Yolk sac cell atlas reveals multiorgan functions during human early development.

The extraembryonic yolk sac (YS) ensures delivery of nutritional support and oxygen to the developing embryo but remains ill-defined in humans. We therefore assembled a comprehensive multiomic reference of the human YS from 3 to 8 postconception weeks by integrating single-cell protein and gene expression data. Beyond its recognized role as a site of hematopoiesis, we highlight roles in metabolism, coagulation, vascular development, and hematopoietic regulation. We reconstructed the emergence and decline of YS hematopoietic stem and progenitor cells from hemogenic endothelium and revealed a YS-specific accelerated route to macrophage production that seeds developing organs. The multiorgan functions of the YS are superseded as intraembryonic organs develop, effecting a multifaceted relay of vital functions as pregnancy proceeds.

High-grade serous ovarian carcinoma organoids as models of chromosomal instability.

High-grade serous ovarian carcinoma (HGSOC) is the most genomically complex cancer, characterized by ubiquitous TP53 mutation, profound chromosomal instability, and heterogeneity. The mutational processes driving chromosomal instability in HGSOC can be distinguished by specific copy number signatures. To develop clinically relevant models of these mutational processes we derived 15 continuous HGSOC patient-derived organoids (PDOs) and characterized them using bulk transcriptomic, bulk genomic, single-cell genomic, and drug sensitivity assays. We show that HGSOC PDOs comprise communities of different clonal populations and represent models of different causes of chromosomal instability including homologous recombination deficiency, chromothripsis, tandem-duplicator phenotype, and whole genome duplication. We also show that these PDOs can be used as exploratory tools to study transcriptional effects of copy number alterations as well as compound-sensitivity tests. In summary, HGSOC PDO cultures provide validated genomic models for studies of specific mutational processes and precision therapeutics.

ZFTA Translocations Constitute Ependymoma Chromatin Remodeling and Transcription Factors.

ZFTA (C11orf95)-a gene of unknown function-partners with a variety of transcriptional coactivators in translocations that drive supratentorial ependymoma, a frequently lethal brain tumor. Understanding the function of ZFTA is key to developing therapies that inhibit these fusion proteins. Here, using a combination of transcriptomics, chromatin immunoprecipitation sequencing, and proteomics, we interrogated a series of deletion-mutant genes to identify a tripartite transformation mechanism of ZFTA-containing fusions, including: spontaneous nuclear translocation, extensive chromatin binding, and SWI/SNF, SAGA, and NuA4/Tip60 HAT chromatin modifier complex recruitment. Thereby, ZFTA tethers fusion proteins across the genome, modifying chromatin to an active state and enabling its partner transcriptional coactivators to promote promiscuous expression of a transforming transcriptome. Using mouse models, we validate further those elements of ZFTA-fusion proteins that are critical for transformation-including ZFTA zinc fingers and partner gene transactivation domains-thereby unmasking vulnerabilities for therapeutic targeting. SIGNIFICANCE: Ependymomas are hard-to-treat brain tumors driven by translocations between ZFTA and a variety of transcriptional coactivators. We dissect the transforming mechanism of these fusion proteins and identify protein domains indispensable for tumorigenesis, thereby providing insights into the molecular basis of ependymoma tumorigenesis and vulnerabilities for therapeutic targeting.This article is highlighted in the In This Issue feature, p. 2113.

KHS101 disrupts energy metabolism in human glioblastoma cells and reduces tumor growth in mice.

Pharmacological inhibition of uncontrolled cell growth with small-molecule inhibitors is a potential strategy for treating glioblastoma multiforme (GBM), the most malignant primary brain cancer. We showed that the synthetic small-molecule KHS101 promoted tumor cell death in diverse GBM cell models, independent of their tumor subtype, and without affecting the viability of noncancerous brain cell lines. KHS101 exerted cytotoxic effects by disrupting the mitochondrial chaperone heat shock protein family D member 1 (HSPD1). In GBM cells, KHS101 promoted aggregation of proteins regulating mitochondrial integrity and energy metabolism. Mitochondrial bioenergetic capacity and glycolytic activity were selectively impaired in KHS101-treated GBM cells. In two intracranial patient-derived xenograft tumor models in mice, systemic administration of KHS101 reduced tumor growth and increased survival without discernible side effects. These findings suggest that targeting of HSPD1-dependent metabolic pathways might be an effective strategy for treating GBM.

