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

Computational strategies for cross-species knowledge transfer and translational biomedicine

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.

Analysis of Evolutionary Conservation, Expression Level, and Genetic Association at a Genome-wide Scale Reveals Heterogeneity Across Polygenic Phenotypes

Understanding the expression level and evolutionary rate of associated genes with human polygenic diseases provides crucial insights into their disease-contributing roles. In this work, we leveraged genome-wide association studies (GWASs) to investigate the relationship between the genetic association and both the evolutionary rate (dN/dS) and expression level of human genes associated with the two polygenic diseases of schizophrenia and coronary artery disease. Our findings highlight a distinct variation in these relationships between the two diseases. Genes associated with both diseases exhibit a significantly greater variance in evolutionary rate compared to those implicated in monogenic diseases. Expanding our analyses to 4,756 complex traits in the GWAS atlas database, we unraveled distinct trait categories with a unique interplay among the evolutionary rate, expression level, and genetic association of human genes. In most polygenic traits, highly expressed genes were more associated with the polygenic phenotypes compared to lowly expressed genes. About 69% of polygenic traits displayed a negative correlation between genetic association and evolutionary rate, while approximately 30% of these traits showed a positive correlation between genetic association and evolutionary rate. Our results demonstrate the presence of a spectrum among complex traits, shaped by natural selection. Notably, at opposite ends of this spectrum, we find metabolic traits being more likely influenced by purifying selection, and immunological traits that are more likely shaped by positive selection. We further established the polygenic evolution portal (evopolygen.de) as a resource for investigating relationships and generating hypotheses in the field of human polygenic trait evolution.

Comparative Mitogenomics and Phylogenetic Implications for Nine Species of the Subfamily Meconematinae (Orthoptera: Tettigoniidae)

Currently, the subfamily Meconematinae encompasses 1029 species, but whole-mitochondrial-genome assemblies have only been made available for 13. In this study, the whole mitochondrial genomes (mitogenomes) of nine additional species in the subfamily Meconematinae were sequenced. The size ranged from 15,627 bp to 17,461 bp, indicating double-stranded circular structures. The length of the control region was the main cause of the difference in mitochondrial genome length among the nine species. All the mitogenomes including 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), 2 ribosomal RNA genes (rRNAs) and a control region (CR). The majority strand encoded 23 genes, and the minority strand encoded 14 genes. A phylogenetic analysis reaffirmed the monophyletic status of each subfamily, but the monophysitism of Xizicus, Xiphidiopsis and Phlugiolopsis was not supported.

Evolutionary rates of and selective constraints on the mitochondrial genomes of Orthoptera insects with different wing types

Orthoptera is the most diverse order of polyneopterans, and the forewing and hindwing of its members exhibit extremely variability from full length to complete loss in many groups; thus, this order provides a good model for studying the effects of insect flight ability on the evolutionary constraints on and evolutionary rate of the mitochondrial genome. Based on a data set of mitochondrial genomes from 171 species, including 43 newly determined, we reconstructed Orthoptera phylogenetic relationships and estimated the divergence times of this group. The results supported Caelifera and Ensifera as two monophyletic groups, and revealed that Orthoptera originated in the Carboniferous (298.997 Mya). The date of divergence between the suborders Caelifera and Ensifera was 255.705 Mya, in the late Permian. The major lineages of Acrididae seemed to have radiated in the Cenozoic, and the six patterns of rearrangement of 171 Orthoptera mitogenomes mostly occurred in the Cretaceous and Cenozoic. Based on phylogenetic relationships and ancestral state reconstruction, we analysed the evolutionary selection pressure on and evolutionary rate of mitochondrial protein-coding genes (mPCGs). The results indicated that during approximately 300 Mya of evolution, these genes experienced purifying selection to maintain their function. Flightless orthopteran insects accumulated more non-synonymous mutations than flying species and experienced more relaxed evolutionary constraints. The different wing types had different evolutionary rates, and the mean evolutionary rate of Orthoptera mitochondrial mPCGs was 13.554 × 10^-9 subs/s/y. The differences in selection pressures and evolutionary rates observed between the mitochondrial genomes suggested that functional constraints due to locomotion play an important role in the evolution of mitochondrial DNA in orthopteran insects with different wing types.

The Evolutionary Traceability of a Protein

Orthologs document the evolution of genes and metabolic capacities encoded in extant and ancient genomes. However, the similarity between orthologs decays with time, and ultimately it becomes insufficient to infer common ancestry. This leaves ancient gene set reconstructions incomplete and distorted to an unknown extent. Here we introduce the “evolutionary traceability” as a measure that quantifies, for each protein, the evolutionary distance beyond which the sensitivity of the ortholog search becomes limiting. Using yeast, we show that genes that were thought to date back to the last universal common ancestor are of high traceability. Their functions mostly involve catalysis, ion transport, and ribonucleoprotein complex assembly. In turn, the fraction of yeast genes whose traceability is not sufficient to infer their presence in last universal common ancestor is enriched for regulatory functions. Computing the traceabilities of genes that have been experimentally characterized as being essential for a self-replicating cell reveals that many of the genes that lack orthologs outside bacteria have low traceability. This leaves open whether their orthologs in the eukaryotic and archaeal domains have been overlooked. Looking at the example of REC8, a protein essential for chromosome cohesion, we demonstrate how a traceability-informed adjustment of the search sensitivity identifies hitherto missed orthologs in the fast-evolving microsporidia. Taken together, the evolutionary traceability helps to differentiate between true absence and nondetection of orthologs, and thus improves our understanding about the evolutionary conservation of functional protein networks. “protTrace,” a software tool for computing evolutionary traceability, is freely available at https://github.com/BIONF/protTrace.git; last accessed February 10, 2019.

