Large, Diverse Population Cohorts of hiPSCs and Derived Hepatocyte-like Cells Reveal Functional Genetic Variation at Blood Lipid-Associated Loci.

paper Cited Public

Authors: Pashos, Evanthia E; Park, YoSon; Wang, Xiao; Raghavan, Avanthi; Yang, Wenli; Abbey, Deepti; Peters, Derek T; Arbelaez, Juan; Hernandez, Mayda; Kuperwasser, Nicolas; Li, Wenjun; Lian, Zhaorui; Liu, Ying; Lv, Wenjian; Lytle-Gabbin, Stacey L; Marchadier, Dawn H; Rogov, Peter; Shi, Jianting; Slovik, Katherine J; Stylianou, Ioannis M; Wang, Li; Yan, Ruilan; Zhang, Xiaolan; Kathiresan, Sekar; Duncan, Stephen A; Mikkelsen, Tarjei S; Morrisey, Edward E; Rader, Daniel J; Brown, Christopher D; Musunuru, Kiran
Year: 2017
Journal: Cell stem cell
PMID: 28388432
DOI: 10.1016/j.stem.2017.03.017
PMCID: PMC5476422

Figure 1

Genome-wide Mapping of Expression Quantitative Trait Loci (eQTLs)(A) Overview of the single-tissue cis-eQTL mapping results. Representative HLC eGenes are indicated.(B) Venn diagram of mapped eGenes with a stringent cutoff of FDR < 5%.See also Figures S1, S2, and S3.

LLM interpretation

Figure A consists of three Manhattan plots showing genome-wide cis-eQTL mapping results across chromosomes for iPSCs (cyan), HLCs (red), and GTEx livers (blue), with the y-axis representing $-\log(P)$ and specific eGenes labeled at the top. Figure B is a Venn diagram illustrating the overlap of eGenes (FDR < 0.05) among the three groups, showing 301 shared eGenes and varying numbers of group-specific eGenes (e.g., 2227 for iPSCs, 1086 for HLCs, and 804 for GTEx livers).

Figure 2

HLC eGenes and Candidate Functional Variants from MPRAs(A) Venn diagram of mapped eGenes at GLGC loci with a stringent cutoff of FDR < 5% (also see Table S7). In bold and underlined are eGenes for which functional studies were performed.(B) Top-ranked GLGC HLC eGenes. The displayed SNP in the third column is the GWAS SNP (or one of a set of GWAS SNPs in perfect LD in the cohort) in the locus that displayed the strongest association with the eGene. The displayed SNP in the seventh column is the GWAS SNP in the locus that displayed the strongest association in a sensitivity analysis with 63 higher-quality HLC samples. In bold are eGenes for which functional studies were performed.(C) MPRAs identified rs2277862, rs10889356, and rs10872142 as the SNPs with the greatest allele-specific regulatory activity in the CPNE1, ANGPTL3, and FRK loci, respectively (also see Table S9). Each candidate SNP was represented on a 145-bp tile that was either left-shifted, centered, or right-shifted relative to the SNP, in order to increase the probability of capturing the correct regulatory context for that SNP. For each tile, the individual signals for the two alleles are shown for two independent experiments (where signal refers to the log of median barcode counts for the given tile divided by median barcode counts for all tiles). In the final two columns, a log-ratio of the signals of the two alleles is calculated, along with a P-value (Mann-Whitney U test) for the null hypothesis that the two alleles generate equal signals.

LLM interpretation

This figure consists of three parts: (A) a Venn diagram showing the overlap of eGenes mapped at GLGC loci across HLCs (32), iPSCs (43), and GTEx livers (30), with specific eGenes bolded and underlined. (B) A table ranking top GLGC HLC eGenes, listing associated SNPs and p-values for HLC, iPSC, and GTEx liver conditions. (C) A data table from MPRAs showing allele-specific regulatory signals, log-ratios, and Mann-Whitney U test p-values for three candidate SNPs (rs2277862, rs10889356, and rs10872142) across different tile positions.

