Matching Items (74)
Description
The biological lipid bilayer on cells or the cell membrane is a surface teeming with activity. Several membrane proteins decorate the lipid bilayer to carry out various functionalities that help a cell interact with the environment, gather resources and communicate with other cells. This provides a repertoire of biological structures and processes that can be mimicked and manipulated. Since its inception in the late 20th century deoxyribonucleic acid (DNA) nanotechnology has been used to create nanoscale objects that can be used for such purposes. Using DNA as the building material provides the user with a programmable and functionalizable tool box to design and demonstrate these ideas. In this dissertation, I describe various DNA nanostructures that can insert or interact with lipid bilayers for cargo transport, diagnostics and therapeutics. First, I describe a reversibly gated DNA nanopore of 20.4nm x 20.4nm cross sectional width. Controlled transport of cargoes of various sizes across a lipid bilayer through a channel formed by the DNA nanopore was demonstrated. This demonstration paves the way for a class of nanopores that can be activated by different stimuli. The membrane insertion capability of the DNA nanopore is further utilized to design a nanopore sensor that can detect oligonucleotides of a specific s equence inside a lipid vesicle. The ease with which the sensor can be modified to i dentify different diagnostic markers for disease detection was shown by designing a sensor that can identify the non small cell lung cancer marker micro ribonucleic acid -21 (miRNA21). Finally, I demonstrate the therapeutic capabilities of DNA devices with a DNA tetrabody that can recruit natural killer cells (NK cells) to target cancer cells. The DNA tetrabody functionalized with cholesterol molecules and Her2 affibody inserts into NK cell membrane leading it to Her2 positive cancer cells. This shows that inthe presence of DNA tetrabody, the NK cell activation gets accelerated.
ContributorsAbraham, Leeza (Author) / Yan, Hao (Thesis advisor) / Liu, Uan (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2023
Description
Exoelectrogenic organisms transfer electrons from their quinone pool to extracellular acceptors over m-scale distances through appendages known as “biological nanowires”. These structures have been described as cytochrome-rich membrane extensions or pili. However, the components and mechanisms of this long-range electron transfer remain largely unknown. This dissertation describes supramolecular assembly of a tetraheme cytochrome into well-defined models of microbial nanowires and uses those structures to explore the mechanisms of ultra-long-range electron transfer. Chiral-induced-spin-selectivity through the cytochrome is also demonstrated. Nanowire extensions in Shewanella oneidensis have been hypothesized to transfer electrons via electron tunneling through proteinaceous structures that reinforce π-π stacking or through electron hopping via redox cofactors found along their lengths. To provide a model to evaluate the possibility of electron hopping along micron-scale distances, the first part of this dissertation describes the construction of a two-component, supramolecular nanostructure comprised of a small tetraheme cytochrome (STC) from Shewanella oneidensis fused to a peptide domain that self-assembles with a β-fibrillizing peptide. Structural and electrical characterization shows that the self-assembled protein fibers have dimensions relevant to understanding ultralong-range electron transfer and conduct electrons along their length via a cytochrome-mediated mechanism of electron transfer. The second part of this dissertations shows that a model three-component fiber construct based on charge complementary peptides and the redox protein can also be assembled. Structural and electrical characterization of the three-component structure also demonstrates desirable dimensions and electron conductivity along the length via a cytochrome-mediated mechanism.
In vivo, it has been hypothesized that cytochromes in the outer surface conduit are spin-selective. However, cytochromes in the periplasm of Shewanella oneidensis have not been shown to be spin selective, and the physiological impact of the chiral-induced-spin-selectivity (CISS) effect on microbial electron transport remains unclear. In the third part of this dissertation, investigations via spin polarization and a spin-dependent conduction study show that STC is spin selective, suggesting that spin selectivity may be an important factor in the electron transport efficiency of exoelectrogens.
In conclusion, this dissertation enables a better understanding of long-range electron transfer in bacterial nanowires and bioelectronic circuitry and offers suggestions for how to construct enhanced biosensors.
