Biology subjects

Werner, B.

Publications and source records attributed to Werner, B..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

Quantification of spatial subclonal interactions enhancing the invasive phenotype of paediatric glioma

Intra-tumour heterogeneity is an intrinsic property of all cancers. In some cases, such variation can be maintained by interactions between tumour subclones with distinct molecular and phenotypic characteristics. In paediatric gliomas, interactions can take the form of enhanced invasive phenotype, a hallmark of these malignancies. However, subclonal interactions are hard to quantify and difficult to distinguish from spatial confounding factors and experimental bias. Here we combine spatial computational modelling of cellular interactions and invasion, with co-evolution experiments of clonally disassembled primary glioma lines derived at autopsy. We design a Bayesian inference framework to quantify spatial subclonal interactions between molecular and phenotypically distinct lineages with different patterns of invasion. We show how this approach could discriminate genuine subclonal interactions where one clone enhanced the invasive phenotype of another, from apparent interactions that were only due to the complex dynamics of subclones growing in space. This study provides a new approach for the identification and quantification of spatial subclonal interactions in cancer.

cancer biology↗

bioRxiv2021

Assessment of the evolutionary consequence of putative driver mutations in colorectal cancer with spatial multiomic data

Cancer genomic medicine relies on targeting driver genes. However, current catalogues of cancer drivers are mostly based on indirect measurements of mutation frequencies, positions or types, rather than their effect on clonal expansions in vivo. Moreover, non-genetic drivers are largely unknown, as are the epigenetic and transcriptomic effects of genetic drivers. Here we perform spatial computational inference on multiomic data with matched whole-genome sequencing, ATAC-seq and RNA-seq. Using 436 samples, we directly quantify the contribution, or lack thereof, of putative driver genes to subclonal expansions in vivo in 30 colorectal carcinomas (4-33 samples per patient, median=15). Although subclonal neutral evolution was widespread (13/26 cases with sufficient data), there were cases with clear evidence of subclonal selection (6/26) in which we measured epigenetic and transcriptomic differences between subclones in vivo. In 7/26 cases we could not distinguish between neutral or selective evolution with the available data. We identified expanding subclones that were not driven by known genetic alterations, and propose candidate epigenetic drivers. We identified the distinguishing patterns of genomic heterogeneity produced in fast, exponentially growing tumours (7/26) versus neoplasms growing only at the periphery (19/26), as well as identifying clonally intermixed (16/28 cases with sufficient data) versus segregated malignancies (10/28). Our model-based approach measures genetic and non-genetic subclonal selection, or lack thereof, in space and time and allows in vivo comparisons of the emergent phenotypic properties of subclones within human tumours.

cancer biology↗

bioRxiv2021

Principles of ecDNA random inheritance drive rapid genome change and therapy resistance in human cancers

The foundational principles of Darwinian evolution are variation, selection, and identity by descent. Oncogene amplification on extrachromosomal DNA (ecDNA) is a common event, driving aggressive tumour growth, drug resistance, and shorter survival in patients1-4. Currently, the impact of non-chromosomal oncogene inheritance--random identity by descent--is not well understood. Neither is the impact of ecDNA on variation and selection. Here, integrating mathematical modeling, unbiased image analysis, CRISPR-based ecDNA tagging, and live-cell imaging, we identify a set of basic "rules" for how random ecDNA inheritance drives oncogene copy number and distribution, resulting in extensive intratumoural ecDNA copy number heterogeneity and rapid adaptation to metabolic stress and targeted cancer treatment. Observed ecDNAs obligatorily benefit host cell survival or growth and can change within a single cell cycle. In studies ranging from well-curated, patient-derived cancer cell cultures to clinical tumour samples from patients with glioblastoma and neuroblastoma treated with oncogene-targeted drugs, we show how these ecDNA inheritance "rules" can predict, a priori, some of the aggressive features of ecDNA-containing cancers. These properties are entailed by their ability to rapidly change their genomes in a way that is not possible for cancers driven by chromosomal oncogene amplification. These results shed new light on how the non-chromosomal random inheritance pattern of ecDNA underlies poor outcomes for cancer patients.

cancer biology↗

bioRxiv2021

Identification of Fluoxetine as a direct NLRP3 inhibitor to treat atrophic macular degeneration

