Rett syndrome is a neurological disorder caused by mutations in a gene called MECP2 on the X-chromosome. Since a mutation is embryonic lethal, it only affects girls who have another wildtype copy, and affects them to varying degrees depending on the fraction of X-chromosomes carrying the mutant allele that are silenced during X-inactivation. To study the potential of rescuing this phenotype by reactivating the X-chromosome carrying the wildtype allele, we use the CRISPR gene editing system to create a mouse carrying a copy of MECP2 tagged with a nano-luciferase reporter. We then isolate clonal cells in which this tagged allele is silenced, in order to test the effects of small hairpin RNAs (shRNAs) and small molecules in reactivating the silenced X-chromosome. This research could open the door to options including genetic therapy and drug targets to mediate the neurological effects of Rett syndrome, as well as shed light on the mechanisms of X-chromosome inactivation.

A growing field in gene therapy is Ultrasound-Mediated Gene Delivery (UMGD). In UMGD, ultrasound is used to cavitate microbubbles near target cells, causing punctures in the cell membrane by which exogenous plasmids can enter. UMGD’s advantages over traditional viral vectors include reduced probability of autoimmune response, improved targeting, and modularity with various genes. Its efficiency is dependent on a variety of ultrasound parameters, but past work has identified pulse duration and pressure as particularly significant. Studies have historically used distinct conditions between cellular and animal models. Most in vivo treatments have favored microsecond pulse durations and high pressure, while in vitro experiments have favored millisecond pulse durations at lower pressure. We tested a wide matrix of pulse durations and pressure conditions in 293T cells in order to determine if there is commonality between UMGD parameters. Our results showed up to an 800-fold increase in expression relative to sham at millisecond pulse durations and low pressure values, but a maximum of 30-fold elevation at pulse durations below 1ms at pressures greater than in vivo. We next tested these short conditions in varying microbubble concentrations to ensure it was not a limiting factor on gene expression. We found that increasing microbubble concentration caused only an additional 3-fold increase in gene expression at a local maximum of 6% microbubble by volume, above which expression decreased. Our results showed no discernible microsecond condition with appreciable expression compared to millisecond pulse conditions. We are now investigating microbubble mechanistic discrepancies in order to explain the discrepancy observed between in vivo and in vitro ultrasound conditions. Further research will explore increased pulse durations, which may permit lower pressure transfection, a key challenge in large animal models. Understanding the correlation between different models will help us scale up experiments, and work towards developing safe and reliable clinical methods.

Bacteria colonizing surfaces often form dense communities with many species. As a consequence of growth in these crowded settings, bacteria have evolved potent antibacterial toxins in order to compete with other organisms. A recently discovered toxin delivery mechanism is the type VI secretion system (T6SS). The T6SS mediates interbacterial competition by injecting toxins from a donor cell into a neighboring competitor cell. I hypothesize that this mechanism can be exploited to develop a novel, cost-effective antibacterial therapeutic strategy that is more efficient in bacterial communities than conventional antibiotics. Instead of treating such a community with exogenous antibiotics, I aim to deploy a genetically engineered non-virulent strain of bacteria that can infiltrate the community and outcompete a target virulent strain via T6S. Here, I report my efforts to engineer substrates of the T6SS for use in developing this therapeutic strain.

During DNA replication, the ligase enzyme plays a pivotal role in the synthesis of the lagging strand by joining together short segments of DNA called Okazaki fragments. In the budding yeast Saccharomyces cerevisiae, it has been observed that extrachromosomal single-stranded DNA (ssDNA) is produced during replication if the ligase is inactive. Our working model is that this ssDNA is generated by the displacement of existing Okazaki fragments by newly synthesized ones. Using a temperature-sensitive S. cerevisiae strain with a ligase mutation (cdc9-1^ts), I am investigating the effect of this accumulation of ssDNA on the viability of cells. I evaluate viability based on the ability of a cell to recover and form a colony after a brief period of ligase inactivation. I have found that cdc9-1^ts cells have significantly reduced viability when grown at the permissive temperature after undergoing replication at the restrictive temperature. This result indicates that the ssDNA generated during replication due to the dysfunction of the ligase enzyme has a definite negative impact on the health of the cells. I am also interested in possible relationships between the ligase and other genes that may explain why this ssDNA arises. To find such a relationship, I am transforming the cdc9-1^ts strain with a genomic library and evaluating viability of the transformants at the restrictive temperature to see if there is any gene that helps cells overcome the negative effects of the ssDNA. Because ssDNA has been implicated in a number of autoimmune disorders, understanding exactly how ssDNA is generated within cells could be a key step in developing treatments for such disorders.

