Molecular Landscapes and Models of Acute Erythroleukemia.
Ontology highlight
ABSTRACT:
SUBMITTER: Fagnan A
PROVIDER: S-EPMC8061683 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Acute erythroid leukemia (AEL) is a high-risk leukemia of poorly understood genetic basis, with controversy regarding diagnosis in the spectrum of myelodysplasia and myeloid leukemia. We compared genomic features of 159 childhood and adult AEL cases with non-AEL myeloid disorders and defined five age-related subgroups with distinct transcriptional profiles: adult, TP53 mutated; NPM1 mutated; KMT2A mutated/rearranged; adult, DDX41 mutated; and pediatric, NUP98 rearranged. Genomic features influenced outcome, with NPM1 mutations and HOXB9 overexpression being associated with a favorable prognosis and TP53, FLT3 or RB1 alterations associated with poor survival. Targetable signaling mutations were present in 45% of cases and included recurrent mutations of ALK and NTRK1, the latter of which drives erythroid leukemogenesis sensitive to TRK inhibition. This genomic landscape of AEL provides the framework for accurate diagnosis and risk stratification of this disease, and the rationale for testing targeted therapies in this high-risk leukemia.
| S-EPMC6828160 | biostudies-literature
Project description:Erythroleukemia belongs to acute myeloid leukemia (AML) type 6 (M6), and treatment remains difficult due to the poor prognosis of the disease. Friend virus (FV) is a complex of two viruses: Friend murine leukemia virus (F-MuLV) strain along with a defective spleen focus-forming virus (SFFV), which can induce acute erythroleukemia in mice. We have previously reported that activation of vagal α7 nicotinic acetylcholine receptor (nAChR) signaling promotes HIV-1 transcription. Whether vagal muscarinic signaling mediates FV-induced erythroleukemia and the underlying mechanisms remain unclear. In this study, sham and vagotomized mice were intraperitoneally injected with FV. FV infection caused anemia in sham mice, and vagotomy reversed this change. FV infection increased erythroblasts ProE, EryA, and EryB cells in the spleen, and these changes were blocked by vagotomy. In bone marrow, FV infection reduced EryC cells in sham mice, an effect that was counteracted by vagotomy. FV infection increased choline acetyltransferase (ChAT) expression in splenic CD4+ and CD8+ T cells, and this change was reversed by vagotomy. Furthermore, the increase of EryA and EryB cells in spleen of FV-infected wild-type mice was reversed after deletion of ChAT in CD4+ T cells. In bone marrow, FV infection reduced EryB and EryC cells in sham mice, whereas lack of ChAT in CD4+ T cells did not affect this change. Activation of muscarinic acetylcholine receptor 4 (mAChR4) by clozapine N-oxide (CNO) significantly increased EryB in the spleen but decreased the EryC cell population in the bone marrow of FV-infected mice. Thus, vagal-mAChR4 signaling in the spleen and bone marrow synergistically promotes the pathogenesis of acute erythroleukemia. We uncover an unrecognized mechanism of neuromodulation in erythroleukemia.
| S-EPMC10311277 | biostudies-literature
Project description: Not available
| S-EPMC9153007 | biostudies-literature
Project description:Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.
| S-EPMC3228428 | biostudies-other
Project description:Hepatocellular carcinoma (HCC) and biliary tract cancer are more frequent in East Asia including Japan than in Europe or North America. A compilation of 1340 multi-ethnic HCC genomes, the largest cohort ever reported, identified a comprehensive landscape of HCC driver genes, comprised of three core drivers (TP53, TERT, and WNT signaling) and combinations of infrequent alterations in various cancer pathways. In contrast, five core driver genes (TP53, ARID1A, KRAS, SMAD4, and BAP1) with characteristic molecular alterations including fusion transcripts involving fibroblast growth factor receptor 2 and the protein kinase A pathway, and IDH1/2 mutation constituted the biliary tract cancer genomes. Consistent with their heterogeneous epidemiological backgrounds, mutational signatures and combinations of non-core driver genes within these cancer genomes were found to be complex. Integrative analyses of multi-omics data identified molecular classifications of these tumors that are associated with clinical outcome and enrichments of potential therapeutic targets, including immune checkpoint molecules. Translating comprehensive molecular-genomic analysis together with further basic research and international collaborations are highly anticipated for developing precise and better treatments, diagnosis, and prevention of these tumor types.