Revisiting avian 'missing' genes from de novo assembled transcripts

Background

Argument remains as to whether birds have lost genes compared with mammals and non-avian vertebrates during speciation. High quality-reference gene sets are necessary for precisely evaluating gene gain and loss. It is essential to explore new reference transcripts from large-scale de novo assembled transcriptomes to recover the potential hidden genes in avian genomes.

Results

We explored 196 high quality transcriptomic datasets from five bird species to reconstruct transcripts for the purpose of discovering potential hidden genes in the avian genomes. We constructed a relatively complete and high-quality bird transcript database (1,623,045 transcripts after quality control in five birds) from a large amount of avian transcriptomic data, and found most of the presumed missing genes (83.2%) could be recovered in at least one bird species. Most of these genes have been identified for the first time in birds. Our results demonstrate that 67.94% genes have GC content over 50%, while 2.91% genes are AT-rich (AT% > 60%). In our results, 239 (53.59%) genes had a tissue-specific expression index of more than 0.9 in chicken. The missing genes also have lower Ka/Ks values than average (genome-wide: Ka/Ks = 0.99; missing gene: Ka/Ks = 0.90; t-test = 1.25E-14). Among all presumed missing genes, there were 135 for which we did not find any meaningful orthologues in any of the 5 species studied.

Conclusion

Insufficient reference genome quality is the major reason for wrongly inferring missing genes in birds. Those presumably missing genes often have a very strong tissue-specific expression pattern. We show multi-tissue transcriptomic data from various species are necessary for inferring gene family evolution for species with only draft reference genomes.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5407-1) contains supplementary material, which is available to authorized users.

Systematic Analyses and Prediction of Human Drug Side Effect Associated Proteins from the Perspective of Protein Evolution

Identification of various factors involved in adverse drug reactions in target proteins to develop therapeutic drugs with minimal/no side effect is very important. In this context, we have performed a comparative evolutionary rate analyses between the genes exhibiting drug side-effect(s) (SET) and genes showing no side effect (NSET) with an aim to increase the prediction accuracy of SET/NSET proteins using evolutionary rate determinants. We found that SET proteins are more conserved than the NSET proteins. The rates of evolution between SET and NSET protein primarily depend upon their noncomplex (protein complex association number = 0) forming nature, phylogenetic age, multifunctionality, membrane localization, and transmembrane helix content irrespective of their essentiality, total druggability (total number of drugs/target), m-RNA expression level, and tissue expression breadth. We also introduced two novel terms—killer druggability (number of drugs with killing side effect(s)/target), essential druggability (number of drugs targeting essential proteins/target) to explain the evolutionary rate variation between SET and NSET proteins. Interestingly, we noticed that SET proteins are younger than NSET proteins and multifunctional younger SET proteins are candidates of acquiring killing side effects. We provide evidence that higher killer druggability, multifunctionality, and transmembrane helices support the conservation of SET proteins over NSET proteins in spite of their recent origin. By employing all these entities, our Support Vector Machine model predicts human SET/NSET proteins to a high degree of accuracy (∼86%).

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.

Complex Patterns of Association between Pleiotropy and Transcription Factor Evolution

Pleiotropy has been claimed to constrain gene evolution but specific mechanisms and extent of these constraints have been difficult to demonstrate. The expansion of molecular data makes it possible to investigate these pleiotropic effects. Few classes of genes have been characterized as intensely as human transcription factors (TFs). We therefore analyzed the evolutionary rates of full TF proteins, along with their DNA binding domains and protein-protein interacting domains (PID) in light of the degree of pleiotropy, measured by the number of TF-TF interactions, or the number of DNA-binding targets. Data were extracted from the ENCODE Chip-Seq dataset, the String v 9.2 database, and the NHGRI GWAS catalog. Evolutionary rates of proteins and domains were calculated using the PAML CodeML package. Our analysis shows that the numbers of TF-TF interactions and DNA binding targets associated with constrained gene evolution; however, the constraint caused by the number of DNA binding targets was restricted to the DNA binding domains, whereas the number of TF-TF interactions constrained the full protein and did so more strongly. Additionally, we found a positive correlation between the number of protein-PIDs and the evolutionary rates of the protein-PIDs. These findings show that not only does pleiotropy associate with constrained protein evolution but the constraint differs by domain function. Finally, we show that GWAS associated TF genes are more highly pleiotropic : The GWAS data illustrates that mutations in highly pleiotropic genes are more likely to be associated with disease phenotypes.

The Somatic Nature of Cancer Allows It to Affect Highly Constrained Genes

Cancer is special among genetic disorders in two major ways: first, cancer is a disease of the most basic of cellular functions, such as cell proliferation, differentiation, and the maintenance of genomic integrity. Second, in contrast to most genetic disorders that are mediated by germline (hereditary) mutations, cancer is largely a somatic disease. Here we show that these two traits are not detached and that it is the somatic nature of cancer that allows it to affect the most basic of cellular functions. We begin by demonstrating that cancer genes are both more functionally central (as measured by their patterns of expression and protein interaction) and more evolutionarily constrained than non-cancer genetic disease genes. We then compare genes that are only modified somatically in cancer (hereinafter referred to as “somatic cancer genes”) to those that can also be modified in a hereditary manner, contributing to cancer development (hereinafter referred to as “hereditary cancer genes”). We show that both somatic and hereditary cancer genes are much more functionally central than genes contributing to non-cancer genetic disorders. At the same time, hereditary cancer genes are only as constrained as non-cancer hereditary disease genes, while somatic cancer genes tend to be much more constrained in evolution. Thus, it appears that it is the somatic nature of cancer that allows it to modify the most constrained genes and, therefore, affect the most basic of cellular functions.