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.

Exposure to Traffic-Related Air Pollution and Serum Inflammatory Cytokines in Children.

BACKGROUND: Long-term exposure to ambient air pollution can lead to adverse health effects in children; however, underlying biological mechanisms are not fully understood. OBJECTIVES: We evaluated the effect of air pollution exposure during different time periods on mRNA expression as well as circulating levels of inflammatory cytokines in children. METHODS: We measured a panel of 10 inflammatory markers in peripheral blood samples from 670 8-y-old children in the Barn/Child, Allergy, Milieu, Stockholm, Epidemiology (BAMSE) birth cohort. Outdoor concentrations of nitrogen dioxide (NO2) and particulate matter (PM) with aerodynamic diameter <10 µm (PM10) from road traffic were estimated for residential, daycare, and school addresses using dispersion modeling. Time-weighted average exposures during infancy and at biosampling were linked to serum cytokine levels using linear regression analysis. Furthermore, gene expression data from 16-year-olds in BAMSE (n=238) were used to evaluate links between air pollution exposure and expression of genes coding for the studied inflammatory markers. RESULTS: A 10 µg/m3 increase of NO2 exposure during infancy was associated with a 13.6% (95% confidence interval (CI): 0.8; 28.1%) increase in interleukin-6 (IL-6) levels, as well as with a 27.8% (95% CI: 4.6, 56.2%) increase in IL-10 levels, the latter limited to children with asthma. However, no clear associations were observed for current exposure. Results were similar using PM10, which showed a high correlation with NO2. The functional analysis identified several differentially expressed genes in response to air pollution exposure during infancy, including IL10, IL13, and TNF;. CONCLUSION: Our results indicate alterations in systemic inflammatory markers in 8-y-old children in relation to early-life exposure to traffic-related air pollution. https://doi.org/10.1289/EHP460.

Genome-Wide Interaction Analysis of Air Pollution Exposure and Childhood Asthma with Functional Follow-up.

RATIONALE: The evidence supporting an association between traffic-related air pollution exposure and incident childhood asthma is inconsistent and may depend on genetic factors. OBJECTIVES: To identify gene-environment interaction effects on childhood asthma using genome-wide single-nucleotide polymorphism (SNP) data and air pollution exposure. Identified loci were further analyzed at epigenetic and transcriptomic levels. METHODS: We used land use regression models to estimate individual air pollution exposure (represented by outdoor NO2 levels) at the birth address and performed a genome-wide interaction study for doctors' diagnoses of asthma up to 8 years in three European birth cohorts (n = 1,534) with look-up for interaction in two separate North American cohorts, CHS (Children's Health Study) and CAPPS/SAGE (Canadian Asthma Primary Prevention Study/Study of Asthma, Genetics and Environment) (n = 1,602 and 186 subjects, respectively). We assessed expression quantitative trait locus effects in human lung specimens and blood, as well as associations among air pollution exposure, methylation, and transcriptomic patterns. MEASUREMENTS AND MAIN RESULTS: In the European cohorts, 186 SNPs had an interaction P < 1 × 10-4 and a look-up evaluation of these disclosed 8 SNPs in 4 loci, with an interaction P < 0.05 in the large CHS study, but not in CAPPS/SAGE. Three SNPs within adenylate cyclase 2 (ADCY2) showed the same direction of the interaction effect and were found to influence ADCY2 gene expression in peripheral blood (P = 4.50 × 10-4). One other SNP with P < 0.05 for interaction in CHS, rs686237, strongly influenced UDP-Gal:betaGlcNAc ß-1,4-galactosyltransferase, polypeptide 5 (B4GALT5) expression in lung tissue (P = 1.18 × 10-17). Air pollution exposure was associated with differential discs, large homolog 2 (DLG2) methylation and expression. CONCLUSIONS: Our results indicated that gene-environment interactions are important for asthma development and provided supportive evidence for interaction with air pollution for ADCY2, B4GALT5, and DLG2.

Cumulative impact of common genetic variants and other risk factors on colorectal cancer risk in 42,103 individuals.

