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Both fatty bone marrow (FBM) and somatic mutations in hematopoietic stem cells (HSCs), also termed clonal hematopoiesis (CH) accumulate with human aging. However it remains unclear whether FBM can modify the evolution of CH. To address this question, we herein present the interaction between CH and FBM in two preclinical male mouse models: after sub-lethal irradiation or after castration. An adipogenesis inhibitor (PPARγ inhibitor) is used in both models as a control. A significant increase in self-renewal can be detected in both human and rodent DNMT3A Mut -HSCs when exposed to FBM. DNMT3A Mut -HSCs derived from older mice interacting with FBM have even higher self-renewal in comparison to DNMT3A Mut -HSCs derived from younger mice. Single cell RNA-sequencing on rodent HSCs after exposing them to FBM reveal a 6-10 fold increase in DNMT3A Mut -HSCs and an activated inflammatory signaling. Cytokine analysis of BM fluid and BM derived adipocytes grown in vitro demonstrates an increased IL-6 levels under FBM conditions. Anti-IL-6 neutralizing antibodies significantly reduce the selective advantage of DNMT3A Mut -HSCs exposed to FBM. Overall, paracrine FBM inflammatory signals promote DNMT3A -driven clonal hematopoiesis, which can be inhibited by blocking the IL-6 pathway. Age related accumulation of adipocytes in the bone marrow could alter normal and leukemic haematopoiesis. Here, in fatty bone marrow (FBM) preclinical models, the authors show that inflammatory cytokines increased in the FBM, such as IL-6, promote DNMT3a driven clonal hematopoiesis.

The mutational mechanisms underlying recurrent deletions in clonal hematopoiesis are not entirely clear. In the current study we inspect the genomic regions around recurrent deletions in myeloid malignancies, and identify microhomology-based signatures in CALR , ASXL1 and SRSF2 loci. We demonstrate that these deletions are the result of double stand break repair by a PARP1 dependent microhomology-mediated end joining (MMEJ) pathway. Importantly, we provide evidence that these recurrent deletions originate in pre-leukemic stem cells. While DNA polymerase theta (POLQ) is considered a key component in MMEJ repair, we provide evidence that pre-leukemic MMEJ (preL-MMEJ) deletions can be generated in POLQ knockout cells. In contrast, aphidicolin (an inhibitor of replicative polymerases and replication) treatment resulted in a significant reduction in preL-MMEJ. Altogether, our data indicate an association between POLQ independent MMEJ and clonal hematopoiesis and elucidate mutational mechanisms involved in the very first steps of leukemia evolution. The mutational mechanisms that produce insertions and deletions that lead to clonal hematopoiesis are poorly understood. Here the authors show evidence that frequent deletions that are relevant to myeloid malignancies could be produced by PARP1-dependent microhomology-mediated end joining.

Stem cell dynamics in vivo are often being studied by lineage tracing methods. Our laboratory has previously developed a retrospective method for reconstructing cell lineage trees from somatic mutations accumulated in microsatellites. This method was applied here to explore different aspects of stem cell dynamics in the mouse colon without the use of stem cell markers. We first demonstrated the reliability of our method for the study of stem cells by confirming previously established facts, and then we addressed open questions. Our findings confirmed that colon crypts are monoclonal and that, throughout adulthood, the process of monoclonal conversion plays a major role in the maintenance of crypts. The absence of immortal strand mechanism in crypts stem cells was validated by the age-dependent accumulation of microsatellite mutations. In addition, we confirmed the positive correlation between physical and lineage proximity of crypts, by showing that the colon is separated into small domains that share a common ancestor. We gained new data demonstrating that colon epithelium is clustered separately from hematopoietic and other cell types, indicating that the colon is constituted of few progenitors and ruling out significant renewal of colonic epithelium from hematopoietic cells during adulthood. Overall, our study demonstrates the reliability of cell lineage reconstruction for the study of stem cell dynamics, and it further addresses open questions in colon stem cells. In addition, this method can be applied to study stem cell dynamics in other systems.

Clonal hematopoiesis can exist as both a driver and a consequence of inflammatory dysregulation.

