Younger genes are less likely to be essential than older genes, and duplicates are less likely to be essential than singletons of the same age

The quest for the best target genes for RNAi-mediated pest control

RNA interference (RNAi) has emerged as an eco-friendly alternative to classic pesticides for pest control. This review highlights the importance of identifying the best target genes for RNAi-mediated pest control. We argue that the knowledge-based approach to predicting effective targets is limited by our current gaps of knowledge, making unbiased screening a superior method for discovering the best target processes and genes. We emphasize the recent evidence that suggests targeting conserved basic cellular processes, such as protein degradation and translation, is more effective than targeting the classic pesticide target processes. We support these claims by comparing the efficacy of previously reported RNAi target genes and classic insecticide targets with data from our genome-wide RNAi screen in the red flour beetle, Tribolium castaneum. Finally, we provide practical advice for identifying excellent target genes in other pests, where large-scale RNAi screenings are typically challenging.

Evolutionary analysis of gene ages across TADs associates chromatin topology with whole-genome duplications

Topologically associated domains (TADs) are interaction subnetworks of chromosomal regions in 3D genomes. TAD boundaries frequently coincide with genome breaks while boundary deletion is under negative selection, suggesting that TADs may facilitate genome rearrangements and evolution. We show that genes co-localize by evolutionary age in humans and mice, resulting in TADs having different proportions of younger and older genes. We observe a major transition in the age co-localization patterns between the genes born during vertebrate whole-genome duplications (WGDs) or before and those born afterward. We also find that genes recently duplicated in primates and rodents are more frequently essential when they are located in old-enriched TADs and interact with genes that last duplicated during the WGD. Therefore, the evolutionary relevance of recent genes may increase when located in TADs with established regulatory networks. Our data suggest that TADs could play a role in organizing ancestral functions and evolutionary novelty.

Mslar: Microbial synthetic lethal and rescue database

Synthetic lethality (SL) occurs when mutations in two genes together lead to cell or organism death, while a single mutation in either gene does not have a significant impact. This concept can also be extended to three or more genes for SL. Computational and experimental methods have been developed to predict and verify SL gene pairs, especially for yeast and Escherichia coli. However, there is currently a lack of a specialized platform to collect microbial SL gene pairs. Therefore, we designed a synthetic interaction database for microbial genetics that collects 13,313 SL and 2,994 Synthetic Rescue (SR) gene pairs that are reported in the literature, as well as 86,981 putative SL pairs got through homologous transfer method in 281 bacterial genomes. Our database website provides multiple functions such as search, browse, visualization, and Blast. Based on the SL interaction data in the S. cerevisiae, we review the issue of duplications' essentiality and observed that the duplicated genes and singletons have a similar ratio of being essential when we consider both individual and SL. The Microbial Synthetic Lethal and Rescue Database (Mslar) is expected to be a useful reference resource for researchers interested in the SL and SR genes of microorganisms. Mslar is open freely to everyone and available on the web at http://guolab.whu.edu.cn/Mslar/.

Feature importance network reveals novel functional relationships between biological features in Arabidopsis thaliana

Understanding how the different cellular components are working together to form a living cell requires multidisciplinary approaches combining molecular and computational biology. Machine learning shows great potential in life sciences, as it can find novel relationships between biological features. Here, we constructed a dataset of 11,801 gene features for 31,522 Arabidopsis thaliana genes and developed a machine learning workflow to identify linked features. The detected linked features are visualised as a Feature Important Network (FIN), which can be mined to reveal a variety of novel biological insights pertaining to gene function. We demonstrate how FIN can be used to generate novel insights into gene function. To make this network easily accessible to the scientific community, we present the FINder database, available at finder.plant.tools.

A pan-Zea genome map for enhancing maize improvement

Background

Maize (Zea mays L.) is at the vanguard facing the upcoming breeding challenges. However, both a super pan-genome for the Zea genus and a comprehensive genetic variation map for maize breeding are still lacking.

Results

Here, we construct an approximately 6.71-Gb pan-Zea genome that contains around 4.57-Gb non-B73 reference sequences from fragmented de novo assemblies of 721 pan-Zea individuals. We annotate a total of 58,944 pan-Zea genes and find around 44.34% of them are dispensable in the pan-Zea population. Moreover, 255,821 common structural variations are identified and genotyped in a maize association mapping panel. Further analyses reveal gene presence/absence variants and their potential roles during domestication of maize. Combining genetic analyses with multi-omics data, we demonstrate how structural variants are associated with complex agronomic traits.

Conclusions

Our results highlight the underexplored role of the pan-Zea genome and structural variations to further understand domestication of maize and explore their potential utilization in crop improvement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02742-7.

Functional compensation of mouse duplicates by their paralogs expressed in the same tissues

Analyses in a number of organisms have shown that duplicated genes are less likely to be essential than singletons. This implies that genes can often compensate for the loss of their paralogs. However, it is unclear why the loss of some duplicates can be compensated by their paralogs, whereas the loss of other duplicates cannot. Surprisingly, initial analyses in mice did not detect differences in the essentiality of duplicates and singletons. Only subsequent analyses, using larger gene knockout datasets and controlling for a number of confounding factors, did detect significant differences. Previous studies have not taken into account the tissues in which duplicates are expressed. We hypothesized that in complex organisms, in order for a gene's loss to be compensated by one or more of its paralogs, such paralogs need to be expressed in at least the same set of tissues as the lost gene. To test our hypothesis, we classified mouse duplicates into two categories based on the expression patterns of their paralogs: "compensable duplicates" (those with paralogs expressed in all the tissues in which the gene is expressed) and "non-compensable duplicates" (those whose paralogs are not expressed in all the tissues where the gene is expressed). In agreement with our hypothesis, the essentiality of non-compensable duplicates is similar to that of singletons, whereas compensable duplicates exhibit a substantially lower essentiality. Our results imply that duplicates can often compensate for the loss of their paralogs, but only if they are expressed in the same tissues. Indeed, the compensation ability is more dependent on expression patterns than on protein sequence similarity. The existence of these two kinds of duplicates with different essentialities, which has been overlooked by prior studies, may have hindered the detection of differences between singletons and duplicates.

A Simple Evolutionary Model of Genetic Robustness After Gene Duplication

When a dispensable gene is duplicated (referred to the ancestral dispensability denoted by O⁺), genetic buffering and duplicate compensation together maintain the duplicate redundancy, whereas duplicate compensation is the only mechanism when an essential gene is duplicated (referred to the ancestral essentiality denoted by O^-). To investigate these evolutionary scenarios of genetic robustness, I formulated a simple mixture model for analyzing duplicate pairs with one of the following states: double dispensable (DD), semi-dispensable (one dispensable one essential, DE), or double essential (EE). This model was applied to the yeast duplicate pairs from a whole-genome duplication (WGD) occurred about 100 million years ago (mya), and the mouse duplicate pairs from a WGD occurred about more than 500 mya. Both case studies revealed that the proportion of essentiality for those duplicates with ancestral essentiality [P_E(O^-)] was much higher than that for those with ancestral dispensability [P_E(O⁺)]. While it was negligible in the yeast duplicate pairs, P_E(O⁺) (about 20%) was shown statistically significant in the mouse duplicate pairs. These findings, together, support the hypothesis that both sub-functionalization and neo-functionalization may play some roles after gene duplication, though the former may be much faster than the later.

