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Glycosylphosphatidylinositol-anchored high density lipoprotein–binding protein 1 (GPIHBP1), the protein that shuttles LPL to the capillary lumen, is essential for plasma triglyceride metabolism. When GPIHBP1 is absent, LPL remains stranded within the interstitial spaces and plasma triglyceride hydrolysis is impaired, resulting in severe hypertriglyceridemia. While the functions of GPIHBP1 in intravascular lipolysis are reasonably well understood, no one has yet identified DNA sequences regulating GPIHBP1 expression. In the current studies, we identified an enhancer element located ∼3.6 kb upstream from exon 1 of mouse Gpihbp1. To examine the importance of the enhancer, we used CRISPR/Cas9 genome editing to create mice lacking the enhancer (Gpihbp1(Enh/Enh)). Removing the enhancer reduced Gpihbp1 expression by >90% in the liver and by ∼50% in heart and brown adipose tissue. The reduced expression of GPIHBP1 was insufficient to prevent LPL from reaching the capillary lumen, and it did not lead to hypertriglyceridemia—even when mice were fed a high-fat diet. Compound heterozygotes (Gpihbp1(Enh/−) mice) displayed further reductions in Gpihbp1 expression and exhibited partial mislocalization of LPL (increased amounts of LPL within the interstitial spaces of the heart), but the plasma triglyceride levels were not perturbed. The enhancer element that we identified represents the first insight into DNA sequences controlling Gpihbp1 expression.

Objective Type 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome-wide association studies (GWAS) have identified > 400 independent signals that encode genetic predisposition. More than 90% of associated single-nucleotide polymorphisms (SNPs) localize to non-coding regions and are enriched in chromatin-defined islet enhancer elements, indicating a strong transcriptional regulatory component to disease susceptibility. Pancreatic islets are a mixture of cell types that express distinct hormonal programs, so each cell type may contribute differentially to the underlying regulatory processes that modulate T2D-associated transcriptional circuits. Existing chromatin profiling methods such as ATAC-seq and DNase-seq, applied to islets in bulk, produce aggregate profiles that mask important cellular and regulatory heterogeneity. Methods We present genome-wide single-cell chromatin accessibility profiles in >1,600 cells derived from a human pancreatic islet sample using single-cell combinatorial indexing ATAC-seq (sci-ATAC-seq). We also developed a deep learning model based on U-Net architecture to accurately predict open chromatin peak calls in rare cell populations. Results We show that sci-ATAC-seq profiles allow us to deconvolve alpha, beta, and delta cell populations and identify cell-type-specific regulatory signatures underlying T2D. Particularly, T2D GWAS SNPs are significantly enriched in beta cell-specific and across cell-type shared islet open chromatin, but not in alpha or delta cell-specific open chromatin. We also demonstrate, using less abundant delta cells, that deep learning models can improve signal recovery and feature reconstruction of rarer cell populations. Finally, we use co-accessibility measures to nominate the cell-specific target genes at 104 non-coding T2D GWAS signals. Conclusions Collectively, we identify the islet cell type of action across genetic signals of T2D predisposition and provide higher-resolution mechanistic insights into genetically encoded risk pathways., Highlights • Single cell chromatin accessibility profiles obtained for 1,456 human pancreatic islet cells. • Unique α, β, and δ cell chromatin accessibility signatures identified. • Deep learning approach accurately predicts chromatin accessibility in rare cells. • Nominate effector cell types, causal variants and target genes at diabetes GWAS loci.

SUMMARY While the immediate and transitory response of breast cancer cells to pathological stiffness in their native microenvironment has been well explored, it remains unclear how stiffness-induced phenotypes are maintained over time after cancer cell dissemination in vivo. Here, we show that fibrotic-like matrix stiffness promotes distinct metastatic phenotypes in cancer cells, which are preserved after transition to softer microenvironments, such as bone marrow. Using differential gene expression analysis of stiffness-responsive breast cancer cells, we establish a multigenic score of mechanical conditioning (MeCo) and find that it is associated with bone metastasis in patients with breast cancer. The maintenance of mechanical conditioning is regulated by RUNX2, an osteogenic transcription factor, established driver of bone metastasis, and mitotic bookmarker that preserves chromatin accessibility at target gene loci. Using genetic and functional approaches, we demonstrate that mechanical conditioning maintenance can be simulated, repressed, or extended, with corresponding changes in bone metastatic potential., Graphical Abstract, In brief Watson et al. demonstrate that mechanical conditioning by stiff microenvironments in breast tumors is maintained in cancer cells after dissemination to softer microenvironments, including bone marrow. They show that mechanical conditioning promotes invasion and osteolysis and establish a mechanical conditioning (MeCo) score, associated with bone metastasis in patients.