The atrophic form of age-related macular degeneration (dry AMD) affects nearly 200 million people worldwide. There is no FDA-approved therapy for this disease, which is the leading cause of irreversible blindness among people over 50 years of age. Vision loss in dry AMD results from degeneration of the retinal pigmented epithelium (RPE). RPE cell death is driven in part by accumulation of Alu RNAs, which are noncoding transcripts of a human retrotransposon. Alu RNA induces RPE degeneration by activating the NLRP3-ASC inflammasome. We report that fluoxetine, an FDA-approved drug for treating clinical depression, binds NLRP3 in silico, in vitro, and in vivo, and that it inhibits activation of the NLRP3-ASC inflammasome in RPE cells and macrophages, two critical cell types in dry AMD. We also demonstrate that fluoxetine, unlike several other anti-depressant drugs, reduces Alu RNA-induced RPE degeneration in mice. Finally, by analyzing two health insurance databases comprising more than 100 million Americans, we report a reduced hazard of developing dry AMD among patients with depression who were treated with fluoxetine. Collectively, these studies triangulate to link fluoxetine as a potential drug repurposing candidate for a major unmet medical need that causes blindness in millions of people in the United States and across the world. Significance StatementDry age-related macular degeneration (AMD) affects the vision of millions of people worldwide. There is currently no FDA-approved treatment for dry AMD. The inflammasome components NLRP3 and ASC have been implicated in the pathogenesis of dry AMD. We report that fluoxetine, which is approved for the treatment of clinical depression, directly binds the NLRP3 protein and prevents the assembly and activation of the NLRP3-ASC inflammasome. As a result, it also blocks the degeneration of retinal pigmented epithelium (RPE) cells in an animal model of dry AMD. Furthermore, we demonstrate through an analysis of health insurance databases that use of this FDA-approved anti-depressant drug is associated with reduced incidence of dry AMD. These studies identify that fluoxetine is a potential repurposing candidate for AMD, a prevalent cause of blindness.

immunology↗

bioRxiv2020

Mutational landscapes of normal breast during age and pregnancy determine cancer risk

The accumulation of somatic mutations in the healthy breast throughout life and pregnancy is poorly understood1-10. Similarly, the mutational landscape of both epithelial and stromal components of the mammary gland has not been investigated. Both are relevant for breast cancer (BC), as the interplay between age, pregnancy, and cancer risk has not been fully characterized11. We describe whole genome sequencing analysis of epithelial and stromal compartments from the normal breast. We show that, in a similar way to other normal organs, the mutational burden of the mammary nulliparous epithelium significantly increases with age. In a nulliparous status, mutated clones are maintained at a consistently small size throughout the life of the individual; however, at parity, pre-existent clones significantly increase in size with age. Both epithelial and stromal compartments of the healthy breast contain pre-existing known cancer mutations, albeit at low rate, indicative of subsequent positive selection for mutations in tissue-specific driver genes. In line with this, both compartments also present gene enrichment in preferentially mutated cancer pathways. Our results show that mutational landscapes differ between the parous and nulliparous epithelium and suggest an explanation for both differential breast cancer risk and development of pregnancy-associated BC (PABC).

genetics↗

bioRxiv2020

Elucidating the mechanism of cyclodextrins in the treatment of Niemann-Pick Disease Type C using crosslinked 2-hydroxypropyl-β-cyclodextrin

