Matching Items (23)

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Vitellogenin Expression and Deformed Wing Virus Replication in Apis mellifera

Description

Vitellogenin (vg) is a precursor protein of egg yolk in honeybees, but it is also known to have immunological functions. The purpose of this experiment was to determine the effect

Vitellogenin (vg) is a precursor protein of egg yolk in honeybees, but it is also known to have immunological functions. The purpose of this experiment was to determine the effect of vg on the viral load of deformed wing virus (DWV) in worker honey bees (Apis mellifera). I hypothesized that a reduction in vg expression would lead to an increase in the viral load. I collected 180 worker bees and split them into four groups: half the bees were subjected to a vg gene knockdown by injections of double stranded vg RNA, and the rest were injected with green fluorescent protein (gfp) double stranded RNA. Half of each group was thereafter injected with DWV, and half given a sham injection. The rate of mortality in all four groups was higher than expected, leaving only 17 bees total. I dissected these bees' fat bodies and extracted their RNA to test for vg and DWV. PCR results showed that, out of the small group of remaining bees, the levels of vg were not statistically different. Furthermore, both groups of virus-injected bees showed similar viral loads. Because of the high mortality rate bees and the lack of differing levels of vg transcript between experimental and control groups, I could not draw conclusions from these results. The high mortality could be caused by several factors: temperature-induced stress, repeated stress from the two injections, and stress from viral infection. In addition, it is possible that the vg dsRNA batch I used was faulty. This thesis exemplifies that information cannot safely be extracted when loss of sampling units result in a small datasets that do not represent the original sampling population.

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Agent

Created

Date Created
  • 2017-12

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Muscle IGF-1 Regulation in Humans with Obesity

Description

Objective: Isoforms of insulin-like growth factor-1 (IGF-1) gene encodes different IGF-1 isoforms by alternative splicing, and which are known to play distinct roles in muscle growth and repair. These isoforms

Objective: Isoforms of insulin-like growth factor-1 (IGF-1) gene encodes different IGF-1 isoforms by alternative splicing, and which are known to play distinct roles in muscle growth and repair. These isoforms in humans exist as IGF-1Ea, IGF-1Eb and IGF-1Ec (the latter is also known as mechano-growth factor). We sought to determine whether mRNA expression of any of these isoforms is impaired in skeletal muscle of humans with obesity, and given that humans with obesity display reduced protein synthesis in muscle. Methods: We studied 10 subjects (3 females/7 males) with obesity (body mass index: 34 ± 1 kg/m2) and 14 subjects (6 females/8 males) that were lean (body mass index: 24 ± 1 kg/m2) and served as controls. The groups represented typical populations of individuals that differed (P < 0.05) in body fat (obese: 32 ± 2; lean: 22 ± 2) and insulin sensitivity (Matsuda insulin sensitivity index, obese: 5 ± 1; lean 11 ± 2). Total RNA was extracted from 20-30 mg of tissue from muscle biopsies performed after an overnight fast. Isolated RNA was used to perform cDNA synthesis. Real-time PCR was performed using predesigned TaqMan® gene expression assays (Thermo Fisher Scientific Inc) for IGF-1Ea (assay ID: Hs01547657_m1), IGF-1Eb (assay ID: Hs00153126_m1) and IGF-1Ec (assay ID: Hs03986524_m1), as well as ACTB (assay ID: Hs01060665_g1), which was used to adjust the IGF-1 isoform mRNA expression. Responses for mRNA expression were calculated using the comparative CT method (2-ΔΔCT). Results: IGF-1Eb mRNA expression was lower in the subjects with obesity compared to the lean controls (0.67 ± 0.09 vs 1.00 ± 0.13; P < 0.05) but that of IGF-1Ea (0.99 ± 0.16 vs 1.00 ± 0.33) or IGF-1Ec (0.83 ± 0.14 vs 1.00 ± 0.21) were not different between groups (P > 0.05). Conclusions: Among the IGF-1 mRNA isoforms, IGF-1Eb mRNA is uniquely decreased in humans with obesity. Lower IGF-1Eb mRNA expression in muscle of humans with obesity may explain the lower protein synthesis observed in these individuals. Furthermore, these findings may have direct implications for muscle growth and repair in humans with obesity.