Figure 3

Evidence for rs2277862-CPNE1 as a Functional SNP-Gene Set(A) Schematics of human chromosome 20q11 locus showing the relative positions of rs2277862, CPNE1, and ERGIC3 and mouse chromosome 2qH1 locus showing the relative positions of rs27324996, Cpne1, and Ergic3.(B) Top panels: heterozygous knock-in of rs2277862 minor allele with a single-strand DNA oligonucleotide. Representative indels in non-knock-in clones are also shown. Bottom panels: homozygous 38-bp deletions (“knockout” or Δ38/Δ38) encompassing rs2277862 using a dual gRNA approach. A representative agarose gel of PCR amplicons is shown. The protospacers are underlined, the PAMs are bolded, and the SNP position is indicated in red.(C) Left panels: gene expression in undifferentiated HUES 8 cells (n = 2 wild-type clones and 1 knock-in clone; 6 wells per clone) and differentiated HUES 8 HLCs (n = 2 wild-type clones and 1 knock-in clone; 6 wells per clone) normalized to mean expression level in wild-type clones. Right panels: gene expression in undifferentiated HUES 8 cells (n = 10 wild-type and 10 knockout clones, 3 wells per clone) and undifferentiated H7 cells (n = 8 wild-type and 6 knockout clones, 3 wells per clone) normalized to mean expression level in wild-type clones.(D) CRISPR interference at the rs2277862 site. The three gRNA protospacers are underlined, the PAMs are bolded, and the SNP position is indicated in red. The graphs show gene expression, normalized to mean expression level in control cells, in HEK 293T cells transfected with catalytically dead Cas9 (dCas9) with the gRNAs (either singly or in combination, 3 wells per condition). Control cells were transfected with dCas9 without an accompanying gRNA.(E) The noncoding rs2277862 site is well conserved in mouse, including allelic variants of the SNP itself, with the murine equivalent being rs27324996. The SNP position is indicated in red, non-conserved nucleotides are indicated in blue, the gRNA protospacer used to generate the knock-in mouse is underlined, and the PAM is bolded. The electropherogram is from a mouse in which the minor allele of rs2277862/rs27324996 (T) has been knocked into one chromosome, along with four non-conserved nucleotides to “humanize” the site.(F) Gene expression in liver from littermates of the C57BL/6J background (n = 18 wild-type mice and 10 homozygous knock-in mice), normalized to mean expression level in wild-type mice.Data are displayed as means and s.e.m. P-values were calculated with two-tailed Welch’s t-tests.

LLM interpretation

This figure consists of genomic schematics, sequence alignments, and bar charts analyzing the functional impact of the rs2277862 SNP on *CPNE1* expression. Panels A, B, D, and E show chromosomal loci, CRISPR targeting strategies, and sequence conservation between humans and mice. Panels C, D, and F utilize bar charts to show that knock-in, knockout, and CRISPR interference at the rs2277862 site generally reduce *CPNE1* gene expression across various cell types (HUES 8, HLCs, H7, HEK 293T) and mouse liver, while *ERGIC3* expression remains largely unaffected. Statistical significance is indicated by p-values and asterisks (p < 0.05 to p < 0.001).

Figure 4

Evidence for rs10889356-DOCK7 and rs10889356-ANGPTL3 as Functional SNP-Gene Sets(A) Schematic of human chromosome 1p31 locus showing the relative positions of rs10889356, DOCK7, and ANGPTL3.(B) Homozygous knock-in of rs10889356 minor allele using a targeting vector with puromycin resistance encoded within a scarless-excision piggyBac transposon. The protospacer is underlined, the PAM is bolded, and the SNP position is indicated in red.(C) Homozygous 36- to 39-bp deletions (“knockout” or Δ/Δ) encompassing rs10889356 using a dual gRNA approach. The protospacer is underlined, the PAM is bolded, and the SNP position is indicated in red.(D) Gene expression in undifferentiated H7 cells (n = 12 wild-type and 10 homozygous knock-in clones, 6 wells per clone) and differentiated H7 HLCs (n = 6 wild-type and 4 homozygous knock-in, 3 wells per clone), normalized to mean expression level in wild-type clones.(E) Gene expression in undifferentiated H7 cells (n = 12 wild-type and 8 knockout clones, 3 wells per clone) and differentiated H7 HLCs (n = 4 wild-type and 4 knockout clones, 2 wells per clone).(F) CRISPR interference at the rs10889356 site. The three gRNA protospacers are underlined, the PAMs are bolded, and the SNP position is indicated in red. The graphs show gene expression, normalized to mean expression level in control cells, in HepG2 cells transfected with dCas9 with the gRNAs (either singly or in combination, 3 wells per condition).Data are displayed as means and s.e.m. P-values were calculated with two-tailed Welch’s t-tests.