Functional Heterogeneity of the Young and Old Duplicate Genes in Tung Tree (Vernicia fordii)

Genes are subject to birth and death during the long evolutionary period. Here, young and old duplicate genes were identified in Vernicia fordii. We performed integrative analyses, including expression pattern, gene complexity, evolution, and functional divergence between young and old duplicate genes. Compared with young genes, old genes have higher values of Ka and Ks, lower Ka/Ks values, and lower average intrinsic structural disorder (ISD) values. Gene ontology and RNA-seq suggested that most young and old duplicate genes contained asymmetric functions. Only old duplicate genes are likely to participate in response to Fusarium wilt infection and exhibit divergent expression patterns. Our data suggest that young genes differ from older genes not only by evolutionary properties but also by their function and structure. These results highlighted the characteristics and diversification of the young and old genes in V. fordii and provided a systematic analysis of these genes in the V. fordii genome.

The fate of drought-related genes after polyploidization in Arachis hypogaea cv. Tifrunner

Drought stress affects plant growth and development. Cultivated peanut (Arachis hypogaea) was formed by a cross between A. duranensis and A. ipaensis. The drought tolerance of A. duranensis and A. ipaensis is reportedly stronger than that of cultivated peanut. However, there has been little study of drought tolerance genes in Arachis. In this study, we compared drought tolerance genes between A. hypogaea cv. Tifrunner and its diploid donors. We have observed that polyploidization does not generate more drought tolerance genes in A. hypogaea cv. Tifrunner but promotes the loss of many ancient drought tolerance genes. Although putative drought tolerance genes occurred on gene duplication events in A. hypogaea cv. Tifrunner, most copies lacked drought tolerance. These findings suggest that the loss of drought tolerance genes in A. hypogaea cv. Tifrunner could possibly result in weaker drought tolerance. In addition, we have observed that the three Arachis species stochastically lost putative drought tolerance genes. The evolution of drought tolerance genes could possibly have correlated with environmental changes. Our results enhance the current understanding of drought tolerance and polyploidy evolution in Arachis species.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-022-01198-0.

Rapid intraspecies evolution of fitness effects of yeast genes

Organisms within species have numerous genetic and phenotypic variations. Growing evidences show intraspecies variation of mutant phenotypes may be more complicated than expected. Current studies on intraspecies variations of mutant phenotypes are limited to just a few strains. This study investigated the intraspecies variation of fitness effects of 5,630 gene mutants in ten Saccharomyces cerevisiae strains using CRISPR–Cas9 screening. We found that the variability of fitness effects induced by gene disruptions is very large across different strains. Over 75% of genes affected cell fitness in a strain-specific manner to varying degrees. The strain specificity of the fitness effect of a gene is related to its evolutionary and functional properties. Subsequent analysis revealed that younger genes, especially those newly acquired in S. cerevisiae species, are more likely to be strongly strain-specific. Intriguingly, there seems to exist a ceiling of fitness effect size for strong strain-specific genes, and among them, the newly acquired genes are still evolving and have yet to reach this ceiling. Additionally, for a large proportion of protein complexes, the strain specificity profile is inconsistent among genes encoding the same complex. Taken together, these results offer a genome-wide map of intraspecies variation for fitness effect as a mutant phenotype and provide an updated insight on intraspecies phenotypic evolution.

Genomic Variations and Mutational Events Associated with Plant-Pathogen Interactions

Phytopathologists are actively researching the molecular basis of plant-pathogen interactions. The mechanisms of responses to pathogens have been studied extensively in model crop plant species and natural populations. Today, with the rapid expansion of genomic technologies such as DNA sequencing, transcriptomics, proteomics, and metabolomics, as well as the development of new methods and protocols, data analysis, and bioinformatics, it is now possible to assess the role of genetic variation in plant-microbe interactions and to understand the underlying molecular mechanisms of plant defense and microbe pathogenicity with ever-greater resolution and accuracy. Genetic variation is an important force in evolution that enables organisms to survive in stressful environments. Moreover, understanding the role of genetic variation and mutational events is essential for crop breeders to produce improved cultivars. This review focuses on genetic variations and mutational events associated with plant-pathogen interactions and discusses how these genome compartments enhance plants' and pathogens' evolutionary processes.

Known allosteric proteins have central roles in genetic disease

Allostery is a form of protein regulation, where ligands that bind sites located apart from the active site can modify the activity of the protein. The molecular mechanisms of allostery have been extensively studied, because allosteric sites are less conserved than active sites, and drugs targeting them are more specific than drugs binding the active sites. Here we quantify the importance of allostery in genetic disease. We show that 1) known allosteric proteins are central in disease networks, contribute to genetic disease and comorbidities much more than non-allosteric proteins, and there is an association between being allosteric and involvement in disease; 2) they are enriched in many major disease types like hematopoietic diseases, cardiovascular diseases, cancers, diabetes, or diseases of the central nervous system; 3) variants from cancer genome-wide association studies are enriched near allosteric proteins, indicating their importance to polygenic traits; and 4) the importance of allosteric proteins in disease is due, at least partly, to their central positions in protein-protein interaction networks, and less due to their dynamical properties.

Identifying features of genome evolution to exploit cancer vulnerabilities

Cancer treatment strategies include exploiting genetic vulnerabilities offered by synthetic lethal (SL) interactions between paralogs. In this issue of Cell Systems, De Kegel et al. (2021) apply a machine learning approach to predict robust SL paralogs in the human genome, highlighting genome evolutionary features as key predictors.

Comprehensive prediction of robust synthetic lethality between paralog pairs in cancer cell lines

Pairs of paralogs may share common functionality and, hence, display synthetic lethal interactions. As the majority of human genes have an identifiable paralog, exploiting synthetic lethality between paralogs may be a broadly applicable approach for targeting gene loss in cancer. However, only a biased subset of human paralog pairs has been tested for synthetic lethality to date. Here, by analyzing genome-wide CRISPR screens and molecular profiles of over 700 cancer cell lines, we identify features predictive of synthetic lethality between paralogs, including shared protein-protein interactions and evolutionary conservation. We develop a machine-learning classifier based on these features to predict which paralog pairs are most likely to be synthetic lethal and to explain why. We show that our classifier accurately predicts the results of combinatorial CRISPR screens in cancer cell lines and furthermore can distinguish pairs that are synthetic lethal in multiple cell lines from those that are cell-line specific. A record of this paper's transparent peer review process is included in the supplemental information.

