Conserved proteins are fragile

Predicting evolutionary targets and parameters of gene deletion from expression data

Motivation

Gene deletion is traditionally thought of as a nonadaptive process that removes functional redundancy from genomes, such that it generally receives less attention than duplication in evolutionary turnover studies. Yet, mounting evidence suggests that deletion may promote adaptation via the "less-is-more" evolutionary hypothesis, as it often targets genes harboring unique sequences, expression profiles, and molecular functions. Hence, predicting the relative prevalence of redundant and unique functions among genes targeted by deletion, as well as the parameters underlying their evolution, can shed light on the role of gene deletion in adaptation.

Results

Here, we present CLOUDe, a suite of machine learning methods for predicting evolutionary targets of gene deletion events from expression data. Specifically, CLOUDe models expression evolution as an Ornstein-Uhlenbeck process, and uses multi-layer neural network, extreme gradient boosting, random forest, and support vector machine architectures to predict whether deleted genes are "redundant" or "unique", as well as several parameters underlying their evolution. We show that CLOUDe boasts high power and accuracy in differentiating between classes, and high accuracy and precision in estimating evolutionary parameters, with optimal performance achieved by its neural network architecture. Application of CLOUDe to empirical data from Drosophila suggests that deletion primarily targets genes with unique functions, with further analysis showing these functions to be enriched for protein deubiquitination. Thus, CLOUDe represents a key advance in learning about the role of gene deletion in functional evolution and adaptation.

Availability and implementation

CLOUDe is freely available on GitHub (https://github.com/anddssan/CLOUDe).

An integrative analysis of genotype-phenotype correlation in Charcot Marie Tooth type 2A disease with MFN2 variants: A case and systematic review

Dominant genetic variants in the mitofusin 2 (MFN2) gene lead to Charcot-Marie-Tooth type 2A (CMT2A), a neurodegenerative disease caused by genetic defects that directly damage axons. In this study, we reported a proband with a pathogenic variant in the GTPase domain of MFN2, c.494A > G (p.His165Arg). To date, at least 184 distinct MFN2 variants identified in 944 independent probands have been reported in 131 references. However, the field of medical genetics has long been challenged by how genetic variation in the MFN2 gene is associated with disease phenotypes. Here, by collating the MFN2 variant data and patient clinical information from Leiden Open Variant Database 3.0, NCBI clinvar database, and available related references in PubMed, we determined the mutation frequency, age of onset, sex ratio, and geographical distribution. Furthermore, the results of an analysis examining the relationship between variants and phenotypes from multiple genetic perspectives indicated that insertion and deletions (indels), copy number variants (CNVs), duplication variants, and nonsense mutations in single nucleotide variants (SNVs) tend to be pathogenic, and the results emphasized the importance of the GTPase domain to the structure and function of MFN2. Overall, three reliable classification methods of MFN2 genotype-phenotype associations provide insights into the prediction of CMT2A disease severity. Of course, there are still many MFN2 variants that have not been given clear clinical significance, which requires clinicians to make more accurate clinical diagnoses.

Predicting gene expression divergence between single-copy orthologs in two species

Predicting gene expression divergence is integral to understanding the emergence of new biological functions and associated traits. Whereas several sophisticated methods have been developed for this task, their applications are either limited to duplicate genes or require expression data from more than two species. Thus, here we present PiXi, the first machine learning framework for predicting gene expression divergence between single-copy orthologs in two species. PiXi models gene expression evolution as an Ornstein-Uhlenbeck process, and overlays this model with multi-layer neural network, random forest, and support vector machine architectures for making predictions. It outputs the predicted class "conserved" or "diverged" for each pair of orthologs, as well as their predicted expression optima in the two species. We show that PiXi has high power and accuracy in predicting gene expression divergence between single-copy orthologs, as well as high accuracy and precision in estimating their expression optima in the two species, across a wide range of evolutionary scenarios, with the globally best performance achieved by a multi-layer neural network. Moreover, application of our best performing PiXi predictor to empirical gene expression data from single-copy orthologs residing at different loci in two species of Drosophila reveals that approximately 23% underwent expression divergence after positional relocation. Further analysis shows that several of these "diverged" genes are involved in the electron transport chain of the mitochondrial membrane, suggesting that new chromatin environments may impact energy production in Drosophila. Thus, by providing a toolkit for predicting gene expression divergence between single-copy orthologs in two species, PiXi can shed light on the origins of novel phenotypes across diverse biological processes and study systems.

