CpG island turnover events predict evolutionary changes in enhancer activity.

BACKGROUND: Genetic changes that modify the function of transcriptional enhancers have been linked to the evolution of biological diversity across species. Multiple studies have focused on the role of nucleotide substitutions, transposition, and insertions and deletions in altering enhancer function. CpG islands (CGIs) have recently been shown to influence enhancer activity, and here we test how their turnover across species contributes to enhancer evolution. RESULTS: We integrate maps of CGIs and enhancer activity-associated histone modifications obtained from multiple tissues in nine mammalian species and find that CGI content in enhancers is strongly associated with increased histone modification levels. CGIs show widespread turnover across species and species-specific CGIs are strongly enriched for enhancers exhibiting species-specific activity across all tissues and species. Genes associated with enhancers with species-specific CGIs show concordant biases in their expression, supporting that CGI turnover contributes to gene regulatory innovation. Our results also implicate CGI turnover in the evolution of Human Gain Enhancers (HGEs), which show increased activity in human embryonic development and may have contributed to the evolution of uniquely human traits. Using a humanized mouse model, we show that a highly conserved HGE with a large CGI absent from the mouse ortholog shows increased activity at the human CGI in the humanized mouse diencephalon. CONCLUSIONS: Collectively, our results point to CGI turnover as a mechanism driving gene regulatory changes potentially underlying trait evolution in mammals.

CpG island turnover events predict evolutionary changes in enhancer activity.

Genetic changes that modify the function of transcriptional enhancers have been linked to the evolution of biological diversity across species. Multiple studies have focused on the role of nucleotide substitutions, transposition, and insertions and deletions in altering enhancer function. Here we show that turnover of CpG islands (CGIs), which contribute to enhancer activation, is broadly associated with changes in enhancer activity across mammals, including humans. We integrated maps of CGIs and enhancer activity-associated histone modifications obtained from multiple tissues in nine mammalian species and found that CGI content in enhancers was strongly associated with increased histone modification levels. CGIs showed widespread turnover across species and species-specific CGIs were strongly enriched for enhancers exhibiting species-specific activity across all tissues and species we examined. Genes associated with enhancers with species-specific CGIs showed concordant biases in their expression, supporting that CGI turnover contributes to gene regulatory innovation. Our results also implicate CGI turnover in the evolution of Human Gain Enhancers (HGEs), which show increased activity in human embryonic development and may have contributed to the evolution of uniquely human traits. Using a humanized mouse model, we show that a highly conserved HGE with a large CGI absent from the mouse ortholog shows increased activity at the human CGI in the humanized mouse diencephalon. Collectively, our results point to CGI turnover as a mechanism driving gene regulatory changes potentially underlying trait evolution in mammals.

Modeling uniquely human gene regulatory function via targeted humanization of the mouse genome.

The evolution of uniquely human traits likely entailed changes in developmental gene regulation. Human Accelerated Regions (HARs), which include transcriptional enhancers harboring a significant excess of human-specific sequence changes, are leading candidates for driving gene regulatory modifications in human development. However, insight into whether HARs alter the level, distribution, and timing of endogenous gene expression remains limited. We examined the role of the HAR HACNS1 (HAR2) in human evolution by interrogating its molecular functions in a genetically humanized mouse model. We find that HACNS1 maintains its human-specific enhancer activity in the mouse embryo and modifies expression of Gbx2, which encodes a transcription factor, during limb development. Using single-cell RNA-sequencing, we demonstrate that Gbx2 is upregulated in the limb chondrogenic mesenchyme of HACNS1 homozygous embryos, supporting that HACNS1 alters gene expression in cell types involved in skeletal patterning. Our findings illustrate that humanized mouse models provide mechanistic insight into how HARs modified gene expression in human evolution.

IL-7 receptor alpha defines heterogeneity and signature of human effector memory CD8+ T cells in high dimensional analysis.

The IL-7 receptor alpha chain (IL-7Rα or CD127) can be differentially expressed in memory CD8+ T cells. Here we investigated whether IL-7Rα could serve as a key molecule in defining a comprehensive landscape of heterogeneity in human effector memory (EM) CD8+ T cells using high-dimensional Cytometry by Time-Of-Flight (CyTOF) and single-cell RNA-seq (scRNA-seq). IL-7Rα had diverse, but organized, expressional relationship in EM CD8+ T cells with molecules related to cell function and gene regulation, which rendered an immune landscape defining heterogeneous cell subsets. The differential expression of these molecules likely has biological implications as we found in vivo signatures of transcription factors and homeostasis cytokine receptors, including T-bet and IL-7Rα. Our findings indicate the existence of heterogeneity in human EM CD8+ T cells as defined by distinct but organized expression patterns of multiple molecules in relationship to IL-7Rα and its possible biological significance in modulating downstream events.

Author Correction: Visualizing structure and transitions in high-dimensional biological data.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Interpretable Neuron Structuring with Graph Spectral Regularization.

While neural networks are powerful approximators used to classify or embed data into lower dimensional spaces, they are often regarded as black boxes with uninterpretable features. Here we propose Graph Spectral Regularization for making hidden layers more interpretable without significantly impacting performance on the primary task. Taking inspiration from spatial organization and localization of neuron activations in biological networks, we use a graph Laplacian penalty to structure the activations within a layer. This penalty encourages activations to be smooth either on a predetermined graph or on a feature-space graph learned from the data via co-activations of a hidden layer of the neural network. We show numerous uses for this additional structure including cluster indication and visualization in biological and image data sets.