LLM interpretation

This figure consists of genomic diagrams and bar charts analyzing the functional impact of the rs10889356 SNP on *DOCK7* and *ANGPTL3* expression. Panels A-C provide schematics of the 1p31 locus and the CRISPR-based strategies used for homozygous knock-in (B) and knockout (C) of the SNP. Panels D-F use bar charts to show normalized gene expression in H7 cells, H7 HLCs, and HepG2 cells, indicating that the minor allele (A/A) and knockout ($\Delta/\Delta$) generally decrease *DOCK7* expression and increase *ANGPTL3* expression, with statistical significance denoted by p-values and asterisks.

Figure 5

Evidence for rs10872142-FRK as a Functional SNP-Gene Set(A) Schematic of human chromosome 6q22 locus showing the relative positions of rs10872142 and FRK.(B) Heterozygous knock-in of rs10872142 minor allele with a single-strand DNA oligonucleotide. The protospacer is underlined, the PAM is bolded, and the SNP position is indicated in red.(C) Gene expression in undifferentiated 1016 cells (n = 3 wild-type and 3 heterozygous clones, 3 wells per clone) and differentiated 1016 HLCs (n = 3 wild-type and 3 heterozygous clones, 4 wells per clone), normalized to mean expression level in wild-type clones.(D) CRISPR interference at the rs10872142 site. The two gRNA protospacers are underlined, the PAMs are bolded, and the SNP position is indicated in red. The graphs show gene expression, normalized to mean expression level in control cells, in HEK 293T cells transfected with dCas9 with the gRNAs (either singly or in combination, 6 wells per condition).Data are displayed as means and s.e.m. P-values were calculated with two-tailed Welch’s t-tests.

LLM interpretation

This figure consists of genomic schematics, sequence alignments, and bar charts analyzing the rs10872142 SNP and its effect on *FRK* expression. Panel A shows the locus on chromosome 6, while panels B and D detail the DNA sequences for knock-in and CRISPR interference targeting the SNP. Panel C contains two bar charts showing normalized *FRK* expression, with a significant decrease in heterozygous knock-in clones compared to wild-type in undifferentiated 1016 cells ($P=0.04$), but no significant difference in 1016 HLCs. Panel D shows a bar chart where CRISPR interference using gRNA1, gRNA2, or both significantly reduces *FRK* expression in HEK 293T cells compared to the control ($P < 0.01$ or $P < 0.001$).

Figure 6

Interrogation of candidate genes for effects on blood lipid levels in mice(A) Proportional changes in blood TG or HDL-C levels before versus after AAV8-mediated gene overexpression in liver, normalized to mice that received control AAV8 vectors (n = 7 mice per group). Two independent experiments are shown for each gene except ERGIC3.(B) Blood triglyceride and cholesterol levels in wild-type versus Angptl3 knockout mice (n = 17 wild-type and 6 knockout mice), normalized to mean expression levels in wild-type mice.Data are displayed as means and s.e.m. P-values were calculated with two-tailed Welch’s t-tests.

LLM interpretation

Figure A consists of several bar charts showing proportional changes in blood HDL-C or TG levels compared to controls following AAV8-mediated gene overexpression in different mouse models. For genes *CPNE1*, *UBE2L3*, and *ACAA2* in C57BL/6J mice, and *VKORC1* in $Ldlr^{-/-}; Apobec1^{-/-}; Tg(APOB)$ mice, significant changes are indicated by p-values ranging from $P=0.03$ to $P=0.001$. Figure B is a bar chart comparing normalized TG and cholesterol levels between wild-type and *Angptl3* knockout C57BL/6J mice, showing a significant decrease in both lipids for the knockout group ($P<0.0001$ for TG and $P=0.005$ for cholesterol).

