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Leveraging the genotype-phenotype relationship to understand the traditional and novel facets of cell biology
No full textGenotypes and phenotypes form two fundamentally different levels of biological abstraction. The challenge has been to understand how they articulate with each other. In how genotypes map onto phenotypes by perturbing cellular networks. Furthermore, how phenotypes interact with the organism’s environment to map back onto the genotype via forces of natural selection. In our effort to explore the genotype-phenotype relationship we will first present work where we developed an experimental and analytical tool that makes observing subtle changes in phenotype in high-throughput more accurate. Then we will present work that employed the power of forward genetics to examine an unexpected phenotype that defied decades of established research. This phenotype was eventually found to be context-dependent and led to the discovery of an alternate enzyme encoded by a previously uncharacterized gene. We will then transition from the established to the evolutionarily novel and review why yeast, Saccharomyces cerevisiae in particular, serves as a crucial model organism for probing the birth of new genes from scratch. Finally, in this dissertation, we will utilize the techniques of reverse genetics to investigate the phenotypic impact of evolutionarily novel sequences (proto-genes). We will also present a detailed analysis of how a prototypical proto-gene might be interacting with existing cellular processes. The focus of this dissertation extends across established and novel cellular systems. It represents a comprehensive exploration of the genotype-phenotype relationship by employing a systems biology approach. This wholistic exploration has resulted in vital insights to the broader realms of genetics and evolutionary biology
Exploring the Cardioprotective Role of Adropin-GPR19 Signaling in Diabetic Cardiomyopathy
No full textDiabetic cardiomyopathy (DCM) represents a distinct and increasingly prevalent complication of diabetes mellitus, characterized by structural and functional cardiac abnormalities independent of traditional cardiovascular risk factors. Diabetic cardiomyopathy features striking changes in cardiomyocyte fuel metabolism, which promote the transition into an advanced pathological state. Alterations in fuel metabolism include an increased reliance on fatty acid oxidation for mechanical energy production, at the expense of other substrates such as glucose. The resultant loss of metabolic flexibility can lead to reduced cardiac work efficiency and contractile dysfunction. The cellular mechanisms that drive changes in fuel substrate utilization are not fully understood, and this deficiency represents a major impediment to the development of novel treatments. This thesis explores the intricate mechanisms underlying DCM, focusing on the role of adropin-GPR19 signaling. Adropin, a novel peptide hormone, has emerged as a potential regulator of metabolic and cardiovascular health, while GPR19, its putative receptor, is implicated in mediating various physiological responses. We hypothesized that adropin-GPR19 signaling mediates the regulation of cardiac substrate use to reverse energy metabolism deficits and improve functional output in the diabetic heart. This research leverages a combination/series of in vivo and ex vivo metabolic approaches, utilizing knockout mutagenesis and pre-clinical animal models of DCM, to investigate the effects of adropin-GPR19 signaling on cardiac function, structure, and metabolic pathways. Results from this thesis demonstrate the potential therapeutic relevance of adropin in DCM, revealing its capacity to modulate cardiac metabolic flexibility, structural remodeling, and contractile function. Moreover, sex-dependent differences in adropin-GPR19 signaling were observed, providing novel insights into the complex interplay between metabolic derangements and cardiac dysfunction in male and female mice. The findings of this thesis shed light on the multifaceted nature of DCM, highlighting the significance of adropin-GPR19 signaling in cardiac pathophysiology. As such, these insights have promising implications for the development of novel therapeutic strategies targeting metabolic and cardiovascular dysfunction in diabetic patients. Collectively, the data presented in this thesis advances our understanding of DCM, and contributes to the ongoing efforts to mitigate its detrimental effects by highlighting the cardioprotective role of adropin-GPR19 signaling, and thus its therapeutic potential for clinical applications
TDP-43 Phosphorylation State Regulates Solubility, Localization, and Cellular Toxicity
