding genes depend on the stringency of functional or structural constraints. The Ka/Ks ratio has been commonly used as an indicator of selective constraints and is typically calculated from interspecies alignments. Recent accumulation of single nucleotide polymorphism (SNP) data has enabled the derivation of Ka/Ks ratios for polymorphism (SNP A/S ratios).

Results

Using data from the dbSNP database, we conducted the first large-scale survey of SNP A/S ratios for different structural and functional properties. We confirmed that the SNP A/S ratio is largely correlated with Ka/Ks for divergence. We observed stronger selective constraints for proteins that have high mRNA expression levels or broad expression patterns, have no paralogs, arose earlier in evolution, have natively disordered regions, are located in cytoplasm and nucleus, or are related to human diseases. On the residue level, we found higher degrees of variation for residues that are exposed to solvent, are in a loop conformation, natively disordered regions or low complexity regions, or are in the signal peptides of secreted proteins. Our analysis also revealed that histones and protein kinases are among the protein families that are under the strongest selective constraints, whereas olfactory and taste receptors are among the most variable groups.

Conclusion

Our study suggests that the SNP A/S ratio is a robust measure for selective constraints. The correlations between SNP A/S ratios and other variables provide valuable insights into the natural selection of various structural or functional properties, particularly for human-specific genes and constraints within the human lineage.

Role of duplicate genes in robustness against deleterious human mutations

It is now widely recognized that robustness is an inherent property of biological systems [1],[2],[3]. The contribution of close sequence homologs to genetic robustness against null mutations has been previously demonstrated in simple organisms [4],[5]. In this paper we investigate in detail the contribution of gene duplicates to back-up against deleterious human mutations. Our analysis demonstrates that the functional compensation by close homologs may play an important role in human genetic disease. Genes with a 90% sequence identity homolog are about 3 times less likely to harbor known disease mutations compared to genes with remote homologs. Moreover, close duplicates affect the phenotypic consequences of deleterious mutations by making a decrease in life expectancy significantly less likely. We also demonstrate that similarity of expression profiles across tissues significantly increases the likelihood of functional compensation by homologs.

Genetic robustness is the ability of an organism to buffer deleterious genetic mutations. It has been previously demonstrated that the functional compensation by duplicates plays an important role in protection against gene deletions in model organisms. Close duplicates often share similar functions, and loss of one paralog may be buffered by others. In the present work we specifically investigate the contribution of gene duplicates to backup against deleterious human mutations. We find that genes with close homologs are significantly less likely to harbor known disease mutations compared to genes with remote homologs. In addition, close duplicates affect the phenotypic consequences of deleterious mutations by making a decrease in life expectancy less likely. Similarity of expression profiles across tissues increases the likelihood of functional compensation by homologs. Taken together, our analysis demonstrates that functional compensation by close duplicates plays an important role in human genetic disease.

Bioinformatic analysis of human CNS-expressed ion channels as candidates for episodic nervous system disorders

As monogenic forms of episodic nervous system disorders are often caused by ion channel mutations, we looked for features of human central nervous system (CNS) expressed ion channels that further our understanding of those phenotypes. To this end, we compared human ion channels with other CNS-expressed genes, which we categorized according to the existence of transmembrane domains. When looking at the phylogenetic distribution of these genes, we observed an increased percentage of ion channels that exist in vertebrate genomes while missing in invertebrate genomes. Because we hypothesized that this pattern may relate to a more specific expression, we searched for characteristics of ion channels that indicate a tighter expression regulation. We found that ion channels have longer intron and protein sequences, features typical of genes with more specific expression. In addition, ion channels have increased human-rodent conservation around their transcription start site, as indicated by a higher fraction of conserved noncoding regions. This points to a high relevance of mutations that regulate ion channel expression. When we finally asked whether vertebrate-specific diversification is also displayed by non-ion channel genes with important roles in the CNS, we found a similar phylogenetic distribution. This concordant phylogenetic pattern suggests that vertebrate-specific adaptations may account for a large part of the shared genetic basis of episodic CNS disorders, including monogenic and genetically complex disease manifestations. Consequently, this phylogenetic pattern may contribute to the prioritization of candidate genes in human genetic studies of episodic CNS disorders.