Insights into gene tissue specificity and protein-protein interactions in the context of purifying selection in humans

BACKGROUND

How much are natural selection and gene characteristics, such as the number of protein-protein interactions (PPIs), tissue specificity (𝞽), and expression level, connected?

METHODS

In order to investigate these relationships, we combined different metrics linked to genetic constraints and analyzed their distribution concerning PPIs, 𝞽 and expression levels.

RESULTS

We discovered a positive correlation between genetic constraints, PPIs, and expression levels in all tissues. On the other hand, we obtained a negative correlation between genetic constraints and 𝞽. Furthermore, the fraction of variance in PPI and 𝞽 explained by the constraints metrics is around 6% and 10%, respectively.

CONCLUSIONS

We observed that the variance of expression of tissue-specific genes seems not related to their level of selection constraints, which is the opposite of what is found on non-tissue-specific genes. Overall these observations would help to elucidate the relationship between natural selection and gene features.

Nanoceria dissolution at acidic pH by breaking off the catalytic loop

This manuscript proves the reproducibility and robustness of cerium oxide nanoparticles, nanoceria, employed as a chemical reagent with oxidizing capacity (as an electron sink) at acidic pH. Unlike nanoceria multi-enzyme-mimetic capabilities at neutral or high pH, nanoceria can behave as a stoichiometric reagent at low pH where insoluble Ce⁴⁺ ions transform into soluble Ce³⁺ in the nanocrystal that finally dissolves. This behaviour can be interpreted as enzyme-like when nanoceria is in excess with respect to the substrate. Under these conditions, the Ce³⁺/Ce⁴⁺ ratio in the NPs can easily be estimated by titration with ferrocyanide. This procedure could become a rapid assessment tool for evaluating nanoceria capacity in liquid environments.

Loss-of-function Mutations K11E or E271K Lead to Novel Tumor Suppression, Implicate Nucleolar Helicase DDX24 Oncogenicity

Purpose: Mutations (K11E or E271K) of DEAD-box RNA helicase 24 (DDX24) were related to multi-organ venous lymphatic malformation syndrome (MOVLD). However, the relationship between these mutations and DDX24-function still remains unknown. Understanding whether K11E and E271K cause "loss-of-function" or "gain-of-function" for DDX24 is significant for related diseases. DDX24 was reported to be related to tumors closely, thus this study aims to explore how K11E and E271K affect DDX24-function in tumor proliferation. Methods: Cell lines stably expressing wild-type DDX24, K11E-DDX24, E271K-DDX24, along with vector only based on Chinese hamster ovary cells (CHO) and Balb/c tumor-bearing mice models were constructed. Then immunofluorescence staining, proliferation assay and colony formation assay in vitro and ¹⁸F-FDG PET/CT-scan were performed. Finally, the tumor tissues were collected to perform transcriptome sequencing to predict the potential mechanism. Results: Contrasted with CHO-WT-DDX24, CHO-K11E-DDX24 or CHO-E271K-DDX24 showed a decreased number of nucleoli, a slower proliferation rate and a lower colony formation rate significantly. Moreover, mice, inoculated with CHO-K11E-DDX24 or CHO-E271K-DDX24 cells, showed lower tumor formation rate, slower tumor growth rate, better prognosis, reduced standard uptake value and K_i of glucose in subcutaneous tumors. Sequencing indicated CHO-K11E-DDX24 or CHO-E271K-DDX24 caused increasing expression of TNF or chemokines and alteration in immune-related signal pathways. Conclusion: K11E or E271K mutation could lead to "loss-of-function" of DDX24 in cell proliferation and tumor bearing mice, which may be acted by non-specific immune killing to inhibit tumor growth.