#	Section	Preview
80	STAR*METHODS — METHOD DETAILS — CRISPR knock-in and knockout mice	The generation of Angptl3 knockout mice was performed essentially as described above, except that a…
81	STAR*METHODS — METHOD DETAILS — Quantitative reverse transcriptase-polymerase chain reaction	Snap-frozen livers were used for RNA preparation with either the QIAsymphony RNA Kit on the…
82	STAR*METHODS — METHOD DETAILS — Quantitative reverse transcriptase-polymerase chain reaction	(Assay ID 4326319E), human ACTB (Assay ID 4310881E), or mouse Actb (Assay ID 4352341E) was used as…
83	STAR*METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS	All details related to the statistical parameters for the reported experiments are included in the…
84	STAR*METHODS — DATA AND SOFTWARE AVAILABILITY — Data Resources	The RNA-seq and genotype data generated as a part of this study have been deposited in dbGaP (Study…

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Self-Organizing Human Induced Pluripotent Stem Cell Hepatocyte 3D Organoids Inform the Biology of the Pleiotropic <i>TRIB1</i> Gene.	Abbey D et al.	—	2020	→
Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression.	Cuomo ASE et al.	—	2020	→
Systematic identification of <i>cis</i>-regulatory variants that cause gene expression differences in a yeast cross.	Renganaath K et al.	—	2020	→
Allele-specific NKX2-5 binding underlies multiple genetic associations with human electrocardiographic traits.	Benaglio P et al.	—	2019	→
Derivation and applications of human hepatocyte-like cells.	Li S et al.	—	2019	→
Functional Genomics and CRISPR Applied to Cardiovascular Research and Medicine.	Li F et al.	—	2019	→
Genetics of Common, Complex Coronary Artery Disease.	Musunuru K et al.	—	2019	→
hiPSCs in cardio-oncology: deciphering the genomics.	Pinheiro EA et al.	—	2019	→
Humanising the mouse genome piece by piece.	Zhu F et al.	—	2019	→
Identifying Extrinsic versus Intrinsic Drivers of Variation in Cell Behavior in Human iPSC Lines from Healthy Donors.	Vigilante A et al.	—	2019	→
Induced pluripotent stem cells in disease modelling and drug discovery.	Rowe RG et al.	—	2019	→
iPSC-Derived Hepatocytes as a Platform for Disease Modeling and Drug Discovery.	Corbett JL et al.	—	2019	→
Modeling Disease with Human Inducible Pluripotent Stem Cells.	Grandy R et al.	—	2019	→
Modeling Leukemia with Human Induced Pluripotent Stem Cells.	Papapetrou EP	—	2019	→
New considerations for hiPSC-based models of neuropsychiatric disorders.	Hoffman GE et al.	—	2019	→
Pharmaceutical Research for Inherited Metabolic Disorders of the Liver Using Human Induced Pluripotent Stem Cell and Genome Editing Technologies.	Yamashita T et al.	—	2019	→
Race, Natriuretic Peptides, and High-Carbohydrate Challenge: A Clinical Trial.	Patel N et al.	—	2019	→
Role of sortilin in lipid metabolism.	Conlon DM	—	2019	→
Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression	Cuomo AS et al.	—	2019	—
Systems-Wide Approaches in Induced Pluripotent Stem Cell Models.	Lau E et al.	—	2019	→
The Use of Human Pluripotent Stem Cells for Modeling Liver Development and Disease.	Heslop JA et al.	—	2019	→
Tissue-specific epigenetics of atherosclerosis-related ANGPT and ANGPTL genes.	Ehrlich KC et al.	—	2019	→
Trouble With Tribbles-1.	Jadhav KS et al.	—	2019	→
Analysis of differentially expressed genes among human hair follicle-derived iPSCs, induced hepatocyte-like cells, and primary hepatocytes.	Xu Z et al.	—	2018	→
Cell-based liver therapies: past, present and future.	Iansante V et al.	—	2018	→