Identification of the cell types from which relapse arises in acute myeloid leukaemia, by following leukaemia propagation from patient-derived leukaemia samples. AML relapse can develop from stem cells Relapse is frequently seen in patients with acute myeloid leukemia (AML). John Dick and colleagues now uncover the cell types from which relapse arises by following leukaemia propagation from patient-derived leukaemia samples. Surprisingly, they found that relapse can arise from two distinct leukaemia cell populations, both of which display stemness features. The first group consisted of rare leukaemia stem cells with a haematopoietic stem/progenitor cell phenotype, and the second were larger subclones of immunophenotypically committed leukaemia cells. These findings may help to better monitor and target relapse AML. In acute myeloid leukaemia, long-term survival is poor as most patients relapse despite achieving remission 1 . Historically, the failure of therapy has been thought to be due to mutations that produce drug resistance, possibly arising as a consequence of the mutagenic properties of chemotherapy drugs 2 . However, other lines of evidence have pointed to the pre-existence of drug-resistant cells 3 . For example, deep sequencing of paired diagnosis and relapse acute myeloid leukaemia samples has provided direct evidence that relapse in some cases is generated from minor genetic subclones present at diagnosis that survive chemotherapy 3 , 4 , 5 , suggesting that resistant cells are generated by evolutionary processes before treatment 3 and are selected by therapy 6 , 7 , 8 . Nevertheless, the mechanisms of therapy failure and capacity for leukaemic regeneration remain obscure, as sequence analysis alone does not provide insight into the cell types that are fated to drive relapse. Although leukaemia stem cells 9 , 10 have been linked to relapse owing to their dormancy and self-renewal properties 11 , 12 , 13 , and leukaemia stem cell gene expression signatures are highly predictive of therapy failure 14 , 15 , experimental studies have been primarily correlative 7 and a role for leukaemia stem cells in acute myeloid leukaemia relapse has not been directly proved. Here, through combined genetic and functional analysis of purified subpopulations and xenografts from paired diagnosis/relapse samples, we identify therapy-resistant cells already present at diagnosis and two major patterns of relapse. In some cases, relapse originated from rare leukaemia stem cells with a haematopoietic stem/progenitor cell phenotype, while in other instances relapse developed from larger subclones of immunophenotypically committed leukaemia cells that retained strong stemness transcriptional signatures. The identification of distinct patterns of relapse should lead to improved methods for disease management and monitoring in acute myeloid leukaemia. Moreover, the shared functional and transcriptional stemness properties that underlie both cellular origins of relapse emphasize the importance of developing new therapeutic approaches that target stemness to prevent relapse.

Abstract Deep targeted sequencing technologies are still not widely used in clinical practice due to the complexity of the methods and their cost. The Molecular Inversion Probes (MIP) technology is cost effective and scalable in the number of targets, however, suffers from low overall performance especially in GC rich regions. In order to improve the MIP performance, we sequenced a large cohort of healthy individuals (n = 4417), with a panel of 616 MIPs, at high depth in duplicates. To improve the previous state-of-the-art statistical model for low variant allele frequency, we selected 4635 potentially positive variants and validated them using amplicon sequencing. Using machine learning prediction tools, we significantly improved precision of 10–56.25% (P < 0.0004) to detect variants with VAF > 0.005. We further developed biochemically modified MIP protocol and improved its turn-around-time to ∼4 h. Our new biochemistry significantly improved uniformity, GC-Rich regions coverage, and enabled 95% on target reads in a large MIP panel of 8349 genomic targets. Overall, we demonstrate an enhancement of the MIP targeted sequencing approach in both detection of low frequency variants and in other key parameters, paving its way to become an ultrafast cost-effective research and clinical diagnostic tool.

Standardized lab tests are central for patient evaluation, differential diagnosis and treatment. Interpretation of these data is nevertheless lacking quantitative and personalized metrics. Here we report on the modeling of 2.1 billion lab measurements of 92 different lab tests from 2.8 million adults over a span of 18 years. Following unsupervised filtering of 131 chronic conditions and 5,223 drug–test pairs we performed a virtual survey of lab tests distributions in healthy individuals. Age and sex alone explain less than 10% of the within-normal test variance in 89 out of 92 tests. Personalized models based on patients’ history explain 60% of the variance for 17 tests and over 36% for half of the tests. This allows for systematic stratification of the risk for future abnormal test levels and subsequent emerging disease. Multivariate modeling of within-normal lab tests can be readily implemented as a basis for quantitative patient evaluation. A new approach based on machine-learning integration of 2.1 billion lab measurements of 92 different lab tests from 2.8 million adults, over a span of 18 years, produces models that can stratify one’s risk of having a future abnormal lab test level and subsequent emerging disease.

