Developmental Acquisition of Regulomes Underlies Innate Lymphoid Cell Functionality

Innate lymphoid cells (ILCs) play key roles in host defense, barrier integrity, and homeostasis and mirror adaptive CD4(+) T helper (Th) cell subtypes in both usage of effector molecules and transcription factors. To better understand the relationship between ILC subsets and their Th cell counterparts, we measured genome-wide chromatin accessibility. We find that chromatin in proximity to effector genes is selectively accessible in ILCs prior to high-level transcription upon activation. Accessibility of these regions is acquired in a stepwise manner during development and changes little after in vitro or in vivo activation. Conversely, dramatic chromatin remodeling occurs in naive CD4(+) T cells during Th cell differentiation using a type-2-infection model. This alteration results in a substantial convergence of Th2 cells toward ILC2 regulomes. Our data indicate extensive sharing of regulatory circuitry across the innate and adaptive compartments of the immune system, in spite of their divergent developing pathways

Dynamic GATA4 enhancers shape the chromatin landscape central to heart development and disease.

How stage-specific enhancer dynamics modulate gene expression patterns essential for organ development, homeostasis and disease is not well understood. Here, we addressed this question by mapping chromatin occupancy of GATA4--a master cardiac transcription factor--in heart development and disease. We find that GATA4 binds and participates in establishing active chromatin regions by stimulating H3K27ac deposition, which facilitates GATA4-driven gene expression. GATA4 chromatin occupancy changes markedly between fetal and adult heart, with a limited binding sites overlap. Cardiac stress restored GATA4 occupancy to a subset of fetal sites, but many stress-associated GATA4 binding sites localized to loci not occupied by GATA4 during normal heart development. Collectively, our data show that dynamic, context-specific transcription factors occupancy underlies stage-specific events in development, homeostasis and disease

Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process.

To understand the impact of epigenetics on human misfolding disease, we apply Gaussian-process regression (GPR) based machine learning (ML) (GPR-ML) through variation spatial profiling (VSP). VSP generates population-based matrices describing the spatial covariance (SCV) relationships that link genetic diversity to fitness of the individual in response to histone deacetylases inhibitors (HDACi). Niemann-Pick C1 (NPC1) is a Mendelian disorder caused by >300 variants in the NPC1 gene that disrupt cholesterol homeostasis leading to the rapid onset and progression of neurodegenerative disease. We determine the sequence-to-function-to-structure relationships of the NPC1 polypeptide fold required for membrane trafficking and generation of a tunnel that mediates cholesterol flux in late endosomal/lysosomal (LE/Ly) compartments. HDACi treatment reveals unanticipated epigenomic plasticity in SCV relationships that restore NPC1 functionality. GPR-ML based matrices capture the epigenetic processes impacting information flow through central dogma, providing a framework for quantifying the effect of the environment on the healthspan of the individual

Meta-analysis of RNA-seq expression data across species, tissues and studies.

BackgroundDifferences in gene expression drive phenotypic differences between species, yet major organs and tissues generally have conserved gene expression programs. Several comparative transcriptomic studies have observed greater similarity in gene expression between homologous tissues from different vertebrate species than between diverse tissues of the same species. However, a recent study by Lin and colleagues reached the opposite conclusion. These studies differed in the species and tissues analyzed, and in technical details of library preparation, sequencing, read mapping, normalization, gene sets, and clustering methods.ResultsTo better understand gene expression evolution we reanalyzed data from four studies, including that of Lin, encompassing 6-13 tissues each from 11 vertebrate species using standardized mapping, normalization, and clustering methods. An analysis of independent data showed that the set of tissues chosen by Lin et al. were more similar to each other than those analyzed by previous studies. Comparing expression in five common tissues from the four studies, we observed that samples clustered exclusively by tissue rather than by species or study, supporting conservation of organ physiology in mammals. Furthermore, inter-study distances between homologous tissues were generally less than intra-study distances among different tissues, enabling informative meta-analyses. Notably, when comparing expression divergence of tissues over time to expression variation across 51 human GTEx tissues, we could accurately predict the clustering of expression for arbitrary pairs of tissues and species.ConclusionsThese results provide a framework for the design of future evolutionary studies of gene expression and demonstrate the utility of comparing RNA-seq data across studies

Unique transcriptomic landscapes identified in idiopathic spontaneous and infection related preterm births compared to normal term births.

