We report a novel DNA sequence motif in human and mouse genomes. This motif has several interesting features indicating that it is highly likely to be an unknown functional sequence element. The motif is highly enriched in promoter regions. Locations of the motif sites in the genome have strong tendency to be clustered together. Motif sites are associated with increased phylogenetic conservation as well as elevated DNase I hypersensitivity (DHS) in ENCODE cell lines. Clustered motif sites are found in promoter regions of a substantial fraction of the protein-coding genes in the genome. All together, these indicate that the motif may have important functions associated with a large number of genes.

The standard methods for detecting differential gene expression are mostly designed for analyzing a single gene expression experiment. When data from multiple related gene expression studies are available, separately analyzing each study is not ideal as it may fail to detect important genes with consistent but relatively weak differential signals in multiple studies. Jointly modeling all data allows one to borrow information across studies to improve the analysis. However, a simple concordance model, in which each gene is assumed to be differential in either all studies or none of the studies, is incapable of handling genes with study-specific differential expression. In contrast, a model that naively enumerates and analyzes all possible differential patterns across studies can deal with study-specificity and allow information pooling, but the complexity of its parameter space grows exponentially as the number of studies increases. Here, we propose a correlation motif approach to address this dilemma. This approach searches for a small number of latent probability vectors called correlation motifs to capture the major correlation patterns among multiple studies. The motifs provide the basis for sharing information among studies and genes. The approach has flexibility to handle all possible study-specific differential patterns. It improves detection of differential expression and overcomes the barrier of exponential model complexity.

Conventional high-throughput genomic technologies for mapping regulatory element activities in bulk samples such as ChIP-seq, DNase-seq and FAIRE-seq cannot analyze samples with small numbers of cells. The recently developed low-input and single-cell regulome mapping technologies such as ATAC-seq and single-cell ATAC-seq (scATAC-seq) allow analyses of small-cell-number and single-cell samples, but their signals remain highly discrete or noisy. Compared to these regulome mapping technologies, transcriptome profiling by RNA-seq is more widely used. Transcriptome data in single-cell and small-cell-number samples are more continuous and often less noisy. Here, we show that one can globally predict chromatin accessibility and infer regulatory element activities using RNA-seq. Genome-wide chromatin accessibility predicted by RNA-seq from 30 cells can offer better accuracy than ATAC-seq from 500 cells. Predictions based on single-cell RNA-seq (scRNA-seq) can more accurately reconstruct bulk chromatin accessibility than using scATAC-seq. Integrating ATAC-seq with predictions from RNA-seq increases the power and value of both methods. Thus, transcriptome-based prediction provides a new tool for decoding gene regulatory circuitry in samples with limited cell numbers.

We evaluate the feasibility of using a biological sample's transcriptome to predict its genome-wide regulatory element activities measured by DNase I hypersensitivity (DH). We develop BIRD, Big Data Regression for predicting DH, to handle this high-dimensional problem. Applying BIRD to the Encyclopedia of DNA Elements (ENCODE) data, we found that to a large extent gene expression predicts DH, and information useful for prediction is contained in the whole transcriptome rather than limited to a regulatory element's neighboring genes. We show applications of BIRD-predicted DH in predicting transcription factor-binding sites (TFBSs), turning publicly available gene expression samples in Gene Expression Omnibus (GEO) into a regulome database, predicting differential regulatory element activities, and facilitating regulome data analyses by serving as pseudo-replicates. Besides improving our understanding of the regulome-transcriptome relationship, this study suggests that transcriptome-based prediction can provide a useful new approach for regulome mapping.

The rapid development of single-cell RNA-sequencing (scRNA-seq) technologies has led to the emergence of many methods for removing systematic technical noises, including imputation methods, which aim to address the increased sparsity observed in single-cell data. Although many imputation methods have been developed, there is no consensus on how methods compare to each other.

Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many computational methods have been developed to infer the pseudo-temporal trajectories of cells within a biological sample, methods that compare pseudo-temporal patterns with multiple samples (or replicates) across different experimental conditions are lacking. Lamian is a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. It can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions, and also to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both simulations and real scRNA-seq data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.