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Agent

Created

Date Created
  • 2019-05

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In Situ RNA Expression Analysis Using Two-Photon Laser Lysis (2PLL) and Microfluidic RT-qPCR

Description

A major goal of the Center for Biosignatures Discovery Automation (CBDA) is to design a diagnostic tool that detects novel cancer biosignatures at the single-cell level. We designed the

A major goal of the Center for Biosignatures Discovery Automation (CBDA) is to design a diagnostic tool that detects novel cancer biosignatures at the single-cell level. We designed the Single-cell QUantitative In situ Reverse Transcription-Polymerase Chain Reaction (SQUIRT-PCR) system by combining a two-photon laser lysis (2PLL) system with a microfluidic reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) platform. It is important to identify early molecular changes from intact tissues as prognosis for premalignant conditions and develop new molecular targets for prevention of cancer progression and improved therapies. This project analyzes RNA expression at the single-cell level and presents itself with two major challenges: (1) detecting low levels of RNA and (2) minimizing RNA absorption in the polydimethylsiloxane (PDMS) microfluidic channel. The first challenge was overcome by successfully detecting picogram (pg) levels of RNA using the Fluidigm (FD) BioMark™ HD System (Fluidigm Corporation, South San Francisco, CA) for RT-qPCR analysis. This technology incorporates a highly precise integrated fluidic circuit (IFC) that allows for high-throughput genetic screening using microarrays. The second challenge entailed collecting data from RNA flow-through samples that were passed through microfluidic channels. One channel was treated with a coating of polyethylene glycol (PEG) and the other remained untreated. Various flow-through samples were subjected to RT-qPCR and analyzed using the FD FLEXsix™ Gene Expression IFC. As predicted, the results showed that the treated PDMS channel absorbed less RNA than the untreated PDMS channel. Once the optimization of the PDMS microfluidic platform is complete, it will be implemented into the 2PLL system. This novel technology will be able to identify cell populations in situ and could have a large impact on cancer diagnostics.

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Agent

Created

Date Created
  • 2014-05

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Bcl11a and Bcl11b Regulation of the Decision for Murine Cells to Proliferate or Differentiate During Skeletal Muscle Development and Repair

Description

The development of skeletal muscle during embryogenesis and repair in adults is dependent on the intricate balance between the proliferation of myogenic progenitor cells and the differentiation of those cells

The development of skeletal muscle during embryogenesis and repair in adults is dependent on the intricate balance between the proliferation of myogenic progenitor cells and the differentiation of those cells into functional muscle fibers. Recent studies demonstrate that the Drosophila melanogaster transcription factor CG9650 is expressed in muscle progenitor cells, where it maintains myoblast numbers. We are interested in the Mus musculus orthologs Bcl11a and Bcl11b (C2H2 zinc finger transcription factors), and understanding their role as molecular switches that control proliferation/differentiation decisions in muscle progenitor cells. Expression analysis revealed that Bcl11b, but not Bcl11a, is expressed in the region of the mouse embryo populated with myogenic progenitor cells; gene expression studies in muscle cell culture confirmed Bcl11b is also selectively transcribed in muscle. Furthermore, Bcl11b is down-regulated with differentiation, which is consistent with the belief that the gene plays a role in cell proliferation.

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Agent

Created

Date Created
  • 2014-05

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Differential Gene Expression in Type II Diabetes

Description

This research project investigated known and novel differential genetic variants and their associated molecular pathways involved in Type II diabetes mellitus for the purpose of improving diagnosis and treatment methods.

This research project investigated known and novel differential genetic variants and their associated molecular pathways involved in Type II diabetes mellitus for the purpose of improving diagnosis and treatment methods. The goal of this investigation was to 1) identify the genetic variants and SNPs in Type II diabetes to develop a gene regulatory pathway, and 2) utilize this pathway to determine suitable drug therapeutics for prevention and treatment. Using a Gene Set Enrichment Analysis (GSEA), a set of 1000 gene identifiers from a Mayo Clinic database was analyzed to determine the most significant genetic variants related to insulin signaling pathways involved in Type II Diabetes. The following genes were identified: NRAS, KRAS, PIK3CA, PDE3B, TSC1, AKT3, SOS1, NEU1, PRKAA2, AMPK, and ACC. In an extensive literature review and cross-analysis with Kegg and Reactome pathway databases, novel SNPs located on these gene variants were identified and used to determine suitable drug therapeutics for treatment. Overall, understanding how genetic mutations affect target gene function related to Type II Diabetes disease pathology is crucial to the development of effective diagnosis and treatment. This project provides new insight into the molecular basis of the Type II Diabetes, serving to help untangle the regulatory complexity of the disease and aid in the advancement of diagnosis and treatment. Keywords: Type II Diabetes mellitus, Gene Set Enrichment Analysis, genetic variants, KEGG Insulin Pathway, gene-regulatory pathway