Learning Retention Mechanisms and Evolutionary Parameters of Duplicate Genes from Their Expression Data

Learning about the roles that duplicate genes play in the origins of novel phenotypes requires an understanding of how their functions evolve. A previous method for achieving this goal, CDROM, employs gene expression distances as proxies for functional divergence and then classifies the evolutionary mechanisms retaining duplicate genes from comparisons of these distances in a decision tree framework. However, CDROM does not account for stochastic shifts in gene expression or leverage advances in contemporary statistical learning for performing classification, nor is it capable of predicting the parameters driving duplicate gene evolution. Thus, here we develop CLOUD, a multi-layer neural network built on a model of gene expression evolution that can both classify duplicate gene retention mechanisms and predict their underlying evolutionary parameters. We show that not only is the CLOUD classifier substantially more powerful and accurate than CDROM, but that it also yields accurate parameter predictions, enabling a better understanding of the specific forces driving the evolution and long-term retention of duplicate genes. Further, application of the CLOUD classifier and predictor to empirical data from Drosophila recapitulates many previous findings about gene duplication in this lineage, showing that new functions often emerge rapidly and asymmetrically in younger duplicate gene copies, and that functional divergence is driven by strong natural selection. Hence, CLOUD represents a major advancement in classifying retention mechanisms and predicting evolutionary parameters of duplicate genes, thereby highlighting the utility of incorporating sophisticated statistical learning techniques to address long-standing questions about evolution after gene duplication.

Prospective enzymes for omega-3 PUFA biosynthesis found in endoparasitic classes within the phylum Platyhelminthes

The free-living infectious stages of macroparasites, specifically, the cercariae of trematodes (flatworms), are likely to be significant (albeit underappreciated) vectors of nutritionally important polyunsaturated fatty acids (PUFA) to consumers within aquatic food webs, and other macroparasites could serve similar roles. In the context of de novo omega-3 (n-3) PUFA biosynthesis, it was thought that most animals lack the fatty acid (FA) desaturase enzymes that convert stearic acid (18:0) into ɑ-linolenic acid (ALA; 18:3n-3), the main FA precursor for n-3 long-chain PUFA. Recently, novel sequences of these enzymes were recovered from 80 species from six invertebrate phyla, with experimental confirmation of gene function in five phyla. Given this wide distribution, and the unusual attributes of flatworm genomes, we conducted an additional search for genes for de novo n-3 PUFA in the phylum Platyhelminthes. Searches with experimentally confirmed sequences from Rotifera recovered nine relevant FA desaturase sequences from eight species in four genera in the two exclusively endoparasite classes (Trematoda and Cestoda). These results could indicate adaptations of these particular parasite species, or may reflect the uneven taxonomic coverage of sequence databases. Although additional genomic data and, particularly, experimental study of gene functionality are important future validation steps, our results indicate endoparasitic platyhelminths may have enzymes for de novo n-3 PUFA biosynthesis, thereby contributing to global PUFA production, but also representing a potential target for clinical antihelmintic applications.

Lineage-Specific Expression Divergence in Grasses Is Associated with Male Reproduction, Host-Pathogen Defense, and Domestication