Adhesion to the host cell is a critical step in establishing urinary tract infection. For uropathogenic E. coli, hair-like appendages known as fimbriae mediate this attachment via active FimH adhesin that sits at its tip. Contrary to classical ‘slip bonds,’ that bind optimally in the absence of stress, FimH-mediated catch bonds require sufficient tensile force to activate and have the longest lifetime when exposed to intermediate forces. A structurally dynamic polymer of FimA tethers the active FimH domain of the fimbriae to the outer membrane of the pathogen. Although FimA itself lacks adhesive property, its ability to uncoil and recoil under physiological condition suggests it has additional unknown function. Here, we explore the hypothesis that FimA’s change in conformation can optimize FimH’s ability to form catch bonds by supplying the right amount of force to the active site. In order to determine the role of FimA’s behavior, we made several mutants that lowered the uncoiling force as measured with Atomic Force Microscopy (AFM). Using flow chamber, we will now be able to observe these mutants’ adhesive behavior and assess the role of FimA in these interactions. If our hypothesis is correct, E. coli with lower FimA uncoiling force should detach at a higher rate when exposed to shear stress, since the rapidly uncoiling tether cannot hold the force on the FimH at an optimal level. Understanding the dynamics of fimbriae-mediated adhesion will be critical in developing new ways to combat urinary tract infections and pave the path to designing novel activatable adhesin.

An accurate quantification of the mutability of individual residues within a protein structure can have utility in computational protein design, especially when used to validate mutant designs or to complement existing methods of high-throughput structure analysis. Mutability can be directly calculated from the sequences of structurally similar or evolutionarily related proteins and therefore may be computed without computationally intensive simulation. We started this work by examining links between an existing metric of evolutionary conservation retrieved from an external group and several dynamic properties measured in-house. It had been previously hypothesized that highly conserved residues should be inflexible and solvent inaccessible – as these residues tend to be at points where the protein structure does not tolerate variance. Ultimately, we found that there was little correlation between these measurements. We found it suspect that the evolutionary conservation of a residue had no link to any of the behaviors demonstrated in-silico; further analysis showed that the evolutionary conservation metric that we retrieved was flawed in that it was based off of simple sequence alignments and did not consider protein structure. This allowed for the mixing of varying structures and sequences in alignments, leading to incorrect evolutionary conservation scores. We believe that we can improve on this metric by aligning protein sequences by structure before quantifying variances at each residue. These analyses are being performed within Dynameomics families, which are groups of protein structures with folding/unfolding behaviors represented by a single target. These families provide a set of comparable structures with which to accurately measure the degree to which mutations are tolerated at each residue. We expect the newly computed mutability metric to better correlate with residue dynamics. We ultimately plan to test our new metric of mutability by guiding a mutant design process with suggestions based on our calculations.

Spurred by recent advancements in modern medicine, the number of implanted medical devices is on the rise. Unfortunately, the rise in implants has contributed to hospital-acquired infections, which is now the fourth leading cause of death in the United States. Implant-derived infections are deadly due to the formation of bacteria-produced biofilms that shield and protect bacteria from the host immune system and conventional antibiotic therapies. Recent research has provided evidence that a fundamental structural component of bacterial biofilms is an aggregated protein similar to amyloid proteins associated with Alzheimer’s and Parkinson’s disease. The aggregation and progression of amyloid diseases are thought to involve a unique secondary structure element, called an alpha sheet, that promotes self-aggregation. Thus, we have an opportunity to prevent the formation of these bacterial biofilms by developing a novel therapeutic that inhibits amyloid aggregation by emulating the alpha sheet conformation. We hypothesize that the efficacy of anti-bacterial therapies and the host immune response’s ability to clear an infection will be improved by inhibiting the formation of the protective biofilm with a peptide therapeutic. Computational molecular dynamics simulations, in conjugation with solid phase protein synthesis, was used to design short peptides with a high propensity for the alpha sheet conformation. The efficacy of these designs in inhibiting amyloid formation was assessed in live biofilms. These designs may set the precedence for a novel class of protein therapeutics that can reduce the 100,000 annual deaths associated with hospital acquired bacterial infections.

Determining protein structure is a critical goal in medicine and biotechnology. Experimental techniques for structure determination are limited to proteins that can be manipulated in vitro. Crystallography, for example, requires stable protein crystals, which are not possible for many proteins. Computational methods that predict structure from protein sequences search for conserved amino acid residues by building a sequence alignment derived from orthologs of a protein of interest. The alignment is used to make a structure prediction; however, the large number of homologous sequences needed prohibit wide application. We are developing a new experimental method for predicting structure that can be applied in vivo to nearly any protein. We rely on the fact that single mutations often disrupt the structure of the protein and decrease its functionality. Some other mutations can reestablish the disrupted intramolecular interaction, restoring functionality. We use a method called Deep Mutational Scanning (DMS) to construct a library of double and single mutations and explore their associated functionality for a single protein in Saccharomyces cerevisiae. By searching for covariation between residues that maintain functionality, it is possible to build a matrix that describes the physical distance for all residue-residue contacts. After building this covariation matrix from large-scale mutagenesis data, we will apply a model to infer residue co-localization in three-dimensional space. The co-localization data will be used in conjunction with the structural modeling software Rosetta to predict the structure of the protein. By searching for residue covariation within a mutational dataset generated by DMS we will bridge the gap between experimental and computational prediction methods and increase the number of proteins whose structure we can predict.