Mineralocorticoid receptor mutations are the principal cause of renal type 1 pseudohypoaldosteronism

Aldosterone plays a key role in electrolyte balance and blood pressure regulation. Type 1 pseudohypoaldosteronism (PHA1) is a primary form of mineralocorticoid resistance characterized in the newborn by salt wasting, hyperkalemia, and failure to thrive. Inactivating mutations of the mineralocorticoid receptor (MR; NR3C2) are responsible for autosomal dominant and some sporadic cases of PHA1. The question as to whether other genes may be involved in the disease is of major importance because of the potential life-threatening character of the disease, the potential cardiovascular effects of compensatory aldosterone excess, and the role of the mineralocorticoid system in human hypertension. We present the first comprehensive study seeking nucleotide substitutions in coding regions, intron-exon junctions, and untranslated exons, as well as for large deletions. A total of 22 MR gene abnormalities were found in 33 patients. We demonstrate that MR mutations are extremely frequent in PHA1 patients classified according to aldosterone and potassium levels and give indications for accurate clinical and biological investigation. In our study the possibility of a genocopy exists in three PHA1 kindreds. The other patients without MR mutations might have different diseases resembling to PHA1 in the neonatal period, which could be identified by extensive clinical and functional exploration.

Evolutionary anatomies of positions and types of disease-associated and neutral amino acid mutations in the human genome

Background

Amino acid mutations in a large number of human proteins are known to be associated with heritable genetic disease. These disease-associated mutations (DAMs) are known to occur predominantly in positions essential to the structure and function of the proteins. Here, we examine how the relative perpetuation and conservation of amino acid positions modulate the genome-wide patterns of 8,627 human disease-associated mutations (DAMs) reported in 541 genes. We compare these patterns with 5,308 non-synonymous Single Nucleotide Polymorphisms (nSNPs) in 2,592 genes from primary SNP resources.

Results

The abundance of DAMs shows a negative relationship with the evolutionary rate of the amino acid positions harboring them. An opposite trend describes the distribution of nSNPs. DAMs are also preferentially found in the amino acid positions that are retained (or present) in multiple vertebrate species, whereas the nSNPs are over-abundant in the positions that have been lost (or absent) in the non-human vertebrates. These observations are consistent with the effect of purifying selection on natural variation, which also explains the existence of lower minor nSNP allele frequencies at highly-conserved amino acid positions. The biochemical severity of the inter-specific amino acid changes is also modulated by natural selection, with the fast-evolving positions containing more radical amino acid differences among species. Similarly, DAMs associated with early-onset diseases are more radical than those associated with the late-onset diseases. A small fraction of DAMs (10%) overlap with the amino acid differences between species within the same position, but are biochemically the most conservative group of amino acid differences in our datasets. Overlapping DAMs are found disproportionately in fast-evolving amino acid positions, which, along with the conservative nature of the amino acid changes, may have allowed some of them to escape natural selection until compensatory changes occur.

Conclusion

The consistency and predictability of genome-wide patterns of disease- associated and neutral amino acid variants reported here underscores the importance of the consideration of evolutionary rates of amino acid positions in clinical and population genetic analyses aimed at understanding the nature and fate of disease-associated and neutral population variation. Establishing such general patterns is an early step in efforts to diagnose the pathogenic potentials of novel amino acid mutations.

Genomics and conservation genetics

In large part, the relevance of genetics to conservation rests on the premise that neutral marker variation in populations reflects levels of detrimental and adaptive genetic variation. Despite its prominence, this tenet has been difficult to evaluate, until now. As we discuss here, genome sequence information and new technological and bioinformatics platforms now enable comprehensive surveys of neutral variation and more direct inferences of detrimental and adaptive variation in species with sequenced genomes and in 'genome-enabled' endangered taxa. Moreover, conservation schemes could begin to consider specific pathological genetic variants. A new conservation genetic agenda would utilize data from enhanced surveys of genomic variation in endangered species to better manage functional genetic variation.

Towards the identification of essential genes using targeted genome sequencing and comparative analysis

Background

The identification of genes essential for survival is of theoretical importance in the understanding of the minimal requirements for cellular life, and of practical importance in the identification of potential drug targets in novel pathogens. With the great time and expense required for experimental studies aimed at constructing a catalog of essential genes in a given organism, a computational approach which could identify essential genes with high accuracy would be of great value.

Results

We gathered numerous features which could be generated automatically from genome sequence data and assessed their relationship to essentiality, and subsequently utilized machine learning to construct an integrated classifier of essential genes in both S. cerevisiae and E. coli. When looking at single features, phyletic retention, a measure of the number of organisms an ortholog is present in, was the most predictive of essentiality. Furthermore, during construction of our phyletic retention feature we for the first time explored the evolutionary relationship among the set of organisms in which the presence of a gene is most predictive of essentiality. We found that in both E. coli and S. cerevisiae the optimal sets always contain host-associated organisms with small genomes which are closely related to the reference. Using five optimally selected organisms, we were able to improve predictive accuracy as compared to using all available sequenced organisms. We hypothesize the predictive power of these genomes is a consequence of the process of reductive evolution, by which many parasites and symbionts evolved their gene content. In addition, essentiality is measured in rich media, a condition which resembles the environments of these organisms in their hosts where many nutrients are provided. Finally, we demonstrate that integration of our most highly predictive features using a probabilistic classifier resulted in accuracies surpassing any individual feature.