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Agent

Created

Date Created
  • 2019-05

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CXCL10-Induced Migration of Triple-Negative Breast Cancer Cells

Description

Inhibitor of growth factor 4 (ING4) is a tumor suppressor of which low expression has been associated with poor patient survival and aggressive tumor progression in breast cancer. ING4 is

Inhibitor of growth factor 4 (ING4) is a tumor suppressor of which low expression has been associated with poor patient survival and aggressive tumor progression in breast cancer. ING4 is characterized as a transcription regulator of inflammatory genes. Among the ING4-regulated genes is CXCL10, a chemokine secreted by endothelial cells during normal inflammation response, which induces chemotactic migration of immune cells to the site. High expression of CXCL10 has been implicated in aggressive breast cancer, but the mechanism is not well understood. A potential signaling molecule downstream of Cxcl10 is Janus Kinase 2 (Jak2), a kinase activated in normal immune response. Deregulation of Jak2 is associated with metastasis, immune evasion, and tumor progression in breast cancer. Thus, we hypothesized that the Ing4/Cxcl10/Jak2 axis plays a key role in breast cancer progression. We first investigated whether Cxcl10 affected breast cancer cell migration. We also investigated whether Cxcl10-mediated migration is dependent on ING4 expression levels. We utilized genetically engineered MDAmb231 breast cancer cells with a CRISPR/Cas9 ING4-knockout construct or a viral ING4 overexpression construct. We performed Western blot analysis to confirm Ing4 expression. Cell migration was assessed using Boyden Chamber assay with or without exogenous Cxcl10 treatment. The results showed that in the presence of Cxcl10, ING4-deficient cells had a two-fold increase in migration as compared to the vector controls, suggesting Ing4 inhibits Cxcl10-induced migration. These findings support our hypothesis that ING4-deficient tumor cells have increased migration when Cxcl10 signaling is present in breast cancer. These results implicate Ing4 is a key regulator of a chemokine-induced tumor migration. Our future plan includes evaluation of Jak2 as an intermediate signaling molecule in Cxcl10/Ing4 pathway. Therapeutic implications of these findings are targeting Cxcl10 and/or Jak2 may be effective in treating ING4-deficient aggressive breast cancer.

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Agent

Created

Date Created
  • 2019-05

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Single cell RT-qPCR based ocean environmental sensing device development

Description

This thesis research focuses on developing a single-cell gene expression analysis method for marine diatom Thalassiosira pseudonana and constructing a chip level tool to realize the single cell RT-qPCR analysis.

This thesis research focuses on developing a single-cell gene expression analysis method for marine diatom Thalassiosira pseudonana and constructing a chip level tool to realize the single cell RT-qPCR analysis. This chip will serve as a conceptual foundation for future deployable ocean monitoring systems. T. pseudonana, which is a common surface water microorganism, was detected in the deep ocean as confirmed by phylogenetic and microbial community functional studies. Six-fold copy number differences between 23S rRNA and 23S rDNA were observed by RT-qPCR, demonstrating the moderate functional activity of detected photosynthetic microbes in the deep ocean including T. pseudonana. Because of the ubiquity of T. pseudonana, it is a good candidate for an early warning system for ocean environmental perturbation monitoring. This early warning system will depend on identifying outlier gene expression at the single-cell level. An early warning system based on single-cell analysis is expected to detect environmental perturbations earlier than population level analysis which can only be observed after a whole community has reacted. Preliminary work using tube-based, two-step RT-qPCR revealed for the first time, gene expression heterogeneity of T. pseudonana under different nutrient conditions. Heterogeneity was revealed by different gene expression activity for individual cells under the same conditions. This single cell analysis showed a skewed, lognormal distribution and helped to find outlier cells. The results indicate that the geometric average becomes more important and representative of the whole population than the arithmetic average. This is in contrast with population level analysis which is limited to arithmetic averages only and highlights the value of single cell analysis. In order to develop a deployable sensor in the ocean, a chip level device was constructed. The chip contains surface-adhering droplets, defined by hydrophilic patterning, that serve as real-time PCR reaction chambers when they are immersed in oil. The chip had demonstrated sensitivities at the single cell level for both DNA and RNA. The successful rate of these chip-based reactions was around 85%. The sensitivity of the chip was equivalent to published microfluidic devices with complicated designs and protocols, but the production process of the chip was simple and the materials were all easily accessible in conventional environmental and/or biology laboratories. On-chip tests provided heterogeneity information about the whole population and were validated by comparing with conventional tube based methods and by p-values analysis. The power of chip-based single-cell analyses were mainly between 65-90% which were acceptable and can be further increased by higher throughput devices. With this chip and single-cell analysis approaches, a new paradigm for robust early warning systems of ocean environmental perturbation is possible.

