Out of the testis, into the ovary: biased outcomes of gene duplication and deletion in Drosophila

New genes driven by segmental duplications share a testis-specific expression pattern in the chromosome-level genome assembly of tree sparrow

Based on a chromosome-level genome assembly, a burst of new genes with different structures but a similar testis-specific expression pattern was detected in tree sparrow.

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).

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.

Where are all the egg genes?

Complementary forward and reverse genetic approaches in several model systems have resulted in a recent burst of fertilization gene discovery. The number of genetically validated gamete surface molecules have more than doubled in the last few years. All the genetically validated sperm fertilization genes encode transmembrane or secreted molecules. Curiously, the discovery of genes that encode oocyte molecules have fallen behind that of sperm genes. This review discusses potential experimental biases and inherent biological reasons that could slow egg fertilization gene discovery. Finally, we shed light on current strategies to identify genes that may result in further identification of egg fertilization genes.

Retrogene Duplication and Expression Patterns Shaped by the Evolution of Sex Chromosomes in Malaria Mosquitoes

Genes that originate during evolution are an important source of novel biological functions. Retrogenes are functional copies of genes produced by retroduplication and as such are located in different genomic positions. To investigate retroposition patterns and retrogene expression, we computationally identified interchromosomal retroduplication events in nine portions of the phylogenetic history of malaria mosquitoes, making use of species that do or do not have classical sex chromosomes to test the roles of sex-linkage. We found 40 interchromosomal events and a significant excess of retroduplications from the X chromosome to autosomes among a set of young retrogenes. These young retroposition events occurred within the last 100 million years in lineages where all species possessed differentiated sex chromosomes. An analysis of available microarray and RNA-seq expression data for Anopheles gambiae showed that many of the young retrogenes evolved male-biased expression in the reproductive organs. Young autosomal retrogenes with increased meiotic or postmeiotic expression in the testes tend to be male biased. In contrast, older retrogenes, i.e., in lineages with undifferentiated sex chromosomes, do not show this particular chromosomal bias and are enriched for female-biased expression in reproductive organs. Our reverse-transcription PCR data indicates that most of the youngest retrogenes, which originated within the last 47.6 million years in the subgenus Cellia, evolved non-uniform expression patterns across body parts in the males and females of An. coluzzii. Finally, gene annotation revealed that mitochondrial function is a prominent feature of the young autosomal retrogenes. We conclude that mRNA-mediated gene duplication has produced a set of genes that contribute to mosquito reproductive functions and that different biases are revealed after the sex chromosomes evolve. Overall, these results suggest potential roles for the evolution of meiotic sex chromosome inactivation in males and of sexually antagonistic conflict related to mitochondrial energy function as the main selective pressures for X-to-autosome gene reduplication and testis-biased expression in these mosquito lineages.

Transcription Factors Drive Opposite Relationships between Gene Age and Tissue Specificity in Male and Female Drosophila Gonads

Evolutionarily young genes are usually preferentially expressed in the testis across species. Although it is known that older genes are generally more broadly expressed than younger genes, the properties that shaped this pattern are unknown. Older genes may gain expression across other tissues uniformly, or faster in certain tissues than others. Using Drosophila gene expression data, we confirmed previous findings that younger genes are disproportionately testis biased and older genes are disproportionately ovary biased. We found that the relationship between gene age and expression is stronger in the ovary than any other tissue and weakest in testis. We performed ATAC-seq on Drosophila testis and found that although genes of all ages are more likely to have open promoter chromatin in testis than in ovary, promoter chromatin alone does not explain the ovary bias of older genes. Instead, we found that upstream transcription factor (TF) expression is highly predictive of gene expression in ovary but not in testis. In the ovary, TF expression is more predictive of gene expression than open promoter chromatin, whereas testis gene expression is similarly influenced by both TF expression and open promoter chromatin. We propose that the testis is uniquely able to express younger genes controlled by relatively few TFs, whereas older genes with more TF partners are broadly expressed with peak expression most likely in the ovary. The testis allows widespread baseline expression that is relatively unresponsive to regulatory changes, whereas the ovary transcriptome is more responsive to trans-regulation and has a higher ceiling for gene expression.

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.

Small Interfering RNAs and RNA Therapeutics in Cardiovascular Diseases

Ribonucleic acid (RNA) is being exploited and understood in its many aspects of function and structure for development of valuable tools in the therapeutics of various diseases such as cardiovascular etc. The expanded knowledge regarding function of RNA in the genomics and inside the cell has dramatically changed the therapeutic strategies in the past few years. RNA has become a spotlight of attention for developing novel therapeutic schemes and hence variety of therapeutic strategies is being coming into the picture that includes RNA interference, use of aptamers, role of microRNA (miRNA) that can alter the complex gene expression patterns. It is due to the fact that RNA offers various advantages in disease management as it can be edited and modified in its various forms such as secondary and tertiary structures. Although scientists are in process of manufacturing RNA-targeting therapies using variety of endogenous gene silencing regulators, Small interfering RNAs (Si RNAs), aptamers and microRNA for cardiovascular diseases yet the development of a novel, risk free therapeutic strategy is a major challenge and need of the hour in cardiovascular medicine. In this regard these agents are required to overcome pleothra of barriers such as stability of drug targets, immunogenicity, adequate binding, targeted delivery etc. to become effective drugs. Recent years have witnessed the progress of RNA therapeutic strategies in cardiovascular diseases that are likely to significantly expand the cardiovascular therapeutic repertoire within the next decade. The present manuscript has been compiled to summarize various approaches of siRNA based therapies in cardiovascular diseases along with the advantages, outcomes and limitations if any in this regard. In addition, the future prospects of RNA therapeutic modalities in cardiovascular diseases are summarized.