Summary Although studies have identified hundreds of loci associated with human traits and diseases, pinpointing causal alleles remains difficult, particularly for non-coding variants. To address this challenge, we adapted the massively parallel reporter assay (MPRA) to identify variants that directly modulate gene expression. We applied it to 32,373 variants from 3,642 cis-expression quantitative trait loci and control regions. Detection by MPRA was strongly correlated with measures of regulatory function. We demonstrate MPRA's capabilities for pinpointing causal alleles, using it to identify 842 variants showing differential expression between alleles, including 53 well-annotated variants associated with diseases and traits. We investigated one in detail, a risk allele for ankylosing spondylitis, and provide direct evidence of a non-coding variant that alters expression of the prostaglandin EP4 receptor. These results create a resource of concrete leads and illustrate the promise of this approach for comprehensively interrogating how non-coding polymorphism shapes human biology.

The human genome can be segmented into topologically associating domains (TADs), which have been proposed to spatially sequester genes and regulatory elements through chromatin looping. Interactions between TADs have also been suggested, presumably due to variable boundary positions across individual cells. However, the nature, extent, and consequence of these dynamic boundaries remain unclear. Here, we combine high-resolution imaging with Oligopaint technology to quantify the interaction frequencies across both weak and strong boundaries. We find that chromatin intermingling across population-defined boundaries is widespread but that the extent of permissibility is locus-specific. Cohesin depletion, which abolishes domain formation at the population level, does not induce ectopic interactions but instead reduces interactions across all boundaries tested. In contrast, WAPL or CTCF depletion increases inter-domain contacts in a cohesin-dependent manner. Reduced chromatin intermingling due to cohesin loss affects the topology and transcriptional bursting frequencies of genes near boundaries. We propose that cohesin occasionally bypasses boundaries to promote incorporation of boundary-proximal genes into neighboring domains.

Tumor necrosis factor-alpha (TNF-α) binds to two distinct receptors, TNFR1/p55 and TNFR2/p75. TNF-α is implicated in the processes of tumor growth, survival, differentiation, invasion, metastases, secretion of cytokines and pro-angiogenic factors. We have shown that TNFR2/p75 signaling promotes ischemia-induced angiogenesis via modulation of several angiogenic growth factors. We hypothesized that TNFR2/p75 may promote tumor growth and angiogenesis. Growth of mouse Lewis lung carcinoma (LLC1) and/or mouse melanoma B16 cell was evaluated in wild type (WT), p75 knockout (KO) and double p55KO/p75KO mouse tumor xenograft models. Compared to WT and p55KO/p75KO mice, growth of tumors in p75KO mice was significantly decreased (two-fold) in both LLC and B16 tumors. Tumor growth inhibition was correlated with decreases in VEGF expression and capillary density, as well as bone marrow (BM)-derived endothelial progenitor cells (EPCs) incorporation into the functional capillary network, and an increase in apoptotic cells in LLC xenografts. Gene array analysis of tumor tissues showed a decrease in gene expression in pathways that promote tumor angiogenesis and cell survival. Blocking p75 by shRNA in cultured LLCs led to increases in TNF-mediated apoptosis, as well as decreases in the constitutive and TNF-mediated expression of angiogenic growth factors (VEGF, HGF, PLGF), and SDF-1α receptor CXCR4. In summary, p75 is essential for tumor angiogenesis and survival in highly vascularized murine lung tumor xenografts. Blocking p75 expression may lead to tumor regression. This may represent new and effective therapy against lung neoplasms and potentially tumors of other origin.

Repeated emergence of SARS-CoV-2 variants with increased transmissibility necessitates rapid detection and characterization of new lineages. To address this need, we developed PyR0, a hierarchical Bayesian multinomial logistic regression model that infers relative transmissibility of all viral lineages across geographic regions, detects lineages increasing in prevalence, and identifies mutations relevant to transmissibility. Applying PyR0 to all publicly available SARS-CoV-2 genomes, we identify numerous substitutions that increase transmissibility, including previously identified spike mutations and many non-spike mutations within the nucleocapsid and nonstructural proteins. PyR0 forecasts growth of new lineages from their mutational profile, identifies viral lineages of concern as they emerge, and prioritizes mutations of biological and public health concern for functional characterization.