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The partitioning of photosynthates between their sites of production (source) and their sites of utilization (sink) is a major determinant of crop yield and the potential of regulating this translocation promises substantial opportunities for yield increases. Ubiquitous overexpression of the plant type I proton pyrophosphatase (H+-PPase) in crops improves several valuable traits including salt tolerance and drought resistance, nutrient and water use efficiencies, and increased root biomass and yield. Originally, type I H+-PPases were described as pyrophosphate (PPi)-dependent proton pumps localized exclusively in vacuoles of mesophyll and meristematic tissues. It has been proposed that in the meristematic tissues, the role of this enzyme would be hydrolyzing PPi originated in biosynthetic reactions and favoring sink strength. Interestingly, this enzyme has been also localized at the plasma membrane of companion cells in the phloem which load and transport photosynthates from source leaves to sinks. Of note, the plasma membrane-localized H+-PPase could only function as a PPi-synthase in these cells due to the steep proton gradient between the apoplast and cytosol. The generated PPi would favor active sucrose loading through the sucrose/proton symporter in the phloem by promoting sucrose hydrolysis through the Sucrose Synthase pathway and providing the ATP required to maintain the proton gradient. To better understand these two different roles of type I H+-PPases, a series of Arabidopsis thaliana transgenic plants were generated. By expressing soluble pyrophosphatases in companion cells of Col-0 ecotype and H+-PPase mutants, impaired photosynthates partitioning was observed, suggesting phloem-localized H+-PPase could generate the PPi required for sucrose loading. Col-0 plants expressed with either phloem- or meristem-specific AVP1 overexpression cassette and the cross between the two tissue specific lines (Cross) were generated. The results showed that the phloem-specific AVP1-overexpressing plants had increased root hair elongation under limited nutrient conditions and both phloem- and meristem-overexpression of AVP1 contributed to improved rhizosphere acidification and drought resistance. It was concluded that H+-PPases localized in both sink and source tissues regulate plant growth and performance under stress through its versatile enzymatic functions (PPi hydrolase and synthase).
ContributorsLi, Lin (Author) / Park, Yujin (Thesis advisor) / Mangone, Marco (Committee member) / Roberson, Robert (Committee member) / Vermaas, Willem (Committee member) / Arizona State University (Publisher)
Created2022
Description
MicroRNAs (miRNAs) are 17-22 nucleotide non-coding RNAs that regulate gene expression by targeting non-complementary elements in the 3’ untranslated regions (3’UTRs) of mRNAs. miRNAs, which form complex networks of interaction that differ by tissue and developmental stage, display conservation in their function across metazoan species. Yet much remains unknown regarding their biogenesis, localization, strand selection, and their absolute abundance due to the difficulty of detecting and amplifying such small molecules. Here, I used an updated HT qPCR-based methodology to follow miRNA expression of 5p and 3p strands for all 190 C. elegans miRNAs described in miRBase throughout all six developmental stages in triplicates (total of 9,708 experiments), and studied their expression levels, tissue localization, and the rules underlying miRNA strand selection. My study validated previous findings and identified novel, conserved patterns of miRNA strand expression throughout C. elegans development, which at times correlate with previously observed developmental phenotypes. Additionally, my results highlighted novel structural principles underlying strand selection, which can be applied to higher metazoans.
Though optimized for use in C. elegans, this method can be easily adapted to other eukaryotic systems, allowing for more scalable quantitative investigation of miRNA biology and/or miRNA diagnostics.
