Epigenomic machinery regulating pediatric AML: Clonal expansion mechanisms, therapies, and future perspectives.

Acute myeloid leukemia (AML) is a heterogeneous disease with a genetic, epigenetic, and transcriptional etiology mainly presenting somatic and germline abnormalities. AML incidence rises with age but can also occur during childhood. Pediatric AML (pAML) accounts for 15-20% of all pediatric leukemias and differs considerably from adult AML. Next-generation sequencing technologies have enabled the research community to "paint" the genomic and epigenomic landscape in order to identify pathology-associated mutations and other prognostic biomarkers in pAML. Although current treatments have improved the prognosis for pAML, chemoresistance, recurrence, and refractory disease remain major challenges. In particular, pAML relapse is commonly caused by leukemia stem cells that resist therapy. Marked patient-to-patient heterogeneity is likely the primary reason why the same treatment is successful for some patients but, at best, only partially effective for others. Accumulating evidence indicates that patient-specific clonal composition impinges significantly on cellular processes, such as gene regulation and metabolism. Although our understanding of metabolism in pAML is still in its infancy, greater insights into these processes and their (epigenetic) modulation may pave the way toward novel treatment options. In this review, we summarize current knowledge on the function of genetic and epigenetic (mis)regulation in pAML, including metabolic features observed in the disease. Specifically, we describe how (epi)genetic machinery can affect chromatin status during hematopoiesis, leading to an altered metabolic profile, and focus on the potential value of targeting epigenetic abnormalities in precision and combination therapy for pAML. We also discuss the possibility of using alternative epidrug-based therapeutic approaches that are already in clinical practice, either alone as adjuvant treatments and/or in combination with other drugs.

Extensive patient-to-patient single nucleus transcriptome heterogeneity in pheochromocytomas and paragangliomas.

Pheochromocytoma, neuroendocrine tumor, single cell RNA-sequencing, transcriptome, heterogeneity, SDHB, RET, paraganglinoma; Pheochromocytomas (PC) and paragangliomas (PG) are rare neuroendocrine tumors with varied genetic makeup and are associated with high cardiovascular morbidity and a variable risk of malignancy. The source of the transcriptional heterogeneity of the disease and the underlying biological processes that determine the outcome of PCPG remain largely unclear. We focused on PCPG tumors with germline SDHB and RET mutations, which represent distinct prognostic groups with worse or better prognoses, respectively. We applied single-nuclei RNA sequencing (snRNA-seq) to tissue samples from 11 patients and found high patient-to-patient transcriptome heterogeneity in neuroendocrine tumor cells. The tumor microenvironment also showed heterogeneous profiles, mainly contributed by macrophages of the immune cell clusters and Schwann cells of the stroma. By performing non-negative matrix factorization, we identified common transcriptional programs active in RET and SDHB, as well as distinct modules, including neuronal development, hormone synthesis and secretion, and DNA replication. Similarities between the transcriptomes of the tumor cells and those of the chromaffin- and precursor cell types suggests different developmental stages at which PC and PG tumors appear to be arrested.

Longitudinal single-cell transcriptomics reveals distinct patterns of recurrence in acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is a heterogeneous and aggressive blood cancer that results from diverse genetic aberrations in the hematopoietic stem or progenitor cells (HSPCs) leading to the expansion of blasts in the hematopoietic system. The heterogeneity and evolution of cancer blasts can render therapeutic interventions ineffective in a yet poorly understood patient-specific manner. In this study, we investigated the clonal heterogeneity of diagnosis (Dx) and relapse (Re) pairs at genetic and transcriptional levels, and unveiled the underlying pathways and genes contributing to recurrence. METHODS: Whole-exome sequencing was used to detect somatic mutations and large copy number variations (CNVs). Single cell RNA-seq was performed to investigate the clonal heterogeneity between Dx-Re pairs and amongst patients. RESULTS: scRNA-seq analysis revealed extensive expression differences between patients and Dx-Re pairs, even for those with the same -presumed- initiating events. Transcriptional differences between and within patients are associated with clonal composition and evolution, with the most striking differences in patients that gained large-scale copy number variations at relapse. These differences appear to have significant molecular implications, exemplified by a DNMT3A/FLT3-ITD patient where the leukemia switched from an AP-1 regulated clone at Dx to a mTOR signaling driven clone at Re. The two distinct AML1-ETO pairs share genes related to hematopoietic stem cell maintenance and cell migration suggesting that the Re leukemic stem cell-like (LSC-like) cells evolved from the Dx cells. CONCLUSIONS: In summary, the single cell RNA data underpinned the tumor heterogeneity not only amongst patient blasts with similar initiating mutations but also between each Dx-Re pair. Our results suggest alternatively and currently unappreciated and unexplored mechanisms leading to therapeutic resistance and AML recurrence.