Ancestry analysis indicates two different sets of essential genes in eukaryotic model species

Essential genes are so-called because they are crucial for organism perpetuation. Those genes are usually related to essential functions to cellular metabolism or multicellular homeostasis. Deleterious alterations on essential genes produce a spectrum of phenotypes in multicellular organisms. The effects range from the impairment of the fertilization process, disruption of fetal development, to loss of reproductive capacity. Essential genes are described as more evolutionarily conserved than non-essential genes. However, there is no consensus about the relationship between gene essentiality and gene age. Here, we identified essential genes in five model eukaryotic species (Saccharomyces cerevisiae, Schizosaccharomyces pombe, Drosophila melanogaster, Caenorhabditis elegans, and Mus musculus) and estimate their evolutionary ancestry and their network properties. We observed that essential genes, on average, are older than other genes in all species investigated. The relationship of network properties and gene essentiality convey with previous findings, showing essential genes as important nodes in biological networks. As expected, we also observed that essential orthologs shared by the five species evaluated here are old. However, all the species evaluated here have a specific set of young essential genes not shared among them. Additionally, these two groups of essential genes are involved with distinct biological functions, suggesting two sets of essential genes: (i) a set of old essential genes common to all the evaluated species, regulating basic cellular functions, and (ii) a set of young essential genes exclusive to each species, which perform specific essential functions in each species.

On the relation of gene essentiality to intron structure: a computational and deep learning approach

Essential genes have been studied by copy number variants and deletions, both associated with introns. The premise of our work is that introns of essential genes have distinct characteristic properties. We provide support for this by training a deep learning model and demonstrating that introns alone can be used to classify essentiality. The model, limited to first introns, performs at an increased level, implicating first introns in essentiality. We identify unique properties of introns of essential genes, finding that their structure protects against deletion and intron-loss events, especially centered on the first intron. We show that GC density is increased in the first introns of essential genes, allowing for increased enhancer activity, protection against deletions, and improved splice site recognition. We find that first introns of essential genes are of remarkably smaller size than their nonessential counterparts, and to protect against common 3' end deletion events, essential genes carry an increased number of (smaller) introns. To demonstrate the importance of the seven features we identified, we train a feature-based model using only these features and achieve high performance.

Region-specific expression of young small-scale duplications in the human central nervous system

BACKGROUND

The duplication of genes is one of the main genetic mechanisms that led to the gain in complexity of biological tissue. Although the implication of duplicated gene expression in brain evolution was extensively studied through comparisons between organs, their role in the regional specialization of the adult human central nervous system has not yet been well described.

RESULTS

Our work explored intra-organ expression properties of paralogs through multiple territories of the human central nervous system (CNS) using transcriptome data generated by the Genotype-Tissue Expression (GTEx) consortium. Interestingly, we found that paralogs were associated with region-specific expression in CNS, suggesting their involvement in the differentiation of these territories. Beside the influence of gene expression level on region-specificity, we observed the contribution of both duplication age and duplication type to the CNS region-specificity of paralogs. Indeed, we found that small scale duplicated genes (SSDs) and in particular ySSDs (SSDs younger than the 2 rounds of whole genome duplications) were more CNS region-specific than other paralogs. Next, by studying the two paralogs of ySSD pairs, we observed that when they were region-specific, they tend to be specific to the same region more often than for other paralogs, showing the high co-expression of ySSD pairs. The extension of this analysis to families of paralogs showed that the families with co-expressed gene members (i.e. homogeneous families) were enriched in ySSDs. Furthermore, these homogeneous families tended to be region-specific families, where the majority of their gene members were specifically expressed in the same region.

CONCLUSIONS

Overall, our study suggests the involvement of ySSDs in the differentiation of human central nervous system territories. Therefore, we show the relevance of exploring region-specific expression of paralogs at the intra-organ level.

The Transposable Element Environment of Human Genes Differs According to Their Duplication Status and Essentiality

Transposable elements (TEs) are major components of eukaryotic genomes and represent approximately 45% of the human genome. TEs can be important sources of novelty in genomes and there is increasing evidence that TEs contribute to the evolution of gene regulation in mammals. Gene duplication is an evolutionary mechanism that also provides new genetic material and opportunities to acquire new functions. To investigate how duplicated genes are maintained in genomes, here, we explored the TE environment of duplicated and singleton genes. We found that singleton genes have more short-interspersed nuclear elements and DNA transposons in their vicinity than duplicated genes, whereas long-interspersed nuclear elements and long-terminal repeat retrotransposons have accumulated more near duplicated genes. We also discovered that this result is highly associated with the degree of essentiality of the genes with an unexpected accumulation of short-interspersed nuclear elements and DNA transposons around the more-essential genes. Our results underline the importance of taking into account the TE environment of genes to better understand how duplicated genes are maintained in genomes.

Machine learning approach to gene essentiality prediction: a review

 

Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to discover relevant features that significantly classify essential genes, improve on the generalizability of prediction models across organisms, and construct a robust gold standard as the class label for the train data to enhance prediction. Findings also show that a significant limitation of the machine learning approach is predicting conditionally essential genes. The essentiality status of a gene can change due to a specific condition of the organism. This review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes. We discussed categories of features and how they contribute to the classification performance of essentiality prediction models. Five categories of features, namely, gene sequence, protein sequence, network topology, homology and gene ontology-based features, were generated for Caenorhabditis elegans to perform a comparative analysis of their essentiality prediction capacity. Gene ontology-based feature category outperformed other categories of features majorly due to its high correlation with the genes' biological functions. However, the topology feature category provided the highest discriminatory power making it more suitable for essentiality prediction. The major limiting factor of machine learning to predict essential genes conditionality is the unavailability of labeled data for interest conditions that can train a classifier. Therefore, cooperative machine learning could further exploit models that can perform well in conditional essentiality predictions.

SHORT ABSTRACT

Identification of essential genes is imperative because it provides an understanding of the core structure and function, accelerating drug targets' discovery, among other functions. Recent studies have applied machine learning to complement the experimental identification of essential genes. However, several factors are limiting the performance of machine learning approaches. This review aims to present the standard procedure and resources available for predicting essential genes in organisms, and also highlight the factors responsible for the current limitation in using machine learning for conditional gene essentiality prediction. The choice of features and ML technique was identified as an important factor to predict essential genes effectively.

OGEE v3: Online GEne Essentiality database with increased coverage of organisms and human cell lines

OGEE is an Online GEne Essentiality database. Gene essentiality is not a static and binary property, rather a context-dependent and evolvable property in all forms of life. In OGEE we collect not only experimentally tested essential and non-essential genes, but also associated gene properties that contributes to gene essentiality. We tagged conditionally essential genes that show variable essentiality statuses across datasets to highlight complex interplays between gene functions and environmental/experimental perturbations. OGEE v3 contains gene essentiality datasets for 91 species; almost doubled from 48 species in previous version. To accommodate recent advances on human cancer essential genes (as known as tumor dependency genes) that could serve as targets for cancer treatment and/or drug development, we expanded the collection of human essential genes from 16 cell lines in previous to 581. These human cancer cell lines were tested with high-throughput experiments such as CRISPR-Cas9 and RNAi; in total, 150 of which were tested by both techniques. We also included factors known to contribute to gene essentiality for these cell lines, such as genomic mutation, methylation and gene expression, along with extensive graphical visualizations for ease of understanding of these factors. OGEE v3 can be accessible freely at https://v3.ogee.info.

Essential Genes of the Parasitic Apicomplexa

Genome-scale mutagenesis screens for genes essential for apicomplexan parasite survival have been completed in three species: Plasmodium falciparum, the major human malaria parasite, Plasmodium berghei, a model rodent malaria parasite, and the more distantly related Toxoplasma gondii, the causative agent of toxoplasmosis. These three species share 2606 single-copy orthologs, 1500 of which have essentiality data in all three screens. In this review, we explore the overlap between these datasets to define the core essential genes of the phylum Apicomplexa. We further discuss the implications of these groundbreaking studies for understanding apicomplexan parasite biology, and we identify promising areas of focus for developing new pan-apicomplexan parasite interventions.