| S-EPMC5980207 | biostudies-literature
Project description:Traditional robots rely for their function on computing, to store internal representations of their goals and environment and to coordinate sensing and any actuation of components required in response. Moving robotics to the single-molecule level is possible in principle, but requires facing the limited ability of individual molecules to store complex information and programs. One strategy to overcome this problem is to use systems that can obtain complex behaviour from the interaction of simple robots with their environment. A first step in this direction was the development of DNA walkers, which have developed from being non-autonomous to being capable of directed but brief motion on one-dimensional tracks. Here we demonstrate that previously developed random walkers-so-called molecular spiders that comprise a streptavidin molecule as an inert 'body' and three deoxyribozymes as catalytic 'legs'-show elementary robotic behaviour when interacting with a precisely defined environment. Single-molecule microscopy observations confirm that such walkers achieve directional movement by sensing and modifying tracks of substrate molecules laid out on a two-dimensional DNA origami landscape. When using appropriately designed DNA origami, the molecular spiders autonomously carry out sequences of actions such as 'start', 'follow', 'turn' and 'stop'. We anticipate that this strategy will result in more complex robotic behaviour at the molecular level if additional control mechanisms are incorporated. One example might be interactions between multiple molecular robots leading to collective behaviour; another might be the ability to read and transform secondary cues on the DNA origami landscape as a means of implementing Turing-universal algorithmic behaviour.
| S-EPMC2907518 | biostudies-literature
Project description:Alisertib (MLN8237, ALS), an Aurora kinase A (AURKA) inhibitor, exerts potent anti-tumor effects in the treatment of solid tumor and hematologic malignancies in preclinical and clinical studies. However, the fully spectrum of molecular targets of ALS and its anticancer effect in the treatment of chronic myeloid leukemia (CML) are not clear. This study aimed to examine the proteomic responses to ALS treatment and unveil the molecular interactome and possible mechanisms for its anticancer effect in K562 cells using stable-isotope labeling by amino acids in cell culture (SILAC) approach. The proteomic data identified that ALS treatment modulated the expression of 1541 protein molecules (570 up; 971 down). The pathway analysis showed that 299 signaling pathways and 459 cellular functional proteins directly responded to ALS treatment in K562 cells. These targeted molecules and signaling pathways were mainly involved in cell growth and proliferation, cell metabolism, and cell survival and death. Subsequently, the effects of ALS on cell cycle distribution, apoptosis, and autophagy were verified. The flow cytometric analysis showed that ALS significantly induced G2/M phase arrest and the Western blotting assays showed that ALS induced apoptosis via mitochondria-dependent pathway and promoted autophagy with the involvement of PI3K/Akt/mTOR, p38 MAPK, and AMPK signaling pathways in K562 cells. Collectively, this study provides a clue to quantitatively evaluate the proteomic responses to ALS and assists in globally identifying the potential molecular targets and elucidating the underlying mechanisms of ALS for CML treatment, which may help develop new efficacious and safe therapies for CML treatment.
| S-EPMC4697722 | biostudies-literature
Project description:Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations.
| S-EPMC4989103 | biostudies-literature
Project description:Protein sequences contain rich information about protein evolution, fitness landscapes, and stability. Here we investigate how latent space models trained using variational auto-encoders can infer these properties from sequences. Using both simulated and real sequences, we show that the low dimensional latent space representation of sequences, calculated using the encoder model, captures both evolutionary and ancestral relationships between sequences. Together with experimental fitness data and Gaussian process regression, the latent space representation also enables learning the protein fitness landscape in a continuous low dimensional space. Moreover, the model is also useful in predicting protein mutational stability landscapes and quantifying the importance of stability in shaping protein evolution. Overall, we illustrate that the latent space models learned using variational auto-encoders provide a mechanism for exploration of the rich data contained in protein sequences regarding evolution, fitness and stability and hence are well-suited to help guide protein engineering efforts.
| S-EPMC6904478 | biostudies-literature
Project description:Recent work has highlighted the tumor microenvironment as a central player in cancer. In particular, interactions between tumor and immune cells may help drive the development of brain tumors such as glioblastoma multiforme (GBM). Despite significant research into the molecular classification of glioblastoma, few studies have characterized in a comprehensive manner the immune infiltrate in situ and within different GBM subtypes.In this study, we use an unbiased, automated immunohistochemistry-based approach to determine the immune phenotype of the four GBM subtypes (classical, mesenchymal, neural and proneural) in a cohort of 98 patients. Tissue Micro Arrays (TMA) were stained for CD20 (B lymphocytes), CD5, CD3, CD4, CD8 (T lymphocytes), CD68 (microglia), and CD163 (bone marrow derived macrophages) antibodies. Using automated image analysis, the percentage of each immune population was calculated with respect to the total tumor cells. Mesenchymal GBMs displayed the highest percentage of microglia, macrophage, and lymphocyte infiltration. CD68+ and CD163+ cells were the most abundant cell populations in all four GBM subtypes, and a higher percentage of CD163+ cells was associated with a worse prognosis. We also compared our results to the relative composition of immune cell type infiltration (using RNA-seq data) across TCGA GBM tumors and validated our results obtained with immunohistochemistry with an external cohort and a different method. The results of this study offer a comprehensive analysis of the distribution and the infiltration of the immune components across the four commonly described GBM subgroups, setting the basis for a more detailed patient classification and new insights that may be used to better apply or design immunotherapies for GBM.
| S-EPMC6902522 | biostudies-literature