Somatic Mutations Increase Hepatic Clonal Fitness and Regeneration in Chronic Liver Disease

Abstract

Normal tissues accumulate genetic changes with age, but it is unknown if somatic mutations promote clonal expansion of non-malignant cells in the setting of chronic degenerative diseases. Exome sequencing of diseased liver samples from 82 patients revealed a complex mutational landscape in cirrhosis. Additional ultra-deep sequencing identified recurrent mutations in PKD1, PPARGC1B, KMT2D, and ARID1A. The number and size of mutant clones increased as a function of fibrosis stage and tissue damage. To interrogate the functional impact of mutated genes, a pooled in vivo CRISPR screening approach was established. In agreement with sequencing results, examination of 147 genes again revealed that loss of Pkd1, Kmt2d, and Arid1a promoted clonal expansion. Conditional heterozygous deletion of these genes in mice was also hepatoprotective in injury assays. Pre-malignant somatic alterations are often viewed through the lens of cancer, but we show that mutations can promote regeneration, likely independent of carcinogenesis.

Figure 1.. Whole exome sequencing reveals mutational burden within diseased livers.

A. Schema for liver tissue sampling. Fresh frozen non-malignant liver tissues from HCC surgical resections or livers removed during transplant surgeries were obtained for genomic DNA. The adjacent tissue was sectioned for histology. Sequenced tissue weights are shown on the right. B. Number of mutations found in each patient sample. Classifications in the pie chart: among 389 total mutations, 260 are missense, 13 are nonsense, 4 are splice-site, 1 is a frameshift deletion and 111 are synonymous mutations. C. Mutation count and rate in liver samples compared with cancer types. Mutation count equals the absolute number of mutations per patient sample with VAF > 5%. Mutation rate equals the sums of 2 times the VAFs for each mutation. The Wilcoxon Rank-Sum Test was used to compare mutation counts and rates between tissue types. D. Classes of missense mutations or SNVs in liver tissues and HCC samples from TCGA. E. The 4 mutation signatures with the highest cosine similarity are shown. F. Correlation between mutation count and fibrosis stage. The p-value is calculated based on the one-way Jonckheere trend test. G. Correlation between mutation count and ALT or AST, which are serum markers of hepatic damage. H. VAF distribution for whole exome data. Mean VAF is 10.5% (+/− 0.514% SEM, with 95% confidence interval) and median VAF is 8.7%. All data are presented as mean ± SEM. *, p<0.05, **, p<0.01,***, p<0.001,****, p<0.0001.

Figure 2.. Ultra-deep targeted sequencing reveals recurrently mutated genes within diseased liver tissues.

Sequencing of 136 genes was performed in 129 liver samples and paired blood from 61 patients. The waterfall plot for 26 genes with recurrent mutations is shown here. Genes previously found to be recurrently mutated in the HCC TCGA study are labeled in red.

Figure 3.. A comparison of mutant clone and nodule volumes indicates clonal expansion with increasing liver fibrosis.

A. The formula for nodule volume calculations based on measured nodule dimensions. B. Individual mutant clone volumes calculated from ultra-deep sequencing VAFs (upper). Mutant clone volumes from F0–3 and F4 livers were compared (lower). There are 15 clones in F0–3 and 85 clones in F4 samples and each clone is represented by a blue circle. C. Individual nodule volumes calculated using the measurements obtained in Figure 3A (upper). Nodule volumes from F0–3 and F4 livers were compared (lower). We measured 22 nodules in 11 F0–3 samples and 100 nodules in 53 F4 samples. Each nodule is a red circle. D. Ratios of individual mutant clone volumes / average nodule volume of each sample (upper). The ratios from F0–3 and F4 livers were compared (lower). Each ratio is a green circle.

Figure 4.. Venn diagrams representing livers sequenced at multiple locations.

In these diagrams, each box represents one patient, each circle represents one piece of liver, and each number represents the mutation count within a piece. Intersecting circles depict mutations that are shared. Circle size scales with mutation number, not tissue size. A table of mutation counts per piece of liver is at the bottom.

Figure 5.. In-vivo CRISPR screening identified genes that increase clonal expansion.