Trained innate immunity, long-lasting epigenetic modulation, and skewed myelopoiesis by heme.

Trained immunity defines long-lasting adaptations of innate immunity based on transcriptional and epigenetic modifications of myeloid cells and their bone marrow progenitors [M. Divangahi et al., Nat. Immunol. 22, 2-6 (2021)]. Innate immune cells, however, do not exclusively differentiate between foreign and self but also react to host-derived molecules referred to as alarmins. Extracellular "labile" heme, released during infections, is a bona fide alarmin promoting myeloid cell activation [M. P. Soares, M. T. Bozza, Curr. Opin. Immunol. 38, 94-100 (2016)]. Here, we report that labile heme is a previously unrecognized inducer of trained immunity that confers long-term regulation of lineage specification of hematopoietic stem cells and progenitor cells. In contrast to previous reports on trained immunity, essentially mediated by pathogen-associated molecular patterns, heme training depends on spleen tyrosine kinase signal transduction pathway acting upstream of c-Jun N-terminal kinases. Heme training promotes resistance to sepsis, is associated with the expansion of self-renewing hematopoetic stem cells primed toward myelopoiesis and to the occurrence of a specific myeloid cell population. This is potentially evoked by sustained activity of Nfix, Runx1, and Nfe2l2 and dissociation of the transcriptional repressor Bach2. Previously reported trained immunity inducers are, however, infrequently present in the host, whereas heme abundantly occurs during noninfectious and infectious disease. This difference might explain the vanishing protection exerted by heme training in sepsis over time with sustained long-term myeloid adaptations. Hence, we propose that trained immunity is an integral component of innate immunity with distinct functional differences on infectious disease outcome depending on its induction by pathogenic or endogenous molecules.

STARR-seq identifies active, chromatin-masked, and dormant enhancers in pluripotent mouse embryonic stem cells.

BACKGROUND: Enhancers are distal regulators of gene expression that shape cell identity and control cell fate transitions. In mouse embryonic stem cells (mESCs), the pluripotency network is maintained by the function of a complex network of enhancers, that are drastically altered upon differentiation. Genome-wide chromatin accessibility and histone modification assays are commonly used as a proxy for identifying putative enhancers and for describing their activity levels and dynamics. RESULTS: Here, we applied STARR-seq, a genome-wide plasmid-based assay, as a read-out for the enhancer landscape in "ground-state" (2i+LIF; 2iL) and "metastable" (serum+LIF; SL) mESCs. This analysis reveals that active STARR-seq loci show modest overlap with enhancer locations derived from peak calling of ChIP-seq libraries for common enhancer marks. We unveil ZIC3-bound loci with significant STARR-seq activity in SL-ESCs. Knock-out of Zic3 removes STARR-seq activity only in SL-ESCs and increases their propensity to differentiate towards the endodermal fate. STARR-seq also reveals enhancers that are not accessible, masked by a repressive chromatin signature. We describe a class of dormant, p53 bound enhancers that gain H3K27ac under specific conditions, such as after treatment with Nocodazol, or transiently during reprogramming from fibroblasts to pluripotency. CONCLUSIONS: In conclusion, loci identified as active by STARR-seq often overlap with those identified by chromatin accessibility and active epigenetic marking, yet a significant fraction is epigenetically repressed or display condition-specific enhancer activity.