Retinitis pigmentosa (RP) and associated inherited retinal diseases (IRDs) are caused by rod photoreceptor degeneration, necessitating therapeutics promoting rod photoreceptor survival. To address this, we tested compounds for neuroprotective effects in multiple zebrafish and mouse RP models, reasoning drugs effective across species and/or independent of disease mutation may translate better clinically. We first performed a large-scale phenotypic drug screen for compounds promoting rod cell survival in a larval zebrafish model of inducible RP. We tested 2934 compounds, mostly human-approved drugs, across six concentrations, resulting in 113 compounds being identified as hits. Secondary tests of 42 high-priority hits confirmed eleven lead candidates. Leads were then evaluated in a series of mouse RP models in an effort to identify compounds effective across species and RP models, that is, potential pan-disease therapeutics. Nine of 11 leads exhibited neuroprotective effects in mouse primary photoreceptor cultures, and three promoted photoreceptor survival in mouse rd1 retinal explants. Both shared and complementary mechanisms of action were implicated across leads. Shared target tests implicated parp1-dependent cell death in our zebrafish RP model. Complementation tests revealed enhanced and additive/synergistic neuroprotective effects of paired drug combinations in mouse photoreceptor cultures and zebrafish, respectively. These results highlight the value of cross-species/multi-model phenotypic drug discovery and suggest combinatorial drug therapies may provide enhanced therapeutic benefits for RP patients.

Transcription factors ThPOK and Runx3 regulate the differentiation of "helper" CD4+ and "cytotoxic" CD8+ T cell lineages respectively, inducing single positive (SP) T cells that enter the periphery with the expression of either the CD4 or CD8 co-receptor. Despite the expectation that these cell fates are mutually exclusive and that mature CD4+CD8+ double positive (DP) T cells are present in healthy individuals and augmented in the context of disease, yet their molecular features and pathophysiologic role are disputed. Here, we show DP T cells in murine and human tumors as a heterogenous population originating from SP T cells which re-express the opposite co-receptor and acquire features of the opposite cell type's phenotype and function following TCR stimulation. We identified distinct clonally expanded DP T cells in human melanoma and lung cancer by scRNA sequencing and demonstrated their tumor reactivity in cytotoxicity assays. Our findings indicate that antigen stimulation induces SP T cells to differentiate into DP T cell subsets gaining in polyfunctional characteristics.

Low-dose human interleukin-2 (hIL-2) treatment is used clinically to treat autoimmune disorders due to the cytokine's preferential expansion of immunosuppressive regulatory T cells (Tregs). However, off-target immune cell activation and short serum half-life limit the clinical potential of IL-2 treatment. Recent work showed that complexes comprising hIL-2 and the anti-hIL-2 antibody F5111 overcome these limitations by preferentially stimulating Tregs over immune effector cells. Although promising, therapeutic translation of this approach is complicated by the need to optimize dosing ratios and by the instability of the cytokine/antibody complex. We leverage structural insights to engineer a single-chain hIL-2/F5111 antibody fusion protein, termed F5111 immunocytokine (IC), which potently and selectively activates and expands Tregs. F5111 IC confers protection in mouse models of colitis and checkpoint inhibitor-induced diabetes mellitus. These results provide a roadmap for IC design and establish a Treg-biased immunotherapy that could be clinically translated for autoimmune disease treatment.

Detection of cyclic-di-adenosine monophosphate (c-di-AMP), a bacterial second messenger, by the host cytoplasmic surveillance pathway (CSP) is known to elicit type I interferon (IFN) responses, which are crucial to antimicrobial defense. However, the mechanisms and role of c-di-AMP signaling in Mycobacterium tuberculosis virulence remain unclear. Here we show that resistance to tuberculosis requires CSP-mediated detection of c-di-AMP produced by M. tuberculosis and that levels of c-di-AMP modulate the fate of infection. We found that a di-adenylate cyclase (disA or dacA)-overexpressing M. tuberculosis strain that secretes excess c-di-AMP activates the interferon regulatory factor (IRF) pathway with enhanced levels of IFN-β, elicits increased macrophage autophagy, and exhibits substantial virulence attenuation in mice. We show that c-di-AMP-mediated IFN-β induction during M. tuberculosis infection requires stimulator of interferon genes (STING)-signaling. We observed that c-di-AMP induction of IFN-β is independent of the cytosolic nucleic acid receptor cyclic GMP-AMP (cGAMP) synthase (cGAS), but cGAS nevertheless contributes substantially to the overall IFN-β response to M. tuberculosis infection. In sum, our results reveal c-di-AMP to be a key mycobacterial pathogen-associated molecular pattern (PAMP) driving host type I IFN responses and autophagy. These findings suggest that modulating the levels of this small molecule may lead to novel immunotherapeutic strategies against tuberculosis.

