Biallelic inactivation of SMARCB1, encoding a member of the SWI/SNF chromatin remodeling complex, is the hallmark genetic aberration of atypical teratoid rhabdoid tumors (ATRT). Here, we report how ...loss of SMARCB1 affects the epigenome in these tumors. Using chromatin immunoprecipitation sequencing (ChIP-seq) on primary tumors for a series of active and repressive histone marks, we identified the chromatin states differentially represented in ATRTs compared with other brain tumors and non-neoplastic brain. Re-expression of SMARCB1 in ATRT cell lines enabled confirmation of our genome-wide findings for the chromatin states. Additional generation of ChIP-seq data for SWI/SNF and Polycomb group proteins and the transcriptional repressor protein REST determined differential dependencies of SWI/SNF and Polycomb complexes in regulation of diverse gene sets in ATRTs.
•ATRT epigenomes display a global depletion of H3K27ac and H3K27me3•Neuronal genes bound by SMARCB1 in normal brain are repressed by EZH2 in ATRT•ATRT harbor many active genes occupied by EZH2 but without occupancy of H3K27me3•Residual SWI/SNF occupancy maintains genes active in the presence of Polycomb complex
Erkek et al. show that in atypical teratoid rhabdoid tumors (ATRT), which often lack the SWI/SNF complex component SMARCB1, a large fraction of SMARCB1 binding loci in normal brain is bound by EZH2 but without H3K27me3 and remains in an active state, and some of these genes are essential for ATRT survival.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Pancreatic ductal adenocarcinoma (PDAC) is associated with a dismal prognosis and poor therapeutic response to current chemotherapy regimens in unselected patient populations. Recently, it has been ...shown that PDAC may be stratified into functionally and therapeutically relevant molecular subgroups and that some of these subtypes can be recapitulated by IHC for KRT81 quasi-mesenchymal (QM)/squamous/basal-like and HNF1A (non-QM, overlap with exocrine/ADEX subtype).
We validated the different outcome of the HNF1A/KRT81 PDAC subtypes in two independent cohorts of surgically treated patients and examined the treatment response to chemotherapy in a third cohort of unresectable patients. The first two cohorts included 262 and 130 patients, respectively, and the third independent cohort comprised advanced-stage PDAC patients who were treated with either FOLFIRINOX (64 patients) or gemcitabine (61 patients).
In both cohorts with resected PDAC, the HNF1A-positive subtype showed the best, the KRT81-positive subtype the worst, and the double-negative subtype an intermediate survival (
< 0.013 and
< 0.009, respectively). In the chemotherapy cohort, the survival difference between the double-negative and the HNF1A-positive subtype was lost, whereas the dismal prognosis of KRT81-positive PDAC patients was retained (
< 0.021). Patients with a KRT81-positive subtype did not benefit from FOLFIRINOX therapy, whereas those with HNF1A-positive tumors responded better compared with gemcitabine-based treatment (
< 0.038).
IHC stratification recapitulating molecular subtypes of PDAC using HNF1A and KRT81 is associated with significantly different outcomes and responses to chemotherapy. These results may pave the way toward future pretherapeutic biomarker-based stratification of PDAC patients.
.
DNA methylation patterns delineate clinically relevant subgroups of meningioma. We previously established the six meningioma methylation classes (MC) benign 1–3, intermediate A and B, and malignant. ...Here, we set out to identify subgroup-specific mutational patterns and gene regulation. Whole genome sequencing was performed on 62 samples across all MCs and WHO grades from 62 patients with matched blood control, including 40 sporadic meningiomas and 22 meningiomas arising after radiation (Mrad). RNA sequencing was added for 18 of these cases and chromatin-immunoprecipitation for histone H3 lysine 27 acetylation (H3K27ac) followed by sequencing (ChIP-seq) for 16 samples. Besides the known mutations in meningioma, structural variants were found as the mechanism of
NF2
inactivation in a small subset (5%) of sporadic meningiomas, similar to previous reports for Mrad. Aberrations of
DMD
were found to be enriched in MCs with
NF2
mutations, and
DMD
was among the most differentially upregulated genes in
NF2
mutant compared to
NF2
wild-type cases. The mutational signature AC3, which has been associated with defects in homologous recombination repair (HRR), was detected in both sporadic meningioma and Mrad, but widely distributed across the genome in sporadic cases and enriched near genomic breakpoints in Mrad. Compared to the other MCs, the number of single nucleotide variants matching the AC3 pattern was significantly higher in the malignant MC, which also exhibited higher genomic instability, determined by the numbers of both large segments affected by copy number alterations and breakpoints between large segments. ChIP-seq analysis for H3K27ac revealed a specific activation of genes regulated by the transcription factor FOXM1 in the malignant MC. This analysis also revealed a super enhancer near the
HOXD
gene cluster in this MC, which, together with general upregulation of
HOX
genes in the malignant MC, indicates a role of
HOX
genes in meningioma aggressiveness. This data elucidates the biological mechanisms rendering different epigenetic subgroups of meningiomas, and suggests leveraging HRR as a novel therapeutic target.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
In this Editorial, our Chief Editor and members of our Advisory Editorial Board discuss recent breakthroughs, current challenges, and emerging opportunities in single‐cell biology and share their ...vision of “where the field is headed.”