Disparate Modes of Evolution Shaped Modern Prion (PRNP) and Prion-Related Doppel (PRND) Variation in Domestic Cattle

Previous investigations aimed at determining whether the mammalian prion protein actually facilitates tangible molecular aspects of either a discrete or pleiotropic functional niche have been debated, especially given the apparent absence of overt behavioral or physiological phenotypes associated with several mammalian prion gene (PRNP) knockout experiments. Moreover, a previous evaluation of PRNP knockout cattle concluded that they were normal, suggesting that the bovine prion protein is physiologically dispensable. Herein, we examined the frequency and distribution of nucleotide sequence variation within the coding regions of bovine PRNP and the adjacent Doppel (PRND) gene, a proximal paralogue to PRNP on BTA13. Evaluation of PRND variation demonstrated that the gene does not depart from a strictly neutral model of molecular evolution, and would therefore not be expected to influence tests of selection within PRNP. Collectively, our analyses confirm that intense purifying selection is indeed occurring directly on bovine PRNP, which is indicative of a protein with an important role. These results suggest that the lack of observed fitness effects may not manifest in the controlled environmental conditions used to care for and raise PRNP knockout animals.

Integrating Diverse Types of Genomic Data to Identify Genes that Underlie Adverse Pregnancy Phenotypes

Progress in understanding complex genetic diseases has been bolstered by synthetic approaches that overlay diverse data types and analyses to identify functionally important genes. Pre-term birth (PTB), a major complication of pregnancy, is a leading cause of infant mortality worldwide. A major obstacle in addressing PTB is that the mechanisms controlling parturition and birth timing remain poorly understood. Integrative approaches that overlay datasets derived from comparative genomics with function-derived ones have potential to advance our understanding of the genetics of birth timing, and thus provide insights into the genes that may contribute to PTB. We intersected data from fast evolving coding and non-coding gene regions in the human and primate lineage with data from genes expressed in the placenta, from genes that show enriched expression only in the placenta, as well as from genes that are differentially expressed in four distinct PTB clinical subtypes. A large fraction of genes that are expressed in placenta, and differentially expressed in PTB clinical subtypes (23–34%) are fast evolving, and are associated with functions that include adhesion neurodevelopmental and immune processes. Functional categories of genes that express fast evolution in coding regions differ from those linked to fast evolution in non-coding regions. Finally, there is a surprising lack of overlap between fast evolving genes that are differentially expressed in four PTB clinical subtypes. Integrative approaches, especially those that incorporate evolutionary perspectives, can be successful in identifying potential genetic contributions to complex genetic diseases, such as PTB.

Dominant and Protective Role of the CYTH4 Primate-Specific GTTT-Repeat Longer Alleles Against Neurodegeneration

Primate-specific genes and regulatory mechanisms could provide insight into human brain functioning and disease. In a genome-scale analysis of the entire protein-coding genes listed in the GeneCards database, we have recently reported human genes that contain "exceptionally long" short tandem repeats (STRs) in their core promoter, which may be of adaptive/selective evolutionary advantage in this species. The longest tetra-nucleotide repeat identified in a human gene core promoter belongs to the CYTH4 gene. This GTTT-repeat is specific to Hominidae and Old World monkeys, and the shortest allele of this repeat, (GTTT)6, is linked with neural dysfunction and type I bipolar disorder in human. In the present study, we sought a possibly broader role for the CYTH4 gene core promoter GTTT-repeat in neural functioning and investigated its allelic distribution in a total of 949 human subjects, consisting of two neurodegenerative disorders, multiple sclerosis (MS) (n = 272) and Alzheimer's disease (AD) (n = 257), and controls (n = 420). The range of the alleles of this GTTT-repeat in the human sample studied was between 6- and 9-repeats. The shortest allele, (GTTT)6, was significantly in excess in the MS and AD patients in comparison with the controls (p < 0.004). The 6/6, 6/7, and 7/7 genotypes were in excess in the MS and AD patients, whereas the overall frequency of all other genotypes (consisting of at least one longer allele, i.e., 8- or 9-repeat) was higher in the controls (p < 0.005), indicating a dominant and protective effect for the longer alleles against neurodegeneration. This is the first indication of the involvement of a primate-specific STR in neurodegeneration in humans. We propose an adaptive evolutionary role for the expansion of the CYTH4 gene core promoter GTTT-repeat in the human brain, which is supported by a link between the shortest allele of this repeat with neuropsychiatric disorders.

Elucidating the genotype-phenotype relationships and network perturbations of human shared and specific disease genes from an evolutionary perspective

To date, numerous studies have been attempted to determine the extent of variation in evolutionary rates between human disease and nondisease (ND) genes. In our present study, we have considered human autosomal monogenic (Mendelian) disease genes, which were classified into two groups according to the number of phenotypic defects, that is, specific disease (SPD) gene (one gene: one defect) and shared disease (SHD) gene (one gene: multiple defects). Here, we have compared the evolutionary rates of these two groups of genes, that is, SPD genes and SHD genes with respect to ND genes. We observed that the average evolutionary rates are slow in SHD group, intermediate in SPD group, and fast in ND group. Group-to-group evolutionary rate differences remain statistically significant regardless of their gene expression levels and number of defects. We demonstrated that disease genes are under strong selective constraint if they emerge through edgetic perturbation or drug-induced perturbation of the interactome network, show tissue-restricted expression, and are involved in transmembrane transport. Among all the factors, our regression analyses interestingly suggest the independent effects of 1) drug-induced perturbation and 2) the interaction term of expression breadth and transmembrane transport on protein evolutionary rates. We reasoned that the drug-induced network disruption is a combination of several edgetic perturbations and, thus, has more severe effect on gene phenotypes.