Genome-wide association studies (GWASs) have become a standard tool for dissecting genetic contributions to disease risk. However, these studies typically require extraordinarily large sample sizes to be adequately powered. Strategies that incorporate functional information alongside genetic associations have proved successful in increasing GWAS power. Following this paradigm, we present the results of 20 different genetic association studies for quantitative traits related to complex diseases, conducted in the Hutterites of South Dakota. To boost the power of these association studies, we collected RNA-sequencing data from lymphoblastoid cell lines for 431 Hutterite individuals. We then used Sherlock, a tool that integrates GWAS and expression quantitative trait locus (eQTL) data, to identify weak GWAS signals that are also supported by eQTL data. Using this approach, we found novel associations with quantitative phenotypes related to cardiovascular disease, including carotid intima-media thickness, left atrial volume index, monocyte count and serum YKL-40 levels.

Single-cell chromatin and RNA analysis Single-cell analyses have begun to provide insight into the differences among and within the individual cells that make up a tissue or organism. However, technological barriers owing to the small amount of material present in each single cell have prevented parallel analyses. Cao et al. present sci-CAR, a pooled barcode method that jointly analyzes both the RNA transcripts and chromatin profiles of single cells. By applying sci-CAR to lung adenocarcinoma cells and mouse kidney tissue, the authors demonstrate precision in assessing expression and genome accessibility at a genome-wide scale. The approach provides an improvement over bulk analysis, which can be confounded by differing cellular subgroups. Science , this issue p. 1380

We applied a combinatorial indexing assay, sci-ATAC-seq, to profile genome-wide chromatin accessibility in ∼100,000 single cells from 13 adult mouse tissues. We identify 85 distinct patterns of chromatin accessibility, most of which can be assigned to cell types, and ∼400,000 differentially accessible elements. We use these data to link regulatory elements to their target genes, to define the transcription factor grammar specifying each cell type, and to discover in vivo correlates of heterogeneity in accessibility within cell types. We develop a technique for mapping single cell gene expression data to single-cell chromatin accessibility data, facilitating the comparison of atlases. By intersecting mouse chromatin accessibility with human genome-wide association summary statistics, we identify cell-type-specific enrichments of the heritability signal for hundreds of complex traits. These data define the in vivo landscape of the regulatory genome for common mammalian cell types at single-cell resolution.

The extent to which non-coding mutations contribute to Mendelian disease is a major unknown in human genetics. Relatedly, the vast majority of candidate regulatory elements have yet to be functionally validated. Here, we describe a CRISPR-based system that uses pairs of guide RNAs (gRNAs) to program thousands of kilobase-scale deletions that deeply scan across a targeted region in a tiling fashion ("ScanDel"). We applied ScanDel to HPRT1, the housekeeping gene underlying Lesch-Nyhan syndrome, an X-linked recessive disorder. Altogether, we programmed 4,342 overlapping 1 and 2 kb deletions that tiled 206 kb centered on HPRT1 (including 87 kb upstream and 79 kb downstream) with median 27-fold redundancy per base. We functionally assayed programmed deletions in parallel by selecting for loss of HPRT function with 6-thioguanine. As expected, sequencing gRNA pairs before and after selection confirmed that all HPRT1 exons are needed. However, HPRT1 function was robust to deletion of any intergenic or deeply intronic non-coding region, indicating that proximal regulatory sequences are sufficient for HPRT1 expression. Although our screen did identify the disruption of exon-proximal non-coding sequences (e.g., the promoter) as functionally consequential, long-read sequencing revealed that this signal was driven by rare, imprecise deletions that extended into exons. Our results suggest that no singular distal regulatory element is required for HPRT1 expression and that distal mutations are unlikely to contribute substantially to Lesch-Nyhan syndrome burden. Further application of ScanDel could shed light on the role of regulatory mutations in disease at other loci while also facilitating a deeper understanding of endogenous gene regulation.