Dynamic CpG methylation delineates subregions within super-enhancers selectively decommissioned at the exit from naive pluripotency.

Clusters of enhancers, referred as to super-enhancers (SEs), control the expression of cell identity genes. The organisation of these clusters, and how they are remodelled upon developmental transitions remain poorly understood. Here, we report the existence of two types of enhancer units within SEs typified by distinctive CpG methylation dynamics in embryonic stem cells (ESCs). We find that these units are either prone for decommissioning or remain constitutively active in epiblast stem cells (EpiSCs), as further established in the peri-implantation epiblast in vivo. Mechanistically, we show a pivotal role for ESRRB in regulating the activity of ESC-specific enhancer units and propose that the developmentally regulated silencing of ESRRB triggers the selective inactivation of these units within SEs. Our study provides insights into the molecular events that follow the loss of ESRRB binding, and offers a mechanism by which the naive pluripotency transcriptional programme can be partially reset upon embryo implantation.

Publisher Correction: Epigenetic modulation of a hardwired 3D chromatin landscape in two naive states of pluripotency.

In the version of the article originally published, extra lines were displayed in Fig. 7. Fig. 7a contained a solid black line that extended into panel b, and Fig. 7c contained two extra scale bars on the left. These have been removed from the figure. The errors have been corrected in the HTML and PDF versions of the article.

Epigenetic modulation of a hardwired 3D chromatin landscape in two naive states of pluripotency.

The mechanisms underlying enhancer activation and the extent to which enhancer-promoter rewiring contributes to spatiotemporal gene expression are not well understood. Using integrative and time-resolved analyses we show that the extensive transcriptome and epigenome resetting during the conversion between 'serum' and '2i' states of mouse embryonic stem cells (ESCs) takes place with minimal enhancer-promoter rewiring that becomes more evident in primed-state pluripotency. Instead, differential gene expression is strongly linked to enhancer activation via H3K27ac. Conditional depletion of transcription factors and allele-specific enhancer analysis reveal an essential role for Esrrb in H3K27 acetylation and activation of 2i-specific enhancers. Restoration of a polymorphic ESRRB motif using CRISPR-Cas9 in a hybrid ESC line restores ESRRB binding and enhancer H3K27ac in an allele-specific manner but has no effect on chromatin interactions. Our study shows that enhancer activation in serum- and 2i-ESCs is largely driven by transcription factor binding and epigenetic marking in a hardwired network of chromatin interactions.

Extensive epigenomic integration of the glucocorticoid response in primary human monocytes and in vitro derived macrophages.

Glucocorticoid receptor is a transcription factor that is ubiquitously expressed. Glucocorticoids are circadian steroids that regulate a wide range of bodily functions, including immunity. Here we report that synthetic glucocorticoids affect 1035 mRNAs in isolated healthy human blood monocytes but only 165 in the respective six day-old monocyte-derived macrophages. The majority of the glucocorticoid response in monocytes concerns genes that are dynamic upon monocyte to macrophage differentiation, whereby macrophage-like mRNA levels are often reached in monocytes within four hours of treatment. Concomitantly, over 5000 chromosomal H3K27ac regions undergo remodelling, of which 60% involve increased H3K27ac signal. We find that chromosomal glucocorticoid receptor binding sites correlate with positive but not with negative local epigenomic effects. To investigate further we assigned our data to topologically associating domains (TADs). This shows that about 10% of macrophage TADs harbour at least one GR binding site and that half of all the glucocorticoid-induced H3K27ac regions are confined to these TADs. Our analyses are therefore consistent with the notion that TADs naturally accommodate information from sets of distal glucocorticoid response elements.

ß-Glucan Reverses the Epigenetic State of LPS-Induced Immunological Tolerance.