A. Sleeping Beauty transposon used for stable hepatocyte expression of FAH, Cas9, and sgRNA. The plasmid was injected intravenously. B. Schema of in vivo loss-of-function screen to identify genes that regulate liver regeneration using the Fah KO hereditary tyrosinemia model. C. Body weights of Fah KO mice undergoing liver repopulation after plasmids were delivered +/− SB100 transposase plasmid by hydrodynamic transfection (HDT). NTBC was withdrawn immediately after HDT. Data are represented as mean ± standard deviation; n = 3 mice per group. D. FAH IHC staining shows FAH+, PTEN negative hepatocytes one week after HDT of plasmids carrying Fah, Cas9, and a Pten sgRNA (scale bar = 50 μm). E. Representative IHC staining one month after transposon HDT (scale bar = 100μm). F. The appearance of livers 1 month after transposon HDT. G. Scatterplot showing average enrichment of individual sgRNAs after liver repopulation from 5 independent replicates. The sgRNA count was defined as the number of sequencing reads that perfectly match the sgRNA target sequence (see Supplemental Table 5). H. Identification of candidate genes using positive robust rank aggregation (RRA) score as assessed by MAGeCK. The RRA score reflected whether or not the distribution of sgRNAs targeting a gene were significantly skewed within a ranked list of all sgRNAs. Assuming that if a gene had no biological effect, then sgRNAs targeting this gene should be uniformly distributed (Li et al., 2014). I. Top 10 gene candidates in the repopulated liver based on RRA scores. The screen was performed in 5 independent mouse replicates. A red square means that the gene was found within the top 10 enriched genes in that screened mouse.

Figure 6.. Arid1a and Kmt2d heterozygosity protect against chemical liver injuries.

A. Schema for Aridla experiments. Arid1a^fl/+ mice or Arid1a^fl/+; Alb-Cre mice were injected with a single dose of CCl₄ to induce acute liver injury. In a separate experiment, DDC diet was given for 2 weeks and then a normal diet for 3 days to evaluate liver injury and recovery. B. Serum alkaline phosphatase (ALKP) from Arid1a^fl/+ mice or Arid1a^fl/+; Alb-Cre mice at baseline and 24 hours after a single dose of CCl₄ (n = 7 and 6 for baseline and 7 and 5 for CCl4). C. Serum total bilirubin after 2 weeks of DDC diet and liver/body mass ratios after 2 weeks of DDC diet and 3 days of normal diet (n = 10 and 8). D. Schema for Kmt2d experiments. AAV-TBG-Cre (5×10¹⁰) was injected intravenously into Kmt2d^fl/+ mice to delete one allele in hepatocytes. AAV-TBG-GFP was injected as control. Two weeks later, mice were injected with one dose of CCl₄. In a separate set of experiments, mice were put on T3 diet for 1 month to enforce hepatocyte proliferation. E. Serum ALT or AST measured 24 hours after CCl₄ (n = 4 and 5). F. H&E staining showing hepatic necrosis 48 hours after CCl₄. Scale bar: 2000μm. G. Quantification of necrosis on H&E and liver/body mass ratios, assessed 48 hours after CCl₄ (n = 4 and 5). H. Proliferation as assessed by Ki-67 one month after T3 diet. Scale bar: 100μm. I. Quantification of Ki-67 positive cells and liver/body mass ratios after T3 diet (n = 3 and 4). All data are presented as mean ± SEM. *, p<0.05, **, p<0.01.

Figure 7.. Pkd1 heterozygosity protects against CCl₄ induced necrosis and fibrosis.

A. Schema for Pkd1 experiments. AAV-TBG-Cre (5×10¹⁰) was injected intravenously into Pkd^fl/+ mice to delete one Pkd1 allele in the liver. AAV-TBG-GFP was injected to generate control mice. Two weeks later, mice were injected with one dose of CCl₄ to induce injury. In a separate experiment, 12 weeks of biweekly CCl₄ was used to induce chronic damage and liver fibrosis. B. Serum ALT and AST in Pkd1 het mice 24 hours after CCl₄ injection (n = 11 and 11). C. Necrotic cells (circled in yellow) in Pkd1 heterozygous livers 48 hours post CCl₄ injection. Scale bar: 200μm. D. Quantification of necrosis 48 hours post CCl₄ injection (n = 11 and 11). E. Sirius Red staining of liver sections after 12 weeks of biweekly CCl₄ injections. Scale bar: 500pm. F. Quantification of Sirius Red staining after chronic CCl₄ injury (n = 9 and 9). All data are presented as mean ± SEM. *, p<0.05, **, p<0.01.