Evolutionary profiling reveals the heterogeneous origins of classes of human disease genes: implications for modeling disease genetics in animals

Background

The recent expansion of whole-genome sequence data available from diverse animal lineages provides an opportunity to investigate the evolutionary origins of specific classes of human disease genes. Previous studies have observed that human disease genes are of particularly ancient origin. While this suggests that many animal species have the potential to serve as feasible models for research on genes responsible for human disease, it is unclear whether this pattern has meaningful implications and whether it prevails for every class of human disease.

Results

We used a comparative genomics approach encompassing a broad phylogenetic range of animals with sequenced genomes to determine the evolutionary patterns exhibited by human genes associated with different classes of disease. Our results support previous claims that most human disease genes are of ancient origin but, more importantly, we also demonstrate that several specific disease classes have a significantly large proportion of genes that emerged relatively recently within the metazoans and/or vertebrates. An independent assessment of the synonymous to non-synonymous substitution rates of human disease genes found in mammals reveals that disease classes that arose more recently also display unexpected rates of purifying selection between their mammalian and human counterparts.

Conclusions

Our results reveal the heterogeneity underlying the evolutionary origins of (and selective pressures on) different classes of human disease genes. For example, some disease gene classes appear to be of uncommonly recent (i.e., vertebrate-specific) origin and, as a whole, have been evolving at a faster rate within mammals than the majority of disease classes having more ancient origins. The novel patterns that we have identified may provide new insight into cases where studies using traditional animal models were unable to produce results that translated to humans. Conversely, we note that the larger set of disease classes do have ancient origins, suggesting that many non-traditional animal models have the potential to be useful for studying many human disease genes. Taken together, these findings emphasize why model organism selection should be done on a disease-by-disease basis, with evolutionary profiles in mind.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-014-0212-1) contains supplementary material, which is available to authorized users.

Long non-coding RNAs as a source of new peptides

Deep transcriptome sequencing has revealed the existence of many transcripts that lack long or conserved open reading frames (ORFs) and which have been termed long non-coding RNAs (lncRNAs). The vast majority of lncRNAs are lineage-specific and do not yet have a known function. In this study, we test the hypothesis that they may act as a repository for the synthesis of new peptides. We find that a large fraction of the lncRNAs expressed in cells from six different species is associated with ribosomes. The patterns of ribosome protection are consistent with the translation of short peptides. lncRNAs show similar coding potential and sequence constraints than evolutionary young protein coding sequences, indicating that they play an important role in de novo protein evolution.

DOI:http://dx.doi.org/10.7554/eLife.03523.001

Despite the terms being largely interchangeable in modern language, ‘DNA’ and ‘gene’ do not mean the same thing. A gene is made of DNA and contains the instructions to make a protein, and it is the protein that performs the function of the gene. However, cells in the body also contain DNA that does not form genes. Far from being ‘junk’ DNA with no biological purpose; this DNA has a variety of roles, including affecting how other genes are used.

To produce a protein, the DNA sequence of a gene is transcribed into an intermediate molecule called RNA, which is then translated to produce a protein. So-called long non-coding RNA (lncRNA) molecules are also transcribed from DNA, but whether these are translated to make proteins has been a subject of much debate. Indeed, the function of the vast majority of lncRNA molecules is unknown.

Ruiz-Orera et al. analyzed RNA sequences collected from earlier experiments on six different species—humans, mice, fish, flies, yeast, and a plant—and found nearly 2500 as yet unstudied lncRNAs in addition to those previously identified. Many of the lncRNAs that Ruiz-Orera et al. investigated could be found lodged inside the cellular machinery used to translate RNA into proteins. Furthermore, these lncRNA molecules are oriented in the machinery as if they are primed and ready for translation, suggesting that many lncRNAs do produce proteins. However, it is unclear how many of these proteins have a useful function.

Very few lncRNAs were found in more than one species, suggesting that they have evolved recently. The properties of lncRNA molecules also show many similarities with the properties of ‘young’—recently evolved—genes that are known to produce proteins. The combined findings of Ruiz-Orera et al. therefore suggest that lncRNAs are important for developing new proteins. The emergence of proteins with new functions has been an important driving force in evolution, and this work provides important clues into the first steps of this process.

DOI:http://dx.doi.org/10.7554/eLife.03523.002

Comparative genome-scale analysis of niche-based stress-responsive genes in Lactobacillus helveticus strains

Next generation sequencing technologies with advanced bioinformatic tools present a unique opportunity to compare genomes from diverse niches. The identification of niche-specific stress-responsive genes can help in characterizing robust strains for multiple applications. In this study, we attempted to compare the stress-responsive genes of a potential probiotic strain, Lactobacillus helveticus MTCC 5463, and a cheese starter strain, Lactobacillus helveticus DPC 4571, from a gut and dairy niche, respectively. Sequencing of MTCC 5463 was done using 454 GS FLX, and contigs were assembled using GS Assembler software. Genome analysis was done using BLAST hits and the prokaryotic annotation server RAST. The MTCC 5463 genome carried multiple orthologs of genes governing stress responses, whereas the DPC 4571 genome lacked in the number of major stress-response proteins. The absence of the bile salt hydrolase gene in DPC 4571 and its presence in MTCC 5463 clearly indicated niche adaptation. Further, MTCC 5463 carried higher copy numbers of genes contributing towards heat, cold, osmotic, and oxidative stress resistance as compared with DPC 4571. Through comparative genomics, we could thus identify stress-responsive gene sets required to adapt to gut and dairy niches.