ContributorsMeadows, Dalton Alexander (Author) / Mangone, Marco (Thesis advisor) / LaBaer, Joshua (Committee member) / Murugan, Vel (Committee member) / Wilson-Rawls, Jeanne (Committee member) / Arizona State University (Publisher)
Created2023
Description
Precise modulation of gene expression is essential for proper tissue and cell-specific differentiation and function. Multiple distinct post-transcriptional regulatory mechanisms, such as miRNA (microRNA)-based regulation and alternative polyadenylation (APA), are an intrinsic part of this modulation and orchestrate intricate pathways to achieve and maintain balanced gene expression.MiRNA-based regulation and APA function through sequence motifs located in the 3’ Untranslated Region (3’UTR) of mRNA transcripts. MiRNAs are short (~22 nt) non-coding RNA molecules that bind target sequences within the 3’UTR of an mRNA transcript, inhibiting its translation or promoting its degradation. APA occurs during RNA transcription termination and leads to the preparation of mature mRNAs with different 3’UTR lengths, allowing shorter 3’UTRs to bypass miRNA regulation.
In addition to these two post-transcriptional forms of regulation, co-transcriptional mechanisms such as alternative RNA splicing, which produces distinct gene products from a precursor mRNA, are also important in controlling gene expression.
While miRNA-based regulation, APA, and alternative RNA splicing are important regulatory mechanisms, there is a lack of comprehensive understanding of how they interact and communicate with each other. This thesis studies these three forms of gene regulation in the nematode C. elegans, with the goal of extracting rules and mechanisms used by each of them in development to establish and maintain somatic tissue identity.
After isolating miRNA targets in multiple C. elegans somatic tissues, it was found that miRNAs can modulate the abundance of hnRNPs and SR proteins, which are known to control alternative RNA splicing in a dosage-dependent manner.To identify tissue-specific miRNAs, a nuclear fluorescent cell sorting (FACS)-based methodology named Nuc-Seq, was developed to isolate and sequence tissue-specific miRNAs from body muscle tissue. Nuc-Seq identified 2,848 muscle-specific protein-coding genes and 16 body muscle-specific miRNAs. This data was used to develop a high-quality body muscle-specific miRNA-APA Interactome which allows studies in regulatory processes in detail.
Taken together, this work highlights some of the complexity of pre- and post-transcriptional gene regulation and sheds light on how miRNA-based regulation, APA, and alternative RNA splicing are interconnected and are responsible for the establishment and maintenance of tissue identity.
ContributorsSchorr, Anna L (Author) / Mangone, Marco (Thesis advisor) / Harris, Robin (Committee member) / Sharma, Shalini (Committee member) / Varsani, Arvind (Committee member) / Arizona State University (Publisher)
Created2023
Description
A description of numerical and analytical work pertaining to models that describe the growth and progression of glioblastoma multiforme (GBM), an aggressive form of primary brain cancer. Two reaction-diffusion models are used: the Fisher-Kolmogorov-Petrovsky-Piskunov equation and a 2-population model that divides the tumor into actively proliferating and quiescent (or necrotic) cells. The numerical portion of this work (chapter 2) focuses on simulating GBM expansion in patients undergoing treatment for recurrence of tumor following initial surgery. The models are simulated on 3-dimensional brain geometries derived from magnetic resonance imaging (MRI) scans provided by the Barrow Neurological Institute. The study consists of 17 clinical time intervals across 10 patients that have been followed in detail, each of whom shows significant progression of tumor over a period of 1 to 3 months on sequential follow up scans. A Taguchi sampling design is implemented to estimate the variability of the predicted tumors to using 144 different choices of model parameters. In 9 cases, model parameters can be identified such that the simulated tumor contains at least 40 percent of the volume of the observed tumor. In the analytical portion of the paper (chapters 3 and 4), a positively invariant region for our 2-population model is identified. Then, a rigorous derivation of the critical patch size associated with the model is performed. The critical patch (KISS) size is the minimum habitat size needed for a population to survive in a region. Habitats larger than the critical patch size allow a population to persist, while smaller habitats lead to extinction. The critical patch size of the 2-population model is consistent with that of the Fisher-Kolmogorov-Petrovsky-Piskunov equation, one of the first reaction-diffusion models proposed for GBM. The critical patch size may indicate that GBM tumors have a minimum size depending on the location in the brain. A theoretical relationship between the size of a GBM tumor at steady-state and its maximum cell density is also derived, which has potential applications for patient-specific parameter estimation based on magnetic resonance imaging data.
