Chromatin structure and 3D architecture define differential functions of PU.1 cis regulatory elements in human blood cell lineages.

The precise spatio-temporal expression of the hematopoietic ETS transcription factor PU.1 that determines the hematopoietic cell fates is tightly regulated at the chromatin level. However, it remains elusive as to how chromatin signatures are linked to this dynamic expression pattern of PU.1 across blood cell lineages. Here we performed an unbiased and in-depth analysis of the relationship between human PU.1 expression, the presence of trans-acting factors, and 3D architecture at various cis-regulatory elements (CRE) proximal to the PU.1 locus. We identified multiple novel CREs at the upstream region of the gene following an integrative inspection for conserved DNA elements at the chromatin-accessible regions in primary human blood lineages. We showed that a subset of CREs localize within a 10 kb-wide cluster that exhibits that exhibit molecular features of a myeloid-specific super-enhancer involved in mediating PU.1 autoregulation, including open chromatin, unmethylated DNA, histone enhancer marks, transcription of enhancer RNAs, and occupancy of the PU.1 protein itself. Importantly, we revealed the presence of common 35-kb-wide CTCF-bound insulated neighborhood that contains the CRE cluster, forming the chromatin territory for lineage-specific and CRE-mediated chromatin interactions. These include functional CRE-promoter interactions in myeloid and B cells but not in erythroid and T cells. Our findings also provide mechanistic insights into the interplay between dynamic chromatin structure and 3D architecture in defining certain CREs as enhancers or silencers in chromatin regulation of PU.1 expression. The study lays the groundwork for further examination of PU.1 CREs as well as epigenetic regulation in malignant hematopoiesis.

Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model.

In recent studies, miRNAs have been found to be extremely influential in many of the essential biological processes. They exhibit a self-regulatory mechanism through which they act as positive/negative regulators of expression of genes and other miRNAs. This has direct implications in the regulation of various pathophysiological conditions, signaling pathways and different types of cancers. Studying miRNA-disease associations has been an extensive area of research; however deciphering miRNA-miRNA network regulatory patterns in several diseases remains a challenge. In this study, we use information diffusion theory to quantify the influence diffusion in a miRNA-miRNA regulation network across multiple disease categories. Our proposed methodology determines the critical disease specific miRNAs which play a causal role in their signaling cascade and hence may regulate disease progression. We extensively validate our framework using existing computational tools from the literature. Furthermore, we implement our framework on a comprehensive miRNA expression data set for alcohol dependence and identify the causal miRNAs for alcohol-dependency in patients which were validated by the phase-shift in their expression scores towards the early stages of the disease. Finally, our computational framework for identifying causal miRNAs implicated in diseases is available as a free online tool for the greater scientific community.

Dynamic social community detection and its applications.

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods.