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Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a new optimization procedure to identify the top network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression. [ABSTRACT FROM AUTHOR]

Biological circuits such as neural or gene regulation networks use internal states to map sensory input to an adaptive repertoire of behavior. Characterizing this mapping is a major challenge for systems biology. Though experiments that probe internal states are developing rapidly, organismal complexity presents a fundamental obstacle given the many possible ways internal states could map to behavior. Using C. elegans as an example, we propose a protocol for systematic perturbation of neural states that limits experimental complexity and could eventually help characterize collective aspects of the neural-behavioral map. We consider experimentally motivated small perturbations—ones that are most likely to preserve natural dynamics and are closer to internal control mechanisms—to neural states and their impact on collective neural activity. Then, we connect such perturbations to the local information geometry of collective statistics, which can be fully characterized using pairwise perturbations. Applying the protocol to a minimal model of C. elegans neural activity, we find that collective neural statistics are most sensitive to a few principal perturbative modes. Dominant eigenvalues decay initially as a power law, unveiling a hierarchy that arises from variation in individual neural activity and pairwise interactions. Highest-ranking modes tend to be dominated by a few, "pivotal" neurons that account for most of the system's sensitivity, suggesting a sparse mechanism of collective control. Author summary: The relationship between underlying biological circuitry and behavior is complex and difficult to probe experimentally. Part of the problem is that, for organisms of even modest size, there are an overwhelming number of possible combinations of interacting circuit components. We develop a theoretical framework to simplify this problem with experiments that change the system minimally with small perturbations. In the realm of small perturbations, not only does system behavior remain close to normal but the range of possible perturbations is greatly reduced to only pairs of experimental targets. We demonstrate such a perturbation using a minimal model of neural activity in the C. elegans worm. We find that a few combinations of "pivotal" neurons strongly affect the statistics of synchronous activity, suggesting they may be important for neural control of behavior. Our work suggests generalizable, feasible, and perturbative experiments to map how the physical components of an organism control emergent collective activity. [ABSTRACT FROM AUTHOR]

Polycomb group (PcG) proteins maintain cell identity by repressing gene expression during development. Surprisingly, emerging studies have recently reported that a number of PcG proteins directly activate gene expression during cell fate determination process. However, the mechanisms by which they direct gene activation in pluripotency remain poorly understood. Here, we show that Phc1, a subunit of canonical polycomb repressive complex 1 (cPRC1), can exert its function in pluripotency maintenance via a PRC1-independent activation of Nanog. Ablation of Phc1 reduces the expression of Nanog and overexpression of Nanog partially rescues impaired pluripotency caused by Phc1 depletion. We find that Phc1 interacts with Nanog and activates Nanog transcription by stabilizing the genome-wide chromatin interactions of the Nanog locus. This adds to the already known canonical function of PRC1 in pluripotency maintenance via a PRC1-dependent repression of differentiation genes. Overall, our study reveals a function of Phc1 to activate Nanog transcription through regulating chromatin architecture and proposes a paradigm for PcG proteins to maintain pluripotency. Phc1 is a subunit of the polycomb repressive complex 1 (PRC1), which represses gene expression during development. Here the authors show that Phc1 acts independently from PRC1 to activate Nanog transcription by stabilizing genome-wide chromatin interactions of the Nanog locus, and in turn stabilize pluripotency. [ABSTRACT FROM AUTHOR]

The global insight into the relationships between miRNAs and their regulatory influences remains poorly understood. And most of complex diseases may be attributed to certain local areas of pathway (subpathway) instead of the entire pathway. Here, we reviewed the studies on miRNA regulations to pathways and constructed a bipartite miRNAs and subpathways network for systematic analyzing the miRNA regulatory influences to subpathways. We found that a small fraction of miRNAs were global regulators, environmental information processing pathways were preferentially regulated by miRNAs, and miRNAs had synergistic effect on regulating group of subpathways with similar function. Integrating the disease states of miRNAs, we also found that disease miRNAs regulated more subpathways than nondisease miRNAs, and for all miRNAs, the number of regulated subpathways was not in proportion to the number of the related diseases. Therefore, the study not only provided a global view on the relationships among disease, miRNA and subpathway, but also uncovered the function aspects of miRNA regulations and potential pathogenesis of complex diseases. A web server to query, visualize and download for all the data can be freely accessed at http://bioinfo.hrbmu.edu.cn/miR2Subpath. [ABSTRACT FROM PUBLISHER]

The investigation of topological properties of proteins in protein--protein interaction network (PPIN) has great potentials to identify basic protein functions and mechanisms of action. Based on human PPIN, previous study has shown that the topological properties of drug targets are significantly distinguished from those of proteins that are not targeted by drugs (non-drug-targets). MicroRNAs (miRNAs) are known to regulate gene expression at the post-transcriptional level. To determine whether the differences in topological properties between drug targets and non-drug-targets are dominated by the proteins that are regulated by miRNA, we divided the drug targets into two sets: those are regulated by miRNA (mir-drug-targets) and those are not regulated by miRNA (non-mir-drug-targets). We compared the probability of interactions and five topological properties among the three types of proteins in human PPIN. Our results demonstrated that mir-drug-targets preferentially interact with other mir-drug-targets and tend to be hub-bottlenecks. However, there was no bias on topological properties between non-mir-drug-targets and non-drug-targets. The same topological features are observed among non-drug targets. These findings indicate that miRNA regulation has an important role in human PPIN, and may be useful in the development of novel drugs. [ABSTRACT FROM AUTHOR]