ContributorsHarris, Duane C. (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric J. (Thesis advisor) / Preul, Mark C. (Committee member) / Crook, Sharon (Committee member) / Gardner, Carl (Committee member) / Arizona State University (Publisher)
Created2023
Description
The most advanced social insects, the eusocial insects, form often large societies in which there is reproductive division of labor, queens and workers, have overlapping generations, and cooperative brood care where daughter workers remain in the nest with their queen mother and care for their siblings. The eusocial insects are composed of representative species of bees and wasps, and all species of ants and termites. Much is known about their organizational structure, but remains to be discovered.
The success of social insects is dependent upon cooperative behavior and adaptive strategies shaped by natural selection that respond to internal or external conditions. The objective of my research was to investigate specific mechanisms that have helped shaped the structure of division of labor observed in social insect colonies, including age polyethism and nutrition, and phenomena known to increase colony survival such as egg cannibalism. I developed various Ordinary Differential Equation (ODE) models in which I applied dynamical, bifurcation, and sensitivity analysis to carefully study and visualize biological outcomes in social organisms to answer questions regarding the conditions under which a colony can survive. First, I investigated how the population and evolutionary dynamics of egg cannibalism and division of labor can promote colony survival. I then introduced a model of social conflict behavior to study the inclusion of different response functions that explore the benefits of cannibalistic behavior and how it contributes to age polyethism, the change in behavior of workers as they age, and its biological relevance. Finally, I introduced a model to investigate the importance of pollen nutritional status in a honeybee colony, how it affects population growth and influences division of labor within the worker caste. My results first reveal that both cannibalism and division of labor are adaptive strategies that increase the size of the worker population, and therefore, the persistence of the colony. I show the importance of food collection, consumption, and processing rates to promote good colony nutrition leading to the coexistence of brood and adult workers. Lastly, I show how taking into account seasonality for pollen collection improves the prediction of long term consequences.
The success of social insects is dependent upon cooperative behavior and adaptive strategies shaped by natural selection that respond to internal or external conditions. The objective of my research was to investigate specific mechanisms that have helped shaped the structure of division of labor observed in social insect colonies, including age polyethism and nutrition, and phenomena known to increase colony survival such as egg cannibalism. I developed various Ordinary Differential Equation (ODE) models in which I applied dynamical, bifurcation, and sensitivity analysis to carefully study and visualize biological outcomes in social organisms to answer questions regarding the conditions under which a colony can survive. First, I investigated how the population and evolutionary dynamics of egg cannibalism and division of labor can promote colony survival. I then introduced a model of social conflict behavior to study the inclusion of different response functions that explore the benefits of cannibalistic behavior and how it contributes to age polyethism, the change in behavior of workers as they age, and its biological relevance. Finally, I introduced a model to investigate the importance of pollen nutritional status in a honeybee colony, how it affects population growth and influences division of labor within the worker caste. My results first reveal that both cannibalism and division of labor are adaptive strategies that increase the size of the worker population, and therefore, the persistence of the colony. I show the importance of food collection, consumption, and processing rates to promote good colony nutrition leading to the coexistence of brood and adult workers. Lastly, I show how taking into account seasonality for pollen collection improves the prediction of long term consequences.
ContributorsRodríguez Messan, Marisabel (Author) / Kang, Yun (Thesis advisor) / Castillo-Chavez, Carlos (Thesis advisor) / Kuang, Yang (Committee member) / Page Jr., Robert E (Committee member) / Gardner, Carl (Committee member) / Arizona State University (Publisher)
Created2018
Description
The role of climate change, as measured in terms of changes in the climatology of geophysical variables (such as temperature and rainfall), on the global distribution and burden of vector-borne diseases (VBDs) remains a subject of considerable debate. This dissertation attempts to contribute to this debate via the use of mathematical (compartmental) modeling and statistical data analysis. In particular, the objective is to find suitable values and/or ranges of the climate variables considered (typically temperature and rainfall) for maximum vector abundance and consequently, maximum transmission intensity of the disease(s) they cause.