Preterm birth (PTB) is leading contributor to infant death in the United States and globally, yet the underlying mechanistic causes are not well understood. Histopathological studies of preterm birth suggest advanced villous maturity may have a role in idiopathic spontaneous preterm birth (isPTB). To better understand pathological and molecular basis of isPTB, we compared placental villous transcriptomes from carefully phenotyped cohorts of PTB due to infection or isPTB between 28-36 weeks gestation and healthy term placentas. Transcriptomic analyses revealed a unique expression signature for isPTB distinct from the age-matched controls that were delivered prematurely due to infection. This signature included the upregulation of three IGF binding proteins (IGFBP1, IGFBP2, and IGFBP6), supporting a role for aberrant IGF signaling in isPTB. However, within the isPTB expression signature, we detected secondary signature of inflammatory markers including TNC, C3, CFH, and C1R, which have been associated with placental maturity. In contrast, the expression signature of the gestational age-matched infected samples included upregulation of proliferative genes along with cell cycling and mitosis pathways. Together, these data suggest an isPTB molecular signature of placental hypermaturity, likely contributing to the premature activation of inflammatory pathways associated with birth and providing a molecular basis for idiopathic spontaneous birth

Viral evolution under the pressure of an adaptive immune system - optimal mutation rates for viral escape

Based on a recent model of evolving viruses competing with an adapting immune system [1], we study the conditions under which a viral quasispecies can maximize its growth rate. The range of mutation rates that allows viruses to thrive is limited from above due to genomic information deterioration, and from below by insufficient sequence diversity, which leads to a quick eradication of the virus by the immune system. The mutation rate that optimally balances these two requirements depends to first order on the ratio of the inverse of the virus' growth rate and the time the immune system needs to develop a specific answer to an antigen. We find that a virus is most viable if it generates exactly one mutation within the time it takes for the immune system to adapt to a new viral epitope. Experimental viral mutation rates, in particular for HIV (human immunodeficiency virus), seem to suggest that many viruses have achieved their optimal mutation rate. [1] C.Kamp and S. Bornholdt, Phys. Rev. Lett., 88, 068104 (2002)Comment: 5 pages RevTeX including 3 figure

Analysis of nucleosome positioning landscapes enables gene discovery in the human malaria parasite Plasmodium falciparum.

BackgroundPlasmodium falciparum, the deadliest malaria-causing parasite, has an extremely AT-rich (80.7 %) genome. Because of high AT-content, sequence-based annotation of genes and functional elements remains challenging. In order to better understand the regulatory network controlling gene expression in the parasite, a more complete genome annotation as well as analysis tools adapted for AT-rich genomes are needed. Recent studies on genome-wide nucleosome positioning in eukaryotes have shown that nucleosome landscapes exhibit regular characteristic patterns at the 5'- and 3'-end of protein and non-protein coding genes. In addition, nucleosome depleted regions can be found near transcription start sites. These unique nucleosome landscape patterns may be exploited for the identification of novel genes. In this paper, we propose a computational approach to discover novel putative genes based exclusively on nucleosome positioning data in the AT-rich genome of P. falciparum.ResultsUsing binary classifiers trained on nucleosome landscapes at the gene boundaries from two independent nucleosome positioning data sets, we were able to detect a total of 231 regions containing putative genes in the genome of Plasmodium falciparum, of which 67 highly confident genes were found in both data sets. Eighty-eight of these 231 newly predicted genes exhibited transcription signal in RNA-Seq data, indicative of active transcription. In addition, 20 out of 21 selected gene candidates were further validated by RT-PCR, and 28 out of the 231 genes showed significant matches using BLASTN against an expressed sequence tag (EST) database. Furthermore, 108 (47%) out of the 231 putative novel genes overlapped with previously identified but unannotated long non-coding RNAs. Collectively, these results provide experimental validation for 163 predicted genes (70.6%). Finally, 73 out of 231 genes were found to be potentially translated based on their signal in polysome-associated RNA-Seq representing transcripts that are actively being translated.ConclusionOur results clearly indicate that nucleosome positioning data contains sufficient information for novel gene discovery. As distinct nucleosome landscapes around genes are found in many other eukaryotic organisms, this methodology could be used to characterize the transcriptome of any organism, especially when coupled with other DNA-based gene finding and experimental methods (e.g., RNA-Seq)

Molecular recording of mammalian embryogenesis.

Ontogeny describes the emergence of complex multicellular organisms from single totipotent cells. This field is particularly challenging in mammals, owing to the indeterminate relationship between self-renewal and differentiation, variation in progenitor field sizes, and internal gestation in these animals. Here we present a flexible, high-information, multi-channel molecular recorder with a single-cell readout and apply it as an evolving lineage tracer to assemble mouse cell-fate maps from fertilization through gastrulation. By combining lineage information with single-cell RNA sequencing profiles, we recapitulate canonical developmental relationships between different tissue types and reveal the nearly complete transcriptional convergence of endodermal cells of extra-embryonic and embryonic origins. Finally, we apply our cell-fate maps to estimate the number of embryonic progenitor cells and their degree of asymmetric partitioning during specification. Our approach enables massively parallel, high-resolution recording of lineage and other information in mammalian systems, which will facilitate the construction of a quantitative framework for understanding developmental processes