Dedicated transcriptomics combined with power analysis lead to functional understanding of genes with weak phenotypic changes in knockout lines

Systematic knockout studies in mice have shown that a large fraction of the gene replacements show no lethal or other overt phenotypes. This has led to the development of more refined analysis schemes, including physiological, behavioral, developmental and cytological tests. However, transcriptomic analyses have not yet been systematically evaluated for non-lethal knockouts. We conducted a power analysis to determine the experimental conditions under which even small changes in transcript levels can be reliably traced. We have applied this to two gene disruption lines of genes for which no function was known so far. Dedicated phenotyping tests informed by the tissues and stages of highest expression of the two genes show small effects on the tested phenotypes. For the transcriptome analysis of these stages and tissues, we used a prior power analysis to determine the number of biological replicates and the sequencing depth. We find that under these conditions, the knockouts have a significant impact on the transcriptional networks, with thousands of genes showing small transcriptional changes. GO analysis suggests that A930004D18Rik is involved in developmental processes through contributing to protein complexes, and A830005F24Rik in extracellular matrix functions. Subsampling analysis of the data reveals that the increase in the number of biological replicates was more important that increasing the sequencing depth to arrive at these results. Hence, our proof-of-principle experiment suggests that transcriptomic analysis is indeed an option to study gene functions of genes with weak or no traceable phenotypic effects and it provides the boundary conditions under which this is possible.

Knockout mice benefit the understanding of gene functions in mammals. However, it has proven difficult for many genes to identify clear phenotypes, related due to lack of sufficient assays. As Lewis Wolpert put it in a famous quote “But did you take them to the opera?”, thus metaphorically alluding to the need to extend phenotyping efforts. This insight led to the establishment of phenotyping pipelines that are nowadays routinely used to characterize knock-out lines. However, transcriptomic approaches based on RNA-Seq have been much less explored for such deep-level studies. We conducted here both, a theoretical power analysis and practical RNA-Seq experiments on two knockout lines with small phenotypic effects to investigate the parameters including sample size, sequencing depth, fold change, and dispersion. Our dedicated RNA-Seq studies discovered thousands of genes with small transcriptional changes and enriched in specific functions in both knockout lines. We find that it is more important to increase the number of samples than to increase the sequencing depth. Our work shows that a deep RNA-Seq study on knockouts is powerful for understanding gene functions in cases of weak phenotypic effects, and provides a guideline for the experimental design of such studies.

Transcriptome and translatome co-evolution in mammals

Gene expression programs define shared and species-specific phenotypes, but their evolution remains largely uncharacterized beyond the transcriptome layer¹. Here we report an analysis of the co-evolution of translatomes and transcriptomes using ribosome-profling and matched RNA-sequencing data for three organs (brain, liver and testis) in fve mammals (human, macaque, mouse, opossum and platypus) and a bird (chicken). Our within-species analyses reveal that translational regulation is widespread in the diferent organs, in particular across the spermatogenic cell types of the testis. The between-species divergence in gene expression is around 20% lower at the translatome layer than at the transcriptome layer owing to extensive buffering between the expression layers, which especially preserved old, essential and housekeeping genes. Translational upregulation specifcally counterbalanced global dosage reductions during the evolution of sex chromosomes and the efects of meiotic sex-chromosome inactivation during spermatogenesis. Despite the overall prevalence of bufering, some genes evolved faster at the translatome layer—potentially indicating adaptive changes in expression; testis tissue shows the highest fraction of such genes. Further analyses incorporating mass spectrometry proteomics data establish that the co-evolution of transcriptomes and translatomes is refected at the proteome layer. Together, our work uncovers co-evolutionary patterns and associated selective forces across the expression layers, and provides a resource for understanding their interplay in mammalian organs.

A spectrum of verticality across genes

Lateral gene transfer (LGT) has impacted prokaryotic genome evolution, yet the extent to which LGT compromises vertical evolution across individual genes and individual phyla is unknown, as are the factors that govern LGT frequency across genes. Estimating LGT frequency from tree comparisons is problematic when thousands of genomes are compared, because LGT becomes difficult to distinguish from phylogenetic artefacts. Here we report quantitative estimates for verticality across all genes and genomes, leveraging a well-known property of phylogenetic inference: phylogeny works best at the tips of trees. From terminal (tip) phylum level relationships, we calculate the verticality for 19,050,992 genes from 101,422 clusters in 5,655 prokaryotic genomes and rank them by their verticality. Among functional classes, translation, followed by nucleotide and cofactor biosynthesis, and DNA replication and repair are the most vertical. The most vertically evolving lineages are those rich in ecological specialists such as Acidithiobacilli, Chlamydiae, Chlorobi and Methanococcales. Lineages most affected by LGT are the α-, β-, γ-, and δ- classes of Proteobacteria and the Firmicutes. The 2,587 eukaryotic clusters in our sample having prokaryotic homologues fail to reject eukaryotic monophyly using the likelihood ratio test. The low verticality of α-proteobacterial and cyanobacterial genomes requires only three partners-an archaeal host, a mitochondrial symbiont, and a plastid ancestor-each with mosaic chromosomes, to directly account for the prokaryotic origin of eukaryotic genes. In terms of phylogeny, the 100 most vertically evolving prokaryotic genes are neither representative nor predictive for the remaining 97% of an average genome. In search of factors that govern LGT frequency, we find a simple but natural principle: Verticality correlates strongly with gene distribution density, LGT being least likely for intruding genes that must replace a preexisting homologue in recipient chromosomes. LGT is most likely for novel genetic material, intruding genes that encounter no competing copy.

Ancient genes can be served as pan-cancer diagnostic and prognostic biomarkers

One important challenge for cancer is efficient biomarkers monitoring its formation and developments remain greatly limited. Although the accumulated big omics data provide great opportunities to the above purpose, the biomarkers identified by the data‐driven strategy often do not work well in new datasets, which is one of the main bottlenecks limiting their utilities. Given that atavistic phenotype is generally observed in cancer cells, we have been suggested that the activity of progenitor genes in tumour could serve as an efficient cancer biomarker. For doing so, we first curated 77 progenitor genes and then proposed a quantitative score to evaluate cancer progenitorness. After applying progenitorness score to ~ 22 000 samples, 33 types of cancers from 81 datasets, this method generally performs well in the diagnosis, prognosis and therapy monitoring of cancers. This study proposed a potential pan‐cancer biomarker and revealed a significant role of atavism in the formation and development of cancers.