Poaceae (grasses) is an agriculturally important and widely distributed family of plants with extraordinary phenotypic diversity, much of which was generated under recent lineage-specific evolution. Yet, little is known about the genes and functional modules involved in the lineage-specific divergence of grasses. Here, I address this question on a genome-wide scale by applying a novel branch-based statistic of lineage-specific expression divergence, LED, to RNA-seq data from nine tissues of the wild grass Brachypodium distachyon and its domesticated relatives Oryza sativa japonica (rice) and Sorghum bicolor (sorghum). I find that LED is generally smallest in B. distachyon and largest in O. sativa japonica, which underwent domestication earlier than S. bicolor, supporting the hypothesis that domestication may increase the rate of lineage-specific expression divergence in grasses. Moreover, in all three species, LED is positively correlated with protein-coding sequence divergence and tissue specificity, and negatively correlated with network connectivity. Further analysis reveals that genes with large LED are often primarily expressed in anther, implicating lineage-specific expression divergence in the evolution of male reproductive phenotypes. Gene ontology enrichment analysis also identifies an overrepresentation of terms related to male reproduction in the two domesticated grasses, as well as to those involved in host-pathogen defense in all three species. Last, examinations of genes with the largest LED reveal that their lineage-specific expression divergence may have contributed to antimicrobial functions in B. distachyon, to enhanced adaptation and yield during domestication in O. sativa japonica, and to defense against a widespread and devastating fungal pathogen in S. bicolor. Together, these findings suggest that lineage-specific expression divergence in grasses may increase under domestication and preferentially target rapidly evolving genes involved in male reproduction, host-pathogen defense, and the origin of domesticated phenotypes.

Natural Selection Drives Rapid Functional Evolution of Young Drosophila Duplicate Genes

Gene duplication is thought to play a major role in phenotypic evolution. Yet the forces involved in the functional divergence of young duplicate genes remain unclear. Here, we use population-genetic inference to elucidate the role of natural selection in the functional evolution of young duplicate genes in Drosophila melanogaster. We find that negative selection acts on young duplicates with ancestral functions, and positive selection on those with novel functions, suggesting that natural selection may determine whether and how young duplicate genes are retained. Moreover, evidence of natural selection is strongest in protein-coding regions and 3' UTRs of young duplicates, indicating that selection may primarily target encoded proteins and regulatory sequences specific to 3' UTRs. Further analysis reveals that natural selection acts immediately after duplication and weakens over time, possibly explaining the observed bias toward the acquisition of new functions by young, rather than old, duplicate gene copies. Last, we find an enrichment of testis-related functions in young duplicates that underwent recent positive selection, but not in young duplicates that did not undergo recent positive selection, or in old duplicates that either did or did not undergo recent positive selection. Thus, our findings reveal that natural selection is a key player in the functional evolution of young duplicate genes, acts rapidly and in a region-specific manner, and may underlie the origin of novel testis-specific phenotypes in Drosophila.

A benchmark of gene expression tissue-specificity metrics

One of the major properties of genes is their expression pattern. Notably, genes are often classified as tissue specific or housekeeping. This property is of interest to molecular evolution as an explanatory factor of, e.g. evolutionary rate, as well as a functional feature which may in itself evolve. While many different methods of measuring tissue specificity have been proposed and used for such studies, there has been no comparison or benchmarking of these methods to our knowledge, and little justification of their use. In this study, we compare nine measures of tissue specificity. Most methods were established for ESTs and microarrays, and several were later adapted to RNA-seq. We analyse their capacity to distinguish gene categories, their robustness to the choice and number of tissues used and their capture of evolutionary conservation signal.

Variation in Mutational Robustness between Different Proteins and the Predictability of Fitness Effects

Random mutations in genes from disparate protein classes may have different distributions of fitness effects (DFEs) depending on different structural, functional, and evolutionary constraints. We measured the fitness effects of 156 single mutations in the genes encoding AraC (transcription factor), AraD (enzyme), and AraE (transporter) used for bacterial growth on l-arabinose. Despite their different molecular functions these genes all had bimodal DFEs with most mutations either being neutral or strongly deleterious, providing a general expectation for the DFE. This contrasts with the unimodal DFEs previously obtained for ribosomal protein genes where most mutations were slightly deleterious. Based on theoretical considerations, we suggest that the 33-fold higher average mutational robustness of ribosomal proteins is due to stronger selection for reduced costs of translational and transcriptional errors. Whereas the large majority of synonymous mutations were deleterious for ribosomal proteins genes, no fitness effects could be detected for the AraCDE genes. Four mutations in AraC and AraE increased fitness, suggesting that slightly advantageous mutations make up a significant fraction of the DFE, but that they often escape detection due to the limited sensitivity of commonly used fitness assays. We show that the fitness effects of amino acid substitutions can be predicted based on evolutionary conservation, but those weakly deleterious mutations are less reliably detected. This suggests that large-effect mutations and the fraction of highly deleterious mutations can be computationally predicted, but that experiments are required to characterize the DFE close to neutrality, where many mutations ultimately fixed in a population will occur.