Contributors

Agent

Created

Date Created
  • 2013

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Structured sparse learning and its applications to biomedical and biological data

Description

Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured

Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.

Contributors

Agent

Created

Date Created
  • 2013

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Effective gene expression annotation approaches for mouse brain images

Description

Understanding the complexity of temporal and spatial characteristics of gene expression over brain development is one of the crucial research topics in neuroscience. An accurate description of the locations and

Understanding the complexity of temporal and spatial characteristics of gene expression over brain development is one of the crucial research topics in neuroscience. An accurate description of the locations and expression status of relative genes requires extensive experiment resources. The Allen Developing Mouse Brain Atlas provides a large number of in situ hybridization (ISH) images of gene expression over seven different mouse brain developmental stages. Studying mouse brain models helps us understand the gene expressions in human brains. This atlas collects about thousands of genes and now they are manually annotated by biologists. Due to the high labor cost of manual annotation, investigating an efficient approach to perform automated gene expression annotation on mouse brain images becomes necessary. In this thesis, a novel efficient approach based on machine learning framework is proposed. Features are extracted from raw brain images, and both binary classification and multi-class classification models are built with some supervised learning methods. To generate features, one of the most adopted methods in current research effort is to apply the bag-of-words (BoW) algorithm. However, both the efficiency and the accuracy of BoW are not outstanding when dealing with large-scale data. Thus, an augmented sparse coding method, which is called Stochastic Coordinate Coding, is adopted to generate high-level features in this thesis. In addition, a new multi-label classification model is proposed in this thesis. Label hierarchy is built based on the given brain ontology structure. Experiments have been conducted on the atlas and the results show that this approach is efficient and classifies the images with a relatively higher accuracy.

Contributors

Agent

Created

Date Created
  • 2016

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MicroRNA regulation of addiction-related gene expression and motivation for cocaine in rats

Description

MicroRNAs are small, non-coding transcripts that post-transcriptionally regulate expression of multiple genes. Recently microRNAs have been linked to the etiology of neuropsychiatric disorders, including drug addiction. Following genome-wide sequence analyses,

MicroRNAs are small, non-coding transcripts that post-transcriptionally regulate expression of multiple genes. Recently microRNAs have been linked to the etiology of neuropsychiatric disorders, including drug addiction. Following genome-wide sequence analyses, microRNA-495 (miR-495) was found to target several genes within the Knowledgebase of Addiction-Related Genes (KARG) database and to be highly expressed in the nucleus accumbens (NAc), a pivotal brain region involved in reward and motivation. The central hypothesis of this dissertation is that NAc miR-495 regulates drug abuse-related behavior by targeting several addiction-related genes (ARGs). I tested this hypothesis in two ways: 1) by examining the effects of viral-mediated miR-495 overexpression or inhibition in the NAc of rats on cocaine abuse-related behaviors and gene expression, and 2) by examining changes in NAc miR-495 and ARG expression as a result of brief (i.e., 1 day) or prolonged (i.e., 22 days) cocaine self-administration. I found that behavioral measures known to be sensitive to motivation for cocaine were attenuated by NAc miR-495 overexpression, including resistance to extinction of cocaine conditioned place preference (CPP), cocaine self-administration on a high effort progressive ratio schedule of reinforcement, and cocaine-seeking behavior during both extinction and cocaine-primed reinstatement. These effects appeared specific to cocaine, as there was no effect of NAc miR-495 overexpression on a progressive ratio schedule of food reinforcement. In contrast, behavioral measures known to be sensitive to cocaine reward were not altered, including expression of cocaine CPP and cocaine self-administration under a low effort FR5 schedule of reinforcement. Importantly, the effects were accompanied by decreases in NAc ARG expression, consistent with my hypothesis. In further support, I found that NAc miR-495 levels were reduced and ARG levels were increased in rats following prolonged, but not brief, cocaine self-administration experience. Surprisingly, inhibition of NAc miR-495 expression also decreased both cocaine-seeking behavior during extinction and NAc ARG expression, which may reflect compensatory changes or unexplained complexities in miR-495 regulatory effects. Collectively, the findings suggest that NAc miR-495 regulates ARG expression involved in motivation for cocaine. Therefore, using microRNAs as tools to target several ARGs simultaneously may be useful for future development of addiction therapeutics.

Contributors

Agent

Created

Date Created
  • 2016