A Novel pH-Regulated, Unusual 603 bp Overlapping Protein Coding Gene pop Is Encoded Antisense to ompA in Escherichia coli O157:H7 (EHEC)

Antisense transcription is well known in bacteria. However, translation of antisense RNAs is typically not considered, as the implied overlapping coding at a DNA locus is assumed to be highly improbable. Therefore, such overlapping genes are systematically excluded in prokaryotic genome annotation. Here we report an exceptional 603 bp long open reading frame completely embedded in antisense to the gene of the outer membrane protein ompA. An active σ70 promoter, transcription start site (TSS), Shine-Dalgarno motif and rho-independent terminator were experimentally validated, providing evidence that this open reading frame has all the structural features of a functional gene. Furthermore, ribosomal profiling revealed translation of the mRNA, the protein was detected in Western blots and a pH-dependent phenotype conferred by the protein was shown in competitive overexpression growth experiments of a translationally arrested mutant versus wild type. We designate this novel gene pop (pH-regulated overlapping protein-coding gene), thus adding another example to the growing list of overlapping, protein coding genes in bacteria.

Computational determination of gene age and characterization of evolutionary dynamics in human

Genes originate at different evolutionary time scales and possess different ages, accordingly presenting diverse functional characteristics and reflecting distinct adaptive evolutionary innovations. In the past decades, progresses have been made in gene age identification by a variety of methods that are principally based on comparative genomics. Here we summarize methods for computational determination of gene age and evaluate the effectiveness of different computational methods for age identification. Our results show that improved age determination can be achieved by combining homolog clustering with phylogeny inference, which enables more accurate age identification in human genes. Accordingly, we characterize evolutionary dynamics of human genes based on an extremely long evolutionary time scale spanning ~4,000 million years from archaea/bacteria to human, revealing that young genes are clustered on certain chromosomes and that Mendelian disease genes (including monogenic disease and polygenic disease genes) and cancer genes exhibit divergent evolutionary origins. Taken together, deciphering genes' ages as well as their evolutionary dynamics is of fundamental significance in unveiling the underlying mechanisms during evolution and better understanding how young or new genes become indispensable integrants coupled with novel phenotypes and biological diversity.

Evolutionary balance between LRR domain loss and young NBS-LRR genes production governs disease resistance in Arachis hypogaea cv. Tifrunner

BACKGROUND

Cultivated peanut (Arachis hypogaea L.) is an important oil and protein crop, but it has low disease resistance; therefore, it is important to reveal the number, sequence features, function, and evolution of genes that confer resistance. Nucleotide-binding site-leucine-rich repeats (NBS-LRRs) are resistance genes that are involved in response to various pathogens.

RESULTS

We identified 713 full-length NBS-LRRs in A. hypogaea cv. Tifrunner. Genetic exchange events occurred on NBS-LRRs in A. hypogaea cv. Tifrunner, which were detected in the same subgenomes and also found in different subgenomes. Relaxed selection acted on NBS-LRR proteins and LRR domains in A. hypogaea cv. Tifrunner. Using quantitative trait loci (QTL), we found that NBS-LRRs were involved in response to late leaf spot, tomato spotted wilt virus, and bacterial wilt in A. duranensis (2 NBS-LRRs), A. ipaensis (39 NBS-LRRs), and A. hypogaea cv. Tifrunner (113 NBS-LRRs). In A. hypogaea cv. Tifrunner, 113 NBS-LRRs were classified as 75 young and 38 old NBS-LRRs, indicating that young NBS-LRRs were involved in response to disease after tetraploidization. However, compared to A. duranensis and A. ipaensis, fewer LRR domains were found in A. hypogaea cv. Tifrunner NBS-LRR proteins, partly explaining the lower disease resistance of the cultivated peanut.

CONCLUSIONS

Although relaxed selection acted on NBS-LRR proteins and LRR domains, LRR domains were preferentially lost in A. hypogaea cv. Tifrunner compared to A. duranensis and A. ipaensis. The QTL results suggested that young NBS-LRRs were important for resistance against diseases in A. hypogaea cv. Tifrunner. Our results provid insight into the greater susceptibility of A. hypogaea cv. Tifrunner to disease compared to A. duranensis and A. ipaensis.

New insights on human essential genes based on integrated analysis and the construction of the HEGIAP web-based platform

Essential genes are those whose loss of function compromises organism viability or results in profound loss of fitness. Recent gene-editing technologies have provided new opportunities to characterize essential genes. Here, we present an integrated analysis that comprehensively and systematically elucidates the genetic and regulatory characteristics of human essential genes. First, we found that essential genes act as ‘hubs’ in protein–protein interaction networks, chromatin structure and epigenetic modification. Second, essential genes represent conserved biological processes across species, although gene essentiality changes differently among species. Third, essential genes are important for cell development due to their discriminate transcription activity in embryo development and oncogenesis. In addition, we developed an interactive web server, the Human Essential Genes Interactive Analysis Platform (http://sysomics.com/HEGIAP/), which integrates abundant analytical tools to enable global, multidimensional interpretation of gene essentiality. Our study provides new insights that improve the understanding of human essential genes.

The characteristic of Arachis duranensis-specific genes and their potential function

Arachis species produce flowers aerially, and then grow into the ground, where they develop into fruits; a feature that is unique to Arachis species. We hypothesized that Arachis species evolved genes specifically involved in the control of aerial flowers and the formation of underground fruits. Arachis duranensis is more resistant to biotic and abiotic stressors. Here, we compared different legume species and identified Arachis duranensis-specific genes. We analyzed gene expression patterns, base substitution patterns and sequence features between genes that are conserved across legume plants and A. duranensis-specific genes. Furthermore, we tested the role of A. duranensis-specific genes during seed development, response to nematode Meloidogyne arenaria infection and drought stress. We found that A. duranensis-specific genes had characteristics of young genes. The gene expression level and breadth were lower in the A. duranensis-specific genes compared to conserved genes. The A. duranensis-specific genes had higher codon usage bias than conserved genes, and the polypeptide length and GC content at the three codon sites were lower compared to conserved genes. Of the A. duranensis-specific genes, single-copy and duplicated genes had different features. The RNA-seq result showed A. duranensis-specific genes were involved in seed development, as well as response to nematode infection and drought stress. In addition, we detected asymmetric functions in A. duranensis-specific duplicated genes in response to nematode infection and drought stress.

Old and young duplicate genes reveal different responses to environmental changes in Arachis duranensis

Old and young duplicate genes have been reported in some organisms. However, little is known about the properties of old and young duplicate genes in Arachis. Here, we have identified old and young duplicate genes in Arachis duranensis, and analyzed the evolution, gene complexity, gene expression pattern, and functional divergence between old and young duplicate genes. Our results showed different evolutionary, gene complexity and gene expression patterns, as well as differing correlations between old and young duplicate genes. Gene ontology results showed that old duplicate genes play a crucial role in lipid and amino acid biosynthesis and the oxidation-reduction process and that young duplicate genes are preferentially involved in photosynthesis and response to biotic stimulus. Transcriptome data sets revealed that most old and young duplicate genes had asymmetric function, and only a few duplicate genes exhibited symmetric function under drought and nematode stress. We found that old duplicate genes are preferentially involved in lipid and amino acid metabolism and response to abiotic stress, while young duplicate genes are likely to participate in photosynthesis and response to biotic stress. This work provides a better understanding of the evolution and functional divergence of old and young duplicate genes in A. duranensis.