Conclusion

Using features obtainable directly from sequence data, we were able to construct a classifier which can predict essential genes with high accuracy. Furthermore, our analysis of the set of genomes in which the presence of a gene is most predictive of essentiality may suggest ways in which targeted sequencing can be used in the identification of essential genes. In summary, the methods presented here can aid in the reduction of time and money invested in essential gene identification by targeting those genes for experimentation which are predicted as being essential with a high probability.

Impacts of Gene Essentiality, Expression Pattern, and Gene Compactness on the Evolutionary Rate of Mammalian Proteins

Understanding the determinants of the rate of protein sequence evolution is of fundamental importance in evolutionary biology. Many recent studies have focused on the yeast because of the availability of many genome-wide expressional and functional data. Yeast studies revealed a predominant role of gene expression level and a minor role of gene essentiality in determining the rate of protein sequence evolution. Whether these rules apply to complex organisms such as mammals is unclear. Here we assemble a list of 1,138 essential and 2,341 nonessential mouse genes based on targeted gene deletion experiments and report a significant impact of gene essentiality on the rate of mammalian protein evolution. Gene expression level has virtually no effect, although tissue specificity in expression pattern has a strong influence. Unexpectedly, gene compactness, measured by average intron size and untranslated region length, has the greatest influence. Hence, the relative importance of the various factors in determining the rate of mammalian protein evolution is gene compactness > gene essentiality approximately tissue specificity > expression level. Our results suggest a considerable variation in rate determinants between unicellular organisms such as the yeast and multicellular organisms such as mammals.

Differences in the evolutionary history of disease genes affected by dominant or recessive mutations

BACKGROUND

Global analyses of human disease genes by computational methods have yielded important advances in the understanding of human diseases. Generally these studies have treated the group of disease genes uniformly, thus ignoring the type of disease-causing mutations (dominant or recessive). In this report we present a comprehensive study of the evolutionary history of autosomal disease genes separated by mode of inheritance.

RESULTS

We examine differences in protein and coding sequence conservation between dominant and recessive human disease genes. Our analysis shows that disease genes affected by dominant mutations are more conserved than those affected by recessive mutations. This could be a consequence of the fact that recessive mutations remain hidden from selection while heterozygous. Furthermore, we employ functional annotation analysis and investigations into disease severity to support this hypothesis.

CONCLUSION

This study elucidates important differences between dominantly- and recessively-acting disease genes in terms of protein and DNA sequence conservation, paralogy and essentiality. We propose that the division of disease genes by mode of inheritance will enhance both understanding of the disease process and prediction of candidate disease genes in the future.

An integrated view of protein evolution

Why do proteins evolve at different rates? Advances in systems biology and genomics have facilitated a move from studying individual proteins to characterizing global cellular factors. Systematic surveys indicate that protein evolution is not determined exclusively by selection on protein structure and function, but is also affected by the genomic position of the encoding genes, their expression patterns, their position in biological networks and possibly their robustness to mistranslation. Recent work has allowed insights into the relative importance of these factors. We discuss the status of a much-needed coherent view that integrates studies on protein evolution with biochemistry and functional and structural genomics.

Accelerated evolutionary rate may be responsible for the emergence of lineage-specific genes in ascomycota

The evolutionary origin of "orphan" genes, genes that lack sequence similarity to any known gene, remains a mystery. One suggestion has been that most orphan genes evolve rapidly so that similarity to other genes cannot be traced after a certain evolutionary distance. This can be tested by examining the divergence rates of genes with different degrees of lineage specificity. Here the lineage specificity (LS) of a gene describes the phylogenetic distribution of that gene's orthologues in related species. Highly lineage-specific genes will be distributed in fewer species in a phylogeny. In this study, we have used the complete genomes of seven ascomycotan fungi and two animals to define several levels of LS, such as Eukaryotes-core, Ascomycota-core, Euascomycetes-specific, Hemiascomycetes-specific, Aspergillus-specific, and Saccharomyces-specific. We compare the rates of gene evolution in groups of higher LS to those in groups with lower LS. Molecular evolutionary analyses indicate an increase in nonsynonymous nucleotide substitution rates in genes with higher LS. Several analyses suggest that LS is correlated with the evolutionary rate of the gene. This correlation is stronger than those of a number of other factors that have been proposed as predictors of a gene's evolutionary rate, including the expression level of genes, gene essentiality or dispensability, and the number of protein-protein interactions. The accelerated evolutionary rates of genes with higher LS may reflect the influence of selection and adaptive divergence during the emergence of orphan genes. These analyses suggest that accelerated rates of gene evolution may be responsible for the emergence of apparently orphan genes.