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Cellular FADD-like interleukin-1β-converting enzyme inhibitory proteins (c-FLIPs; isoforms c-FLIP long c-FLIPL, c-FLIP short c-FLIPS, and c-FLIP Raji c-FLIPR) regulate caspase-8 activation and death ...receptor (DR)-induced apoptosis. In this study, using a combination of mathematical modeling, imaging, and quantitative Western blots, we present a new mathematical model describing caspase-8 activation in quantitative terms, which highlights the influence of c-FLIP proteins on this process directly at the CD95 death-inducing signaling complex. We quantitatively define how the stoichiometry of c-FLIP proteins determines sensitivity toward CD95-induced apoptosis. We show that c-FLIPL has a proapoptotic role only upon moderate expression in combination with strong receptor stimulation or in the presence of high amounts of one of the short c-FLIP isoforms, c-FLIPS or c-FLIPR. Our findings resolve the present controversial discussion on the function of c-FLIPL as a pro- or antiapoptotic protein in DR-mediated apoptosis and are important for understanding the regulation of CD95-induced apoptosis, where subtle differences in c-FLIP concentrations determine life or death of the cells.
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The circadian clock and the cell cycle are major cellular systems that organize global physiology in temporal fashion. It seems conceivable that the potentially conflicting programs are coordinated. ...We show here that overexpression of MYC in U2OS cells attenuates the clock and conversely promotes cell proliferation while downregulation of MYC strengthens the clock and reduces proliferation. Inhibition of the circadian clock is crucially dependent on the formation of repressive complexes of MYC with MIZ1 and subsequent downregulation of the core clock genes BMAL1 (ARNTL), CLOCK and NPAS2. We show furthermore that BMAL1 expression levels correlate inversely with MYC levels in 102 human lymphomas. Our data suggest that MYC acts as a master coordinator that inversely modulates the impact of cell cycle and circadian clock on gene expression.
After hundreds of generations of adaptive evolution at exponential growth, Escherichia coli grows as predicted using flux balance analysis (FBA) on genome‐scale metabolic models (GEMs). However, it ...is not known whether the predicted pathway usage in FBA solutions is consistent with gene and protein expression in the wild‐type and evolved strains. Here, we report that >98% of active reactions from FBA optimal growth solutions are supported by transcriptomic and proteomic data. Moreover, when E. coli adapts to growth rate selective pressure, the evolved strains upregulate genes within the optimal growth predictions, and downregulate genes outside of the optimal growth solutions. In addition, bottlenecks from dosage limitations of computationally predicted essential genes are overcome in the evolved strains. We also identify regulatory processes that may contribute to the development of the optimal growth phenotype in the evolved strains, such as the downregulation of known regulons and stringent response suppression. Thus, differential gene and protein expression from wild‐type and adaptively evolved strains supports observed growth phenotype changes, and is consistent with GEM‐computed optimal growth states.
Synopsis
When prokaryotes are maintained at early‐ to mid‐log phase growth through serial passaging for hundreds of generations, the strains improve fitness and evolve a higher growth rate (Lenski and Travisano, 1994; Ibarra et al, 2002). This increased growth rate is the result of the appearance of a few causal mutations (Herring et al, 2006; Conrad et al, 2009). In Escherichia coli, these altered growth phenotypes are consistent with predictions from genome‐scale models of metabolism (GEMs) (Ibarra et al, 2002; Fong and Palsson, 2004). However, it is still not known (1) whether absolute gene and protein expression levels and expression changes are consistent with optimal growth predictions from in silico GEMs or (2) whether measured expression changes can be linked to physiological changes that are based on known mechanisms or pathways. In this study, we begin to address these questions using constraint‐based modeling of E. coli K‐12 metabolism (Feist and Palsson, 2008) to analyze omic data that document the expression changes in E. coli under adaptive evolution in three different growth conditions.
Mapping high‐throughput data to a network can be useful for interpretation. However, it does not account for upstream and downstream effects of gene and protein expression changes. The analysis of data in the context of GEMs can suggest if predicted activity is consistent with the data. For this work, we used a variant of flux balance analysis (FBA), called Parsimonious enzyme usage FBA (pFBA) (Figure 1), to classify all genes according to whether they are used in the optimal growth solutions. Results from these models were compared with the data to assess whether the data were consistent with genes and proteins within the predicted optimal solutions, and whether the expression changes were consistent with measured physiology. Through this analysis, we find that the data provide a high coverage of genes that contribute to the optimal growth solutions (Figure 1B). In fact, the union of the proteomic and transcriptomic data for non‐essential genes provides support for 97.7% of all non‐essential gene‐associated reactions within the optimal growth predictions. Thus, the spectrum of expressed genes and proteins is consistent with the pathway utilization that is predicted for these optimal growth phenotypes.
