Understanding tissue expression evolution: from expression phylogeny to phylogenetic network

Cell penetrating peptides are versatile tools for enhancing multimodal uptake into cells from pest insects

Cell penetrating peptides (CPPs) are small peptides defined by their ability to deliver molecular cargo into cells. While the subject of frequent investigation for pharmaceutical drug delivery, little consideration has been given to the possibility of CPPs for use as insecticides or insecticide enhancers. Here, we characterize the entry of four fluorescently tagged CPPs into two insect cell lines and dissected midgut tissues in terms of both total quantity and mode of penetration. Fluorescent microscopy showed that substantial amounts of CPPs penetrate the plasma membrane via endosomal uptake in ovarian (Sf9) and midgut derived (AW1) lepidopteran cells and that this process was sensitive to selected endocytosis inhibitors. Differences in the quantity of uptake was observed between CPPs, and further differences were found in the ability CPP-1838 to efficiently penetrate membranes through passive diffusion. These findings were extended to primary midgut derived cells and dissected tissues suggesting that CPPs show a preference for goblet cells and that CPP-1838 shows far higher rates of penetration. CPP-1838 thus shows extraordinary abilities to penetrate cells efficiency in both a diffusional and endocytotic manner. From these results more sophisticated delivery methods based on the utilization of CPPs can be developed.

Gene family evolution underlies cell-type diversification in the hypothalamus of teleosts

Hundreds of cell types form the vertebrate brain but it is largely unknown how similar cellular repertoires are between or within species or how cell-type diversity evolves. To examine cell-type diversity across and within species, we performed single-cell RNA sequencing of ~130,000 hypothalamic cells from zebrafish (Danio rerio) and surface and cave morphs of Mexican tetra (Astyanax mexicanus). We found that over 75% of cell types were shared between zebrafish and Mexican tetra, which diverged from a common ancestor over 150 million years ago. Shared cell types displayed shifts in paralogue expression that were generated by subfunctionalization after genome duplication. Expression of terminal effector genes, such as neuropeptides, was more conserved than the expression of their associated transcriptional regulators. Species-specific cell types were enriched for the expression of species-specific genes and characterized by the neofunctionalization of expression patterns of members of recently expanded or contracted gene families. Comparisons between surface and cave morphs revealed differences in immune repertoires and transcriptional changes in neuropeptidergic cell types associated with genomic differences. The single-cell atlases presented here are a powerful resource to explore hypothalamic cell types and reveal how gene family evolution and shifts in paralogue expression contribute to cellular diversity.

Advances, challenges and opportunities of phylogenetic and social network analysis using COVID-19 data

Coronavirus disease 2019 (COVID-19) has attracted research interests from all fields. Phylogenetic and social network analyses based on connectivity between either COVID-19 patients or geographic regions and similarity between syndrome coronavirus 2 (SARS-CoV-2) sequences provide unique angles to answer public health and pharmaco-biological questions such as relationships between various SARS-CoV-2 mutants, the transmission pathways in a community and the effectiveness of prevention policies. This paper serves as a systematic review of current phylogenetic and social network analyses with applications in COVID-19 research. Challenges in current phylogenetic network analysis on SARS-CoV-2 such as unreliable inferences, sampling bias and batch effects are discussed as well as potential solutions. Social network analysis combined with epidemiology models helps to identify key transmission characteristics and measure the effectiveness of prevention and control strategies. Finally, future new directions of network analysis motivated by COVID-19 data are summarized.

TreeExp2: An Integrated Framework for Phylogenetic Transcriptome Analysis

Recent innovations of next-generation sequencing such as RNA-seq have generated an enormous amount of comparative transcriptome data, which have shed lights on our understanding of the complexity of transcriptional regulatory systems. Despite numerous RNA-seq analyses, statistical methods and computational tools designed for phylogenetic transcriptome analysis and evolution have not been well developed. In response to this need, we developed software TreeExp2 specifically for RNA-seq data. The R-package TreeExp2 has implemented a suite of advanced, recently developed methods for transcriptome evolutionary analysis. Its main functions include the ancestral transcriptome inference, estimation of the strength of expression conservation, new expression distance, and the relative expression rate test. TreeExp2 provides an integrated, statistically sound framework for phylogenetic transcriptome analysis. It will considerably enhance our analytical capability for exploring the evolution and selection at the transcriptome level. The current version of TreeExp2 is available under GPLv3 license at the Github developer site https://github.com/jingwyang/TreeExp; last accessed November 12, 2019, and its online tutorial which describes the biological theories in details and fully worked case studies with real data can be found at https://jingwyang.github.io/TreeExp-Tutorial; last accessed November 12, 2019.

Quartet-based inference of cell differentiation trees from ChIP-Seq histone modification data

Understanding cell differentiation-the process of generation of distinct cell-types-plays a pivotal role in developmental and evolutionary biology. Transcriptomic information and epigenetic marks are useful to elucidate hierarchical developmental relationships among cell-types. Standard phylogenetic approaches such as maximum parsimony, maximum likelihood and neighbor joining have previously been applied to ChIP-Seq histone modification data to infer cell-type trees, showing how diverse types of cells are related. In this study, we demonstrate the applicability and suitability of quartet-based phylogenetic tree estimation techniques for constructing cell-type trees. We propose two quartet-based pipelines for constructing cell phylogeny. Our methods were assessed for their validity in inferring hierarchical differentiation processes of various cell-types in H3K4me3, H3K27me3, H3K36me3, and H3K27ac histone mark data. We also propose a robust metric for evaluating cell-type trees.

Estimating the strength of expression conservation from high throughput RNA-seq data

Motivation

Evolution of gene across species is usually subject to the stabilizing selection to maintain the optimal expression level. While it is generally accepted that the resulting expression conservation may vary considerably among genes, statistically reliable estimation remains challenging, due to few species included in current comparative RNA-seq data with high number of unknown parameters.

Results

In this paper, we develop a gamma distribution model to describe how the strength of expression conservation (denoted by W) varies among genes. Given the high throughput RNA-seq datasets from multiple species, we then formulate an empirical Bayesian procedure to estimate W for each gene. Our case studies showed that those W-estimates are useful to study the evolutionary pattern of expression conservation.

Availability and implementation

Our method has been implemented in the R-package software, TreeExp, which is publically available at Github develop site https://github.com/hr1912/TreeExp. It involves three functions: estParaGamma, estParaQ and estParaWBayesian. The manual for software TreeExp is available at https://github.com/hr1912/TreeExp/tree/master/vignettes. For any question, one may contact Dr Hang Ruan (Hang.Ruan@uth.tmc.edu).

Pervasive Correlated Evolution in Gene Expression Shapes Cell and Tissue Type Transcriptomes

The evolution and diversification of cell types is a key means by which animal complexity evolves. Recently, hierarchical clustering and phylogenetic methods have been applied to RNA-seq data to infer cell type evolutionary history and homology. A major challenge for interpreting this data is that cell type transcriptomes may not evolve independently due to correlated changes in gene expression. This nonindependence can arise for several reasons, such as common regulatory sequences for genes expressed in multiple tissues, that is, pleiotropic effects of mutations. We develop a model to estimate the level of correlated transcriptome evolution (LCE) and apply it to different data sets. The results reveal pervasive correlated transcriptome evolution among different cell and tissue types. In general, tissues related by morphology or developmental lineage exhibit higher LCE than more distantly related tissues. Analyzing new data collected from bird skin appendages suggests that LCE decreases with the phylogenetic age of tissues compared, with recently evolved tissues exhibiting the highest LCE. Furthermore, we show correlated evolution can alter patterns of hierarchical clustering, causing different tissue types from the same species to cluster together. To identify genes that most strongly contribute to the correlated evolution signal, we performed a gene-wise estimation of LCE on a data set with ten species. Removing genes with high LCE allows for accurate reconstruction of evolutionary relationships among tissue types. Our study provides a statistical method to measure and account for correlated gene expression evolution when interpreting comparative transcriptome data.

Identification of Odorant-Binding Proteins (OBPs) and Functional Analysis of Phase-Related OBPs in the Migratory Locust

Olfactory plasticity, which is one of the major characteristics of density-dependent phase polyphenism, plays critical roles in the large-scale aggregation formation of Locusta migratoria. It is still unknown whether odorant-binding proteins (OBPs) are involved in phase-related olfactory plasticity of locusts, despite the confirmed involvement of several types of olfactory perception genes. In this study, we performed a large-scale search for OBPs and verified their expression patterns in the migratory locust. We identified 17 OBPs in the L. migratoria genome, of which 10 were novel, and we found their scattering distribution characteristics by mapping the genomic loci. Next, we revealed that these OBPs with close phylogenic relationships displayed similar tissue-specific expression profiles by a combined analysis of qRT-PCR and phylogenetic tree reconstruction. In all identified locust OBPs, seven OBPs showed differential mRNA expression levels in antenna tissue between gregarious and solitarious nymphs. Six of these seven OBPs displayed higher mRNA expression in the antennae of gregarious nymphs. The mRNA expression of LmigOBP2 and LmigOBP4 increased during gregarization and decreased during solitarization. RNAi experiments confirmed that only LmigOBP4 regulates the behavioral traits to affect gregarious behavior. These results demonstrated that OBPs also play important roles in the regulation of phase-related behavior of the locusts.

Brownian model of transcriptome evolution and phylogenetic network visualization between tissues

While phylogenetic analysis of transcriptomes of the same tissue is usually congruent with the species tree, the controversy emerges when multiple tissues are included, that is, whether species from the same tissue are clustered together, or different tissues from the same species are clustered together. Recent studies have suggested that phylogenetic network approach may shed some lights on our understanding of multi-tissue transcriptome evolution; yet the underlying evolutionary mechanism remains unclear. In this paper we develop a Brownian-based model of transcriptome evolution under the phylogenetic network that can statistically distinguish between the patterns of species-clustering and tissue-clustering. Our model can be used as a null hypothesis (neutral transcriptome evolution) for testing any correlation in tissue evolution, can be applied to cancer transcriptome evolution to study whether two tumors of an individual appeared independently or via metastasis, and can be useful to detect convergent evolution at the transcriptional level.