Niemann-Pick Disease Type C (NPC) is a severe neurovisceral disorder that is pathophysiologically characterized by intracellular transport abnormalities leading to cytoplasmic accumulation of lipids such as cholesterol and multiple sphingolipids, including sphingosine. The compound 2-hydroxypropyl-{beta}-cyclodextrin (HP{beta}CD) is a compound with high cholesterol complexation capacity and is currently under clinical investigation for the treatment of NPC. However, due to its short blood half-life, high doses are required to produce a therapeutic effect. It has been reported in mice that HP{beta}CDs circulation time and efficacy can be improved by increasing its size via polymerization, but the biodegradable nature of these systems did not allow the contribution of the macromolecule to the activity to be determined. In this work, stable forms of polymerized HP{beta}CD were generated (via epichlorohydrin crosslinking) to investigate their in vitro mechanisms of action and in vivo effects. Crosslinked CDs (8-312 kDa) displayed a 10-fold greater complexation capacity towards cholesterol than monomeric HP{beta}CD but were taken up by cells to a lower extent (in a size-dependent fashion), resulting in an overall comparable in vitro effect on intracellular cholesterol accumulation that was dependent on cholesterol complexation. When tested in vivo, the crosslinked 19.3 kDa HP{beta}CD exhibited a longer terminal half-life than the monomeric HP{beta}CD. However, it did not increase the life span of Npc1 mice, possibly due to reduced organ penetration and brain diffusion consequence of its large molecular weight. This could be circumvented by the application of magnetic resonance imaging-guided low intensity-pulsed focused ultrasound (MRIg-FUS), which increased the brain penetration of the CD. In conclusion, stable forms of polymerized HP{beta}CD constitute valuable tools to elucidate CDs mechanism of action. Moreover, the use of MRIg-FUS to maximize CDs tissue penetration warrants further investigation, as it may be key to harnessing CDs full therapeutic potential in the treatment of NPC. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/230136v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@3dbaf1org.highwire.dtl.DTLVardef@bc255dorg.highwire.dtl.DTLVardef@390e7borg.highwire.dtl.DTLVardef@1e5e5c5_HPS_FORMAT_FIGEXP M_FIG C_FIG The 2-hydroxypropyl-{beta}-cyclodextrin (HP{beta}CD) is a well-established pharmaceutical excipient that can complex cholesterol and is currently under clinical investigation to treat Niemann-Pick Disease Type C (NPC). However, high doses of the drug are needed to achieve a therapeutic effect. Using stable and long circulating crosslinked HP{beta}CDs, this study attempts to further understand the mechanisms behind CDs activity.

pharmacology and toxicology↗

bioRxiv2020

dN/dS dynamics quantify tumour immunogenicity and predict response to immunotherapy

Immunoediting is a major force during cancer evolution that selects for clones with low immunogenicity (adaptation), or clones with mechanisms of immune evasion (escape). However, quantifying immunogenicity in the cancer genome and how the tumour-immune coevolutionary dynamics impact patient outcomes remain unexplored. Here we show that the ratio of nonsynonymous to synonymous mutations (dN/dS) in the immunopeptidome quantifies tumor immunogenicity and differentiates between adaptation and escape. We analysed 8,543 primary tumors from TCGA and validated immune dN/dS as a measure of selection associated with immune infiltration in immune-adapted tumours. In a cohort of 308 metastatic patients that received immunotherapy, pre-treatment lesions in non-responders showed increased immune selection (dN/dS<1), whereas responders did not and instead harboured a higher proportion of genetic escape mechanisms. Ultimately, these findings highlight the potential of evolutionary genomic measures to predict clinical response to immunotherapy.

genomics↗

bioRxiv2020

Host-induced gene silencing involves transfer of dsRNA-derived siRNA via extracellular vesicles

Small (s)RNAs and their double-stranded (ds)RNA precursors have been adopted to control diseases in crop plants through expression in transgenic plants and targeted gene silencing (host-induced gene silencing, HIGS). While HIGS strategies proved to be effective, the mechanism of RNA transfer at the plant - pathogen interface is widely unknown. Here we show that extracellular vesicles (EVs) purified from Arabidopsis thaliana plants expressing CYP3RNA, a dsRNA originally designed to target the three CYP51 genes of the fungal pathogen Fusarium graminearum, contain CYP3RNA-derived small interfering (si)RNAs as shown by RNA sequencing (RNA-seq) analysis. These transgene specific siRNAs had a length of 21 and 22 nucleotides with a bias towards 5-uracil (U) and 5-adenine (A). Notably, stringent protease and RNase treated EV fractions contained >70% less CYP3RNA-derived siRNAs, suggesting the presence of co-purified extravesicular nucleoprotein complexes stabilizing siRNAs outside of EVs. In addition, mutants of the ESCRT-III complex showed a loss of HIGS-mediated disease resistance and EVs isolated from these mutants were free of CYP3RNA-derived siRNAs. Together, these findings support the view that endosomal vesicle trafficking is required for HIGS mediating the transfer of transgene-derived siRNAs between donor host cells and recipient fungal cells probably in an EV-independent manner.