We present CisGenome, a software system for analyzing genome-wide chromatin immunoprecipitation (ChIP) data. CisGenome is designed to meet all basic needs of ChIP data analyses, including visualization, data normalization, peak detection, false discovery rate computation, gene-peak association, and sequence and motif analysis. In addition to implementing previously published ChIP-microarray (ChIP-chip) analysis methods, the software contains statistical methods designed specifically for ChlP sequencing (ChIP-seq) data obtained by coupling ChIP with massively parallel sequencing. The modular design of CisGenome enables it to support interactive analyses through a graphic user interface as well as customized batch-mode computation for advanced data mining. A built-in browser allows visualization of array images, signals, gene structure, conservation, and DNA sequence and motif information. We demonstrate the use of these tools by a comparative analysis of ChIP-chip and ChIP-seq data for the transcription factor NRSF/REST, a study of ChIP-seq analysis with or without a negative control sample, and an analysis of a new motif in Nanog- and Sox2-binding regions.

How oncogenes modulate the self-renewal properties of cancer-initiating cells is incompletely understood. Activating KRAS and NRAS mutations are among the most common oncogenic lesions detected in human cancer, and occur in myeloproliferative disorders (MPDs) and leukemias. We investigated the effects of expressing oncogenic Kras(G12D) from its endogenous locus on the proliferation and tumor-initiating properties of murine hematopoietic stem and progenitor cells. MPD could be initiated by Kras(G12D) expression in a highly restricted population enriched for hematopoietic stem cells (HSCs), but not in common myeloid progenitors. Kras(G12D) HSCs demonstrated a marked in vivo competitive advantage over wild-type cells. Kras(G12D) expression also increased the fraction of proliferating HSCs and reduced the overall size of this compartment. Transplanted Kras(G12D) HSCs efficiently initiated acute T-lineage leukemia/lymphoma, which was associated with secondary Notch1 mutations in thymocytes. We conclude that MPD-initiating activity is restricted to the HSC compartment in Kras(G12D) mice, and that distinct self-renewing populations with cooperating mutations emerge during cancer progression.

During embryonic development, undifferentiated progenitor cells balance the generation of additional progenitor cells with differentiation. Within the developing limb, cartilage cells differentiate from mesodermal progenitors in an ordered process that results in the specification of the correct number of appropriately sized skeletal elements. The internal pathways by which these cells maintain an undifferentiated state while preserving their capacity to differentiate is unknown. Here, we report that the arginine methyltransferase PRMT5 has a crucial role in maintaining progenitor cells. Mouse embryonic buds lacking PRMT5 have severely truncated bones with wispy digits lacking joints. This novel phenotype is caused by widespread cell death that includes mesodermal progenitor cells that have begun to precociously differentiate into cartilage cells. We propose that PRMT5 maintains progenitor cells through its regulation of Bmp4 Intriguingly, adult and embryonic stem cells also require PRMT5 for maintaining pluripotency, suggesting that similar mechanisms might regulate lineage-restricted progenitor cells during organogenesis.

Although previous studies suggest that exposure to traffic-related pollution during childhood increases the risk of childhood overweight or obesity (COWO), the role of early life exposure to fine particulate matter (aerodynamic diameter <2.5 μm; PM2.5) and its joint effect with the mother’s prepregnancy body mass index (MPBMI) on COWO remain unclear.

How distinct transcriptional programs are enacted to generate cellular heterogeneity and plasticity, and enable complex fate decisions are important open questions. One key regulator is the cell's epigenome state that drives distinct transcriptional programs by regulating chromatin accessibility. Genome-wide chromatin accessibility measurements can impart insights into regulatory sequences (in)accessible to DNA-binding proteins at a single-cell resolution. This review outlines molecular methods and bioinformatic tools for capturing cell-to-cell chromatin variation using single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) in a scalable fashion. It also covers joint profiling of chromatin with transcriptome/proteome measurements, computational strategies to integrate multi-omic measurements, and predictive bioinformatic tools to infer chromatin accessibility from single-cell transcriptomic datasets. Methodological refinements that increase power for cell discovery through robust chromatin coverage and integrate measurements from multiple modalities will further expand our understanding of gene regulation during homeostasis and disease.