Persistence time of loss-of-function mutations at nonessential loci affecting eye color in Drosophila melanogaster

Persistence time of a mutant allele, the expected number of generations before its elimination from the population, can be estimated as the ratio of the number of segregating mutations per individual over the mutation rate per generation. We screened two natural populations of Drosophila melanogaster for mutations causing clear-cut eye phenotypes and detected 25 mutant alleles, falling into 19 complementation groups, in 1164 haploid genomes, which implies 0.021 eye mutations/genome. The de novo haploid mutation rate for the same set of loci was estimated as 2 x 10(-4) in a 10-generation mutation-accumulation experiment. Thus, the average persistence time of all mutations causing clear-cut eye phenotypes is approximately 100 generations (95% confidence interval: 61-219). This estimate shows that the strength of selection against phenotypically drastic alleles of nonessential loci is close to that against recessive lethals. In both cases, deleterious alleles are apparently eliminated by selection against heterozygous individuals, which show no visible phenotypic differences from wild type.

Distribution of the strength of selection against amino acid replacements in human proteins

The impact of an amino acid replacement on the organism's fitness can vary from lethal to selectively neutral and even, in rare cases, beneficial. Substantial data are available on either pathogenic or acceptable replacements. However, the whole distribution of coefficients of selection against individual replacements is not known for any organism. To ascertain this distribution for human proteins, we combined data on pathogenic missense mutations, on human non-synonymous SNPs and on human-chimpanzee divergence of orthologous proteins. Fractions of amino acid replacements which reduce fitness by >10(-2), 10(-2)-10(-4), 10(-4)-10(-5) and <10(-5) are 25, 49, 14 and 12%, respectively. On average, the strength of selection against a replacement is substantially higher when chemically dissimilar amino acids are involved, and the Grantham's index of a replacement explains 35% of variance in the average logarithm of selection coefficients associated with different replacements. Still, the impact of a replacement depends on its context within the protein more than on its own nature. Reciprocal replacements are often associated with rather different selection coefficients, in particular, replacements of non-polar amino acids with polar ones are typically much more deleterious than replacements in the opposite direction. However, differences between evolutionary fluxes of reciprocal replacements are only weakly correlated with the differences between the corresponding selection coefficients.

Comparative primate genomics: the year of the chimpanzee

This is the year of the chimpanzee genome. Chimpanzee chromosome 22 has been sequenced and soon will be followed by the whole genome, and thousands of chimpanzee cDNA sequences are available for comparative analysis. Not only does this genomic information allow us to identify human-specific changes in particular genes that are potentially under selection, but also to understand molecular evolutionary dynamics characterizing the two most closely related mammalian genomes sequenced so far. Studies comparing gene expression in chimpanzees and other closely related primates reveal significant species differences in brain, liver and fibroblasts. New empirical data, in combination with models of speciation, are giving insight into how humans and chimpanzees speciated.

Microarray and EST database estimates of mRNA expression levels differ: the protein length versus expression curve for C. elegans

Background

Various methods for estimating protein expression levels are known. The level of correlation between these methods is only fair, and systematic biases in each of the methods cannot be ruled out. We here investigate systematic biases in the estimation of gene expression rates from microarray data and from abundance within the Expressed Sequence Tag (EST) database. We suggest that length is a significant factor in biases to measured gene expression rates.

As a specific example of the importance of the bias of expression rate with length, we address the following evolutionary question: Does the average C. elegans protein length increase or decrease with expression level? Two different answers to this question have been reported in the literature, one method using expression levels estimated by abundance within the EST database and another using microarrays. We have investigated this issue by constructing the full protein length versus expression curve for C. elegans, using both methods for estimating expression levels.

Results

The microarray data show a monotonic decrease of length with expression level, whereas the abundance within the EST database data show a non-monotonic behavior. Furthermore, the ratio of the expression level estimated by the EST database to that measured by microarrays is not constant, but rather systematically biased with gene length.

Conclusions

It is suggested that the length bias may lie primarily in the abundance within the EST database method, being not ameliorated by internal standards as it is in the microarray data, and that this bias should be removed before data interpretation. When this is done, both the microarray and the abundance within the EST database give a monotonic decrease of spliced length with expression level, and the correlation between the EST and microarray data becomes larger. We suggest that standard RNA controls be used to normalize for length bias in any method that measures expression.