OBJECTIVE: Colorectal cancer (CRC) has a substantial heritable component. Common genetic variation has been shown to contribute to CRC risk. A study was conducted in a large multi-population study to assess the feasibility of CRC risk prediction using common genetic variant data combined with other risk factors. A risk prediction model was built and applied to the Scottish population using available data. DESIGN: Nine populations of European descent were studied to develop and validate CRC risk prediction models. Binary logistic regression was used to assess the combined effect of age, gender, family history (FH) and genotypes at 10 susceptibility loci that individually only modestly influence CRC risk. Risk models were generated from case-control data incorporating genotypes alone (n=39,266) and in combination with gender, age and FH (n=11,324). Model discriminatory performance was assessed using 10-fold internal cross-validation and externally using 4187 independent samples. The 10-year absolute risk was estimated by modelling genotype and FH with age- and gender-specific population risks. RESULTS: The median number of risk alleles was greater in cases than controls (10 vs 9, p<2.2 × 10(-16)), confirmed in external validation sets (Sweden p=1.2 × 10(-6), Finland p=2 × 10(-5)). The mean per-allele increase in risk was 9% (OR 1.09; 95% CI 1.05 to 1.13). Discriminative performance was poor across the risk spectrum (area under curve for genotypes alone 0.57; area under curve for genotype/age/gender/FH 0.59). However, modelling genotype data, FH, age and gender with Scottish population data shows the practicalities of identifying a subgroup with >5% predicted 10-year absolute risk. CONCLUSION: Genotype data provide additional information that complements age, gender and FH as risk factors, but individualised genetic risk prediction is not currently feasible. Nonetheless, the modelling exercise suggests public health potential since it is possible to stratify the population into CRC risk categories, thereby informing targeted prevention and surveillance.

Common variation near CDKN1A, POLD3 and SHROOM2 influences colorectal cancer risk.

We performed a meta-analysis of five genome-wide association studies to identify common variants influencing colorectal cancer (CRC) risk comprising 8,682 cases and 9,649 controls. Replication analysis was performed in case-control sets totaling 21,096 cases and 19,555 controls. We identified three new CRC risk loci at 6p21 (rs1321311, near CDKN1A; P = 1.14 × 10(-10)), 11q13.4 (rs3824999, intronic to POLD3; P = 3.65 × 10(-10)) and Xp22.2 (rs5934683, near SHROOM2; P = 7.30 × 10(-10)) This brings the number of independent loci associated with CRC risk to 20 and provides further insight into the genetic architecture of inherited susceptibility to CRC.

Multiple common susceptibility variants near BMP pathway loci GREM1, BMP4, and BMP2 explain part of the missing heritability of colorectal cancer.

Genome-wide association studies (GWAS) have identified 14 tagging single nucleotide polymorphisms (tagSNPs) that are associated with the risk of colorectal cancer (CRC), and several of these tagSNPs are near bone morphogenetic protein (BMP) pathway loci. The penalty of multiple testing implicit in GWAS increases the attraction of complementary approaches for disease gene discovery, including candidate gene- or pathway-based analyses. The strongest candidate loci for additional predisposition SNPs are arguably those already known both to have functional relevance and to be involved in disease risk. To investigate this proposition, we searched for novel CRC susceptibility variants close to the BMP pathway genes GREM1 (15q13.3), BMP4 (14q22.2), and BMP2 (20p12.3) using sample sets totalling 24,910 CRC cases and 26,275 controls. We identified new, independent CRC predisposition SNPs close to BMP4 (rs1957636, P = 3.93×10(-10)) and BMP2 (rs4813802, P = 4.65×10(-11)). Near GREM1, we found using fine-mapping that the previously-identified association between tagSNP rs4779584 and CRC actually resulted from two independent signals represented by rs16969681 (P = 5.33×10(-8)) and rs11632715 (P = 2.30×10(-10)). As low-penetrance predisposition variants become harder to identify-owing to small effect sizes and/or low risk allele frequencies-approaches based on informed candidate gene selection may become increasingly attractive. Our data emphasise that genetic fine-mapping studies can deconvolute associations that have arisen owing to independent correlation of a tagSNP with more than one functional SNP, thus explaining some of the apparently missing heritability of common diseases.