ContributorsNWACHUKWU, JUSTUS NMADUKA (Author) / Jones, Anne K. (Thesis advisor) / Mills, Jeremy (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2023
Description
The natural healing process for bone has multiple signaling cascades where several soluble factors are expressed at specific times to encourage regeneration. Human mesenchymal stromal cells (hMSCs) have three stages of osteogenic differentiation: an increase in cell number (day 1-4), early cell differentiation showing alkaline phosphatase (ALP) expression (day 5-14), and deposition of calcium and phosphate (day 14-28). The first two stages are of particular interest since cell adhesion peptides have been shown to have biological significance during these early stages of bone regeneration. However, far less is known about the temporal dependence of these signals. To mimic these complex systems, developing dynamic biomaterials has become a popular research area over the past decade. Advances in chemistry, materials science, and manufacturing have enabled the development of complex biomaterials that can mimic dynamic cues in the extracellular matrix. One specific area of interest is spatiotemporal control of multiple biomolecules; however, this has generally required diverse chemical approaches making the process difficult and impractical. To circumvent these issues, I developed a novel method that combines a photoresponsive hydrogel with single-stranded DNA to spatiotemporally control multiple biomolecules using a single conjugation scheme.
Here, I describe a detailed protocol to manufacture a fully reversible, spatiotemporal platform using DNA handles. Norbornene-modified hyaluronic acid hydrogels were used to spatially control biomolecule presentation while single-stranded DNA was used to temporally control biomolecule presentation via toehold-mediated strand displacement. This platform was used to orthogonally control the presentation of multiple biomolecules with simple and complex spatial patterning, as well as control the cell morphology of hMSCs by tuning the presentation of the cell adhesion peptide RGDS.
Then, this system was applied to study the temporal presentation of cell adhesion peptides and their effect on early osteogenic differentiation of hMSCs in vitro. The peptides used were RGDS, HAVDI, and OGP. OGP alone expressed higher ALP when presented from day 7-14 than day 0-7 or 0-14. When RGDS, HAVDI, and OGP were combined, there was an increase in ALP activity when HAVDI was presented from day 0-3 indicating that HAVDI plays an important role at earlier time points during osteogenic differentiation.
ContributorsFumasi, Fallon Marie (Author) / Holloway, Julianne L (Thesis advisor) / Stephanopoulos, Nicholas (Committee member) / Green, Matthew D (Committee member) / Stabenfeldt, Sarah E (Committee member) / Acharya, Abhinav (Committee member) / Arizona State University (Publisher)
Created2021
Description
Without a doubt, protein is the most crucial biomolecule performing life and biological functions of any living cell. Profiling various protein expression in individual cells has raised a great interest for scientist and researchers over decades in attempts to reveal cell-to-cell variation, which used to be masked in many previous population average measurement methods. Immunofluorescence (IF) has been a well-established single cell protein analysis technique as for its fast and high-resolution detection and localization, simple and adaptable workflows, and affordable instrumentation. However, inadequate detection sensitivity and multiplexing capability are the two limitation of this platform that remain incompletely addressed in many decades. In this work, several improvements have been proposed and demonstrated to improve existing drawbacks of conventional immunofluorescence. An azide-based linker featured in the novel fluorescent probes synthesis has enable iterative protein staining on the same tissue sample, which subsequently increase the multiplex capacity of IF. Additionally, the multiple fluorophore introduction to the proteins target via either layer by layer biotin-cleavable fluorescent streptavidin or tyramide signal amplification (TSA) have significantly increase the detection sensitivity of the platform. With these advances, IF has the potential to detect, image and quantify up to 100 protein targets in single cell in the tissue sample. In addition of desirable features of IF, these improvements have further turned the technique into a powerful proteomic study platform for not only research setting but also clinical study setting. It is anticipated this highly sensitive and multiplexed, renovated IF method will soon be translated into biomedical studies.
