Search

Significance During infection, extracellular “labile” heme, released from damaged red blood or parenchymal cells, acts as prototypical alarmin stimulating myeloid cells. A characteristic hallmark of myeloid cell activation is the development of trained immunity, specified as long-lasting adaptations based on transcriptional and epigenetic modifications. In vivo, this is maintained by the rerouting of hematopoiesis. We found that heme is a previously unrecognized trained immunity inducer promoting resistance to bacterial infection in mice. This goes along with extensive long-lasting epigenetic memory in hematopoietic stem cells provoking drastic changes in the transcription factor–binding landscape of myeloid progenitor cells. Given the critical role of heme during infections, we propose that trained immunity is a more general component of innate immunity than previously suggested., 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.

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., Clusters of enhancers, called super-enhancers (SEs), control the expression of cell identity genes. Here, the authors identify two types of enhancer units within SEs in ESCs, characterised by distinctive CpG methylation dynamics, and find that ESRRB regulates the enhancer units decommissioned at exit from naive pluripotency.

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.

Contains fulltext : 152194.PDF (Publisher’s version ) (Open Access) 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 naive 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 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.

Contains fulltext : 149260.pdf (Publisher’s version ) (Closed access) 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.

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/. © 2014 Megchelenbrink et al.