Principles of Interplay Between miRNAs and Transcription Factors in The Cancer Genome

Digitized for IUPUI ScholarWorks inclusion in 2021.miRNAs are small non-coding RNA that play a vital role in post-transcriptional gene regulation. They are involved in several important biological processes; hence their dysregulation has been associated with several diseases. In this study we propose a novel method to identify dysregulated miRNAs using tumor matched expression data. Applying the method to expression datasets of nine cancers from TCGA we identify dysregulated miRNAs in each of these cancers. In six cancers we see that more than 50% of the dysregulated miRNAs are up-regulated, suggesting a general trend of upregulation. We then identify transcription factors (TFs) that control the expression of dysregulated miRNAs in cancer by footprinting their upstream regions in order to build a high confidence transcriptional regulatory network contributing to the dysregulation of miRNAs. We observe that these TFs are predominantly responsible for up-regulation of miRNAs across cancers. In addition, we find that TFs that are identified in six or more cancers have different network centralities in the TF-Tf regulatory network when compared to TFs identified to contribute to dysregulation of miRNAs in a single cancer. Finally, we build cancer specific dysregulated TF-miRNA networks and identified several novel motifs including feedback loops involving TFs and miRNAs. These patterns of interactions show how TFs and miRNAs interact in a cancer specific manner and how dysregulation at one level affects the other

MEDALT: Single-cell copy number lineage tracing enabling gene discovery

We present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles, and a statistical routine named lineage speciation analysis (LSA), whichty facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees. MEDALT appears more accurate than phylogenetics approaches in reconstructing copy number lineage. From data from 20 triple-negative breast cancer patients, our approaches effectively prioritize genes that are essential for breast cancer cell fitness and predict patient survival, including those implicating convergent evolution.The source code of our study is available at https://github.com/KChen-lab/MEDALT

Probabilistic Anonymous Routing in Mobile Ad-Hoc Networks

In this paper, we propose a routing protocol which ensures route anonymity, for the user. Amongst all suboptimal paths between source and destination, path for data transfer is chosen randomly at each intermediate node. Route anonymity becomes essential for preventing attacks like traffic monitoring. Bayesian approach has been employed for route discovery phase of the protocol. The protocol is simulated using NCTUNS network simulator. The robustness of our protocol is evaluated against known security attacks

Identifying signatures of EV secretion in metastatic breast cancer through functional single-cell profiling

Summary: Extracellular vesicles (EVs) regulate the tumor microenvironment by facilitating transport of biomolecules. Despite extensive investigation, heterogeneity in EV secretion among cancer cells and the mechanisms that support EV secretion are not well characterized. We developed an integrated method to identify individual cells with differences in EV secretion and performed linked single-cell RNA-sequencing on cloned single cells from the metastatic breast cancer cells. Differential gene expression analyses identified a four-gene signature of breast cancer EV secretion: HSP90AA1, HSPH1, EIF5, and DIAPH3. We functionally validated this gene signature by testing it across cell lines with different metastatic potential in vitro. Analysis of the TCGA and METABRIC datasets showed that this signature is associated with poor survival, invasive breast cancer types, and poor CD8+ T cell infiltration in human tumors. We anticipate that our method for directly identifying the molecular determinants of EV secretion will have broad applications across cell types and diseases