Visualizing structure and transitions in high-dimensional biological data.

The high-dimensional data created by high-throughput technologies require visualization tools that reveal data structure and patterns in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure using an information-geometric distance between data points. We compare PHATE to other tools on a variety of artificial and biological datasets, and find that it consistently preserves a range of patterns in data, including continual progressions, branches and clusters, better than other tools. We define a manifold preservation metric, which we call denoised embedding manifold preservation (DEMaP), and show that PHATE produces lower-dimensional embeddings that are quantitatively better denoised as compared to existing visualization methods. An analysis of a newly generated single-cell RNA sequencing dataset on human germ-layer differentiation demonstrates how PHATE reveals unique biological insight into the main developmental branches, including identification of three previously undescribed subpopulations. We also show that PHATE is applicable to a wide variety of data types, including mass cytometry, single-cell RNA sequencing, Hi-C and gut microbiome data.

Dissecting alterations in human CD8+ T cells with aging by high-dimensional single cell mass cytometry.

We investigated the effect of aging on the multi-dimensional characteristics and heterogeneity of human peripheral CD8+ T cells defined by the expression of a set of molecules at the single cell level using the recently developed mass cytometry or Cytometry by Time-Of-Flight (CyTOF) and computational algorithms. CD8+ T cells of young and older adults had differential expression of molecules, especially those related to cell activation and migration, permitting the clustering of young and older adults through an unbiased approach. The changes in the expression of individual molecules were collectively reflected in the altered high-dimensional profiles of CD8+ T cells in older adults as visualized by the dimensionality reduction analysis tools principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). A combination of PhenoGraph clustering and t-SNE analysis revealed heterogeneous subsets of CD8+ T cells that altered with aging. Furthermore, intermolecular quantitative relationships in CD8+ T cells appeared to change with age as determined by the computational algorithm conditional-Density Resampled Estimate of Mutual Information (DREMI). The results of our study showed that heterogeneity, multidimensional characteristics, and intermolecular quantitative relationships in human CD8+ T cells altered with age, distinctively clustering young and older adults through an unbiased approach.

Recovering Gene Interactions from Single-Cell Data Using Data Diffusion.

Single-cell RNA sequencing technologies suffer from many sources of technical noise, including under-sampling of mRNA molecules, often termed "dropout," which can severely obscure important gene-gene relationships. To address this, we developed MAGIC (Markov affinity-based graph imputation of cells), a method that shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. We validate MAGIC on several biological systems and find it effective at recovering gene-gene relationships and additional structures. Applied to the epithilial to mesenchymal transition, MAGIC reveals a phenotypic continuum, with the majority of cells residing in intermediate states that display stem-like signatures, and infers known and previously uncharacterized regulatory interactions, demonstrating that our approach can successfully uncover regulatory relations without perturbations.

Battle of the Bacteria: Characterizing the Evolutionary Advantage of Stationary Phase Growth.

Providing students with authentic research opportunities has been shown to enhance learning and increase retention in STEM majors. Accordingly, we have developed a novel microbiology lab module, which focuses on the molecular mechanisms of evolution in E. coli, by examining the growth advantage in stationary phase (GASP) phenotype. The GASP phenotype is demonstrated by growing cells into long-term stationary phase (LTSP) and then competing them against un-aged cells in a fresh culture. This module includes learning goals related to strengthening practical laboratory skills and improving student understanding of evolution. In addition, the students generate novel data regarding the effects of different environmental stresses on GASP and the relationship between evolution, genotypic change, mutation frequency, and cell stress. Pairs of students are provided with the experimental background, select a specific aspect of the growth medium to modify, and generate a hypothesis regarding how this alteration will impact the GASP phenotype. From this module, we have demonstrated that students are able to achieve the established learning goals and have produced data that has furthered our understanding of the GASP phenotype. Journal of Microbiology & Biology Education.

Comparative transcriptomics of maturity-associated color change in Hawaiian spiders.

Discontinuous variation within individuals is increasingly recognized as playing a role in diversification and ecological speciation. This study is part of an effort to investigate the molecular genetic underpinnings of adaptive radiation in Hawaiian spiders (genus Tetragnatha). This radiation is found throughout the Hawaiian Islands, showing a common pattern of evolutionary progression from older to younger islands. Moreover, the species are characterized by repeated evolution of similar ecomorphs that can be recognized on the basis of color--Green, Maroon, Large Brown, and Small Brown. However, 2 species (including T. kauaiensis from Kauai) are polyphenic, changing from 1 ecomorph (Green) to another (Maroon) at a specific developmental period. The current study focuses on the age-associated color change in the early stages of the radiation to determine whether this ancestral flexibility in phenotype may have translated into diversification of more derived taxa. We conducted a comparative analysis of transcriptome data (expressed genes) from the Maroon morph of T. kauaiensis and T. perreirai (Oahu), which exhibits a single fixed ecomorph (Maroon). Over 70 million sequence reads were generated using Illumina sequencing of messenger RNA. Using reciprocal best hit BLAST searches, 9027 orthologous genes were identified, of which 32 showed signatures of positive selection between the 2 taxa and may be involved in the loss of the ancestral developmental polyphenism and associated switch to separate monophenic ecomorphs. These results provide critical groundwork that will allow us to advance our understanding of the genomic elements associated with adaptive radiations.