Clonal Lineage Tracing with Somatic Delivery of Recordable Barcodes Reveals Migration Histories of Metastatic Prostate Cancer.

The patterns by which primary tumors spread to metastatic sites remain poorly understood. Here, we define patterns of metastatic seeding in prostate cancer (PCa) using a novel injection-based mouse model - EvoCaP (Evolution in Cancer of the Prostate), featuring aggressive metastatic cancer to bone, liver, lungs, and lymph nodes. To define migration histories between primary and metastatic sites, we used our EvoTraceR pipeline to track distinct tumor clones containing recordable barcodes. We detected widespread intratumoral heterogeneity from the primary tumor in metastatic seeding, with few clonal populations (CPs) instigating most migration. Metastasis-to-metastasis seeding was uncommon, as most cells remained confined within the tissue. Migration patterns in our model were congruent with human PCa seeding topologies. Our findings support the view of metastatic PCa as a systemic disease driven by waves of aggressive clones expanding their niche, infrequently overcoming constraints that otherwise keep them confined in the primary or metastatic site.

Single-Cell Transcription Mapping of Murine and Human Mammary Organoids Responses to Female Hormones.

During female adolescence and pregnancy, rising levels of hormones result in a cyclic source of signals that control the development of mammary tissue. While such alterations are well understood from a whole-gland perspective, the alterations that such hormones bring to organoid cultures derived from mammary glands have yet to be fully mapped. This is of special importance given that organoids are considered suitable systems to understand cross species breast development. Here we utilized single-cell transcriptional profiling to delineate responses of murine and human normal breast organoid systems to female hormones across evolutionary distinct species. Collectively, our study represents a molecular atlas of epithelial dynamics in response to estrogen and pregnancy hormones.

Long-Read Sequencing Reveals Rapid Evolution of Immunity- and Cancer-Related Genes in Bats.

Bats are exceptional among mammals for their powered flight, extended lifespans, and robust immune systems and therefore have been of particular interest in comparative genomics. Using the Oxford Nanopore Technologies long-read platform, we sequenced the genomes of two bat species with key phylogenetic positions, the Jamaican fruit bat (Artibeus jamaicensis) and the Mesoamerican mustached bat (Pteronotus mesoamericanus), and carried out a comprehensive comparative genomic analysis with a diverse collection of bats and other mammals. The high-quality, long-read genome assemblies revealed a contraction of interferon (IFN)-α at the immunity-related type I IFN locus in bats, resulting in a shift in relative IFN-ω and IFN-α copy numbers. Contradicting previous hypotheses of constitutive expression of IFN-α being a feature of the bat immune system, three bat species lost all IFN-α genes. This shift to IFN-ω could contribute to the increased viral tolerance that has made bats a common reservoir for viruses that can be transmitted to humans. Antiviral genes stimulated by type I IFNs also showed evidence of rapid evolution, including a lineage-specific duplication of IFN-induced transmembrane genes and positive selection in IFIT2. In addition, 33 tumor suppressors and 6 DNA-repair genes showed signs of positive selection, perhaps contributing to increased longevity and reduced cancer rates in bats. The robust immune systems of bats rely on both bat-wide and lineage-specific evolution in the immune gene repertoire, suggesting diverse immune strategies. Our study provides new genomic resources for bats and sheds new light on the extraordinary molecular evolution in this critically important group of mammals.

DNA-sequence and epigenomic determinants of local rates of transcription elongation.

Across all branches of life, transcription elongation is a crucial, regulated phase in gene expression. Many recent studies in eukaryotes have focused on the regulation of promoter-proximal pausing of RNA Polymerase II (Pol II), but rates of productive elongation also vary substantially throughout the gene body, both within and across genes. Here, we introduce a probabilistic model for systematically evaluating potential determinants of the local elongation rate based on nascent RNA sequencing (NRS) data. Our model is derived from a unified model for both the kinetics of Pol II movement along the DNA template and the generation of NRS read counts at steady state. It allows for a continuously variable elongation rate along the gene body, with the rate at each nucleotide defined by a generalized linear relationship with nearby genomic and epigenomic features. High-dimensional feature vectors are accommodated through a sparse-regression extension. We show with simulations that the model allows accurate detection of associated features and accurate prediction of local elongation rates. In an analysis of public PRO-seq and epigenomic data, we identify several features that are strongly associated with reductions in the local elongation rate, including DNA methylation, splice sites, RNA stem-loops, CTCF binding sites, and several histone marks, including H3K36me3 and H4K20me1. By contrast, low-complexity sequences and H3K79me2 marks are associated with increases in elongation rate. In an analysis of DNA k-mers, we find that cytosine nucleotides are strongly associated with reductions in local elongation rate, particularly when preceded by guanines and followed by adenines or thymines. Increases in elongation rate are associated with thymines and A+T-rich k-mers. These associations are generally shared across cell types, and by considering them our model is effective at predicting features of held-out PRO-seq data. Overall, our analysis is the first to permit genome-wide predictions of relative nucleotide-specific elongation rates based on complex sets of genomic and epigenomic covariates. We have made predictions available for the K562, CD14+, MCF-7, and HeLa-S3 cell types in a UCSC Genome Browser track.