Faster Evolving Primate Genes Are More Likely to Duplicate

An attractive and long-standing hypothesis regarding the evolution of genes after duplication posits that the duplication event creates new evolutionary possibilities by releasing a copy of the gene from constraint. Apparent support was found in numerous analyses, particularly, the observation of higher rates of evolution in duplicated as compared with singleton genes. Could it, instead, be that more duplicable genes (owing to mutation, fixation, or retention biases) are intrinsically faster evolving? To uncouple the measurement of rates of evolution from the determination of duplicate or singleton status, we measure the rates of evolution in singleton genes in outgroup primate lineages but classify these genes as to whether they have duplicated or not in a crown group of great apes. We find that rates of evolution are higher in duplicable genes prior to the duplication event. In part this is owing to a negative correlation between coding sequence length and rate of evolution, coupled with a bias toward smaller genes being more duplicable. The effect is masked by difference in expression rate between duplicable genes and singletons. Additionally, in contradiction to the classical assumption, we find no convincing evidence for an increase in dN/dS after duplication, nor for rate asymmetry between duplicates. We conclude that high rates of evolution of duplicated genes are not solely a consequence of the duplication event, but are rather a predictor of duplicability. These results are consistent with a model in which successful gene duplication events in mammals are skewed toward events of minimal phenotypic impact.

The Essentiality Status of Mouse Duplicate Gene Pairs Correlates with Developmental Co-Expression Patterns

During the evolution of multicellular eukaryotes, gene duplication occurs frequently to generate new genes and/or functions. A duplicated gene may have a similar function to its ancestral gene. Therefore, it may be expected that duplicated genes are less likely to be critical for the survival of an organism, since there are multiple copies of the gene rendering each individual copy redundant. In this study, we explored the developmental expression patterns of duplicate gene pairs and the relationship between development co-expression and phenotypes resulting from the knockout of duplicate genes in the mouse. We define genes that generate lethal phenotypes in single gene knockout experiments as essential genes. We found that duplicate gene pairs comprised of two essential genes tend to be expressed at different stages of development, compared to duplicate gene pairs with at least one non-essential member, showing that the timing of developmental expression affects the ability of one paralogue to compensate for the loss of the other. Gene essentiality, developmental expression and gene duplication are thus closely linked.

Intronic CNVs and gene expression variation in human populations

Introns can be extraordinarily large and they account for the majority of the DNA sequence in human genes. However, little is known about their population patterns of structural variation and their functional implication. By combining the most extensive maps of CNVs in human populations, we have found that intronic losses are the most frequent copy number variants (CNVs) in protein-coding genes in human, with 12,986 intronic deletions, affecting 4,147 genes (including 1,154 essential genes and 1,638 disease-related genes). This intronic length variation results in dozens of genes showing extreme population variability in size, with 40 genes with 10 or more different sizes and up to 150 allelic sizes. Intronic losses are frequent in evolutionarily ancient genes that are highly conserved at the protein sequence level. This result contrasts with losses overlapping exons, which are observed less often than expected by chance and almost exclusively affect primate-specific genes. An integrated analysis of CNVs and RNA-seq data showed that intronic loss can be associated with significant differences in gene expression levels in the population (CNV-eQTLs). These intronic CNV-eQTLs regions are enriched for intronic enhancers and can be associated with expression differences of other genes showing long distance intron-promoter 3D interactions. Our data suggests that intronic structural variation of protein-coding genes makes an important contribution to the variability of gene expression and splicing in human populations.

Characterization of proteins in different subcellular localizations for Escherichia coli K12

Knowing the comprehensive knowledge about the protein subcellular localization is an important step to understand the function of the proteins. Recent advances in system biology have allowed us to develop more accurate methods for characterizing the proteins at subcellular localization level. In this study, the analysis method was developed to characterize the topological properties and biological properties of the cytoplasmic proteins, inner membrane proteins, outer membrane proteins and periplasmic proteins in Escherichia coli (E. coli). Statistical significant differences were found in all topological properties and biological properties among proteins in different subcellular localizations. In addition, investigation was carried out to analyze the differences in 20 amino acid compositions for four protein categories. We also found that there were significant differences in all of the 20 amino acid compositions. These findings may be helpful for understanding the comprehensive relationship between protein subcellular localization and biological function.

Correlates of evolutionary rates in the murine sperm proteome

Background

Protein-coding genes expressed in sperm evolve at different rates. To gain deeper insight into the factors underlying this heterogeneity we examined the relative importance of a diverse set of previously described rate correlates in determining the evolution of murine sperm proteins.

Results

Using partial rank correlations we detected several major rate indicators: Phyletic gene age, numbers of protein-protein interactions, and survival essentiality emerged as particularly important rate correlates in murine sperm proteins. Tissue specificity, numbers of paralogs, and untranslated region lengths also correlate significantly with sperm genes’ evolutionary rates, albeit to a lesser extent. Multifunctionality, coding sequence or average intron lengths, and mean expression level have insignificant or virtually no independent effects on evolutionary rates in murine sperm genes. Gene ontology enrichment analyses of three equally sized murine sperm protein groups classified based on their evolutionary rates indicate strongest sperm-specific functional specialization in the most quickly evolving gene class.

Conclusions

We propose a model according to which slowly evolving murine sperm proteins tend to be constrained by factors such as survival essentiality, network connectivity, and/or broad expression. In contrast, evolutionary change may arise especially in less constrained sperm proteins, which might, moreover, be prone to specialize to reproduction-related functions. Our results should be taken into account in future studies on rate variations of reproductive genes.

Electronic supplementary material

The online version of this article (10.1186/s12862-018-1157-6) contains supplementary material, which is available to authorized users.

Characterization and fine mapping of qkc7.03: a major locus for kernel cracking in maize

A major locus conferring kernel cracking in maize was characterized and fine mapped to an interval of 416.27 kb. Meanwhile, combining the results of transcriptomic analysis, the candidate gene was inferred. Seed development requires a proper structural and physiological balance between the maternal tissues and the internal structures of the seeds. In maize, kernel cracking is a disorder in this balance that seriously limits quality and yield and is characterized by a cracked pericarp at the kernel top and endosperm everting. This study elucidated the genetic basis and characterization of kernel cracking. Primarily, a near isogenic line (NIL) with a B73 background exhibited steady kernel cracking across environments. Therefore, deprived mapping populations were developed from this NIL and its recurrent parent B73. A major locus on chromosome 7, qkc7.03, was identified to be associated with the cracking performance. According to a progeny test of recombination events, qkc7.03 was fine mapped to a physical interval of 416.27 kb. In addition, obvious differences were observed in embryo development and starch granule arrangement within the endosperm between the NIL and its recurrent parent upon the occurrence of kernel cracking. Moreover, compared to its recurrent parent, the transcriptome of the NIL showed a significantly down-regulated expression of genes related to zeins, carbohydrate synthesis and MADS-domain transcription factors. The transcriptomic analysis revealed ten annotated genes within the target region of qkc7.03, and only GRMZM5G899476 was differently expressed between the NIL and its recurrent parent, indicating that this gene might be a candidate gene for kernel cracking. The results of this study facilitate the understanding of the potential mechanism underlying kernel cracking in maize.