Motivated by the fact that understanding the dynamics of disease vector is crucial to understanding the transmission and control of the VBDs they cause, a novel weather-driven deterministic model for the population biology of the mosquito is formulated and rigorously analyzed. Numerical simulations, using relevant weather and entomological data for Anopheles mosquito (the vector for malaria), show that maximum mosquito abundance occurs when temperature and rainfall values lie in the range [20-25]C and [105-115] mm, respectively.
The Anopheles mosquito ecology model is extended to incorporate human dynamics. The resulting weather-driven malaria transmission model, which includes many of the key aspects of malaria (such as disease transmission by asymptomatically-infectious humans, and enhanced malaria immunity due to repeated exposure), was rigorously analyzed. The model which also incorporates the effect of diurnal temperature range (DTR) on malaria transmission dynamics shows that increasing DTR shifts the peak temperature value for malaria transmission from 29C (when DTR is 0C) to about 25C (when DTR is 15C).
Finally, the malaria model is adapted and used to study the transmission dynamics of chikungunya, dengue and Zika, three diseases co-circulating in the Americas caused by the same vector (Aedes aegypti). The resulting model, which is fitted using data from Mexico, is used to assess a few hypotheses (such as those associated with the possible impact the newly-released dengue vaccine will have on Zika) and the impact of variability in climate variables on the dynamics of the three diseases. Suitable temperature and rainfall ranges for the maximum transmission intensity of the three diseases are obtained.
Motivated by the fact that understanding the dynamics of disease vector is crucial to understanding the transmission and control of the VBDs they cause, a novel weather-driven deterministic model for the population biology of the mosquito is formulated and rigorously analyzed. Numerical simulations, using relevant weather and entomological data for Anopheles mosquito (the vector for malaria), show that maximum mosquito abundance occurs when temperature and rainfall values lie in the range [20-25]C and [105-115] mm, respectively.
The Anopheles mosquito ecology model is extended to incorporate human dynamics. The resulting weather-driven malaria transmission model, which includes many of the key aspects of malaria (such as disease transmission by asymptomatically-infectious humans, and enhanced malaria immunity due to repeated exposure), was rigorously analyzed. The model which also incorporates the effect of diurnal temperature range (DTR) on malaria transmission dynamics shows that increasing DTR shifts the peak temperature value for malaria transmission from 29C (when DTR is 0C) to about 25C (when DTR is 15C).
Finally, the malaria model is adapted and used to study the transmission dynamics of chikungunya, dengue and Zika, three diseases co-circulating in the Americas caused by the same vector (Aedes aegypti). The resulting model, which is fitted using data from Mexico, is used to assess a few hypotheses (such as those associated with the possible impact the newly-released dengue vaccine will have on Zika) and the impact of variability in climate variables on the dynamics of the three diseases. Suitable temperature and rainfall ranges for the maximum transmission intensity of the three diseases are obtained.
ContributorsOkuneye, Kamaldeen O (Author) / Gumel, Abba B (Thesis advisor) / Kuang, Yang (Committee member) / Smith, Hal (Committee member) / Thieme, Horst (Committee member) / Nagy, John (Committee member) / Arizona State University (Publisher)
Created2018
Description
Rapid intraoperative diagnosis of brain tumors is of great importance for planning treatment and guiding the surgeon about the extent of resection. Currently, the standard for the preliminary intraoperative tissue analysis is frozen section biopsy that has major limitations such as tissue freezing and cutting artifacts, sampling errors, lack of immediate interaction between the pathologist and the surgeon, and time consuming.