ACE: a probabilistic model for characterizing gene-level essentiality in CRISPR screens.

High-throughput CRISPR-Cas9 knockout screens are widely used to evaluate gene essentiality in cancer research. Here we introduce a probabilistic modeling framework, Analysis of CRISPR-based Essentiality (ACE), that accounts for multiple sources of variation in CRISPR-Cas9 screens and enables new statistical tests for essentiality. We show using simulations that ACE is effective at predicting both absolute and differential essentiality. When applied to publicly available data, ACE identifies known and novel candidates for genotype-specific essentiality, including RNA m6-A methyltransferases that exhibit enhanced essentiality in the presence of inactivating TP53 mutations. ACE provides a robust framework for identifying genes responsive to subtype-specific therapeutic targeting.

Characterization of Gene Expression Signatures for the Identification of Cellular Heterogeneity in the Developing Mammary Gland.

The developing mammary gland depends on several transcription-dependent networks to define cellular identities and differentiation trajectories. Recent technological advancements that allow for single-cell profiling of gene expression have provided an initial picture into the epithelial cellular heterogeneity across the diverse stages of gland maturation. Still, a deeper dive into expanded molecular signatures would improve our understanding of the diversity of mammary epithelial and non-epithelial cellular populations across different tissue developmental stages, mouse strains and mammalian species. Here, we combined differential mammary gland fractionation approaches and transcriptional profiles obtained from FACS-isolated mammary cells to improve our definitions of mammary-resident, cellular identities at the single-cell level. Our approach yielded a series of expression signatures that illustrate the heterogeneity of mammary epithelial cells, specifically those of the luminal fate, and uncovered transcriptional changes to their lineage-defined, cellular states that are induced during gland development. Our analysis also provided molecular signatures that identified non-epithelial mammary cells, including adipocytes, fibroblasts and rare immune cells. Lastly, we extended our study to elucidate expression signatures of human, breast-resident cells, a strategy that allowed for the cross-species comparison of mammary epithelial identities. Collectively, our approach improved the existing signatures of normal mammary epithelial cells, as well as elucidated the diversity of non-epithelial cells in murine and human breast tissue. Our study provides a useful resource for future studies that use single-cell molecular profiling strategies to understand normal and malignant breast development.

Genomic islands of differentiation in a rapid avian radiation have been driven by recent selective sweeps.

Numerous studies of emerging species have identified genomic "islands" of elevated differentiation against a background of relative homogeneity. The causes of these islands remain unclear, however, with some signs pointing toward "speciation genes" that locally restrict gene flow and others suggesting selective sweeps that have occurred within nascent species after speciation. Here, we examine this question through the lens of genome sequence data for five species of southern capuchino seedeaters, finch-like birds from South America that have undergone a species radiation during the last ∼50,000 generations. By applying newly developed statistical methods for ancestral recombination graph inference and machine-learning methods for the prediction of selective sweeps, we show that previously identified islands of differentiation in these birds appear to be generally associated with relatively recent, species-specific selective sweeps, most of which are predicted to be soft sweeps acting on standing genetic variation. Many of these sweeps coincide with genes associated with melanin-based variation in plumage, suggesting a prominent role for sexual selection. At the same time, a few loci also exhibit indications of possible selection against gene flow. These observations shed light on the complex manner in which natural selection shapes genome sequences during speciation.

Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease.