ContributorsPham, Thai Huy (Author) / Guo, Jia (Thesis advisor) / Stephanopoulos, Nicholas (Committee member) / Chiu, Po-Lin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Molecular recognition forms the basis of all protein interactions, and therefore is crucial for maintaining biological functions and pathways. It can be governed by many factors, but in case of proteins and peptides, the amino acids sequences of the interacting entities play a huge role. It is molecular recognition that helps a protein identify the correct sequences residues necessary for an interaction, among the vast number of possibilities from the combinatorial sequence space. Therefore, it is fundamental to study how the interacting amino acid sequences define the molecular interactions of proteins. In this work, sparsely sampled peptide sequences from the combinatorial sequence space were used to study the molecular recognition observed in proteins, especially monoclonal antibodies. A machine learning based approach was used to study the molecular recognition characteristics of 11 monoclonal antibodies, where a neural network (NN) was trained on data from protein binding experiments performed on high-throughput random-sequence peptide microarrays. The use of random-sequence microarrays allowed for the peptides to be sparsely sampled from sequence space. Post-training, a sequence vs. binding relationship was deduced by the NN, for each antibody. This in silico relationship was then extended to larger libraries of random peptides, as well as to the biologically relevant sequences (target antigens, and proteomes). The NN models performed well in predicting the pertinent interactions for 6 out of the 11 monoclonal antibodies, in all aspects. The interactions of the other five monoclonal antibodies could not be predicted well by the models, due to their poor recognition of the residues that were omitted from the array. Furthermore, NN predicted sequence vs. binding relationships for 3 other proteins were experimentally probed using surface plasmon resonance (SPR). This was done to explore the relationship between the observed and predicted binding to the arrays and the observed binding on different assay platforms. It was noted that there was a general motif dependent correlation between predicted and SPR-measured binding. This study also indicated that a combined reiterative approach using in silico and in vitro techniques is a powerful tool for optimizing the selectivity of the protein-binding peptides.
ContributorsBisarad, Pritha (Author) / Woodbury, Neal W (Thesis advisor) / Green, Alexander A (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2022
Description
Two distinct aspects of synthetic biology were investigated: the development of viral structures for new methods of studying self-assembly and nanomanufacturing, and the designs of genetic controls systems based on controlling the secondary structure of nucleic acids. Viral structures have been demonstrated as building blocks for molecular self-assembly of diverse structures, but the ease with which viral genomes can be modified to create specific structures depends on the mechanisms by which the viral coat proteins self-assemble. The experiments conducted demonstrate how the mechanisms that guide bacteriophage lambda’s self-assembly make it a useful and flexible platform for further research into biologically enabled self-assembly. While the viral platform investigations focus on the creation of new structures, the genetic control systems research focuses on new methods for signal interpretation in biological systems. Regulators of genetic activity that operate based on the secondary structure formation of ribonucleic acid (RNA), also known as riboswitches, are genetically compact devices for controlling protein translation. The toehold switch ribodevice can be modified to enable multiplexed logical operations with RNA inputs, requiring no additional protein transcription factors to regulate activity, but they cannot receive chemical inputs. RNA sequences generated to bind to specific chemicals, known as aptamers, can be used in riboswitches to confer genetic activity upon binding their target chemical. But attempts to use aptamers for logical operations and genetic circuits are difficult to generalize due to differences in sequence and binding strength. The experiments conducted demonstrate a ribodevice structure in which aptamers can be used semi-interchangeably to translate chemical inputs into the toehold switch paradigm, marrying the programmability and orthogonality of toehold switches with the broad sensing potential of aptamer-based ribodevices.