Laboratory‐evolved strains attain a higher growth rate. This higher growth rate is usually associated with an increased substrate uptake rate (Ibarra et al, 2002; Fong et al, 2005) and in some cases more efficient metabolism (Ibarra et al, 2002). Both of these properties are also witnessed in the strains studied here. It has been reported that in most cases, evolved strain growth phenotype is consistent with GEM predictions (Ibarra et al, 2002; Teusink et al, 2009). Here, we evaluate whether the laboratory‐evolved strains adjust the gene and protein expression levels in accordance with pathway usage in the optimal growth predictions. Essential and non‐essential genes and proteins within the optimal growth solutions are significantly upregulated (Figure 1B). This suggests that these proteins may be acting as bottlenecks that are relieved through the adaptive process, thereby allowing for a higher substrate uptake rate and growth rate. However, genes and proteins associated with reactions that cannot carry a flux in the given growth conditions are downregulated in the evolved strains (Figure 1B). Furthermore, there is downregulation of genes associated with less efficient pathways (Figure 5C). Thus, the omic data support the emergence of the predicted optimal growth states, consistent with the increased substrate uptake upstream and the increased biomass production downstream of these internal pathways.
Regulatory mechanisms, both known and unknown, are responsible for the changes seen here. Across all data sets, several metabolic regulons are significantly downregulated. However, no known regulons were enriched among upregulated genes or proteins for all but one data set. Aside from just regulating the metabolic pathways directly, these mechanisms lead to additional physiological changes. For example, in the minimal media growth conditions used here, the stringent response normally represses growth while upregulating amino‐acid biosynthetic processes. However, evolved strain gene expression shows a suppression of the stringent response, as evolved strain gene expression shows either no expression change or changes opposite to the normal stringent response.
The implications of this work are as follows: (1) genome‐scale gene and protein expression data are consistent with FBA computed optimal growth states, and evolved strains reinforce these optimal states; (2) genome‐scale models will have an important function bridging the gap between genotype and phenotype; and (3) the development of additional genome‐scale models of other growth‐related processes such as transcription and translation (Thiele et al, 2009) will have an important function in elucidating the mechanisms that contribute the most to altered phenotypes (Lewis et al, 2009a). In addition, reconstruction of the transcriptional regulation network will aid in identifying the control of expression changes seen in the other systems.
Proteomic and transcriptomic data from wild‐type and laboratory‐evolved strains of Escherichia coli are consistent with predicted pathway usage from optimal growth rate solutions.
In laboratory‐evolved strains, there is an upregulation of the pathways in the computed optimal growth states, and downregulation of non‐functional pathways.
Known regulatory mechanisms are only partially responsible for altered metabolic pathway activity.
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
What makes embryogenesis a robust and canalized process is an important question in developmental biology. A bone morphogenetic protein (BMP) morphogen gradient plays a key role in embryonic ...development, and we are beginning to understand how the self-regulating properties of its signaling circuitry ensure robust embryonic patterning. An unexplored question is why the BMP signaling circuit is organized as a modular synexpression group, with a prevalence of feedback inhibitors. Here, we provide evidence from direct experimentation and mathematical modeling that the synexpressed feedback inhibitors BAMBI, SMAD6, and SMAD7 (i) expand the dynamic BMP signaling range essential for proper embryonic patterning and (ii) reduce interindividual phenotypic and molecular variability in Xenopus embryos. Thereby, negative feedback linearizes signaling responses and confers robust patterning, thus promoting canalized development. The presence of negative feedback inhibitors in other growth factor synexpression groups suggests that these properties may constitute a general principle.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Studies of higher-order chromatin arrangements are an essential part of ongoing attempts to explore changes in epigenome structure and their functional implications during development and cell ...differentiation. However, the extent and cell-type-specificity of three-dimensional (3D) chromosome arrangements has remained controversial. In order to overcome technical limitations of previous studies, we have developed tools that allow the quantitative 3D positional mapping of all chromosomes simultaneously. We present unequivocal evidence for a probabilistic 3D order of prometaphase chromosomes, as well as of chromosome territories (CTs) in nuclei of quiescent (G0) and cycling (early S-phase) human diploid fibroblasts (46, XY). Radial distance measurements showed a probabilistic, highly nonrandom correlation with chromosome size: small chromosomes-independently of their gene density-were distributed significantly closer to the center of the nucleus or prometaphase rosette, while large chromosomes were located closer to the nuclear or rosette rim. This arrangement was independently confirmed in both human fibroblast and amniotic fluid cell nuclei. Notably, these cell types exhibit flat-ellipsoidal cell nuclei, in contrast to the spherical nuclei of lymphocytes and several other human cell types, for which we and others previously demonstrated gene-density-correlated radial 3D CT arrangements. Modeling of 3D CT arrangements suggests that cell-type-specific differences in radial CT arrangements are not solely due to geometrical constraints that result from nuclear shape differences. We also found gene-density-correlated arrangements of higher-order chromatin shared by all human cell types studied so far. Chromatin domains, which are gene-poor, form a layer beneath the nuclear envelope, while gene-dense chromatin is enriched in the nuclear interior. We discuss the possible functional implications of this finding.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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