A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here, we introduce a unified population-genetic and machine-learning model, called Linear Allele-Specific Selection InferencE (LASSIE), for estimating the fitness effects of all observed and potential single-nucleotide variants, based on polymorphism data and predictive genomic features. We applied LASSIE to 51 high-coverage genome sequences annotated with 33 genomic features and constructed a map of allele-specific selection coefficients across all protein-coding sequences in the human genome. This map is generally consistent with previous inferences of the bulk distribution of fitness effects but reveals pervasive weak negative selection against synonymous mutations. In addition, the estimated selection coefficients are highly predictive of inherited pathogenic variants and cancer driver mutations, outperforming state-of-the-art variant prioritization methods. By contrasting our estimated model with ultrahigh coverage ExAC exome-sequencing data, we identified 1118 genes under unusually strong negative selection, which tend to be exclusively expressed in the central nervous system or associated with autism spectrum disorder, as well as 773 genes under unusually weak selection, which tend to be associated with metabolism. This combination of classical population genetic theory with modern machine-learning and large-scale genomic data is a powerful paradigm for the study of both human evolution and disease.

Nascent RNA sequencing reveals a dynamic global transcriptional response at genes and enhancers to the natural medicinal compound celastrol.

Most studies of responses to transcriptional stimuli measure changes in cellular mRNA concentrations. By sequencing nascent RNA instead, it is possible to detect changes in transcription in minutes rather than hours and thereby distinguish primary from secondary responses to regulatory signals. Here, we describe the use of PRO-seq to characterize the immediate transcriptional response in human cells to celastrol, a compound derived from traditional Chinese medicine that has potent anti-inflammatory, tumor-inhibitory, and obesity-controlling effects. Celastrol is known to elicit a cellular stress response resembling the response to heat shock, but the transcriptional basis of this response remains unclear. Our analysis of PRO-seq data for K562 cells reveals dramatic transcriptional effects soon after celastrol treatment at a broad collection of both coding and noncoding transcription units. This transcriptional response occurred in two major waves, one within 10 min, and a second 40-60 min after treatment. Transcriptional activity was generally repressed by celastrol, but one distinct group of genes, enriched for roles in the heat shock response, displayed strong activation. Using a regression approach, we identified key transcription factors that appear to drive these transcriptional responses, including members of the E2F and RFX families. We also found sequence-based evidence that particular transcription factors drive the activation of enhancers. We observed increased polymerase pausing at both genes and enhancers, suggesting that pause release may be widely inhibited during the celastrol response. Our study demonstrates that a careful analysis of PRO-seq time-course data can disentangle key aspects of a complex transcriptional response, and it provides new insights into the activity of a powerful pharmacological agent.

Distinguishing noise from signal in patterns of genomic divergence in a highly polymorphic avian radiation.

Recently diverged taxa provide the opportunity to search for the genetic basis of the phenotypes that distinguish them. Genomic scans aim to identify loci that are diverged with respect to an otherwise weakly differentiated genetic background. These loci are candidates for being past targets of selection because they behave differently from the rest of the genome that has either not yet differentiated or that may cross species barriers through introgressive hybridization. Here we use a reduced-representation genomic approach to explore divergence among six species of southern capuchino seedeaters, a group of recently radiated sympatric passerine birds in the genus Sporophila. For the first time in these taxa, we discovered a small proportion of markers that appeared differentiated among species. However, when assessing the significance of these signatures of divergence, we found that similar patterns can also be recovered from random grouping of individuals representing different species. A detailed demographic inference indicates that genetic differences among Sporophila species could be the consequence of neutral processes, which include a very large ancestral effective population size that accentuates the effects of incomplete lineage sorting. As these neutral phenomena can generate genomic scan patterns that mimic those of markers involved in speciation and phenotypic differentiation, they highlight the need for caution when ascertaining and interpreting differentiated markers between species, especially when large numbers of markers are surveyed. Our study provides new insights into the demography of the southern capuchino radiation and proposes controls to distinguish signal from noise in similar genomic scans.

The draft genome sequence of the ferret (Mustela putorius furo) facilitates study of human respiratory disease.

The domestic ferret (Mustela putorius furo) is an important animal model for multiple human respiratory diseases. It is considered the 'gold standard' for modeling human influenza virus infection and transmission. Here we describe the 2.41 Gb draft genome assembly of the domestic ferret, constituting 2.28 Gb of sequence plus gaps. We annotated 19,910 protein-coding genes on this assembly using RNA-seq data from 21 ferret tissues. We characterized the ferret host response to two influenza virus infections by RNA-seq analysis of 42 ferret samples from influenza time-course data and showed distinct signatures in ferret trachea and lung tissues specific to 1918 or 2009 human pandemic influenza virus infections. Using microarray data from 16 ferret samples reflecting cystic fibrosis disease progression, we showed that transcriptional changes in the CFTR-knockout ferret lung reflect pathways of early disease that cannot be readily studied in human infants with cystic fibrosis disease.