ContributorsMcCutcheon, Griffin Cooper (Author) / Green, Alexander (Thesis advisor) / Hariadi, Rizal (Committee member) / Stephanopoulos, Nicholas (Committee member) / Wang, Xiao (Committee member) / Arizona State University (Publisher)
Created2022
Description
Nucleic acid nanotechnology is a field of nanoscale engineering where the sequences of deoxyribonucleicacid (DNA) and ribonucleic acid (RNA) molecules are carefully designed to create self–assembled nanostructures with higher spatial resolution than is available to top–down fabrication methods. In the 40 year history
of the field, the structures created have scaled from small tile–like structures constructed from a few hundred
individual nucleotides to micron–scale structures assembled from millions of nucleotides using the technique
of “DNA origami”. One of the key drivers of advancement in any modern engineering field is the parallel
development of software which facilitates the design of components and performs in silico simulation of the
target structure to determine its structural properties, dynamic behavior, and identify defects. For nucleic acid
nanotechnology, the design software CaDNAno and simulation software oxDNA are the most popular choices
for design and simulation, respectively. In this dissertation I will present my work on the oxDNA software
ecosystem, including an analysis toolkit, a web–based graphical interface, and a new molecular visualization
tool which doubles as a free–form design editor that covers some of the weaknesses of CaDNAno’s lattice–based design paradigm. Finally, as a demonstration of the utility of these new tools I show oxDNA simulation
and subsequent analysis of a nanoscale leaf–spring engine capable of converting chemical energy into dynamic motion. OxDNA simulations were used to investigate the effects of design choices on the behavior of
the system and rationalize experimental results.
ContributorsPoppleton, Erik (Author) / Sulc, Petr (Thesis advisor) / Yan, Hao (Committee member) / Forrest, Stephanie (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2022
Description
Protein-nucleic acid interactions are ubiquitous in biological systems playing a pivotal role in fundamental processes such as replication, transcription and translation. These interactions have been extensively used to develop biosensors, imaging techniques and diagnostic tools.This dissertation focuses on design of a small molecule responsive biosensor that employs transcription factor/deoxyribonucleic acid (DNA) interactions to detect 10 different analytes including antibiotics such as tetracyclines and erythromycin. The biosensor harnesses the multi-turnover collateral cleavage activity of Cas12a to provide signal amplification in less than an hour that can be monitored using fluorescence as well as on paper based diagnostic devices. In addition, the functionality of this assay was preserved when testing tap water and wastewater spiked with doxycycline. Overall, this biosensor has potential to expand the range of small molecule detection and can be used to identify environmental contaminants.
In second part of the dissertation, interactions between nonribosomal peptide synthetases (NRPS) and ribonucleic acid (RNA) were utilized for programming the synthesis of nonribosomal peptides. RNA scaffolds harboring peptide binding aptamers and interconnected using kissing loops to guide the assembly of NRPS modules modified with corresponding aptamer-binding peptides were built. A successful chimeric assembly of Ent synthetase modules was shown that was characterized by the production of Enterobactin siderophore. It was found that the programmed RNA/NRPS assembly could achieve up to 60% of the yield of wild-type biosynthetic pathway of the iron-chelator enterobactin.
Finally, a cas12a-based detection method for discriminating short tandem repeats where a toehold exchange mechanism was designed to distinguish different numbers of repeats found in Huntington’s disease, Spinocerebellar ataxia type 10 and type 36. It was observed that the system discriminates well when lesser number of repeats are present and provides weaker resolution as the size of DNA strands increases. Additionally, the system can identify Kelch13 mutations such as P553L, N458Y and F446I from the wildtype sequence for Artemisinin resistance detection.
This dissertation demonstrates the great utility of harnessing protein-nucleic acid interactions to construct biomolecular devices for detecting clinically relevant nucleic acid mutations, a variety of small molecule analyte and programming the production of useful molecules.
