Precancerous liver diseases do not cause increased mutagenesis in liver stem cells.

Inflammatory liver disease increases the risk of developing primary liver cancer. The mechanism through which liver disease induces tumorigenesis remains unclear, but is thought to occur via increased mutagenesis. Here, we performed whole-genome sequencing on clonally expanded single liver stem cells cultured as intrahepatic cholangiocyte organoids (ICOs) from patients with alcoholic cirrhosis, non-alcoholic steatohepatitis (NASH), and primary sclerosing cholangitis (PSC). Surprisingly, we find that these precancerous liver disease conditions do not result in a detectable increased accumulation of mutations, nor altered mutation types in individual liver stem cells. This finding contrasts with the mutational load and typical mutational signatures reported for liver tumors, and argues against the hypothesis that liver disease drives tumorigenesis via a direct mechanism of induced mutagenesis. Disease conditions in the liver may thus act through indirect mechanisms to drive the transition from healthy to cancerous cells, such as changes to the microenvironment that favor the outgrowth of precancerous cells.

Publisher Correction: The mutational impact of culturing human pluripotent and adult stem cells.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The mutational impact of culturing human pluripotent and adult stem cells.

Genetic changes acquired during in vitro culture pose a risk for the successful application of stem cells in regenerative medicine. To assess the genetic risks induced by culturing, we determined all mutations in individual human stem cells by whole genome sequencing. Individual pluripotent, intestinal, and liver stem cells accumulate 3.5 ± 0.5, 7.2 ± 1.1 and 8.3 ± 3.6 base substitutions per population doubling, respectively. The annual in vitro mutation accumulation rate of adult stem cells is nearly 40-fold higher than the in vivo mutation accumulation rate. Mutational signature analysis reveals that in vitro induced mutations are caused by oxidative stress. Reducing oxygen tension in culture lowers the mutational load. We use the mutation rates, spectra, and genomic distribution to model the accumulation of oncogenic mutations during typical in vitro expansion, manipulation or screening experiments using human stem cells. Our study provides empirically defined parameters to assess the mutational risk of stem cell based therapies.

Identifying small RNAs derived from maternal- and somatic-type rRNAs in zebrafish development.

rRNAs are non-coding RNAs present in all prokaryotes and eukaryotes. In eukaryotes there are four rRNAs: 18S, 5.8S, 28S, originating from a common precursor (45S), and 5S. We have recently discovered the existence of two distinct developmental types of rRNA: a maternal-type, present in eggs and a somatic-type, expressed in adult tissues. Lately, next-generation sequencing has allowed the discovery of new small-RNAs deriving from longer non-coding RNAs, including small-RNAs from rRNAs (srRNAs). Here, we systemically investigated srRNAs of maternal- or somatic-type 18S, 5.8S, 28S, with small-RNAseq from many zebrafish developmental stages. We identified new srRNAs for each rRNA. For 5.8S, we found srRNA consisting of the 5' or 3' halves, with only the latter having different sequence for the maternal- and somatic-types. For 18S, we discovered 21 nt srRNA from the 5' end of the 18S rRNA with a striking resemblance to microRNAs; as it is likely processed from a stem-loop precursor and present in human and mouse Argonaute-complexed small-RNA. For 28S, an abundant 80 nt srRNA from the 3' end of the 28S rRNA was found. The expression levels during embryogenesis of these srRNA indicate they are not generated from rRNA degradation and might have a role in the zebrafish development.

Expression of distinct maternal and somatic 5.8S, 18S, and 28S rRNA types during zebrafish development.

There is mounting evidence that the ribosome is not a static translation machinery, but a cell-specific, adaptive system. Ribosomal variations have mostly been studied at the protein level, even though the essential transcriptional functions are primarily performed by rRNAs. At the RNA level, oocyte-specific 5S rRNAs are long known for Xenopus. Recently, we described for zebrafish a similar system in which the sole maternal-type 5S rRNA present in eggs is replaced completely during embryonic development by a somatic-type. Here, we report the discovery of an analogous system for the 45S rDNA elements: 5.8S, 18S, and 28S. The maternal-type 5.8S, 18S, and 28S rRNA sequences differ substantially from those of the somatic-type, plus the maternal-type rRNAs are also replaced by the somatic-type rRNAs during embryogenesis. We discuss the structural and functional implications of the observed sequence differences with respect to the translational functions of the 5.8S, 18S, and 28S rRNA elements. Finally, in silico evidence suggests that expansion segments (ES) in 18S rRNA, previously implicated in ribosome-mRNA interaction, may have a preference for interacting with specific mRNA genes. Taken together, our findings indicate that two distinct types of ribosomes exist in zebrafish during development, each likely conducting the translation machinery in a unique way.

Linking maternal and somatic 5S rRNA types with different sequence-specific non-LTR retrotransposons.

5S rRNA is a ribosomal core component, transcribed from many gene copies organized in genomic repeats. Some eukaryotic species have two 5S rRNA types defined by their predominant expression in oogenesis or adult tissue. Our next-generation sequencing study on zebrafish egg, embryo, and adult tissue identified maternal-type 5S rRNA that is exclusively accumulated during oogenesis, replaced throughout the embryogenesis by a somatic-type, and thus virtually absent in adult somatic tissue. The maternal-type 5S rDNA contains several thousands of gene copies on chromosome 4 in tandem repeats with small intergenic regions, whereas the somatic-type is present in only 12 gene copies on chromosome 18 with large intergenic regions. The nine-nucleotide variation between the two 5S rRNA types likely affects TFIII binding and riboprotein L5 binding, probably leading to storage of maternal-type rRNA. Remarkably, these sequence differences are located exactly at the sequence-specific target site for genome integration by the 5S rRNA-specific Mutsu retrotransposon family. Thus, we could define maternal- and somatic-type MutsuDr subfamilies. Furthermore, we identified four additional maternal-type and two new somatic-type MutsuDr subfamilies, each with their own target sequence. This target-site specificity, frequently intact maternal-type retrotransposon elements, plus specific presence of Mutsu retrotransposon RNA and piRNA in egg and adult tissue, suggest an involvement of retrotransposons in achieving the differential copy number of the two types of 5S rDNA loci.

Improving small RNA-seq by using a synthetic spike-in set for size-range quality control together with a set for data normalization.

There is an increasing interest in complementing RNA-seq experiments with small-RNA (sRNA) expression data to obtain a comprehensive view of a transcriptome. Currently, two main experimental challenges concerning sRNA-seq exist: how to check the size distribution of isolated sRNAs, given the sensitive size-selection steps in the protocol; and how to normalize data between samples, given the low complexity of sRNA types. We here present two separate sets of synthetic RNA spike-ins for monitoring size-selection and for performing data normalization in sRNA-seq. The size-range quality control (SRQC) spike-in set, consisting of 11 oligoribonucleotides (10-70 nucleotides), was tested by intentionally altering the size-selection protocol and verified via several comparative experiments. We demonstrate that the SRQC set is useful to reproducibly track down biases in the size-selection in sRNA-seq. The external reference for data-normalization (ERDN) spike-in set, consisting of 19 oligoribonucleotides, was developed for sample-to-sample normalization in differential-expression analysis of sRNA-seq data. Testing and applying the ERDN set showed that it can reproducibly detect differential expression over a dynamic range of 2(18). Hence, biological variation in sRNA composition and content between samples is preserved while technical variation is effectively minimized. Together, both spike-in sets can significantly improve the technical reproducibility of sRNA-seq.