On the retention of gene duplicates prone to dominant deleterious mutations

Recent studies have shown that gene families from different functional categories have been preferentially expanded either by small scale duplication (SSD) or by whole-genome duplication (WGD). In particular, gene families prone to dominant deleterious mutations and implicated in cancers and other genetic diseases in human have been greatly expanded through two rounds of WGD dating back from early vertebrates. Here, we strengthen this intriguing observation, showing that human oncogenes involved in different primary tumors have retained many WGD duplicates compared to other human genes. In order to rationalize this evolutionary outcome, we propose a consistent population genetics model to analyze the retention of SSD and WGD duplicates taking into account their propensity to acquire dominant deleterious mutations. We solve a deterministic haploid model including initial duplicated loci, their retention through sub-functionalization or their neutral loss-of-function or deleterious gain-of-function at one locus. Extensions to diploid genotypes are presented and population size effects are analyzed using stochastic simulations. The only difference between the SSD and WGD scenarios is the initial number of individuals with duplicated loci. While SSD duplicates need to spread through the entire population from a single individual to reach fixation, WGD duplicates are de facto fixed in the small initial post-WGD population arising through the ploidy incompatibility between post-WGD individuals and the rest of the pre-WGD population. WGD duplicates prone to dominant deleterious mutations are then shown to be indirectly selected through purifying selection in post-WGD species, whereas SSD duplicates typically require positive selection. These results highlight the long-term evolution mechanisms behind the surprising accumulation of WGD duplicates prone to dominant deleterious mutations and are shown to be consistent with cancer genome data on the prevalence of human oncogenes with WGD duplicates.

How old is my gene?

Gene functions, interactions, disease associations, and ecological distributions are all correlated with gene age. However, it is challenging to estimate the intricate series of evolutionary events leading to a modern-day gene and then to reduce this history to a single age estimate. Focusing on eukaryotic gene families, we introduce a framework that can be used to compare current strategies for quantifying gene age, discuss key differences between these methods, and highlight several common problems. We argue that genes with complex evolutionary histories do not have a single well-defined age. As a result, care must be taken to articulate the goals and assumptions of any analysis that uses gene age estimates. Recent algorithmic advances offer the promise of gene age estimates that are fast, accurate, and consistent across gene families. This will enable a shift to integrated genome-wide analyses of all events in gene evolutionary histories in the near future.

Human monogenic disease genes have frequently functionally redundant paralogs

Mendelian disorders are often caused by mutations in genes that are not lethal but induce functional distortions leading to diseases. Here we study the extent of gene duplicates that might compensate genes causing monogenic diseases. We provide evidence for pervasive functional redundancy of human monogenic disease genes (MDs) by duplicates by manifesting 1) genes involved in human genetic disorders are enriched in duplicates and 2) duplicated disease genes tend to have higher functional similarities with their closest paralogs in contrast to duplicated non-disease genes of similar age. We propose that functional compensation by duplication of genes masks the phenotypic effects of deleterious mutations and reduces the probability of purging the defective genes from the human population; this functional compensation could be further enhanced by higher purification selection between disease genes and their duplicates as well as their orthologous counterpart compared to non-disease genes. However, due to the intrinsic expression stochasticity among individuals, the deleterious mutations could still be present as genetic diseases in some subpopulations where the duplicate copies are expressed at low abundances. Consequently the defective genes are linked to genetic disorders while they continue propagating within the population. Our results provide insight into the molecular basis underlying the spreading of duplicated disease genes.

Neocortical development as an evolutionary platform for intragenomic conflict

Embryonic development in mammals has evolved a platform for genomic conflict between mothers and embryos and, by extension, between maternal and paternal genomes. The evolutionary interests of the mother and embryo may be maximized through the promotion of sex-chromosome genes and imprinted alleles, resulting in the rapid evolution of postzygotic phenotypes preferential to either the maternal or paternal genome. In eutherian mammals, extraordinary in utero maternal investment in the brain, and neocortex especially, suggests that convergent evolution of an expanded mammalian neocortex along divergent lineages may be explained, in part, by parent-of-origin-linked gene expression arising from parent-offspring conflict. The influence of this conflict on neocortical development and evolution, however, has not been investigated at the genomic level. In this hypothesis and theory article, we provide preliminary evidence for positive selection in humans in the regions of two platforms of intragenomic conflict—chromosomes 15q11-q13 and X—and explore the potential relevance of cis-regulated imprinted domains to neocortical expansion in mammalian evolution. We present the hypothesis that maternal- and paternal-specific pressures on the developing neocortex compete intragenomically to influence neocortical expansion in mammalian evolution.

Gene family evolution in green plants with emphasis on the origination and evolution of Arabidopsis thaliana genes

Gene family size variation is an important mechanism that shapes the natural variation for adaptation in various species. Despite its importance, the pattern of gene family size variation in green plants is still not well understood. In particular, the evolutionary pattern of genes and gene families remains unknown in the model plant Arabidopsis thaliana in the context of green plants. In this study, eight representative genomes of green plants are sampled to study gene family evolution and characterize the origination of A. thaliana genes, respectively. Four important insights gained are that: (i) the rate of gene gains and losses is about 0.001359 per gene every million years, similar to the rate in yeast, Drosophila, and mammals; (ii) some gene families evolved rapidly with extreme expansions or contractions, and 2745 gene families present in all the eight species represent the 'core' proteome of green plants; (iii) 70% of A. thaliana genes could be traced back to 450 million years ago; and (iv) intriguingly, A. thaliana genes with early origination are under stronger purifying selection and more conserved. In summary, the present study provides genome-wide insights into evolutionary history and mechanisms of genes and gene families in green plants and especially in A. thaliana.