ContributorsChaudhary, Soma (Author) / Green, Alexander (Thesis advisor) / Stephanopoulos, Nicholas (Committee member) / Mangone, Marco (Committee member) / Arizona State University (Publisher)
Created2022
Description
Microfluidics has enabled many biological and biochemical applications such as high-throughput drug testing or point-of-care diagnostics. Dielectrophoresis (DEP) has recently achieved prominence as a powerful microfluidic technique for nanoparticle separation. Novel electric field-assisted insulator-based dielectrophoresis (iDEP) microfluidic devices have been employed to fractionate rod-shaped nanoparticles like Single-walled carbon nanotubes (SWNTs) and manipulate biomolecules like Deoxyribonucleic acid (DNA) and proteins. This dissertation involves the development of traditional as well as 3D-printed iDEP devices for the manipulation of nm-to-µm scale analytes. First, novel iDEP microfluidic constriction-based sorting devices were developed to introduce inhomogeneous electric field gradients to fractionate SWNTs by length. SWNTs possess length-specific optical and electrical properties, expanding their potential applications for future nanoscale devices. Standard synthesis procedures yield SWNTs in large-length polydispersity and chirality. Thus, an iDEP-based fractionation tool for desired lengths of SWNTs may be beneficial. This dissertation presents the first study of DEP characterization and fractionation of SWNTs using an iDEP microfluidic device. Using this iDEP constriction sorter device, two different length distributions of SWNTs were sorted with a sorting efficiency of >90%. This study provides the fundamentals of fractionating SWNTs by length, which can help separate and purify SWNTs for future nanoscale-based applications. Manipulation of nm-scale analytes requires achieving high electric field gradients in an iDEP microfluidic device, posing one of the significant challenges for DEP applications. Introducing nm-sized constrictions in an iDEP device can help generate a higher electric field gradient. However, this requires cumbersome and expensive fabrication methods. In recent years, 3D printing has drawn tremendous attention in microfluidics, alleviating complications associated with complex fabrication methods. A high-resolution 3D-printed iDEP device was developed and fabricated for iDEP-based manipulation of analytes. A completely 3D-printed device with 2 µm post-gaps was realized, and fluorescent polystyrene (PS) beads, λ-DNA, and phycocyanin protein trapping were demonstrated. Furthermore, a nm-resolution 3D-printed iDEP device was successfully printed. In the future, these high-resolution 3D-printed devices may lead to exploring DEP characteristics of nanoscale analytes like single protein molecules and viruses. The electric field-assisted unique fractionation phenomena in microfluidic platforms will become a critical solution for nanoparticle separation and manipulating biomolecules.
ContributorsRabbani, Mohammad Towshif (Author) / Ros, Alexandra (Thesis advisor) / Stephanopoulos, Nicholas (Committee member) / Buttry, Daniel (Committee member) / Arizona State University (Publisher)
Created2022
Description
Biopolymers perform the majority of essential functions necessary for life. From a small amount of components emerges considerable complexity in both structure and function. The separated timescales of dynamic processes and intricate intra- and inter-molecular interactions of these molecules necessitate the development and utilization of computational approaches for biopolymer study and nanotechnology applications. Biopolymer nanotechnology exploits the natural chemistry of biopolymers to perform novel functions at the nanoscale. Molecular dynamics is the numerical simulation of chemical entities according to the physical laws of motion and statistical mechanics. The number of atoms in biopolymers require coarse-grained methods to fully sample the dynamics of the system with reasonable resources. Accordingly, a coarse-grained molecular dynamics model for the characterization of hybrid nucleic acid-protein nanotechnology was developed. Proteins are represented as an anisotropic network model (ANM) which show good agreement with experimentally derived protein dynamics for a small computational cost. The model was subsequently applied to hybrid DNA-protein cages systems and exhibited excellent agreement with experimental results. Ongoing development efforts look to apply network models to oxDNA origami to create multiscale models for DNA origami. The network approximation will allow for detailed simulation of DNA origami association, of concern to DNA crystal and lattice formation. Identification and design of target-specific binders (aptamers) has received considerable attention on account of their diagnostic and therapeutic potential. Generated in selection cycles from extensive random libraries, biopolymer aptamers are of particular interest due to their potential non-immunogenic properties. Machine learning leverages the use of powerful statistical principles to train a model to transform an input into a desired output. Parameters of the model are iteratively adjusted according to the gradient of the cost function. An unsupervised and generative machine learning model was applied to Thrombin aptamer sequence data. From the model, sequence characteristics necessary for binding were identified and new aptamers capable of binding Thrombin were sampled and verified experimentally. Future work on the development and utilization of an unsupervised and interpretable machine learning model for unaligned sequence data is also discussed.
ContributorsProcyk, Jonah (Author) / Sulc, Petr (Thesis advisor) / Stephanopoulos, Nicholas (Thesis advisor) / Hariadi, Rizal (Committee member) / Heyden, Matthias (Committee member) / Arizona State University (Publisher)
Created2022