A Comprehensive Overview of Online Resources to Identify and Predict Bacterial Essential Genes

Genes critical for the survival or reproduction of an organism in certain circumstances are classified as essential genes. Essential genes play a significant role in deciphering the survival mechanism of life. They may be greatly applied to pharmaceutics and synthetic biology. The continuous progress of experimental method for essential gene identification has accelerated the accumulation of gene essentiality data which facilitates the study of essential genes in silico. In this article, we present some available online resources related to gene essentiality, including bioinformatic software tools for transposon sequencing (Tn-seq) analysis, essential gene databases and online services to predict bacterial essential genes. We review several computational approaches that have been used to predict essential genes, and summarize the features used for gene essentiality prediction. In addition, we evaluate the available online bacterial essential gene prediction servers based on the experimentally validated essential gene sets of 30 bacteria from DEG. This article is intended to be a quick reference guide for the microbiologists interested in the essential genes.

A database for orphan genes in Poaceae

Orphan genes refer to a group of protein-coding genes lacking recognizable homologs in the other organisms. Extensive studies have demonstrated that numerous newly sequenced genomes contain a significant number of orphan genes, which have important roles in plant's responses to the environment. Due to a lack of phylogenetic conservation, the origin of orphan genes and their functions are currently not well defined. In the present study, a Poaceae orphan genes database (POGD; http://bioinfo.ahau.edu.cn/pogd) was established to serve as a user-friendly web interface for entry browsing, searching and downloading orphan genes from various plants. Four Poaceae species, including Brachypodium distachyon, Oryza sativa, Sorghum bicolor and Zea mays, are included in the current version of POGD. The database provides gene descriptions (chromosome strands, physical location), gene product records (protein length, isoelectric point, molecular weight as well as gene and protein sequences) and functional annotations (cellular role, gene ontology category, subcellular localization prediction). Basic Local Alignment Search Tool and comparative analyses were also provided on the website. POGD will serve as a comprehensive and reliable repository, which will help uncover regulatory mechanisms of orphan genes and may assist in the development of comparative genomics in plant biology.

OGEE v2: an update of the online gene essentiality database with special focus on differentially essential genes in human cancer cell lines

OGEE is an Online GEne Essentiality database. To enhance our understanding of the essentiality of genes, in OGEE we collected experimentally tested essential and non-essential genes, as well as associated gene properties known to contribute to gene essentiality. We focus on large-scale experiments, and complement our data with text-mining results. We organized tested genes into data sets according to their sources, and tagged those with variable essentiality statuses across data sets as conditionally essential genes, intending to highlight the complex interplay between gene functions and environments/experimental perturbations. Developments since the last public release include increased numbers of species and gene essentiality data sets, inclusion of non-coding essential sequences and genes with intermediate essentiality statuses. In addition, we included 16 essentiality data sets from cancer cell lines, corresponding to 9 human cancers; with OGEE, users can easily explore the shared and differentially essential genes within and between cancer types. These genes, especially those derived from cell lines that are similar to tumor samples, could reveal the oncogenic drivers, paralogous gene expression pattern and chromosomal structure of the corresponding cancer types, and can be further screened to identify targets for cancer therapy and/or new drug development. OGEE is freely available at http://ogee.medgenius.info.

What Signatures Dominantly Associate with Gene Age?

As genes originate at different evolutionary times, they harbor distinctive genomic signatures of evolutionary ages. Although previous studies have investigated different gene age-related signatures, what signatures dominantly associate with gene age remains unresolved. Here we address this question via a combined approach of comprehensive assignment of gene ages, gene family identification, and multivariate analyses. We first provide a comprehensive and improved gene age assignment by combining homolog clustering with phylogeny inference and categorize human genes into 26 age classes spanning the whole tree of life. We then explore the dominant age-related signatures based on a collection of 10 potential signatures (including gene composition, gene length, selection pressure, expression level, connectivity in protein–protein interaction network and DNA methylation). Our results show that GC content and connectivity in protein–protein interaction network (PPIN) associate dominantly with gene age. Furthermore, we investigate the heterogeneity of dominant signatures in duplicates and singletons. We find that GC content is a consistent primary factor of gene age in duplicates and singletons, whereas PPIN is more strongly associated with gene age in singletons than in duplicates. Taken together, GC content and PPIN are two dominant signatures in close association with gene age, exhibiting heterogeneity in duplicates and singletons and presumably reflecting complex differential interplays between natural selection and mutation.

Genomic Complexity Places Less Restrictions on the Evolution of Young Coexpression Networks than Protein-Protein Interactions

The differences in evolutionary patterns of young protein–protein interactions (PPIs) among distinct species have long been a puzzle. However, based on our genome-wide analysis of available integrated experimental data, we confirm that young genes preferentially integrate into ancestral PPI networks, and that this manner is consistent in all of six model organisms with widely different levels of phenotypic complexity. We demonstrate that the level of restrictions placed on the evolution of biological networks declines with a decrease of phenotypic complexity. Compared with young PPI networks, new co-expression links have less evolutionary restrictions, so a young gene with a high possibility to be coexpressed other young genes relatively frequently emerges in the four simpler genomes among the six studied. However, it is not favorable for such young–young coexpression in terms of a young gene evolving into a coexpression hub, so the coexpression pattern could gradually decline. To explain this apparent contradiction, we suggest that young genes that are initially peripheral to networks are temporarily coexpressed with other young genes, driving functional evolution because of low selective pressure. However, as the expression levels of genes increase and they gradually develop a greater effect on fitness, young genes start to be coexpressed more with members of ancestral networks and less with other young genes. Our findings provide new insights into the evolution of biological networks.

Old genes experience stronger translational selection than young genes

Selection on synonymous codon usage for translation efficiency and/or accuracy has been identified as a widespread mechanism in many living organisms. However, it remains unknown whether translational selection associates closely with gene age and acts differentially on genes with different evolutionary ages. To address this issue, here we investigate the strength of translational selection acting on different aged genes in human. Our results show that old genes present stronger translational selection than young genes, demonstrating that translational selection correlates positively with gene age. We further explore the difference of translational selection in duplicates vs. singletons and in housekeeping vs. tissue-specific genes. We find that translational selection acts comparably in old singletons and old duplicates and stronger translational selection in old genes is contributed primarily by housekeeping genes. For young genes, contrastingly, singletons experience stronger translational selection than duplicates, presumably due to redundant function of duplicated genes during their early evolutionary stage. Taken together, our results indicate that translational selection acting on a gene would not be constant during all stages of evolution, associating closely with gene age.

Comparative analysis of housekeeping and tissue-selective genes in human based on network topologies and biological properties

Housekeeping genes are genes that are turned on most of the time in almost every tissue to maintain cellular functions. Tissue-selective genes are predominantly expressed in one or a few biologically relevant tissue types. Benefitting from the massive gene expression microarray data obtained over the past decades, the properties of housekeeping and tissue-selective genes can now be investigated on a large-scale manner. In this study, we analyzed the topological properties of housekeeping and tissue-selective genes in the protein-protein interaction (PPI) network. Furthermore, we compared the biological properties and amino acid usage between these two gene groups. The results indicated that there were significant differences in topological properties between housekeeping and tissue-selective genes in the PPI network, and housekeeping genes had higher centrality properties and may play important roles in the complex biological network environment. We also found that there were significant differences in multiple biological properties and many amino acid compositions. The functional genes enrichment and subcellular localizations analysis was also performed to investigate the characterization of housekeeping and tissue-selective genes. The results indicated that the two gene groups showed significant different enrichment in drug targets, disease genes and toxin targets, and located in different subcellular localizations. At last, the discriminations between the properties of two gene groups were measured by the F-score, and expression stage had the most discriminative index in all properties. These findings may elucidate the biological mechanisms for understanding housekeeping and tissue-selective genes and may contribute to better annotate housekeeping and tissue-selective genes in other organisms.