Confirmation of Causal rs9349379- PHACTR1 Expression Quantitative Trait Locus in Human-Induced Pluripotent Stem Cell Endothelial Cells.	Wang X et al.	—	2018	→
CRISPR Gene Editing in the Kidney.	Cruz NM et al.	—	2018	→
Emerging applications of genome-editing technology to examine functionality of GWAS-associated variants for complex traits.	Smith AJP et al.	—	2018	→
From Genotype to Phenotype: A Primer on the Functional Follow-up of Genome-Wide Association Studies in Cardiovascular Disease.	Lin J et al.	—	2018	→
Functional Assays to Screen and Dissect Genomic Hits: Doubling Down on the National Investment in Genomic Research.	Musunuru K et al.	—	2018	→
Functional genomics and assays of regulatory activity detect mechanisms at loci for lipid traits and coronary artery disease.	Roman TS et al.	—	2018	→
Genetics of Alcohol Use Disorder: A Role for Induced Pluripotent Stem Cells?	Prytkova I et al.	—	2018	→
Humanity in a Dish: Population Genetics with iPSCs.	Warren CR et al.	—	2018	→
Human Pluripotent Stem Cell-Derived Endoderm for Modeling Development and Clinical Applications.	Yiangou L et al.	—	2018	→
Impact of regulatory variation across human iPSCs and differentiated cells.	Banovich NE et al.	—	2018	→
Induced Pluripotent Stem Cells for Cardiovascular Disease Modeling and Precision Medicine: A Scientific Statement From the American Heart Association.	Musunuru K et al.	—	2018	→
Interdisciplinary Models for Research and Clinical Endeavors in Genomic Medicine: A Scientific Statement From the American Heart Association.	Musunuru K et al.	—	2018	→
Interrogation of the Atherosclerosis-Associated <i>SORT1</i> (Sortilin 1) Locus With Primary Human Hepatocytes, Induced Pluripotent Stem Cell-Hepatocytes, and Locus-Humanized Mice.	Wang X et al.	—	2018	→
Mapping regulatory variants in hiPSC models.	Hoffman GE et al.	—	2018	→
Modelling Sporadic Alzheimer's Disease Using Induced Pluripotent Stem Cells.	Rowland HA et al.	—	2018	→
Molecular and functional variation in iPSC-derived sensory neurons.	Schwartzentruber J et al.	—	2018	→
Patterns of shared signatures of recent positive selection across human populations.	Johnson KE et al.	—	2018	→
Renal compartment-specific genetic variation analyses identify new pathways in chronic kidney disease.	Qiu C et al.	—	2018	→
Stem cell modeling of lipid genetics.	Musunuru K	—	2018	→
Stem Cells, Genome Editing, and the Path to Translational Medicine.	Soldner F et al.	—	2018	→
The future role of pharmacogenomics in anticancer agent-induced cardiovascular toxicity.	Magdy T et al.	—	2018	→
Using Human Induced Pluripotent Stem Cell-derived Hepatocyte-like Cells for Drug Discovery.	Liu JT et al.	—	2018	→
Cardiovascular endocrinology: Is ANGPTL3 the next PCSK9?	Musunuru K et al.	—	2017	→
Genome Editing: The Recent History and Perspective in Cardiovascular Diseases.	Musunuru K	—	2017	→
Lessons from ten years of genome-wide association studies of asthma.	Vicente CT et al.	—	2017	→
Modeling Inborn Errors of Hepatic Metabolism Using Induced Pluripotent Stem Cells.	Pournasr B et al.	—	2017	→
The NextGen Genetic Association Studies Consortium: A Foray into In Vitro Population Genetics.	Warren CR et al.	—	2017	→
Treatment of Dyslipidemia Using CRISPR/Cas9 Genome Editing.	Chadwick AC et al.	—	2017	→
Variability of human pluripotent stem cell lines.	Ortmann D et al.	—	2017	→
What do polymorphisms tell us about the mechanisms of COPD?	Li Y et al.	—	2017	→
[Leukocyte count of puerperal sows].	Mäde D et al.	—	1996	→