No full textThe hyperphosphorylated and insoluble TDP-43 inclusions are a pathological hallmark of several neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS), frontal temporal dementia (FTD), and Alzheimer's Disease (AD). The mechanism by which phosphorylation regulates TDP-43 physiological functions and its contribution to the observed neuropathology remain unclear. To assess this, we generated recombinant TDP-43 proteins that mimic the hyperphosphorylated and hypophosphorylated condition to study the effect of TDP-43 phosphorylation of the LCD on phase separation, cellular localization, and cytotoxicity. Our studies show that both hyper- and hypo- phosphorylation mimetics promote TDP-43 mislocalization to the cytoplasm , enhance the propensity and stability of the TDP-43 C-terminal domain protein droplets, and disrupt phase separation. However, in all models it is only the phosphodeficient condition that reduces TDP-43 solubility and promotes the formation of cytotoxic aggregates. Furthermore, phosphodeficient TDP-43 induces the sequestration of endogenous TDP-43 to these insoluble aggregates that ultimately diminish TDP-43 splicing capacity. Taken together, our efforts provide further evidence that insoluble, cytoplasmic TDP-43 inclusions are cytotoxic and that phosphorylation might actually play a protective role, inhibiting the formation of insoluble, toxic, cytoplasmic TDP-43 aggregates
Tim-3: Regulation of T Cell Functions and Mechanism of Expression
No full textTim-3 is a transmembrane protein that is commonly used as a marker for terminally exhausted CD8+ T cells in the contexts of chronic infections and tumors. However, Tim-3 is widely expressed on Th1 CD4+ T cells, regulatory T cells, dendritic cells and mast cells, in addition to activated CD8+ T cells. While Tim-3 has been shown to induce death in Th1 cells, it is co- stimulatory in T cells during acute stimulation and has been shown to activate the PI3K pathway. The germline deletion of Tim-3 in mice was previously shown to skew the CD8+ T cell response towards a memory phenotype in the context of LCMV Armstrong infection but the effects of Tim- 3 on CD8+ T cells at the effector stage of the infection remain unknown. In addition, it is not known whether different transcription factors regulate the expression of Tim-3 in conditions of acute vs. chronic stimulation. The transcription factor Blimp-1 has also been shown to co-express with Tim- 3 in acute and chronic infections. Although there are no predicted Blimp-1 binding sites in the Havcr2 locus, there are binding sites for E2A, leading us to predict that a Blimp-1-Id3-E2A transcription factor circuit may play a role in Tim-3 expression.
Using TIRF imaging, I have shown here that Tim-3 localizes to the immunological synapse during T cell activation. Using a novel Tim-3 truncation mutant and a CD8-specific knockout mouse model, I have also shown that Tim-3 is required for a robust CD8+ effector T cell response to LCMV Armstrong and Tim-3 expressing effector CD8+ T cells have an enhanced effector-like phenotype. Additionally, Blimp-1 and Id3 conditional knockout mice in chronic and acute infections show that Blimp-1 and Id3 regulate the expression of Tim-3 in CD8+ T cells
Restoring Sensory Feedback in People with Lower-limb Amputation Using Epidural Spinal Cord Stimulation
No full textEach year, approximately 150,000 individuals experience lower-limb amputation, and this figure is growing rapidly. Existing prosthetic limbs do not provide somatosensory feedback from the missing limb. Restoring somatosensory feedback in individuals with lower-limb amputations would reduce the risk of falls and alleviate phantom limb pain. Although research employing peripheral nerve stimulation has established a foundation for these devices, translating these approaches into clinical practice remains a challenge. Furthermore, while more intuitive sensations (as opposed to unnatural ones) appear to provide additional functional benefits, we currently do not have a quantifiable measure to assess the intuitiveness of the sensation, which hinders systematic advancement of sensory neuroprosthetics.
This dissertation examines the feasibility of spinal cord stimulation (SCS) to restore missing sensations in three individuals with transtibial amputation. We first demonstrate that SCS can evoke sensations from the missing foot, with control over their location and intensity, using commercially available electrodes used to treat chronic pain. Using evoked sensation as sensory feedback during functional tasks improved balance control and gait stability. Over the duration of the study, we measured a decrease in phantom limb pain. We also characterized the spinal reflexes evoked from SCS and evaluated any effect on voluntary muscle activity. We observed a broad activation of the residual muscles, but the reflex activity did not cause any unwanted muscle activation during gait. Finally, we tested a recently proposed measure based on the crossmodal congruency effect to quantify the intuitiveness of sensation in the lower-limb. We demonstrate that the proposed paradigm is not robust and highlight the challenges that need to be overcome for future studies to implement such a measure in sensory neuroprosthetics.