Handheld, portable confocal laser endomicroscopy (CLE) is being explored in neurosurgery for its ability to image histopathological features of tissue at cellular resolution in real time during brain tumor surgery. Over the course of examination of the surgical tumor resection, hundreds to thousands of images may be collected. The high number of images requires significant time and storage load for subsequent reviewing, which motivated several research groups to employ deep convolutional neural networks (DCNNs) to improve its utility during surgery. DCNNs have proven to be useful in natural and medical image analysis tasks such as classification, object detection, and image segmentation.
This thesis proposes using DCNNs for analyzing CLE images of brain tumors. Particularly, it explores the practicality of DCNNs in three main tasks. First, off-the shelf DCNNs were used to classify images into diagnostic and non-diagnostic. Further experiments showed that both ensemble modeling and transfer learning improved the classifier’s accuracy in evaluating the diagnostic quality of new images at test stage. Second, a weakly-supervised learning pipeline was developed for localizing key features of diagnostic CLE images from gliomas. Third, image style transfer was used to improve the diagnostic quality of CLE images from glioma tumors by transforming the histology patterns in CLE images of fluorescein sodium-stained tissue into the ones in conventional hematoxylin and eosin-stained tissue slides.
These studies suggest that DCNNs are opted for analysis of CLE images. They may assist surgeons in sorting out the non-diagnostic images, highlighting the key regions and enhancing their appearance through pattern transformation in real time. With recent advances in deep learning such as generative adversarial networks and semi-supervised learning, new research directions need to be followed to discover more promises of DCNNs in CLE image analysis.
Handheld, portable confocal laser endomicroscopy (CLE) is being explored in neurosurgery for its ability to image histopathological features of tissue at cellular resolution in real time during brain tumor surgery. Over the course of examination of the surgical tumor resection, hundreds to thousands of images may be collected. The high number of images requires significant time and storage load for subsequent reviewing, which motivated several research groups to employ deep convolutional neural networks (DCNNs) to improve its utility during surgery. DCNNs have proven to be useful in natural and medical image analysis tasks such as classification, object detection, and image segmentation.
This thesis proposes using DCNNs for analyzing CLE images of brain tumors. Particularly, it explores the practicality of DCNNs in three main tasks. First, off-the shelf DCNNs were used to classify images into diagnostic and non-diagnostic. Further experiments showed that both ensemble modeling and transfer learning improved the classifier’s accuracy in evaluating the diagnostic quality of new images at test stage. Second, a weakly-supervised learning pipeline was developed for localizing key features of diagnostic CLE images from gliomas. Third, image style transfer was used to improve the diagnostic quality of CLE images from glioma tumors by transforming the histology patterns in CLE images of fluorescein sodium-stained tissue into the ones in conventional hematoxylin and eosin-stained tissue slides.
These studies suggest that DCNNs are opted for analysis of CLE images. They may assist surgeons in sorting out the non-diagnostic images, highlighting the key regions and enhancing their appearance through pattern transformation in real time. With recent advances in deep learning such as generative adversarial networks and semi-supervised learning, new research directions need to be followed to discover more promises of DCNNs in CLE image analysis.