Nutrient availability and stresses impact a cell's decision to enter a growth state or a quiescent state. Acetyl-CoA stimulates cell growth under nutrient-limiting conditions, but how cells generate acetyl-CoA under starvation stress is less understood. Here, we show that general stress response factors, Msn2 and Msn4, function as master transcriptional regulators of yeast glycolysis via directly binding and activating genes encoding glycolytic enzymes. Yeast cells lacking Msn2 and Msn4 exhibit prevalent repression of glycolytic genes and a significant delay of acetyl-CoA accumulation and reentry into growth from quiescence. Thus Msn2/4 exhibit a dual role in activating carbohydrate metabolism genes and stress response genes. These results suggest a possible mechanism by which starvation-induced stress response factors may prime quiescent cells to reenter growth through glycolysis when nutrients are limited.

COVID-19 mRNA vaccines elicit strong T and B cell responses to the SARS-CoV-2 spike glycoprotein in both SARS-CoV-2 naïve and experienced patients. However, it is unknown whether the post-vaccine CD4+ T cell responses seen in patients with a history of COVID-19 are due to restimulation of T cell clonotypes that were first activated during natural infection or if they are the result of new clones activated by the vaccine.

The nuclear factor kappa B (NF-κB) family of transcription factors orchestrates signal-induced gene expression in diverse cell types. Cellular responses to NF-κB activation are regulated at the level of cell and signal specificity, as well as differential use of family members (subunit specificity). Here we used time-dependent multi-omics to investigate the selective functions of Rel and RelA, two closely related NF-κB proteins, in primary B lymphocytes activated via the B cell receptor. Despite large numbers of shared binding sites genome wide, Rel and RelA directed kinetically distinct cascades of gene expression in activated B cells. Single-cell RNA sequencing revealed marked heterogeneity of Rel- and RelA-specific responses, and sequential binding of these factors was not a major mechanism of protracted transcription. Moreover, nuclear co-expression of Rel and RelA led to functional antagonism between the factors. By rigorously identifying the target genes of each NF-κB subunit, these studies provide insights into exclusive functions of Rel and RelA in immunity and cancer.

The larynx enables speech while regulating swallowing and respiration. Larynx function hinges on the laryngeal epithelium which originates as part of the anterior foregut and undergoes extensive remodeling to separate from the esophagus and form vocal folds that interface with the adjacent trachea. Here we find that sonic hedgehog (SHH) is essential for epithelial integrity in the mouse larynx as well as the anterior foregut. During larynx-esophageal separation, low Shh expression marks specific domains of actively remodeling epithelium that undergo an epithelial-to-mesenchymal transition (EMT) characterized by the induction of N-Cadherin and movement of cells out of the epithelial layer. Consistent with a role for SHH signaling in regulating this process, Shh mutants undergo an abnormal EMT throughout the anterior foregut and larynx, marked by a cadherin switch, movement out of the epithelial layer and cell death. Unexpectedly, Shh mutant epithelial cells are replaced by a new population of FOXA2-negative cells that likely derive from adjacent pouch tissues and form a rudimentary epithelium. These findings have important implications for interpreting the etiology of HH-dependent birth defects within the foregut. We propose that SHH signaling has a default role in maintaining epithelial identity throughout the anterior foregut and that regionalized reductions in SHH trigger epithelial remodeling.

Epigenetic information defines tissue identity and is largely inherited in development through DNA methylation. While studied mostly for mean differences, methylation also encodes stochastic change, defined as entropy in information theory. Analyzing allele-specific methylation in 49 human tissue sample datasets, we find that methylation entropy is associated with specific DNA binding motifs, regulatory DNA, and CpG density. Then applying information theory to 42 mouse embryo methylation datasets, we find that the contribution of methylation entropy to time- and tissue-specific patterns of development is comparable to the contribution of methylation mean, and methylation entropy is associated with sequence and chromatin features conserved with human. Moreover, methylation entropy is directly related to gene expression variability in development, suggesting a role for epigenetic entropy in developmental plasticity.