This dissertation marks a significant advancement in the clinical translation of somatosensory neuroprosthetics for people with lower-limb amputation. We demonstrate that SCS is a clinically viable approach to restore sensation and improve quality of life for people with transtibial amputation
Flexible FPGA Acceleration Architecture for Real-Time Neuromorphic Optical Flow
No full textEvent-based vision sensors have become popular due to their high temporal resolution, low power consumption, and high dynamic range. These properties make the sensors more attractive compared to traditional cameras for many computer-vision tasks that require low latency in resource-constrained environments. An important aspect in many computer-vision tasks is optical flow, which is the estimation of an object's velocity. Traditional approaches use frame-by-frame displacement of an object for estimating optical flow. However, with event-based sensors, the paradigm has shifted to using pixel-wise event information. The implementation of these algorithms needs to be able to achieve high performance to keep up with the low-latency event streams from the sensors. Hardware such as field-programmable gate arrays (FPGAs) are commonly used as accelerator platforms because they allow for reconfigurable hardware to tailor the algorithmic implementation. Existing solutions for accelerating event-based optical flow are either too computationally expensive to run on embedded platforms or sacrifice precision in favor of enhanced speed. This research presents an FPGA-based acceleration architecture of a plane-fitting algorithm using a Savitzky-Golay filter for event-based optical-flow calculation implemented on an FPGA. This architecture uses a small history of recent events, unlike traditional methods that store a sensor frame. This optimization enables the architecture to be expanded to higher-resolution sensors without the limitation of requiring enough on-chip memory to store the entire frame. The developed architecture has multiple parameters that can be configured to allow for a tailored FPGA implementation. The architecture was tested on two datasets, Bar-Square and Shapes-Rotation. Our experimental results show that this design reaches real-time performance on an embedded platform. The architecture can achieve a throughput of 506.12~Kevts/s with an event-by-event latency of 1.98~s. The architecture is agnostic to input sensor resolution and can be configured to optimize for accuracy or throughput performance
Identification and Evaluation of Bio-Based Sorbents for The Remediation of Short-Chain PFAS Contaminated Water.
No full textPer- and polyfluoroalkyl substances (PFAS) are a class of thousands of synthetic chemicals that have been produced since the late 1940s and have been used in different consumer products since then. Because human exposure to some of these chemicals have been linked to various health effects, legislative actions and voluntary programs have been set in place to limit the use of long-chain PFAS. This resulted in an increase in the production and use of short-chain PFAS, however these short-chain substances have properties of concern, as comprehensive data on toxicity of all these substances is still lacking. Conventional treatment methods are unable to remove these short-chain PFAS from contaminated aqueous media. Therefore, to help advance the current research on PFAS remediation, this dissertation focused on identifying protein-based sorbents targeting the removal of short-chain PFAS. First, we conducted a comprehensive critical review to understand the current state of the science on the removal of short-chain PFAS from contaminated water. Results highlighted the clear lack of research focus on short-chain PFAS and the limited adsorption capacity of the currently used adsorbent materials. Based on these results, we proposed a treatment train for removal of these short-chain substances. Because of the known interactions between PFAS and proteins, we screened proteins that might be able to bind to short-chain PFAS and tested phospholipase A2, a small commercially available protein, for its use as a bio sorbent of short-chain PFAS using in silico and in vitro approaches. Results showed that while there is promising binding potential of this protein to PFAS of different chain-lengths, therefore opening new areas of research to PFAS remediation route, there may be toxicity implications for organisms expressing this protein. Finally, we employed surface plasmon resonance (SPR) method, for the first time, to investigate binding kinetics between PFAS and proteins in real-time. The results showed promising success of this approach in capturing these interactions, where moderate affinities were observed. This study highlighted some of the challenges of this approach and proposed ways to overcome them. Overall, this work contributes to advancing current research on PFAS remediation via adsorption
Enhancing Alzheimer's prognostic models with cross-domain self-supervised learning and MRI data harmonization
No full textIn the rapidly evolving field of medical imaging, the development of effective artificial intelligence systems requires both advanced deep learning algorithms and substantial, high-quality datasets. However, the acquisition and annotation of such data, particularly in specialized domains like clinical disease prognostics, is often prohibitively expensive and time-consuming. This research explores the potential of cross-domain self-supervised learning (CDSSL) as an innovative solution to these challenges, with a specific focus on enhancing Alzheimer's disease progression models using brain Magnetic Resonance Imaging (MRI) data.