Sequencing each cell of the nematode Single-cell sequencing is challenging owing to the limited biological material available in an individual cell and the high cost of sequencing across multiple cells. Cao et al. developed a two-step combinatorial barcoding method to profile both single-cell and single-nucleus transcriptomes without requiring physical isolation of each cell. The authors profiled almost 50,000 single cells from an individual Caenorhabditis elegans larval stage and were able to identify and recover information from different, even rare, cell types. Science , this issue p. 661

Recent genome-wide association studies (GWAS) have identified a number of novel genetic associations with complex human diseases. In spite of these successes, results from GWAS generally explain only a small proportion of disease heritability, an observation termed the ‘missing heritability problem’. Several sources for the missing heritability have been proposed, including the contribution of many common variants with small individual effect sizes, which cannot be reliably found using the standard GWAS approach. The goal of our study was to explore a complimentary approach, which combines GWAS results with functional data in order to identify novel genetic associations with small effect sizes. To do so, we conducted a GWAS for lymphocyte count, a physiologic quantitative trait associated with asthma, in 462 Hutterites. In parallel, we performed a genome-wide gene expression study in lymphoblastoid cell lines from 96 Hutterites. We found significant support for genetic associations using the GWAS data when we considered variants near the 193 genes whose expression levels across individuals were most correlated with lymphocyte counts. Interestingly, these variants are also enriched with signatures of an association with asthma susceptibility, an observation we were able to replicate. The associated loci include genes previously implicated in asthma susceptibility as well as novel candidate genes enriched for functions related to T cell receptor signaling and adenosine triphosphate synthesis. Our results, therefore, establish a new set of asthma susceptibility candidate genes. More generally, our observations support the notion that many loci of small effects influence variation in lymphocyte count and asthma susceptibility.

Linking regulatory DNA elements to their target genes, which may be located hundreds of kilobases away, remains challenging. Here we introduce Cicero, an algorithm that identifies coaccessible pairs of DNA elements using single-cell chromatin accessibility data, and so connects regulatory elements to their putative target genes. We apply Cicero to investigate how dynamically accessible elements orchestrate gene regulation in differentiating myoblasts. Groups of Cicero-linked regulatory elements meet criteria of "chromatin hubs" — they are enriched for physical proximity, interact with a common set of transcription factors, and undergo coordinated changes in histone marks that are predictive of changes in gene expression. Pseudotemporal analysis revealed that most DNA elements remain in chromatin hubs throughout differentiation. A subset of elements bound by MYOD in myoblasts exhibit early opening in a PBX1- and MEIS1-dependent manner. This strategy can be applied to dissect the architecture, sequence determinants, and mechanisms of cis-regulation on a genome-wide scale.

Plasma lipoprotein(a) (Lp[a]) level is an independent risk factor of cardiovascular disease that is under strong genetic control. We conducted a genome-wide association study of plasma Lp(a) in 386 members of a founder population that adheres to a communal lifestyle, proscribes cigarette smoking, and prepares and eats meals communally. We identified associations with 77 single nucleotide polymorphisms (SNPs) spanning 12.5 Mb on chromosome 6q26-q27 that met criteria for genome-wide significance (Por= 1.3 x 10(-7)) and were within or flanking nine genes, including LPA. We show that variation in at least six genes in addition to LPA are significantly associated with Lp(a) levels independent of each other and of the kringle IV repeat polymorphism in the LPA gene. One novel SNP in intron 37 of the LPA gene was also associated with Lp(a) levels and carotid artery disease number in unrelated Caucasians (P = 7.3 x 10(-12) and 0.024, respectively), also independent of kringle IV number. This study suggests a complex genetic architecture of Lp(a) levels that may involve multiple loci on chromosome 6q26-q27.

Don't Ape Protein Variation Changes in DNA and messenger RNA (mRNA) expression levels have been used to estimate evolutionary changes between species. However protein expression levels may better reflect selection on divergent and constrained phenotypes. Khan et al. (p. 1100 , published online 17 October; see the Perspective by Vogel ) measured the differences among and within species between mRNA expression and protein levels in humans, chimpanzees, and rhesus macaques, identifying protein transcripts that seem to be under lineage-specific constraint between humans and chimpanzees.

Gene expression differences are shaped by selective pressures and contribute to phenotypic differences between species. We identified 964 copy number differences (CNDs) of conserved sequences across three primate species and examined their potential effects on gene expression profiles. Samples with copy number different genes had significantly different expression than samples with neutral copy number. Genes encoding regulatory molecules differed in copy number and were associated with significant expression differences. Additionally, we identified 127 CNDs that were processed pseudogenes and some of which were expressed. Furthermore, there were copy number-different regulatory regions such as ultraconserved elements and long intergenic noncoding RNAs with the potential to affect expression. We postulate that CNDs of these conserved sequences fine-tune developmental pathways by altering the levels of RNA.