plant biology↗

bioRxiv2019

Stochastic dynamics of extra-chromosomal DNA

Nuclear extra-chromosomal DNA (ecDNA) is highly prevalent in human tumours. ecDNA amplifications can promote accessible chromatin, oncogen over-expression and imply a worse clinical prognosis. Yet, little is known about the evolutionary process of ecDNA in human cancers. Here, we develop the theoretical foundation of the ecDNA somatic evolutionary process combining mathematical, computational and experimental approaches. We show that random ecDNA segregation leads to unintuitive dynamics even if ecDNA is under very strong positive selection. Patterns of inter- and intra-tumour ecDNA and chromosomal heterogeneity differ markedly and standard approaches are not directly applicable to quantify ecDNA evolution. We show that evolutionary informed modelling leads to testable predictions on how to distinguish positively selected from neutral ecDNA dynamics. Our predictions describe the dynamics of circular amplicons in GBM39 cell line experiments, suggesting a 300% fitness increase due to circular extra-chromosomal EGFRvIII amplifications. Our work lies the basis for further studies to quantitate ecDNA somatic evolutionary processes.

cancer biology↗

bioRxiv2019

Study on the efficacy of dsRNAs with increasing length in RNAi-based silencing of the Fusarium CYP51 genes

Previously, we have demonstrated that transgenic Arabidopsis and barley plants, expressing a 791 nucleotide (nt) dsRNA (CYP3RNA) that targets all three CYP51 genes (FgCYP51A, FgCYP51B, FgCYP51C) in Fusarium graminearum (Fg), inhibited fungal infection via a process designated as host-induced gene silencing (HIGS). More recently, we have shown that spray applications of CYP3RNA also protect barley from fungal infection via a process termed spray-induced gene silencing (SIGS). Thus, RNAi technology may have the potential to revolutionize plant protection in agriculture. Therefore, successful field application will require optimization of RNAi design necessary to maximize the efficacy of the RNA silencing construct for making RNAi-based strategies a realistic and sustainable approach.\n\nPrevious studies indicate that silencing is correlated with the number of siRNAs generated from a dsRNA precursor. To prove the hypothesis that silencing efficiency is correlated with the number of siRNAs processed out of the dsRNA precursor, we tested in a HIGS and SIGS approach dsRNA precursors of increasing length ranging from 400 nt to 1500 nt to assess gene silencing efficiency of individual FgCYP51 genes. Concerning HIGS-mediated disease control, we found that there is no significant correlation between the length of the dsRNA precursor and the reduction of Fg infection on CYP51-dsRNA expressing Arabidopsis plants. Importantly and in clear contrast to HIGS, we measured a decrease in SIGS-mediated Fg disease resistance that significantly correlates with the length of the dsRNA construct that was sprayed, indicating that the size of the dsRNA interferes with a sufficient uptake of dsRNAs by the fungus.

plant biology↗

bioRxiv2019

RNA-spray-mediated silencing of Fusarium graminearum AGO and DCL genes improve barley disease resistance

Over the last decade, several studies have revealed the enormous potential of RNA-silencing strategies as a potential alternative to conventional pesticides for plant protection. We have previously shown that targeted gene silencing mediated by an in planta expression of non-coding inhibitory double-stranded RNAs (dsRNAs) can protect host plants against various diseases with unprecedented efficiency. In addition to the generation of RNA-silencing (RNAi) signals in planta, plants can be protected from pathogens and pests by spray-applied RNA-based biopesticides. Despite the striking efficiency of RNA-silencing-based technologies holds for agriculture, the molecular mechanisms underlying spray-induced gene silencing (SIGS) strategies are virtually unresolved, a requirement for successful future application in the field. Based on our previous work, we predict that the molecular mechanism of SIGS is controlled by the fungal-silencing machinery. In this study, we used SIGS to compare the silencing efficiencies of computationally-designed versus manually-designed dsRNA constructs targeting ARGONAUTE and DICER genes of Fusarium graminearum (Fg). We found that targeting key components of the fungal RNAi machinery via SIGS could protect barley leaves from Fg infection and that the manual design of dsRNAs resulted in higher gene-silencing efficiencies than the tool-based design. Moreover, our results indicate the possibility of cross-kingdom RNA silencing in the Fg-barley interaction, a phenomenon in which sRNAs operate as effector molecules to induce gene silencing between species from different kingdoms, such as a plant host and their interacting pathogens.