Our study introduces a novel CDSSL approach tailored for disease prognostic modeling, emphasizing regression tasks in medical imaging. Using Alzheimer's disease progression prediction from brain MRI as a case study, we demonstrate that self-supervised pretraining significantly improves prognostic accuracy. Notably, models pretrained on extended, unlabeled brain MRI datasets consistently outperform those using natural images, with an optimal combination of both data sources yielding the best results.
Furthermore, we address the critical issue of data harmonization in medical imaging, investigating the impact of scanner-specific variations arising from diverse manufacturers and models. Our findings highlight CDSSL's potential in ensuring data consistency across different scanner environments, thereby enhancing data comparability and reproducibility. Specifically, we propose two methods Augmentation CDSSL and Auxiliary CDSSL, and show improved prognostic model and scanner variability reduction.
Additionally, we compare our methods with an unsupervised harmonization model, demonstrating that our approach achieves better results in most of the datasets.
This research underscores the significance of scanner-aware self-supervised learning in refining medical imaging methodologies, particularly in the context of Alzheimer's disease (AD) progression modeling. The proposed approach not only improves model accuracy and robustness in limited data scenarios but also offers a promising solution for mitigating scanner variability. These advancements have profound implications for the application of Artificial Intelligence (AI) in clinical settings, potentially leading to more accurate and reliable prognostic tools for AD
A framework for intelligent crowdsourced enforcement of access rights in shared spectrum networks
No full textTraditional spectrum allocation policy statically grants spectrum bands to licensed primary users for exclusive access. Such a policy prevents non-primary users from accessing these spectrum bands, even when they are idle. As a result, licensed frequency bands remain underutilized for extended periods of time. The exponential increase in utilization of wireless services, however, has led to a growing demand for spectrum use. The need to address spectrum scarcity in the public domain has spurred the exploration of spectrum-sharing strategies to optimize spectrum utilization. In response, the Federal Communications Commission (FCC) announced the establishment of the Citizens Broadband Radio Service (CBRS) to facilitate shared federal and non-federal use of the 3550-3700 MHz band, which allows unlicensed secondary users to opportunistically access licensed spectrum bands when they are idle.
While spectrum sharing improves spectrum utilization, it also introduces the risk of illegitimate access to licensed spectrum. This gave rise to the need for effective access rights enforcement in shared spectrum networks. It is to be noted that timing is paramount to the applicability of spectrum enforcement, depending on whether it is applied either before or after a potentially harmful action has occurred. The former enforcement is referred to as ex-ante, while the latter is referred to as ex-post.
The focus of this dissertation is on ex-post spectrum access rights enforcement, an important component of the CBRS framework. To achieve effective ex-post enforcement, two fundamental requirements must be addressed: (i) coverage of the area of enforcement and (ii) accurate and robust detection of spectrum access violations. This dissertation develops a framework and related protocols to address the ex-post spectrum enforcement problem while fulfilling the two requirements.
The main contributions of the dissertation are the development of (i) a shared spectrum enforcement architecture, focused on a volunteer-based crowdsourced approach to achieve cost-effective and scalable spectrum monitoring and misuse detection; (ii) a methodology, that harnesses variants of the Secretary and Stable Matching algorithms, for effective selection of spectrum monitoring volunteers to ensure successful and comprehensive enforcement of spectrum access rights across the spectrum enforcement area. The selected volunteers are assigned to coverage regions based on their qualifications, as reflected by their reputation and the likelihood of their availability in a coverage region for an extended monitoring time interval; iii) a novel spectrum sampling scheme to enable accurate and robust detection of access violations, which takes into consideration the dynamically changing aspects of the volunteers' monitoring capabilities and behaviors and the intruders' misuse strategies; iv) a Machine Learning-based framework to predict volunteers' future locations and a method to estimate the sojourn time of a volunteer within a specific region.
An extensive analysis and assessment of the proposed framework's main components demonstrate the viability and effectiveness of the methodologies used to achieve ex-post enforcement of spectrum access rights, in a scalable and cost-effective manner