On the expansion of "dangerous" gene repertoires by whole-genome duplications in early vertebrates

The emergence and evolutionary expansion of gene families implicated in cancers and other severe genetic diseases is an evolutionary oddity from a natural selection perspective. Here, we show that gene families prone to deleterious mutations in the human genome have been preferentially expanded by the retention of "ohnolog" genes from two rounds of whole-genome duplication (WGD) dating back from the onset of jawed vertebrates. We further demonstrate that the retention of many ohnologs suspected to be dosage balanced is in fact indirectly mediated by their susceptibility to deleterious mutations. This enhanced retention of "dangerous" ohnologs, defined as prone to autosomal-dominant deleterious mutations, is shown to be a consequence of WGD-induced speciation and the ensuing purifying selection in post-WGD species. These findings highlight the importance of WGD-induced nonadaptive selection for the emergence of vertebrate complexity, while rationalizing, from an evolutionary perspective, the expansion of gene families frequently implicated in genetic disorders and cancers.

Evolutionary history of human disease genes reveals phenotypic connections and comorbidity among genetic diseases

The extent to which evolutionary changes have impacted the phenotypic relationships among human diseases remains unclear. In this work, we report that phenotypically similar diseases are connected by the evolutionary constraints on human disease genes. Human disease groups can be classified into slowly or rapidly evolving classes, where the diseases in the slowly evolving class are enriched with morphological phenotypes and those in the rapidly evolving class are enriched with physiological phenotypes. Our findings establish a clear evolutionary connection between disease classes and disease phenotypes for the first time. Furthermore, the high comorbidity found between diseases connected by similar evolutionary constraints enables us to improve the predictability of the relative risk of human diseases. We find the evolutionary constraints on disease genes are a new layer of molecular connection in the network-based exploration of human diseases.

Quantifying protein modularity and evolvability: a comparison of different techniques

Modularity increases evolvability by reducing constraints on adaptation and by allowing preexisting parts to function in new contexts for novel uses. Protein evolution provides an excellent context to study the causes and consequences of biological modularity. In order to address such questions, however, an index for protein modularity is necessary. This paper proposes a simple index for protein modularity-"module density"-which is the number of evolutionarily independent modules that compose a protein divided by the number of amino acids in the protein. The decomposition of proteins into constituent modules can be accomplished by either of two classes of methods. The first class of methods relies on "suppositional" criteria to assign amino acids to modules, whereas the second class of methods relies on "coevolutionary" criteria for this task. One simple and practical method from the first class consists of approximating the number of modules in a protein as the number of regular secondary structure elements (i.e., helices and sheets). Methods based on coevolutionary criteria require more elaborate data, but they have the advantage of being able to specify modules without prior assumptions about why they exist. Given the increasing availability of datasets sampling protein mutational spectra (e.g., from comparative genomics, experimental evolution, and computational prediction), methods based on coevolutionary criteria will likely become more promising in the near future. The ability to meaningfully quantify protein modularity via simple indices has the potential to aid future efforts to understand protein evolutionary rate determinants, improve molecular evolution models and engineer novel proteins.

ProteinHistorian: tools for the comparative analysis of eukaryote protein origin

The evolutionary history of a protein reflects the functional history of its ancestors. Recent phylogenetic studies identified distinct evolutionary signatures that characterize proteins involved in cancer, Mendelian disease, and different ontogenic stages. Despite the potential to yield insight into the cellular functions and interactions of proteins, such comparative phylogenetic analyses are rarely performed, because they require custom algorithms. We developed ProteinHistorian to make tools for performing analyses of protein origins widely available. Given a list of proteins of interest, ProteinHistorian estimates the phylogenetic age of each protein, quantifies enrichment for proteins of specific ages, and compares variation in protein age with other protein attributes. ProteinHistorian allows flexibility in the definition of protein age by including several algorithms for estimating ages from different databases of evolutionary relationships. We illustrate the use of ProteinHistorian with three example analyses. First, we demonstrate that proteins with high expression in human, compared to chimpanzee and rhesus macaque, are significantly younger than those with human-specific low expression. Next, we show that human proteins with annotated regulatory functions are significantly younger than proteins with catalytic functions. Finally, we compare protein length and age in many eukaryotic species and, as expected from previous studies, find a positive, though often weak, correlation between protein age and length. ProteinHistorian is available through a web server with an intuitive interface and as a set of command line tools; this allows biologists and bioinformaticians alike to integrate these approaches into their analysis pipelines. ProteinHistorian's modular, extensible design facilitates the integration of new datasets and algorithms. The ProteinHistorian web server, source code, and pre-computed ages for 32 eukaryotic genomes are freely available under the GNU public license at http://lighthouse.ucsf.edu/ProteinHistorian/.