Functional Diversification after Gene Duplication: Paralog Specific Regions of Structural Disorder and Phosphorylation in p53, p63, and p73

Conformational and functional flexibility promote protein evolvability. High evolvability allows related proteins to functionally diverge and perhaps to neostructuralize. p53 is a multifunctional protein frequently referred to as the Guardian of the Genome–a hub for e.g. incoming and outgoing signals in apoptosis and DNA repair. p53 has been found to be structurally disordered, an extreme form of conformational flexibility. Here, p53, and its paralogs p63 and p73, were studied for further insights into the evolutionary dynamics of structural disorder, secondary structure, and phosphorylation. This study is focused on the post gene duplication phase for the p53 family in vertebrates, but also visits the origin of the protein family and the early domain loss and gain events. Functional divergence, measured by rapid evolutionary dynamics of protein domains, structural properties, and phosphorylation propensity, is inferred across vertebrate p53 proteins, in p63 and p73 from fish, and between the three paralogs. In particular, structurally disordered regions are redistributed among paralogs, but within clades redistribution of structural disorder also appears to be an ongoing process. Despite its deemed importance as the Guardian of the Genome, p53 is indeed a protein with high evolvability as seen not only in rearranged structural disorder, but also in fluctuating domain sequence signatures among lineages.

Global investigation of the co-evolution of MIRNA genes and microRNA targets during soybean domestication

Although the selection of coding genes during plant domestication has been well studied, the evolution of MIRNA genes (MIRs) and the interaction between microRNAs (miRNAs) and their targets in this process are poorly understood. Here, we present a genome-wide survey of the selection of MIRs and miRNA targets during soybean domestication and improvement. Our results suggest that, overall, MIRs have higher evolutionary rates than miRNA targets. Nonetheless, they do demonstrate certain similar evolutionary patterns during soybean domestication: MIRs and miRNA targets with high expression and duplication status, and with greater numbers of partners, exhibit lower nucleotide divergence than their counterparts without these characteristics, suggesting that expression level, duplication status, and miRNA-target interaction are essential for evolution of MIRs and miRNA targets. Further investigation revealed that miRNA-target pairs that are subjected to strong purifying selection have greater similarities than those that exhibited genetic diversity. Moreover, mediated by domestication and improvement, the similarities of a large number of miRNA-target pairs in cultivated soybean populations were increased compared to those in wild soybeans, whereas a small number of miRNA-target pairs exhibited decreased similarity, which may be associated with the adoption of particular domestication traits. Taken together, our results shed light on the co-evolution of MIRs and miRNA targets during soybean domestication.

Universal distribution of mutational effects on protein stability, uncoupling of protein robustness from sequence evolution and distinct evolutionary modes of prokaryotic and eukaryotic proteins