ContributorsIzady Yazdanabadi, Mohammadhassan (Author) / Preul, Mark (Thesis advisor) / Yang, Yezhou (Thesis advisor) / Nakaji, Peter (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2019
Description
The fundamental building blocks for constructing complex synthetic gene networks are effective biological parts with wide dynamic range, low crosstalk, and modularity. RNA-based components are promising sources of such parts since they can provide regulation at the level of transcription and translation and their predictable base pairing properties enable large libraries to be generated through in silico design. This dissertation studies two different approaches for initiating interactions between RNA molecules to implement RNA-based components that achieve translational regulation. First, single-stranded domains known as toeholds were employed for detection of the highly prevalent foodborne pathogen norovirus. Toehold switch riboregulators activated by trigger RNAs from the norovirus RNA genome are designed, validated, and coupled with paper-based cell-free transcription-translation systems. Integration of paper-based reactions with synbody enrichment and isothermal RNA amplification enables as few as 160 copies/mL of norovirus from clinical samples to be detected in reactions that do not require sophisticated equipment and can be read directly by eye. Second, a new type of riboregulator that initiates RNA-RNA interactions through the loop portions of RNA stem-loop structures was developed. These loop-initiated RNA activators (LIRAs) provide multiple advantages compared to toehold-based riboregulators, exhibiting ultralow signal leakage in vivo, lacking any trigger RNA sequence constraints, and appending no additional residues to the output protein. Harnessing LIRAs as modular parts, logic gates that exploit loop-mediated control of mRNA folding state to implement AND and OR operations with up to three sequence-independent input RNAs were constructed. LIRA circuits can also be ported to paper-based cell-free reactions to implement portable systems with molecular computing and sensing capabilities. LIRAs can detect RNAs from a variety of different pathogens, such as HIV, Zika, dengue, yellow fever, and norovirus, and after coupling to isothermal amplification reactions, provide visible test results down to concentrations of 20 aM (12 RNA copies/µL). And the logic functionality of LIRA circuits can be used to specifically identify different HIV strains and influenza A subtypes. These findings demonstrate that toehold- and loop-mediated RNA-RNA interactions are both powerful strategies for implementing RNA-based computing systems for intracellular and diagnostic applications.
ContributorsMA, DUO (Author) / Green, Alexander (Thesis advisor) / Mangone, Marco (Committee member) / Liu, Yan (Committee member) / Arizona State University (Publisher)
Created2019
Description
Multicellular organisms use precise gene regulation, executed throughout development, to build and sustain various cell and tissue types. Post-transcriptional gene regulation is essential for metazoan development and acts on mRNA to determine its localization, stability, and translation. MicroRNAs (miRNAs) and RNA binding proteins (RBPs) are the principal effectors of post-transcriptional gene regulation and act by targeting the 3'untranslated regions (3'UTRs) of mRNA. MiRNAs are small non-coding RNAs that have the potential to regulate hundreds to thousands of genes and are dysregulated in many prevalent human diseases such as diabetes, Alzheimer's disease, Duchenne muscular dystrophy, and cancer. However, the precise contribution of miRNAs to the pathology of these diseases is not known.
MiRNA-based gene regulation occurs in a tissue-specific manner and is implemented by an interplay of poorly understood and complex mechanisms, which control both the presence of the miRNAs and their targets. As a consequence, the precise contributions of miRNAs to gene regulation are not well known. The research presented in this thesis systematically explores the targets and effects of miRNA-based gene regulation in cell lines and tissues.
I hypothesize that miRNAs have distinct tissue-specific roles that contribute to the gene expression differences seen across tissues. To address this hypothesis and expand our understanding of miRNA-based gene regulation, 1) I developed the human 3'UTRome v1, a resource for studying post-transcriptional gene regulation. Using this resource, I explored the targets of two cancer-associated miRNAs miR-221 and let-7c. I identified novel targets of both these miRNAs, which present potential mechanisms by which they contribute to cancer. 2) Identified in vivo, tissue-specific targets in the intestine and body muscle of the model organism Caenorhabditis elegans. The results from this study revealed that miRNAs regulate tissue homeostasis, and that alternative polyadenylation and miRNA expression patterns modulate miRNA targeting at the tissue-specific level. 3) Explored the functional relevance of miRNA targeting to tissue-specific gene expression, where I found that miRNAs contribute to the biogenesis of mRNAs, through alternative splicing, by regulating tissue-specific expression of splicing factors. These results expand our understanding of the mechanisms that guide miRNA targeting and its effects on tissue-specific gene expression.
MiRNA-based gene regulation occurs in a tissue-specific manner and is implemented by an interplay of poorly understood and complex mechanisms, which control both the presence of the miRNAs and their targets. As a consequence, the precise contributions of miRNAs to gene regulation are not well known. The research presented in this thesis systematically explores the targets and effects of miRNA-based gene regulation in cell lines and tissues.