Human genomic disease variants: a neutral evolutionary explanation

Many perspectives on the role of evolution in human health include nonempirical assumptions concerning the adaptive evolutionary origins of human diseases. Evolutionary analyses of the increasing wealth of clinical and population genomic data have begun to challenge these presumptions. In order to systematically evaluate such claims, the time has come to build a common framework for an empirical and intellectual unification of evolution and modern medicine. We review the emerging evidence and provide a supporting conceptual framework that establishes the classical neutral theory of molecular evolution (NTME) as the basis for evaluating disease- associated genomic variations in health and medicine. For over a decade, the NTME has already explained the origins and distribution of variants implicated in diseases and has illuminated the power of evolutionary thinking in genomic medicine. We suggest that a majority of disease variants in modern populations will have neutral evolutionary origins (previously neutral), with a relatively smaller fraction exhibiting adaptive evolutionary origins (previously adaptive). This pattern is expected to hold true for common as well as rare disease variants. Ultimately, a neutral evolutionary perspective will provide medicine with an informative and actionable framework that enables objective clinical assessment beyond convenient tendencies to invoke past adaptive events in human history as a root cause of human disease.

Endocytosis and signaling: cell logistics shape the eukaryotic cell plan

Our understanding of endocytosis has evolved remarkably in little more than a decade. This is the result not only of advances in our knowledge of its molecular and biological workings, but also of a true paradigm shift in our understanding of what really constitutes endocytosis and of its role in homeostasis. Although endocytosis was initially discovered and studied as a relatively simple process to transport molecules across the plasma membrane, it was subsequently found to be inextricably linked with almost all aspects of cellular signaling. This led to the notion that endocytosis is actually the master organizer of cellular signaling, providing the cell with understandable messages that have been resolved in space and time. In essence, endocytosis provides the communications and supply routes (the logistics) of the cell. Although this may seem revolutionary, it is still likely to be only a small part of the entire story. A wealth of new evidence is uncovering the surprisingly pervasive nature of endocytosis in essentially all aspects of cellular regulation. In addition, many newly discovered functions of endocytic proteins are not immediately interpretable within the classical view of endocytosis. A possible framework, to rationalize all this new knowledge, requires us to "upgrade" our vision of endocytosis. By combining the analysis of biochemical, biological, and evolutionary evidence, we propose herein that endocytosis constitutes one of the major enabling conditions that in the history of life permitted the development of a higher level of organization, leading to the actuation of the eukaryotic cell plan.

Predicting the lethal phenotype of the knockout mouse by integrating comprehensive genomic data

MOTIVATION

The phenotypes of knockout mice provide crucial information for understanding the biological functions of mammalian genes. Among various knockout phenotypes, lethality is of great interest because those involved genes play essential roles. With the availability of large-scale genomic data, we aimed to assess how well the integration of various genomic features can predict the lethal phenotype of single-gene knockout mice.

RESULTS

We first assembled a comprehensive list of 491 candidate genomic features derived from diverse data sources. Using mouse genes with a known phenotype as the training set, we integrated the informative genomic features to predict the knockout lethality through three machine learning methods. Based on cross-validation, our models could achieve a good performance (accuracy = 73% and recall = 63%). Our results serve as a valuable practical resource in the mouse genetics research community, and also accelerate the translation of the knowledge of mouse genes into better strategies for studying human disease.

Evolutionary meta-analysis of association studies reveals ancient constraints affecting disease marker discovery

Genome-wide disease association studies contrast genetic variation between disease cohorts and healthy populations to discover single nucleotide polymorphisms (SNPs) and other genetic markers revealing underlying genetic architectures of human diseases. Despite scores of efforts over the past decade, many reproducible genetic variants that explain substantial proportions of the heritable risk of common human diseases remain undiscovered. We have conducted a multispecies genomic analysis of 5,831 putative human risk variants for more than 230 disease phenotypes reported in 2,021 studies. We find that the current approaches show a propensity for discovering disease-associated SNPs (dSNPs) at conserved genomic positions because the effect size (odds ratio) and allelic P value of genetic association of an SNP relates strongly to the evolutionary conservation of their genomic position. We propose a new measure for ranking SNPs that integrates evolutionary conservation scores and the P value (E-rank). Using published data from a large case-control study, we demonstrate that E-rank method prioritizes SNPs with a greater likelihood of bona fide and reproducible genetic disease associations, many of which may explain greater proportions of genetic variance. Therefore, long-term evolutionary histories of genomic positions offer key practical utility in reassessing data from existing disease association studies, and in the design and analysis of future studies aimed at revealing the genetic basis of common human diseases.

On the origins of Mendelian disease genes in man: the impact of gene duplication

Over 3,000 human diseases are known to be linked to heritable genetic variation, mapping to over 1,700 unique genes. Dating of the evolutionary age of these disease-associated genes has suggested that they have a tendency to be ancient, specifically coming into existence with early metazoa. The approach taken by past studies, however, assumes that the age of a disease is the same as the age of its common ancestor, ignoring the fundamental contribution of duplication events in the evolution of new genes and function. Here, we date both the common ancestor and the duplication history of known human disease-associated genes. We find that the majority of disease genes (80%) are genes that have been duplicated in their evolutionary history. Periods for which there are more disease-associated genes, for example, at the origins of bony vertebrates, are explained by the emergence of more genes at that time, and the majority of these are duplicates inferred to have arisen by whole-genome duplication. These relationships are similar for different disease types and the disease-associated gene's cellular function. This indicates that the emergence of duplication-associated diseases has been ongoing and approximately constant (relative to the retention of duplicate genes) throughout the evolution of life. This continued until approximately 390 Ma from which time relatively fewer novel genes came into existence on the human lineage, let alone disease genes. For single-copy genes associated with disease, we find that the numbers of disease genes decreases with recency. For the majority of duplicates, the disease-associated mutation is associated with just one of the duplicate copies. A universal explanation for heritable disease is, thus, that it is merely a by-product of the evolutionary process; the evolution of new genes (de novo or by duplication) results in the potential for new diseases to emerge.