The bacterial composition of the human fecal microbiome is influenced by many lifestyle factors, notably diet. It is less clear, however, what role host genetics plays in dictating the composition of bacteria living in the gut. In this study, we examined the association of ~200K host genotypes with the relative abundance of fecal bacterial taxa in a founder population, the Hutterites, during two seasons (n = 91 summer, n = 93 winter, n = 57 individuals collected in both). These individuals live and eat communally, minimizing variation due to environmental exposures, including diet, which could potentially mask small genetic effects. Using a GWAS approach that takes into account the relatedness between subjects, we identified at least 8 bacterial taxa whose abundances were associated with single nucleotide polymorphisms in the host genome in each season (at genome-wide FDR of 20%). For example, we identified an association between a taxon known to affect obesity (genus Akkermansia) and a variant near PLD1, a gene previously associated with body mass index. Moreover, we replicate a previously reported association from a quantitative trait locus (QTL) mapping study of fecal microbiome abundance in mice (genus Lactococcus, rs3747113, P = 3.13 x 10−7). Finally, based on the significance distribution of the associated microbiome QTLs in our study with respect to chromatin accessibility profiles, we identified tissues in which host genetic variation may be acting to influence bacterial abundance in the gut.

One goal of human genetics is to understand how the information for precise and dynamic gene expression programs is encoded in the genome. The interactions of transcription factors (TFs) with DNA regulatory elements clearly play an important role in determining gene expression outputs, yet the regulatory logic underlying functional transcription factor binding is poorly understood. Many studies have focused on characterizing the genomic locations of TF binding, yet it is unclear to what extent TF binding at any specific locus has functional consequences with respect to gene expression output. To evaluate the context of functional TF binding we knocked down 59 TFs and chromatin modifiers in one HapMap lymphoblastoid cell line. We then identified genes whose expression was affected by the knockdowns. We intersected the gene expression data with transcription factor binding data (based on ChIP-seq and DNase-seq) within 10 kb of the transcription start sites of expressed genes. This combination of data allowed us to infer functional TF binding. Using this approach, we found that only a small subset of genes bound by a factor were differentially expressed following the knockdown of that factor, suggesting that most interactions between TF and chromatin do not result in measurable changes in gene expression levels of putative target genes. We found that functional TF binding is enriched in regulatory elements that harbor a large number of TF binding sites, at sites with predicted higher binding affinity, and at sites that are enriched in genomic regions annotated as “active enhancers.”, Author Summary An important question in genomics is to understand how a class of proteins called “transcription factors” controls the expression level of other genes in the genome in a cell-type-specific manner – a process that is essential to human development. One major approach to this problem is to study where these transcription factors bind in the genome, but this does not tell us about the effect of that binding on gene expression levels and it is generally accepted that much of the binding does not strongly influence gene expression. To address this issue, we artificially reduced the concentration of 59 different transcription factors in the cell and then examined which genes were impacted by the reduced transcription factor level. Our results implicate some attributes that might influence what binding is functional, but they also suggest that a simple model of functional vs. non-functional binding may not suffice.

Epstein-Barr virus (EBV) transformed lymphoblastoid cell lines (LCLs) provide a conveniently accessible and renewable resource for functional genomic studies in humans. The ability to accumulate multidimensional data pertaining to the same individual cell lines, from complete genomic sequences to detailed gene regulatory profiles, further enhances the utility of LCLs as a model system. A lingering concern, however, is that the changes associated with EBV transformation of B cells reduce the usefulness of LCLs as a surrogate model for primary tissues. To evaluate the validity of this concern, we compared global gene expression and methylation profiles between CD20+ primary B cells sampled from six individuals and six independent replicates of transformed LCLs derived from each sample. These data allowed us to obtain a detailed catalog of the genes and pathways whose regulation is affected by EBV transformation. We found that the expression levels and promoter methylation profiles of more than half of the studied genes were affected by the EBV transformation, including enrichments of genes involved in transcription regulation, cell cycle and immune response. However, we show that most of the differences in gene expression levels between LCLs and B cells are of small magnitude, and that LCLs can often recapitulate the naturally occurring gene expression variation in primary B cells. Thus, our observations suggest that inference of the genetic architecture that underlies regulatory variation in LCLs can typically be generalized to primary B cells. In contrast, inference based on functional studies in LCLs may be more limited to the cell lines.