Using Disease-Associated Coding Sequence Variation to Investigate Functional Compensation by Human Paralogous Proteins

Gene duplication enables the functional diversification in species. It is thought that duplicated genes may be able to compensate if the function of one of the gene copies is disrupted. This possibility is extensively debated with some studies reporting proteome-wide compensation, whereas others suggest functional compensation among only recent gene duplicates or no compensation at all. We report results from a systematic molecular evolutionary analysis to test the predictions of the functional compensation hypothesis. We contrasted the density of Mendelian disease-associated single nucleotide variants (dSNVs) in proteins with no discernable paralogs (singletons) with the dSNV density in proteins found in multigene families. Under the functional compensation hypothesis, we expected to find greater numbers of dSNVs in singletons due to the lack of any compensating partners. Our analyses produced an opposite pattern; paralogs have over 35% higher dSNV density than singletons. We found that these patterns are concordant with similar differences in the rates of amino acid evolution (ie, functional constraints), as the proteins with paralogs have evolved 33% slower than singletons. Our evolutionary constraint explanation is robust to differences in family sizes, ages (young vs. old duplicates), and degrees of amino acid sequence similarities among paralogs. Therefore, disease-associated human variation does not exhibit significant signals of functional compensation among paralogous proteins, but rather an evolutionary constraint hypothesis provides a better explanation for the observed patterns of disease-associated and neutral polymorphisms in the human genome.

Whole-Genome Resequencing Reveals Extensive Natural Variation in the Model Green Alga Chlamydomonas reinhardtii

We performed whole-genome resequencing of 12 field isolates and eight commonly studied laboratory strains of the model organism Chlamydomonas reinhardtii to characterize genomic diversity and provide a resource for studies of natural variation. Our data support previous observations that Chlamydomonas is among the most diverse eukaryotic species. Nucleotide diversity is ∼3% and is geographically structured in North America with some evidence of admixture among sampling locales. Examination of predicted loss-of-function mutations in field isolates indicates conservation of genes associated with core cellular functions, while genes in large gene families and poorly characterized genes show a greater incidence of major effect mutations. De novo assembly of unmapped reads recovered genes in the field isolates that are absent from the CC-503 assembly. The laboratory reference strains show a genomic pattern of polymorphism consistent with their origin as the recombinant progeny of a diploid zygospore. Large duplications or amplifications are a prominent feature of laboratory strains and appear to have originated under laboratory culture. Extensive natural variation offers a new source of genetic diversity for studies of Chlamydomonas, including naturally occurring alleles that may prove useful in studies of gene function and the dissection of quantitative genetic traits.

Identifying and characterising key alternative splicing events in Drosophila development

BACKGROUND

In complex Metazoans a given gene frequently codes for multiple protein isoforms, through processes such as alternative splicing. Large scale functional annotation of these isoforms is a key challenge for functional genomics. This annotation gap is increasing with the large numbers of multi transcript genes being identified by technologies such as RNASeq. Furthermore attempts to characterise the functions of splicing in an organism are complicated by the difficulty in distinguishing functional isoforms from those produced by splicing errors or transcription noise. Tools to help prioritise candidate isoforms for testing are largely absent.

RESULTS

In this study we implement a Time-course Switch (TS) score for ranking isoforms by their likelihood of producing additional functions based on their developmental expression profiles, as reported by modENCODE. The TS score allows us to better investigate functional roles of different isoforms expressed in multi transcript genes. From this analysis, we find that isoforms with high TS scores have sequence feature changes consistent with more deterministic splicing and functional changes and tend to gain domains or whole exons which could carry additional functions. Furthermore these functions appear to be particularly important for essential regulatory roles, establishing functional isoform switching as key for regulatory processes. Based on the TS score we develop a Transcript Annotations Pipeline for Alternative Splicing (TAPAS) that identifies functional neighbourhoods of potentially interesting isoforms.

CONCLUSIONS

We have identified a subset of protein isoforms which appear to have high functional significance, particularly in regulation. This has been made possible through the development of novel methods that make use of transcript expression profiles. The methods and analyses we present here represent important first steps in the development of tools to address the near complete lack of isoform specific function annotation. In turn the tools allow us to better characterise the regulatory functions of alternative splicing in more detail.

Considerations for the use of transcriptomics in identifying the ‘genes that matter’ for environmental adaptation

Transcriptomics has emerged as a powerful approach for exploring physiological responses to the environment. However, like any other experimental approach, transcriptomics has its limitations. Transcriptomics has been criticized as an inappropriate method to identify genes with large impacts on adaptive responses to the environment because: (1) genes with large impacts on fitness are rare; (2) a large change in gene expression does not necessarily equate to a large effect on fitness; and (3) protein activity is most relevant to fitness, and mRNA abundance is an unreliable indicator of protein activity. In this review, these criticisms are re-evaluated in the context of recent systems-level experiments that provide new insight into the relationship between gene expression and fitness during environmental stress. In general, these criticisms remain valid today, and indicate that exclusively using transcriptomics to screen for genes that underlie environmental adaptation will overlook constitutively expressed regulatory genes that play major roles in setting tolerance limits. Standard practices in transcriptomic data analysis pipelines may also be limiting insight by prioritizing highly differentially expressed and conserved genes over those genes that undergo moderate fold-changes and cannot be annotated. While these data certainly do not undermine the continued and widespread use of transcriptomics within environmental physiology, they do highlight the types of research questions for which transcriptomics is best suited and the need for more gene functional analyses. Such information is pertinent at a time when transcriptomics has become increasingly tractable and many researchers may be contemplating integrating transcriptomics into their research programs.

Investigating different duplication pattern of essential genes in mouse and human

Gene duplication is one of the major driving forces shaping genome and organism evolution and thought to be itself regulated by some intrinsic properties of the gene. Comparing the essential genes among mouse and human, we observed that the essential genes avoid duplication in mouse while prefer to remain duplicated in humans. In this study, we wanted to explore the reasons behind such differences in gene essentiality by cross-species comparison of human and mouse. Moreover, we examined essential genes that are duplicated in humans are functionally more redundant than that in mouse. The proportion of paralog pseudogenization of essential genes is higher in mouse than that of humans. These duplicates of essential genes are under stringent dosage regulation in human than in mouse. We also observed slower evolutionary rate in the paralogs of human essential genes than the mouse counterpart. Together, these results clearly indicate that human essential genes are retained as duplicates to serve as backed up copies that may shield themselves from harmful mutations.

Characterization of proteins in S. cerevisiae with subcellular localizations

Acquiring comprehensive knowledge of protein in various subcellular localizations is one of the fundamental goals in cell biology and proteomics.