Exploring the potential relevance of human-specific genes to complex disease

Although human disease genes generally tend to be evolutionarily more ancient than non-disease genes, complex disease genes appear to be represented more frequently than Mendelian disease genes among genes of more recent evolutionary origin. It is therefore proposed that the analysis of human-specific genes might provide new insights into the genetics of complex disease. Cross-comparison with the Human Gene Mutation Database (http://www.hgmd.org) revealed a number of examples of disease-causing and disease-associated mutations in putatively human-specific genes. A sizeable proportion of these were missense polymorphisms associated with complex disease. Since both human-specific genes and genes associated with complex disease have often experienced particularly rapid rates of evolutionary change, either due to weaker purifying selection or positive selection, it is proposed that a significant number of human-specific genes may play a role in complex disease.

Different level of population differentiation among human genes

BACKGROUND

During the colonization of the world, after dispersal out of African, modern humans encountered changeable environments and substantial phenotypic variations that involve diverse behaviors, lifestyles and cultures, were generated among the different modern human populations.

RESULTS

Here, we study the level of population differentiation among different populations of human genes. Intriguingly, genes involved in osteoblast development were identified as being enriched with higher FST SNPs, a result consistent with the proposed role of the skeletal system in accounting for variation among human populations. Genes involved in the development of hair follicles, where hair is produced, were also found to have higher levels of population differentiation, consistent with hair morphology being a distinctive trait among human populations. Other genes that showed higher levels of population differentiation include those involved in pigmentation, spermatid, nervous system and organ development, and some metabolic pathways, but few involved with the immune system. Disease-related genes demonstrate excessive SNPs with lower levels of population differentiation, probably due to purifying selection. Surprisingly, we find that Mendelian-disease genes appear to have a significant excessive of SNPs with high levels of population differentiation, possibly because the incidence and susceptibility of these diseases show differences among populations. As expected, microRNA regulated genes show lower levels of population differentiation due to purifying selection.

CONCLUSION

Our analysis demonstrates different level of population differentiation among human populations for different gene groups.

Broker genes in human disease

Genes that underlie human disease are important subjects of systems biology research. In the present study, we demonstrate that Mendelian and complex disease genes have distinct and consistent protein-protein interaction (PPI) properties. We show that five different network properties can be reduced to two independent metrics when applied to the human PPI network. These two metrics largely coincide with the degree (number of connections) and the clustering coefficient (the number of connections among the neighbors of a particular protein). We demonstrate that disease genes have simultaneously unusually high degree and unusually low clustering coefficient. Such genes can be described as brokers in that they connect many proteins that would not be connected otherwise. We show that these results are robust to the effect of gene age and inspection bias variation. Notably, genes identified in genome-wide association study (GWAS) have network patterns that are almost indistinguishable from the network patterns of nondisease genes and significantly different from the network patterns of complex disease genes identified through non-GWAS means. This suggests either that GWAS focused on a distinct set of diseases associated with an unusual set of genes or that mapping of GWAS-identified single nucleotide polymorphisms onto the causally affected neighboring genes is error prone.

Genes, mutations, and human inherited disease at the dawn of the age of personalized genomics

The number of reported germline mutations in human nuclear genes, either underlying or associated with inherited disease, has now exceeded 100,000 in more than 3,700 different genes. The availability of these data has both revolutionized the study of the morbid anatomy of the human genome and facilitated "personalized genomics." With approximately 300 new "inherited disease genes" (and approximately 10,000 new mutations) being identified annually, it is pertinent to ask how many "inherited disease genes" there are in the human genome, how many mutations reside within them, and where such lesions are likely to be located? To address these questions, it is necessary not only to reconsider how we define human genes but also to explore notions of gene "essentiality" and "dispensability."Answers to these questions are now emerging from recent novel insights into genome structure and function and through complete genome sequence information derived from multiple individual human genomes. However, a change in focus toward screening functional genomic elements as opposed to genes sensu stricto will be required if we are to capitalize fully on recent technical and conceptual advances and identify new types of disease-associated mutation within noncoding regions remote from the genes whose function they disrupt.

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.

Relaxed purifying selection and possibly high rate of adaptation in primate lineage-specific genes

Genes in the same organism vary in the time since their evolutionary origin. Without horizontal gene transfer, young genes are necessarily restricted to a few closely related species, whereas old genes can be broadly distributed across the phylogeny. It has been shown that young genes evolve faster than old genes; however, the evolutionary forces responsible for this pattern remain obscure. Here, we classify human–chimp protein-coding genes into different age classes, according to the breath of their phylogenetic distribution. We estimate the strength of purifying selection and the rate of adaptive selection for genes in different age classes. We find that older genes carry fewer and less frequent nonsynonymous single-nucleotide polymorphisms than younger genes suggesting that older genes experience a stronger purifying selection at the protein-coding level. We infer the distribution of fitness effects of new deleterious mutations and find that older genes have proportionally more slightly deleterious mutations and fewer nearly neutral mutations than younger genes. To investigate the role of adaptive selection of genes in different age classes, we determine the selection coefficient (γ = 2N_es) of genes using the MKPRF approach and estimate the ratio of the rate of adaptive nonsynonymous substitution to synonymous substitution (ω_A) using the DoFE method. Although the proportion of positively selected genes (γ > 0) is significantly higher in younger genes, we find no correlation between ω_A and gene age. Collectively, these results provide strong evidence that younger genes are subject to weaker purifying selection and more tenuous evidence that they also undergo adaptive evolution more frequently.