Consequences of interleukin 1ß-triggered chronic inflammation in the mouse prostate gland: Altered architecture associated with prolonged CD4+ infiltration mimics human proliferative inflammatory atrophy.

BACKGROUND: Elevated expression of the proinflammatory cytokine interleukin 1ß (IL-1ß) has been observed in expressed prostatic secretions of patients with chronic prostatitis/chronic pelvic pain syndrome, and genetic polymorphisms associated with the IL1B gene are linked to increased risk for aggressive prostate cancer. METHODS: To study the role of IL-1ß expression in prostate inflammation, we examined IL1B expression in human prostatic proliferative inflammatory atrophy (PIA) lesions and developed a tetracycline-regulated human IL1B transgene in the mouse prostate. RESULTS: Here, we demonstrate that IL1B expression is a common finding in human PIA lesions, which harbored focal IL1B expression in epithelial and stromal compartments. Human IL1B expression in the mouse prostate elicited acute and chronic inflammation. Penetrance and expressivity were variable and tunable by altering transgene dosage and the presence of an exogenous inducible marker antigen (green fluorescent protein). Inflammation was characterized by infiltration of CD4+ T cells, demonstrating an adaptive immune response. Chronic inflammation persisted after doxycycline (Dox) withdrawal. Reactive epithelia increased expression of downstream cytokines, and altered glandular architecture was observed upon sustained induction of IL1B. Immunohistochemical analyses revealed a higher proliferative index and decreased Nkx3.1 expression in inflamed mouse prostates. CONCLUSIONS: These data implicate IL-1ß in human prostate pathology and this model provides a versatile platform to interrogate molecular mechanisms of inflammation-associated prostate pathologies associated with episodic or sustained IL-1ß expression.

Transcriptome data are insufficient to control false discoveries in regulatory network inference.

Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.

Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks.

Background: Gene co-expression networks (GCNs) describe relationships among expressed genes key to maintaining cellular identity and homeostasis. However, the small sample size of typical RNA-seq experiments which is several orders of magnitude fewer than the number of genes is too low to infer GCNs reliably. recount3, a publicly available dataset comprised of 316,443 uniformly processed human RNA-seq samples, provides an opportunity to improve power for accurate network reconstruction and obtain biological insight from the resulting networks. Results: We compared alternate aggregation strategies to identify an optimal workflow for GCN inference by data aggregation and inferred three consensus networks: a universal network, a non-cancer network, and a cancer network in addition to 27 tissue context-specific networks. Central network genes from our consensus networks were enriched for evolutionarily constrained genes and ubiquitous biological pathways, whereas central context-specific network genes included tissue-specific transcription factors and factorization based on the hubs led to clustering of related tissue contexts. We discovered that annotations corresponding to context-specific networks inferred from aggregated data were enriched for trait heritability beyond known functional genomic annotations and were significantly more enriched when we aggregated over a larger number of samples. Conclusion: This study outlines best practices for network GCN inference and evaluation by data aggregation. We recommend estimating and regressing confounders in each data set before aggregation and prioritizing large sample size studies for GCN reconstruction. Increased statistical power in inferring context-specific networks enabled the derivation of variant annotations that were enriched for concordant trait heritability independent of functional genomic annotations that are context-agnostic. While we observed strictly increasing held-out log-likelihood with data aggregation, we noted diminishing marginal improvements. Future directions aimed at alternate methods for estimating confounders and integrating orthogonal information from modalities such as Hi-C and ChIP-seq can further improve GCN inference.

Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms.

Background: Understanding the genetic causes for variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from diverse distinct projects and laboratories. Results: We report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1,454 unique donor individuals across 653 studies from public databases, we catalog 23,381 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often strongly linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility and diverse complex human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes. Conclusions: Our work provides a proof of principle for caQTL calling from previously ungenotyped samples, and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.

Human telomere length is chromosome end-specific and conserved across individuals.

Short telomeres cause age-related disease, and long telomeres contribute to cancer; however, the mechanisms regulating telomere length are unclear. We developed a nanopore-based method, which we call Telomere Profiling, to determine telomere length at nearly single-nucleotide resolution. Mapping telomere reads to chromosome ends showed chromosome end-specific length distributions that could differ by more than six kilobases. Examination of telomere lengths in 147 individuals revealed that certain chromosome ends were consistently longer or shorter. The same rank order was found in newborn cord blood, suggesting that telomere length is determined at birth and that chromosome end-specific telomere length differences are maintained as telomeres shorten with age. Telomere Profiling makes precision investigation of telomere length widely accessible for laboratory, clinical, and drug discovery efforts and will allow deeper insights into telomere biology.

Human telomere length is chromosome specific and conserved across individuals.