Robustness to destabilizing effects of mutations is thought of as a key factor of protein evolution. The connections between two measures of robustness, the relative core size and the computationally estimated effect of mutations on protein stability (ΔΔG), protein abundance and the selection pressure on protein-coding genes (dN/dS) were analyzed for the organisms with a large number of available protein structures including four eukaryotes, two bacteria and one archaeon. The distribution of the effects of mutations in the core on protein stability is universal and indistinguishable in eukaryotes and bacteria, centered at slightly destabilizing amino acid replacements, and with a heavy tail of more strongly destabilizing replacements. The distribution of mutational effects in the hyperthermophilic archaeon Thermococcus gammatolerans is significantly shifted toward strongly destabilizing replacements which is indicative of stronger constraints that are imposed on proteins in hyperthermophiles. The median effect of mutations is strongly, positively correlated with the relative core size, in evidence of the congruence between the two measures of protein robustness. However, both measures show only limited correlations to the expression level and selection pressure on protein-coding genes. Thus, the degree of robustness reflected in the universal distribution of mutational effects appears to be a fundamental, ancient feature of globular protein folds whereas the observed variations are largely neutral and uncoupled from short term protein evolution. A weak anticorrelation between protein core size and selection pressure is observed only for surface residues in prokaryotes but a stronger anticorrelation is observed for all residues in eukaryotic proteins. This substantial difference between proteins of prokaryotes and eukaryotes is likely to stem from the demonstrable higher compactness of prokaryotic proteins.

Genome-wide distribution comparative and composition analysis of the SSRs in Poaceae

Background

The Poaceae family is of great importance to human beings since it comprises the cereal grasses which are the main sources for human food and animal feed. With the rapid growth of genomic data from Poaceae members, comparative genomics becomes a convinent method to study genetics of diffierent species. The SSRs (Simple Sequence Repeats) are widely used markers in the studies of Poaceae for their high abundance and stability.

Results

In this study, using the genomic sequences of 9 Poaceae species, we detected 11,993,943 SSR loci and developed 6,799,910 SSR primer pairs. The results show that SSRs are distributed on all the genomic elements in grass. Hexamer is the most frequent motif and AT/TA is the most frequent motif in dimer. The abundance of the SSRs has a positive linear relationship with the recombination rate. SSR sequences in the coding regions involve a higher GC content in the Poaceae than that in the other species. SSRs of 70-80 bp in length showed the highest AT/GC base ratio among all of these loci. The result shows the highest polymorphism rate belongs to the SSRs ranged from 30 bp to 40 bp. Using all the SSR primers of Japonica, nineteen universal primers were selected and located on the genome of the grass family. The information of SSR loci, the SSR primers and the tools of mining and analyzing SSR are provided in the PSSRD (Poaceae SSR Database, http://biodb.sdau.edu.cn/pssrd/).

Conclusions

Our study and the PSSRD database provide a foundation for the comparative study in the Poaceae and it will accelerate the study on markers application, gene mapping and molecular breeding.

Pleiotropy constrains the evolution of protein but not regulatory sequences in a transcription regulatory network influencing complex social behaviors

It is increasingly apparent that genes and networks that influence complex behavior are evolutionary conserved, which is paradoxical considering that behavior is labile over evolutionary timescales. How does adaptive change in behavior arise if behavior is controlled by conserved, pleiotropic, and likely evolutionary constrained genes? Pleiotropy and connectedness are known to constrain the general rate of protein evolution, prompting some to suggest that the evolution of complex traits, including behavior, is fuelled by regulatory sequence evolution. However, we seldom have data on the strength of selection on mutations in coding and regulatory sequences, and this hinders our ability to study how pleiotropy influences coding and regulatory sequence evolution. Here we use population genomics to estimate the strength of selection on coding and regulatory mutations for a transcriptional regulatory network that influences complex behavior of honey bees. We found that replacement mutations in highly connected transcription factors and target genes experience significantly stronger negative selection relative to weakly connected transcription factors and targets. Adaptively evolving proteins were significantly more likely to reside at the periphery of the regulatory network, while proteins with signs of negative selection were near the core of the network. Interestingly, connectedness and network structure had minimal influence on the strength of selection on putative regulatory sequences for both transcription factors and their targets. Our study indicates that adaptive evolution of complex behavior can arise because of positive selection on protein-coding mutations in peripheral genes, and on regulatory sequence mutations in both transcription factors and their targets throughout the network.