Developing Improved miRNA Target Prediction Tools; Improved Methods to Analyze RNA-seq Data
These researchers are involved in two projects using MSI resources. The first is an investigation into microRNAs (miRNAs), a class of newly discovered genes capable of post-transcriptionally regulating the expression of other genes (their “targets”), by binding to the non-coding regions of those genes, leading to cleavage of transcripts and/or repression of translation. Despite many efforts paid by several research groups, the mechanism of the miRNA targeting remains elusive. The group has started a collaborative project with two groups of researchers at Stanford, who have provided two sets of AGO2 immunoprecipitation microarray datasets and two sets of gene expression microarray datasets. These high-quality datasets offer a unique opportunity for elucidating the miRNA targeting mechanism. The researchers are applying a strategy where configurations for a dynamic programming-based scoring scheme are randomly created and summarized with supervised and unsupervised machine learning-based analysis methods, for the purpose of achieving accurate miRNA targeting criteria with much improved coverage than existing methods.
The second project consists of two parts. In the first, the researchers are developing improved methods of mapping and quantifying RNA-seq data. RNA-seq is a new technology and existing methods are far from perfect. The group is developing improved methods that aim to increase the accuracy in read mapping, including the mapping with exon-exon junctions, and increase the coverage/completeness of mapping, i.e., accounting for higher percentage of the reads than existing, published methods. The second part of the project uses methods developed in the first part to map all (or most) public available human RNA-seq datasets (around 30 of them), to create a close-to-complete transcriptome resource, based on which they will: investigate the completeness of existing gene model databases and estimate the number of unannotated genes; predict coding potential of novel genes, predict functions of coding genes, and assess evolutionary conservation of non-coding genes; and investigate complexity of alternative splicing and alternative transcription start site/stop site usage.
Stochastic Analysis of Dynamic Quasibrittle Fracture
Impact resistance is a critical design consideration for many defense structures. Direct experimental investigation of structural impact resistance is often limited to certain structural geometries and sizes due to the constraints of the test set-ups. Therefore, one has to rely on numerical modeling. Modern defense structures are often made of brittle heterogenous (quasibrittle) materials such as engineering ceramics and composite materials. One of the salient features of quasibrittle materials is that they generally exhibit a strain-softening behavior. This leads to the spurious mesh sensitivity in finite element (FE) calculations, which severely limits the prediction capability of the FE models. Furthermore, for quasibrittle materials, both material microstructure and local material properties are also subjected to significant variability. Therefore, the resulting structural response under impact loading is highly stochastic. Developing a predictive stochastic numerical model is of paramount importance for reliability-based design of quasibrittle structures under impact loading.
This research aims to develop a novel multiscale numerical model for probabilistic analysis of quasibrittle structures under impact. The proposed model will be anchored by a stochastic FE model, where the probability distribution functions of the relevant material properties will be determined by a rate-dependent finite weakest link model and a stochastic micromechanical model. The finite weakest link model will statistically represent the damage localization mechanism and naturally involve the length scales associated with the stochastic material damage process. Consequently, the weakest link model will be able to correctly capture the dependence of the probability distribution functions of the material properties on the finite element mesh size, which is essential for mitigating the spurious mesh sensitivity for the stochastic FE simulations of dynamic quasibrittle fracture. The finite weakest link model will be further calibrated and validated through a stochastic micromechanical model, which can explicitly represent the random grain sizes, pre-existing flaws and fracture properties of grain boundaries. Therefore, through this multiscale model, the variability of the material properties for the FE model will be physically related to the random microstruc- tural features as well as the random fracture properties at the micro-scale. This research will use silicon carbide (SiC) structures as a model system. The proposed numerical model will be calibrated and validated by a set of impact tests on SiC beams in collaboration with the Impact Physics Branch at the Army Research Laboratory (ARL).
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Pathway Targeted Deep Brain Stimulation for Parkinson's Disease
Deep brain stimulation (DBS) is a surgical procedure used to treat various neurological and neuropsychiatric disorders, including Parkinson’s disease, essential tremor, dystonia, and obsessive-compulsive disorder. To date, over 100,000 patients worldwide have undergone DBS surgery, and this number is expected to increase significantly. DBS involves the surgical implantation of an electrode into a specific brain target, for delivery of electrical stimulation, alleviating disease symptoms. However, one of the ambiguities of this procedure is that the clinical outcomes can vary greatly across patients. One plausible explanation for the differences may lie in unoptimized DBS lead placement and the stimulation of undesired anatomical structures and white-matter pathways. Thus, the success of this surgical technique is critically dependent on the precise placement of the DBS electrode. At present, DBS surgery relies on a two-step procedure: initial target localization is based on stereotactic imaging combined with cadaveric atlas-derived consensus coordinates. However, current clinical imaging methods do not allow for a clear visualization of DBS target structures, which can result in electrode placement errors. Consequently, this step is followed by an invasive microelectrode recording procedure that is used for target validation, but carries risk.