I hypothesize that miRNAs have distinct tissue-specific roles that contribute to the gene expression differences seen across tissues. To address this hypothesis and expand our understanding of miRNA-based gene regulation, 1) I developed the human 3'UTRome v1, a resource for studying post-transcriptional gene regulation. Using this resource, I explored the targets of two cancer-associated miRNAs miR-221 and let-7c. I identified novel targets of both these miRNAs, which present potential mechanisms by which they contribute to cancer. 2) Identified in vivo, tissue-specific targets in the intestine and body muscle of the model organism Caenorhabditis elegans. The results from this study revealed that miRNAs regulate tissue homeostasis, and that alternative polyadenylation and miRNA expression patterns modulate miRNA targeting at the tissue-specific level. 3) Explored the functional relevance of miRNA targeting to tissue-specific gene expression, where I found that miRNAs contribute to the biogenesis of mRNAs, through alternative splicing, by regulating tissue-specific expression of splicing factors. These results expand our understanding of the mechanisms that guide miRNA targeting and its effects on tissue-specific gene expression.
ContributorsKotagama, Kasuen Indrajith Bandara (Author) / Mangone, Marco (Thesis advisor) / LaBaer, Joshua (Committee member) / Newbern, Jason (Committee member) / Rawls, Alan (Committee member) / Arizona State University (Publisher)
Created2019
Description
The RASopathies are a collection of developmental diseases caused by germline mutations in components of the RAS/MAPK signaling pathway and is one of the world’s most common set of genetic diseases. A majority of these mutations result in an upregulation of RAS/MAPK signaling and cause a variety of both physical and neurological symptoms. Neurodevelopmental symptoms of the RASopathies include cognitive and motor delays, learning and intellectual disabilities, and various behavioral problems. Recent noninvasive imaging studies have detected widespread abnormalities within white matter tracts in the brains of RASopathy patients. These abnormalities are believed to be indicative of underlying connectivity deficits and a possible source of the behavioral and cognitive deficits. To evaluate these long-range connectivity and behavioral issues in a cell-autonomous manner, MEK1 loss- and gain-of-function (LoF and GoF) mutations were induced solely in the cortical glutamatergic neurons using a Nex:Cre mouse model. Layer autonomous effects of the cortex were also tested in the GoF mouse using a layer 5 specific Rbp4:Cre mouse. Immunohistochemical analysis showed that activated ERK1/2 (P-ERK1/2) was expressed in high levels in the axonal compartments and reduced levels in the soma when compared to control mice. Axonal tract tracing using a lipophilic dye and an adeno-associated viral (AAV) tract tracing vector, identified significant corticospinal tract (CST) elongation deficits in the LoF and GoF Nex:Cre mouse and in the GoF Rbp4:Cre mouse. AAV tract tracing was further used to identify significant deficits in axonal innervation of the contralateral cortex, the dorsal striatum, and the hind brain of the Nex:Cre GoF mouse and the contralateral cortex and dorsal striatum of the Rbp4:Cre mouse. Behavioral testing of the Nex:Cre GoF mouse indicated deficits in motor learning acquisition while the Rbp4:Cre GoF mouse showed no failure to acquire motor skills as tested. Analysis of the expression levels of the immediate early gene ARC in Nex:Cre and Rbp4:Cre mice showed a specific reduction in a cell- and layer-autonomous manner. These findings suggest that hyperactivation of the RAS/MAPK pathway in cortical glutamatergic neurons, induces changes to the expression patterns of P-ERK1/2, disrupts axonal elongation and innervation patterns, and disrupts motor learning abilities.
ContributorsBjorklund, George Reed (Author) / Newbern, Jason M (Thesis advisor) / Neisewander, Janet (Committee member) / Smith, Brian (Committee member) / Orchinik, Miles (Committee member) / Mangone, Marco (Committee member) / Arizona State University (Publisher)
Created2018