Short telomeres cause age-related disease and long telomeres predispose to cancer; however, the mechanisms regulating telomere length are unclear. To probe these mechanisms, we developed a nanopore sequencing method, Telomere Profiling, that is easy to implement, precise, and cost effective with broad applications in research and the clinic. We sequenced telomeres from individuals with short telomere syndromes and found similar telomere lengths to the clinical FlowFISH assay. We mapped telomere reads to specific chromosome end and identified both chromosome end-specific and haplotype-specific telomere length distributions. In the T2T HG002 genome, where the average telomere length is 5kb, we found a remarkable 6kb difference in lengths between some telomeres. Further, we found that specific chromosome ends were consistently shorter or longer than the average length across 147 individuals. The presence of conserved chromosome end-specific telomere lengths suggests there are new paradigms in telomere biology that are yet to be explored. Understanding the mechanisms regulating length will allow deeper insights into telomere biology that can lead to new approaches to disease.

Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes.

Genome-wide association studies (GWAS) have become well-powered to detect loci associated with telomere length. However, no prior work has validated genes nominated by GWAS to examine their role in telomere length regulation. We conducted a multi-ancestry meta-analysis of 211,369 individuals and identified five novel association signals. Enrichment analyses of chromatin state and cell-type heritability suggested that blood/immune cells are the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6 or POP5 and demonstrated that both lengthened telomeres. CRISPR/Cas9 deletion of the predicted causal regions in K562 blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5. Our results demonstrate the utility of telomere length GWAS in the identification of telomere length regulation mechanisms and validate KBTBD6 and POP5 as genes affecting telomere length regulation.

Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects.

Differential allele-specific expression (ASE) is a powerful tool to study context-specific cis-regulation of gene expression. Such effects can reflect the interaction between genetic or epigenetic factors and a measured context or condition. Single-cell RNA sequencing (scRNA-seq) allows the measurement of ASE at individual-cell resolution, but there is a lack of statistical methods to analyze such data. We present Differential Allelic Expression using Single-Cell data (DAESC), a powerful method for differential ASE analysis using scRNA-seq from multiple individuals, with statistical behavior confirmed through simulation. DAESC accounts for non-independence between cells from the same individual and incorporates implicit haplotype phasing. Application to data from 105 induced pluripotent stem cell (iPSC) lines identifies 657 genes dynamically regulated during endoderm differentiation, with enrichment for changes in chromatin state. Application to a type-2 diabetes dataset identifies several differentially regulated genes between patients and controls in pancreatic endocrine cells. DAESC is a powerful method for single-cell ASE analysis and can uncover novel insights on gene regulation.

Genetic determinants of telomere length from 109,122 ancestrally diverse whole-genome sequences in TOPMed.

Genetic studies on telomere length are important for understanding age-related diseases. Prior GWAS for leukocyte TL have been limited to European and Asian populations. Here, we report the first sequencing-based association study for TL across ancestrally-diverse individuals (European, African, Asian and Hispanic/Latino) from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We used whole genome sequencing (WGS) of whole blood for variant genotype calling and the bioinformatic estimation of telomere length in n=109,122 individuals. We identified 59 sentinel variants (p-value <5×10-9) in 36 loci associated with telomere length, including 20 newly associated loci (13 were replicated in external datasets). There was little evidence of effect size heterogeneity across populations. Fine-mapping at OBFC1 indicated the independent signals colocalized with cell-type specific eQTLs for OBFC1 (STN1). Using a multi-variant gene-based approach, we identified two genes newly implicated in telomere length, DCLRE1B (SNM1B) and PARN. In PheWAS, we demonstrated our TL polygenic trait scores (PTS) were associated with increased risk of cancer-related phenotypes.

Tel1 Activation by the MRX Complex Is Sufficient for Telomere Length Regulation but Not for the DNA Damage Response in Saccharomyces cerevisiae.

Previous models suggested that regulation of telomere length in Saccharomyces cerevisiae by Tel1(ATM) and Mec1(ATR) would parallel the established pathways regulating the DNA damage response. Here, we provide evidence that telomere length regulation differs from the DNA damage response in both the Tel1 and Mec1 pathways. We found that Rad53 mediates a Mec1 telomere length regulation pathway but is dispensable for Tel1 telomere length regulation, whereas in the DNA damage response, Rad53 is regulated by both Mec1 and Tel1 Using epistasis analysis with a Tel1 hypermorphic allele, Tel1-hy909, we found that the MRX complex is not required downstream of Tel1 for telomere elongation but is required downstream of Tel1 for the DNA damage response. Our data suggest that nucleolytic telomere end processing is not a required step for telomerase to elongate telomeres.