plant biology↗

bioRxiv2019

Measuring the distribution of fitness effects in somatic evolution by combining clonal dynamics with dN/dS ratios

The distribution of fitness effects (DFE) defines how new mutations spread through an evolving population. The ratio of non-synonymous to synonymous mutations (dN/dS) has become a popular method to detect selection in somatic cells, however the link, in somatic evolution, between dN/dS values and fitness coefficients is missing. Here we present a quantitative model of somatic evolutionary dynamics that yields the selective coefficients from individual driver mutations from dN/dS estimates, and then measure the DFE for somatic mutant clones in ostensibly normal oesophagus and skin. We reveal a broad distribution of fitness effects, with the largest fitness increases found for TP53 and NOTCH1 mutants (proliferative bias 1-5%). Accurate measurement of the per-gene DFE in cancer evolution is precluded by the quality of currently available sequencing data. This study provides the theoretical link between dN/dS values and selective coefficients in somatic evolution, and reveals the DFE for mutations in human tissues.

bioinformatics↗

bioRxiv2019

How many samples are needed to infer truly clonal mutations from heterogenous tumours?

BackgroundModern cancer treatment strategies aim to target tumour specific genetic (or epigenetic) alterations. Treatment response improves if these alterations are clonal, i.e. present in all cancer cells within tumours. However, the identification of truly clonal alterations is impaired by the tremendous intra-tumour genetic heterogeneity and unavoidable sampling biases.\n\nMethodsHere, we investigate the underlying causes of these spatial sampling biases and how the distribution and sizes of biopsies in sampling protocols can be optimized to minimize such biases.\n\nResultsWe find that in the ideal case, less than a handful of samples can be enough to infer truly clonal mutations. The frequency of the largest sub-clone at diagnosis is the main factor determining the accuracy of truncal mutation estimation in structured tumours. If the first sub-clone is dominating the tumour, higher spatial dispersion of samples and larger sample size can increase the accuracy of the estimation. In such an improved sampling scheme, fewer samples will enable the detection of truly clonal alterations with the same probability.\n\nConclusionsTaking spatial tumour structure into account will decrease the probability to misclassify a sub-clonal mutation as clonal and promises better informed treatment decisions.

cancer biology↗

bioRxiv2019

Model-based tumor subclonal reconstruction

The vast majority of cancer next-generation sequencing data consist of bulk samples composed of mixtures of cancer and normal cells. To study tumor evolution, subclonal reconstruction approaches based on machine learning are used to separate subpopulation of cancer cells and reconstruct their ancestral relationships. However, current approaches are entirely data-driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in subclonal reconstruction if tumor evolution is not accounted for, and that those errors increase when multiple samples are taken from the same tumor. To address this issue, we present a novel approach for model-based subclonal reconstruction that combines data-driven machine learning with evolutionary theory. Using public, synthetic and newly generated data, we show the method is more robust and accurate than current techniques in both single-sample and multi-region sequencing data. With careful data curation and interpretation, we show how the method allows minimizing the confounding factors that affect non-evolutionary methods, leading to a more accurate recovery of the evolutionary history of human tumors.

bioinformatics↗

bioRxiv2019

Measuring single cell divisions in human cancers from multi-region sequencing data

Cancer is driven by complex evolutionary dynamics involving billions of cells. Increasing effort has been dedicated to sequence single tumour cells, but obtaining robust measurements remains challenging. Here we show that multi-region sequencing of bulk tumour samples contains quantitative information on single-cell divisions that is accessible if combined with evolutionary theory. Using high-throughput data from 16 human cancers, we measured the in vivo per-cell point mutation rate (mean: 1.69x10-8 bp per cell division) and per-cell survival rate (mean: 0.57) in individual patient tumours from colon, lung and renal cancers. Per-cell mutation rates varied 50-fold between individuals, and per-cell survival rates were between nearly-homeostatic and almost perfect cell doublings, equating to tumour ages between 1 and 19 years. Furthermore, reanalysing a recent dataset of 89 whole-genome sequenced healthy haematopoietic stem cells, we find 1.14 mutations per genome per cell division and near perfect cell doublings (per-cell survival rate: 0.96) during early haematopoietic development. Our analysis measures in vivo the most fundamental properties of human cancer and healthy somatic evolution at single-cell resolution within single individuals.