In acute myeloid leukaemia (AML), the cell of origin, nature and biological consequences of initiating lesions, and order of subsequent mutations remain poorly understood, as AML is typically diagnosed without observation of a pre-leukaemic phase. Here, highly purified haematopoietic stem cells (HSCs), progenitor and mature cell fractions from the blood of AML patients were found to contain recurrent DNMT3A mutations ( DNMT3A mut ) at high allele frequency, but without coincident NPM1 mutations ( NPM1c ) present in AML blasts. DNMT3A mut -bearing HSCs showed a multilineage repopulation advantage over non-mutated HSCs in xenografts, establishing their identity as pre-leukaemic HSCs. Pre-leukaemic HSCs were found in remission samples, indicating that they survive chemotherapy. Therefore DNMT3A mut arises early in AML evolution, probably in HSCs, leading to a clonally expanded pool of pre-leukaemic HSCs from which AML evolves. Our findings provide a paradigm for the detection and treatment of pre-leukaemic clones before the acquisition of additional genetic lesions engenders greater therapeutic resistance. The authors identify pre-leukaemic haematopoietic stem cells (HSCs) in patients with acute myeloid leukaemia; these pre-leukaemic HSCs have the capacity of normal multi-lineage haematopoietic differentiation with a competitive growth advantage over wild-type HSCs, and owing to their persistence may serve as a reservoir for therapeutic resistance and relapse. Pre-cancer processes in leukaemia It is thought that almost all cancers are clonal — the progeny of a single mutated cell — but the evolutionary pathways that lead from a first mutation to the many different forms of cancer remain largely unknown. John Dick and colleagues examined peripheral blood and bone marrow samples from patients with acute myeloid leukaemia (AML) and identified leukaemic blasts with both DNMT3A mut and NPM1c mutations in a large proportion of patients. Also present were pre-leukaemic haematopoietic stem cells (HSCs) that carried DNMT3A mut without NPM1c . These cells retained the ability to generate different cell types and thereby sustain normal haematopoiesis but have a competitive repopulation advantage over wild-type HSCs and can persist after remission following chemotherapy, so may act as a reservoir for the accumulation of further mutations and therapeutic resistance. This work points to mutations in DNMT3A and other genes that give rise to pre-leukaemic HSCs as possible drug targets and suggests that the identification and treatment of pre-leukaemic clones may help combat therapeutic resistance.

Organism cells proliferate and die to build, maintain, renew and repair it. The cellular history of an organism up to any point in time can be captured by a cell lineage tree in which vertices represent all organism cells, past and present, and directed edges represent progeny relations among them. The root represents the fertilized egg, and the leaves represent extant and dead cells. Somatic mutations accumulated during cell division endow each organism cell with a genomic signature that is unique with a very high probability. Distances between such genomic signatures can be used to reconstruct an organism's cell lineage tree. Cell populations possess unique features that are absent or rare in organism populations (e.g., the presence of stem cells and a small number of generations since the zygote) and do not undergo sexual reproduction, hence the reconstruction of cell lineage trees calls for careful examination and adaptation of the standard tools of population genetics. Our lab developed a method for reconstructing cell lineage trees by examining only mutations in highly variable microsatellite loci (MS, also called short tandem repeats, STR). In this study we use experimental data on somatic mutations in MS of individual cells in human and mice in order to validate and quantify the utility of known lineage tree reconstruction algorithms in this context. We employed extensive measurements of somatic mutations in individual cells which were isolated from healthy and diseased tissues of mice and humans. The validation was done by analyzing the ability to infer known and clear biological scenarios. In general, we found that if the biological scenario is simple, almost all algorithms tested can infer it. Another somewhat surprising conclusion is that the best algorithm among those tested is Neighbor Joining where the distance measure used is normalized absolute distance. We include our full dataset in Tables S1, S2, S3, S4, S5 to enable further analysis of this data by others.

Background The question of a genetic contribution to the higher prevalence and incidence of end stage kidney disease (ESKD) among African Americans (AA) remained unresolved, until recent findings using admixture mapping pointed to the association of a genomic locus on chromosome 22 with this disease phenotype. In the current study we utilize this example to demonstrate the utility of applying a multi-step admixture mapping approach. Methods A multi-step case only admixture mapping study, consisted of the following steps was designed: 1) Assembly of the sample dataset (ESKD AA); 2) Design of the estimated mutual information ancestry informative markers (n = 2016) screening panel 3); Genotyping the sample set whose size was determined by a power analysis (n = 576) appropriate for the initial screening panel; 4) Inference of local ancestry for each individual and identification of regions with increased AA ancestry using two different ancestry inference statistical approaches; 5) Enrichment of the initial screening panel; 6) Power analysis of the enriched panel 7) Genotyping of additional samples. 8) Re-analysis of the genotyping results to identify a genetic risk locus. Results The initial screening phase yielded a significant peak using the ADMIXMAP ancestry inference program applying case only statistics. Subgroup analysis of 299 ESKD patients with no history of diabetes yielded peaks using both the ANCESTRYMAP and ADMIXMAP ancestry inference programs. The significant peak was found on chromosome 22. Genotyping of additional ancestry informative markers on chromosome 22 that took into account linkage disequilibrium in the ancestral populations, and the addition of samples increased the statistical significance of the finding. Conclusions A multi-step admixture mapping analysis of AA ESKD patients replicated the finding of a candidate risk locus on chromosome 22, contributing to the heightened susceptibility of African Americans to develop non-diabetic ESKD, and underscores the importance of using mutual information and multiple ancestry inference approaches to achieve a robust analysis, using relatively small datasets of \"affected\" only individuals. The current study suggests solutions to some limitations of existing admixture mapping methodologies, such as considerations regarding the distribution of ancestry information along the genome and its effects on power calculations and sample size.