Signaling pathways differentially affect RNA polymerase II initiation, pausing, and elongation rate in cells.

RNA polymerase II (Pol II) transcribes hundreds of kilobases of DNA, limiting the production of mRNAs and lncRNAs. We used global run-on sequencing (GRO-seq) to measure the rates of transcription by Pol II following gene activation. Elongation rates vary as much as 4-fold at different genomic loci and in response to two distinct cellular signaling pathways (i.e., 17ß-estradiol [E2] and TNF-α). The rates are slowest near the promoter and increase during the first ~15 kb transcribed. Gene body elongation rates correlate with Pol II density, resulting in systematically higher rates of transcript production at genes with higher Pol II density. Pol II dynamics following short inductions indicate that E2 stimulates gene expression by increasing Pol II initiation, whereas TNF-α reduces Pol II residence time at pause sites. Collectively, our results identify previously uncharacterized variation in the rate of transcription and highlight elongation as an important, variable, and regulated rate-limiting step during transcription.

A rapid, extensive, and transient transcriptional response to estrogen signaling in breast cancer cells.

We report the immediate effects of estrogen signaling on the transcriptome of breast cancer cells using global run-on and sequencing (GRO-seq). The data were analyzed using a new bioinformatic approach that allowed us to identify transcripts directly from the GRO-seq data. We found that estrogen signaling directly regulates a strikingly large fraction of the transcriptome in a rapid, robust, and unexpectedly transient manner. In addition to protein-coding genes, estrogen regulates the distribution and activity of all three RNA polymerases and virtually every class of noncoding RNA that has been described to date. We also identified a large number of previously undetected estrogen-regulated intergenic transcripts, many of which are found proximal to estrogen receptor binding sites. Collectively, our results provide the most comprehensive measurement of the primary and immediate estrogen effects to date and a resource for understanding rapid signal-dependent transcription in other systems.

Widespread regulatory activity of vertebrate microRNA* species.

An obligate intermediate during microRNA (miRNA) biogenesis is an ~22-nucleotide RNA duplex, from which the mature miRNA is preferentially incorporated into a silencing complex. Its partner miRNA* species is generally regarded as a passenger RNA, whose regulatory capacity has not been systematically examined in vertebrates. Our bioinformatic analyses demonstrate that a substantial fraction of miRNA* species are stringently conserved over vertebrate evolution, collectively exhibit greatest conservation in their seed regions, and define complementary motifs whose conservation across vertebrate 3'-UTR evolution is statistically significant. Functional tests of 22 miRNA expression constructs revealed that a majority could repress both miRNA and miRNA* perfect match reporters, and the ratio of miRNA:miRNA* sensor repression was correlated with the endogenous ratio of miRNA:miRNA* reads. Analysis of microarray data provided transcriptome-wide evidence for the regulation of seed-matched targets for both mature and star strand species of several miRNAs relevant to oncogenesis, including mir-17, mir-34a, and mir-19. Finally, 3'-UTR sensor assays and mutagenesis tests confirmed direct repression of five miR-19* targets via star seed sites. Overall, our data demonstrate that miRNA* species have demonstrable impact on vertebrate regulatory networks and should be taken into account in studies of miRNA functions and their contribution to disease states.

Reconstructing histories of complex gene clusters on a phylogeny.

Clusters of genes that have evolved by repeated segmental duplication present difficult challenges throughout genomic analysis, from sequence assembly to functional analysis. These clusters are one of the major sources of evolutionary innovation, and they are linked to multiple diseases, including HIV and a variety of cancers. Understanding their evolutionary histories is a key to the application of comparative genomics methods in these regions of the genome. We propose a probabilistic model of gene cluster evolution on a phylogeny, and an MCMC algorithm for reconstruction of duplication histories from genomic sequences in multiple species. Several projects are underway to obtain high quality BAC-based assemblies of duplicated clusters in multiple species, and we anticipate use of our methods in their analysis.

Positive selection on apoptosis related genes.