Innate immune memory is the phenomenon whereby innate immune cells such as monocytes or macrophages undergo functional reprogramming after exposure to microbial components such as lipopolysaccharide (LPS). We apply an integrated epigenomic approach to characterize the molecular events involved in LPS-induced tolerance in a time-dependent manner. Mechanistically, LPS-treated monocytes fail to accumulate active histone marks at promoter and enhancers of genes in the lipid metabolism and phagocytic pathways. Transcriptional inactivity in response to a second LPS exposure in tolerized macrophages is accompanied by failure to deposit active histone marks at promoters of tolerized genes. In contrast, ß-glucan partially reverses the LPS-induced tolerance in vitro. Importantly, ex vivo ß-glucan treatment of monocytes from volunteers with experimental endotoxemia re-instates their capacity for cytokine production. Tolerance is reversed at the level of distal element histone modification and transcriptional reactivation of otherwise unresponsive genes. VIDEO ABSTRACT.

WeGET: predicting new genes for molecular systems by weighted co-expression.

We have developed the Weighted Gene Expression Tool and database (WeGET, http://weget.cmbi.umcn.nl) for the prediction of new genes of a molecular system by correlated gene expression. WeGET utilizes a compendium of 465 human and 560 murine gene expression datasets that have been collected from multiple tissues under a wide range of experimental conditions. It exploits this abundance of expression data by assigning a high weight to datasets in which the known genes of a molecular system are harmoniously up- and down-regulated. WeGET ranks new candidate genes by calculating their weighted co-expression with that system. A weighted rank is calculated for human genes and their mouse orthologs. Then, an integrated gene rank and p-value is computed using a rank-order statistic. We applied our method to predict novel genes that have a high degree of co-expression with Gene Ontology terms and pathways from KEGG and Reactome. For each query set we provide a list of predicted novel genes, computed weights for transcription datasets used and cell and tissue types that contributed to the final predictions. The performance for each query set is assessed by 10-fold cross-validation. Finally, users can use the WeGET to predict novel genes that co-express with a custom query set.

Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm.

MOTIVATION: Genome-scale metabolic networks can be modeled in a constraint-based fashion. Reaction stoichiometry combined with flux capacity constraints determine the space of allowable reaction rates. This space is often large and a central challenge in metabolic modeling is finding the biologically most relevant flux distributions. A widely used method is flux balance analysis (FBA), which optimizes a biologically relevant objective such as growth or ATP production. Although FBA has proven to be highly useful for predicting growth and byproduct secretion, it cannot predict the intracellular fluxes under all environmental conditions. Therefore, alternative strategies have been developed to select flux distributions that are in agreement with experimental "omics" data, or by incorporating experimental flux measurements. The latter, unfortunately can only be applied to a limited set of reactions and is currently not feasible at the genome-scale. On the other hand, it has been observed that micro-organisms favor a suboptimal growth rate, possibly in exchange for a more "flexible" metabolic network. Instead of dedicating the internal network state to an optimal growth rate in one condition, a suboptimal growth rate is used, that allows for an easier switch to other nutrient sources. A small decrease in growth rate is exchanged for a relatively large gain in metabolic capability to adapt to changing environmental conditions. RESULTS: Here, we propose Maximum Metabolic Flexibility (MMF) a computational method that utilizes this observation to find the most probable intracellular flux distributions. By mapping measured flux data from central metabolism to the genome-scale models of Escherichia coli and Saccharomyces cerevisiae we show that i) indeed, most of the measured fluxes agree with a high adaptability of the network, ii) this result can be used to further reduce the space of feasible solutions iii) this reduced space improves the quantitative predictions made by FBA and contains a significantly larger fraction of the measured fluxes compared to the flux space that was reduced by a uniform sampling approach and iv) MMF can be used to select reactions in the network that contribute most to the steady-state flux space. Constraining the selected reactions improves the quantitative predictions of FBA considerably more than adding an equal amount of flux constraints, selected using a more naïve approach. Our method can be applied to any cell type without requiring prior information. AVAILABILITY: MMF is freely available as a MATLAB plugin at: http://cs.ru.nl/~wmegchel/mmf.

Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival.

Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. As experimental genome-wide SDL screens are still scarce, here we introduce a network-level computational modeling framework that quantitatively predicts human SDLs in metabolism. For each enzyme pair (A, B) we systematically knock out the flux through A combined with a stepwise flux increase through B and search for pairs that reduce cellular growth more than when either enzyme is perturbed individually. The predictive signal of the emerging network of 12,000 SDLs is demonstrated in five different ways. (i) It can be successfully used to predict gene essentiality in shRNA cancer cell line screens. Moving to clinical tumors, we show that (ii) SDLs are significantly underrepresented in tumors. Furthermore, breast cancer tumors with SDLs active (iii) have smaller sizes and (iv) result in increased patient survival, indicating that activation of SDLs increases cancer vulnerability. Finally, (v) patient survival improves when multiple SDLs are present, pointing to a cumulative effect. This study lays the basis for quantitative identification of cancer SDLs in a model-based mechanistic manner. The approach presented can be used to identify SDLs in species and cell types in which "omics" data necessary for data-driven identification are missing.

Predicting human genetic interactions from cancer genome evolution.

Synthetic Lethal (SL) genetic interactions play a key role in various types of biological research, ranging from understanding genotype-phenotype relationships to identifying drug-targets against cancer. Despite recent advances in empirical measuring SL interactions in human cells, the human genetic interaction map is far from complete. Here, we present a novel approach to predict this map by exploiting patterns in cancer genome evolution. First, we show that empirically determined SL interactions are reflected in various gene presence, absence, and duplication patterns in hundreds of cancer genomes. The most evident pattern that we discovered is that when one member of an SL interaction gene pair is lost, the other gene tends not to be lost, i.e. the absence of co-loss. This observation is in line with expectation, because the loss of an SL interacting pair will be lethal to the cancer cell. SL interactions are also reflected in gene expression profiles, such as an under representation of cases where the genes in an SL pair are both under expressed, and an over representation of cases where one gene of an SL pair is under expressed, while the other one is over expressed. We integrated the various previously unknown cancer genome patterns and the gene expression patterns into a computational model to identify SL pairs. This simple, genome-wide model achieves a high prediction power (AUC = 0.75) for known genetic interactions. It allows us to present for the first time a comprehensive genome-wide list of SL interactions with a high estimated prediction precision, covering up to 591,000 gene pairs. This unique list can potentially be used in various application areas ranging from biotechnology to medical genetics.

optGpSampler: an improved tool for uniformly sampling the solution-space of genome-scale metabolic networks.

UNLABELLED: Constraint-based models of metabolic networks are typically underdetermined, because they contain more reactions than metabolites. Therefore the solutions to this system do not consist of unique flux rates for each reaction, but rather a space of possible flux rates. By uniformly sampling this space, an estimated probability distribution for each reaction's flux in the network can be obtained. However, sampling a high dimensional network is time-consuming. Furthermore, the constraints imposed on the network give rise to an irregularly shaped solution space. Therefore more tailored, efficient sampling methods are needed. We propose an efficient sampling algorithm (called optGpSampler), which implements the Artificial Centering Hit-and-Run algorithm in a different manner than the sampling algorithm implemented in the COBRA Toolbox for metabolic network analysis, here called gpSampler. Results of extensive experiments on different genome-scale metabolic networks show that optGpSampler is up to 40 times faster than gpSampler. Application of existing convergence diagnostics on small network reconstructions indicate that optGpSampler converges roughly ten times faster than gpSampler towards similar sampling distributions. For networks of higher dimension (i.e. containing more than 500 reactions), we observed significantly better convergence of optGpSampler and a large deviation between the samples generated by the two algorithms. AVAILABILITY: optGpSampler for Matlab and Python is available for non-commercial use at: http://cs.ru.nl/~wmegchel/optGpSampler/.