cancer biology↗

bioRxiv2019

Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data

Quantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constrains, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from bulk sequencing data and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We present a statistical inference framework that takes into account the spatial effects of a growing tumour and allows inferring the evolutionary dynamics from patient genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors requires a mechanistic model-based approach that captures the sources of noise in the data. SummarySequencing the DNA of cancer cells from human tumours has become one of the main tools to study cancer biology. However, sequencing data are complex and often difficult to interpret. In particular, the way in which the tissue is sampled and the data are collected, impact the interpretation of the results significantly. We argue that understanding cancer genomic data requires mathematical models and computer simulations that tell us what we expect the data to look like, with the aim of understanding the impact of confounding factors and biases in the data generation step. In this study, we develop a spatial simulation of tumour growth that also simulates the data generation process, and demonstrate that biases in the sampling step and current technological limitations severely impact the interpretation of the results. We then provide a statistical framework that can be used to overcome these biases and more robustly measure aspects of the biology of tumours from the data.

bioinformatics↗

bioRxiv2019

Evolutionary dynamics of neoantigens in growing tumours

Cancer evolution is driven by the acquisition of somatic mutations that provide cells with a beneficial phenotype in a changing microenvironment. However, mutations that give rise to neoantigens, novel cancer-specific peptides that elicit an immune response, are likely to be disadvantageous. Here we show how the clonal structure and immunogenotype of growing tumours is shaped by negative selection in response to neoantigenic mutations. We construct a mathematical model of neoantigen evolution in a growing tumour, and verify the model using genomic sequencing data. The model predicts that, in the absence of active immune escape mechanisms, tumours either evolve clonal neoantigens (antigen- hot), or have no clonally- expanded neoantigens at all (antigen- cold), whereas antigen- warm tumours (with high frequency subclonal neoantigens) form only following the evolution of immune evasion. Counterintuitively, strong negative selection for neoantigens during tumour formation leads to an increased number of antigen- warm or - hot tumours, as a consequence of selective pressure for immune escape. Further, we show that the clone size distribution under negative selection is effectively- neutral, and moreover, that stronger negative selection paradoxically leads to more neutral- like dynamics. Analysis of antigen clone sizes and immune escape in colorectal cancer exome sequencing data confirms these results. Overall, we provide and verify a mathematical framework to understand the evolutionary dynamics and clonality of neoantigens in human cancers that may inform patient- specific immunotherapy decision- making.

cancer biology↗

bioRxiv2018

Variation of mutational burden in healthy human tissues suggests non-random strand segregation and allows measuring somatic mutation rates

The immortal strand hypothesis poses that stem cells could produce differentiated progeny while conserving the original template strand, thus avoiding accumulating somatic mutations. However, quantitating the extent of non-random DNA strand segregation in human stem cells remains difficult in vivo. Here we show that the change of the mean and variance of the mutational burden with age in healthy human tissues allows estimating strand segregation probabilities and somatic mutation rates. We analysed deep sequencing data from healthy human colon, small intestine, liver, skin and brain. We found highly effective non-random DNA strand segregation in all adult tissues (mean strand segregation probability: 0.98, standard error bounds (0.97,0.99)). In contrast, non-random strand segregation efficiency is reduced to 0.87 (0.78,0.88) in neural tissue during early development, suggesting stem cell pool expansions due to symmetric self-renewal. Healthy somatic mutation rates differed across tissue types, ranging from 3.5x10-9 /bp/division in small intestine to 1.6x10-7/bp/division in skin.\n\nAuthor SummaryCairn proposed in 1975 that upon proliferation, cells might not segregate DNA strands randomly into daughter cells, but preferentially keep the ancestral (blue print) template strand in stem cells. This mechanism would allow to drastically reduce the rate of mutation accumulation in human tissues. Testing the hypothesis in human stem cells within their natural tissue environment remains challenging. Here we show that the patterns of mutation accumulation in human tissues with age support highly effective non-random DNA strand segregation after adolescence. In contrast, during early development in infants, DNA strand segregation is less effective, likely because stem cell populations are continuing to grow.

cancer biology↗