Apoptosis is a form of programmed cell death crucial for development, homeostasis, immunity, spermatogenesis, and prevention of cancer. Positive selection acting on mammalian apoptosis related genes targets protein interfaces that interact with pathogens and also elements of signaling complexes. Selection appears primarily to be driven by the immune/defense related function of these genes. Moreover, competitive interactions could be driving positive selection among sperm cells, as well as the need for protection against female anti-sperm immune responses. Trade-offs in fitness are expected out of these selective pressures, which could explain the involvement of these genes in various diseases, including cancer.

Sequencing and comparative analysis of a conserved syntenic segment in the Solanaceae.

Comparative genomics is a powerful tool for gaining insight into genomic function and evolution. However, in plants, sequence data that would enable detailed comparisons of both coding and noncoding regions have been limited in availability. Here we report the generation and analysis of sequences for an unduplicated conserved syntenic segment (CSS) in the genomes of five members of the agriculturally important plant family Solanaceae. This CSS includes a 105-kb region of tomato chromosome 2 and orthologous regions of the potato, eggplant, pepper, and petunia genomes. With a total neutral divergence of 0.73-0.78 substitutions/site, these sequences are similar enough that most noncoding regions can be aligned, yet divergent enough to be informative about evolutionary dynamics and selective pressures. The CSS contains 17 distinct genes with generally conserved order and orientation, but with numerous small-scale differences between species. Our analysis indicates that the last common ancestor of these species lived approximately 27-36 million years ago, that more than one-third of short genomic segments (5-15 bp) are under selection, and that more than two-thirds of selected bases fall in noncoding regions. In addition, we identify genes under positive selection and analyze hundreds of conserved noncoding elements. This analysis provides a window into 30 million years of plant evolution in the absence of polyploidization.

Genomic analyses of transcription factor binding, histone acetylation, and gene expression reveal mechanistically distinct classes of estrogen-regulated promoters.

To explore the global mechanisms of estrogen-regulated transcription, we used chromatin immunoprecipitation coupled with DNA microarrays to determine the localization of RNA polymerase II (Pol II), estrogen receptor alpha (ERalpha), steroid receptor coactivator proteins (SRC), and acetylated histones H3/H4 (AcH) at estrogen-regulated promoters in MCF-7 cells with or without estradiol (E2) treatment. In addition, we correlated factor occupancy with gene expression and the presence of transcription factor binding elements. Using this integrative approach, we defined a set of 58 direct E2 target genes based on E2-regulated Pol II occupancy and classified their promoters based on factor binding, histone modification, and transcriptional output. Many of these direct E2 target genes exhibit interesting modes of regulation and biological activities, some of which may be relevant to the onset and proliferation of breast cancers. Our studies indicate that about one-third of these direct E2 target genes contain promoter-proximal ERalpha-binding sites, which is considerably more than previous estimates. Some of these genes represent possible novel targets for regulation through the ERalpha/AP-1 tethering pathway. Our studies have also revealed several previously uncharacterized global features of E2-regulated gene expression, including strong positive correlations between Pol II occupancy and AcH levels, as well as between the E2-dependent recruitment of ERalpha and SRC at the promoters of E2-stimulated genes. Furthermore, our studies have revealed new mechanistic insights into E2-regulated gene expression, including the absence of SRC binding at E2-repressed genes and the presence of constitutively bound, promoter-proximally paused Pol IIs at some E2-regulated promoters. These mechanistic insights are likely to be relevant for understanding gene regulation by a wide variety of nuclear receptors.

Efficient approximations for learning phylogenetic HMM models from data.

MOTIVATION: We consider models useful for learning an evolutionary or phylogenetic tree from data consisting of DNA sequences corresponding to the leaves of the tree. In particular, we consider a general probabilistic model described in Siepel and Haussler that we call the phylogenetic-HMM model which generalizes the classical probabilistic models of Neyman and Felsenstein. Unfortunately, computing the likelihood of phylogenetic-HMM models is intractable. We consider several approximations for computing the likelihood of such models including an approximation introduced in Siepel and Haussler, loopy belief propagation and several variational methods. RESULTS: We demonstrate that, unlike the other approximations, variational methods are accurate and are guaranteed to lower bound the likelihood. In addition, we identify a particular variational approximation to be best-one in which the posterior distribution is variationally approximated using the classic Neyman-Felsenstein model. The application of our best approximation to data from the cystic fibrosis transmembrane conductance regulator gene region across nine eutherian mammals reveals a CpG effect.