This project aims to improve the imaging-based target localization and visualizations for DBS surgery. Capitalizing on the advantages of high-field (7 Tesla) MRI, combined with several image post-processing and visualization techniques, these researchers will develop a patient-specific 3D volumetric model of the DBS target, the surrounding white matter tracks, and the neighboring structures. These unique imaging and visualization capabilities will provide unparalleled anatomical and connectivity characterization of each patient. This work is innovative in that it will bring state of the art imaging techniques into a clinical setting – a clear example of translation and implementation of cutting edge basic science methods into the clinical realm. By merging the information obtained via each imaging approach, a comprehensive, patient-specific, 3D model of each patient’s target area will be generated. Each anatomical model will include the DBS target structure of interest, adjacent white matter bundles, as well as along the intended trajectory of the DBS electrode. In addition to the pre-surgery patient-specific anatomical model, a postoperative CT image will be obtained and co-registered to preoperative MR images, including the final electrode location into the model.
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Cosolute Interactions With Nucleic Acids
Biochemical reactions involving DNA, RNA, or proteins often involve large changes in the surface area of these biopolymers. The surface area changes expose (or bury) chemical functional groups that interact with the solvent and all its solute components. The thermodynamic favorability of the interactions between solvent and the surface area exposed or buried during DNA, RNA, and protein biochemical reactions can dramatically attenuate or enhance the rate of these reactions. Thus, we have the potential to use solvent and its solutes to probe the chemical composition of surface area changes during DNA, RNA, and protein biochemical reactions and develop a general method to ascertain biopolymer structural changes.
For several years this research group has quantified the interaction of neutral organic molecules like urea or amino acids (which are generically called cosolutes) with the surface area of nucleic acids. The researchers use a mix of uv-absorbance, differential scanning calorimetry, vapor pressure osmometry, solubility measurements, and molecular dynamics simulations to determine the excess (or deficiency) of these cosolutes near the nucleic acid surface area. If the interactions between cosolutes and the chemical functional groups in the newly exposed nucleic acid surface area after unfolding are thermodynamically favorable, the stability of the folded nucleic acid will be lower in aqueous cosolute solutions relative to water alone.
The group has recently begun exploring cosolute interactions with nucleobases, model compounds, and nucleosides using a novel partition assay at St. Olaf College. Briefly, they determine the partition coefficient of a model compound between an organic hexanol layer and an aqueous layer containing cosolute. Favorable model compound-cosolute interactions result in an increase in model compound concentration in the aqueous layer. The ultimate goal is to quantify cosolute interactions with specific chemical functional groups on the model compounds so that they can predict the magnitude and chemical composition of the surface area exposed or buried during biopolymer binding.
In order to this analysis, the researchers need reliable surface area calculations for their model compounds. They use software available through MSI to perform these calculations.
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Molecular Mechanisms of NRAS-Mediated Leukemia Stem Cell Self-Renewal
Acute myeloid leukemia (AML) is frequently fatal because patients who initially respond to chemotherapy eventually relapse. Leukemia stem cells (LSCs) recapitulate the disease by self-renewal. LSC self-renewal is therefore critical to relapse. NRASG12V is required for self-renewal in a murine AML model. Most anticancer therapies are designed to inhibit proliferation. Yet, in hematopoietic stem cells, the mechanisms of NRAS-mediated proliferation are distinct from self-renewal. Consequently, targeting proliferation may explain the failure of traditional chemotherapy to eradicate this disease. To study NRAS-mediated leukemia self-renewal, these researchers use a transgenic mouse model of AML with an Mll-AF9 fusion and a tetracycline repressible, NRASG12V. Doxycycline abolishes NRASG12V expression leading to leukemia remission. These researchers hypothesize that NRAS-activated pathways required for self-renewal are limited to a subpopulation of cells with the LSC immunophenotype.
During 2015, the researchers examined the NRAS-activated stem-cell containing subpopulation of their mouse model by capturing single cells (~200 cells) and performing single cell RNA sequencing (along with bulk RNA population controls). They also started to capture cells from human AML samples (~100 cells) to investigate if subpopulations exist in these cells and if a self-renewing signature could be detected. They were able to implement this analysis using MSI HPC and storage resources. During 2016, they plan to capture and sequence more mouse model cells (~300) and human AML patient cells (~500). Both of these projects will include the proper population controls. They also plan to process a small set (20 samples) of bulk RNA sequencing on sorted subpopulations that they have identified in the mouse model.
The group is actively investigating if these important biomarkers of self-renewal using this NRAS AML mouse model are present in the human AML cells. Ultimately, the goal of this research is to target this self-renewing population therapeutically so that relapse of AML can be dramatically reduced.
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