First, a study of the thermoelectric effect in single DNA molecules is reported. By varying the molecular length and sequence, the charge transport in DNA was tuned to either a hopping- or tunneling-dominated regimes. In the hopping regime, the thermoelectric effect is small and insensitive to the molecular length. Meanwhile, in the tunneling regime, the thermoelectric effect is large and sensitive to the length. These findings indicate that by varying its sequence and length, the thermoelectric effect in DNA can be controlled. The experimental results are then described in terms of hopping and tunneling charge transport models.
Then, I showed that the electron transfer reaction of a single ferrocene molecule can be controlled with a mechanical force. I monitor the redox state of the molecule from its characteristic conductance, detect the switching events of the molecule from reduced to oxidized states with the force, and determine a negative shift of ~34 mV in the redox potential under force. The theoretical modeling is in good agreement with the observations, and reveals the role of the coupling between the electronic states and structure of the molecule.
Finally, conclusions and perspectives were discussed to point out the implications of the above works and future studies that can be performed based on the findings.
In this dissertation, I developed a nonculture-based AST using an imaging and cell tracking technology. I track individual Escherichia coli O157:H7 (E. coli O157:H7) Uropathogenic Escherichia Coli (UPEC) cells, widely implicated in food-poisoning outbreaks and urinary tract infections respectively. Cells tethered to a surface are tracked on the nanometer scale, and phenotypic motion is correlated with bacterial metabolism. Antibiotic action significantly slows down motion of tethered bacterial cells, which is used to perform antibiotic susceptibility testing. Using this technology, the clinical minimum bactericidal concentration of an antibiotic against UPEC pathogens was calculated within 2 hours directly in urine samples as compared to 3 days using current gold standard tools.
Such technologies can make a tremendous impact to improve the efficacy and efficiency of infectious disease treatment. This has the potential to reduce the antibiotic mis-prescription steeply, which can drastically decrease the annual 2M+ hospitalizations and 23,000+ deaths caused due to antibiotic resistance bacteria along with saving billions of dollars to payers, patients, and hospitals.
In order to meet these requirements, three generations of novel colorimetric sensors have been developed. The first generation is mosaic colorimetric sensors based on tiny sensor blocks and by detecting absorbance change after each air sample injection, the target analyte concentration can be measured. The second generation is a gradient-based colorimetric sensor. Lateral transport of analytes across the colorimetric sensor surface creates a color gradient that shifts along the transport direction over time, and the sensor tracks the gradient shift and converts it into analyte concentration in real-time. The third generation is gradient-based colorimetric arrays fabricated by inkjet-printing method that integrates multiple sensors on a miniaturized sensor chip. Unlike traditional colorimetric sensors, such as detection tubes and optoelectronic nose, that are typically for one-time use, the presented three generations of colorimetric sensors aim to continuously monitor multiple air pollutants and the sensor lifetime and fabrication methods have been improved over each generation. Ozone, nitrogen dioxide, formaldehyde and carbon monoxide are chosen as analytes of interest. The performance of sensors has been validated in the lab and field tests, proving the capability of the sensors to be used for personal exposure monitoring.
ano-oscillators, are developed to measure the binding-induced charge change instead of the mass change, which enables quantification of the binding kinetics for both large and small molecules.
In particular, the nano-oscillator approach provides a unique capability to image individual nanoparticles and measure the size and charge of each nanoparticle simultaneously. This approach is applied to measure one of the smallest biological particles - single protein molecules. By tracking the oscillation of each protein molecule, the size, charge, and mobility are measured in real-time with high precision. This capability also allows to monitor the conformation and charge changes of single protein molecules upon ligand binding. Measuring the size and charge of single proteins opens a new revenue to protein analysis and disease biomarker detection at the single molecule level.
The virion
ano-oscillators and the single protein approach employ a scheme where a particle is tethered to the surface with a polymer molecule. The dynamics of the particle is governed by two important forces: One is entropic force arising from the conformational change of the molecular tether, and the other is solvent damping on the particle and the molecule. The dynamics is studied by varying the type of the tether molecule, size of the particle, and viscosity of the solvent. The findings provide insights into single molecule studies using not only tethered particles, but also other approaches, including force spectroscopy using atomic force microscopy and nanopores.
Quantifying the interactions of bacteria with external ligands is fundamental to the understanding of pathogenesis, antibiotic resistance, immune evasion, and mechanism of antimicrobial action. Due to inherent cell-to-cell heterogeneity in a microbial population, each bacterium interacts differently with its environment. This large variability is washed out in bulk assays, and there is a need of techniques that can quantify interactions of bacteria with ligands at the single bacterium level. In this work, we present a label-free and real-time plasmonic imaging technique to measure the binding kinetics of ligand interactions with single bacteria, and perform statistical analysis of the heterogeneity. Using the technique, we have studied interactions of antibodies with single Escherichia coli O157:H7 cells and demonstrated a capability of determining the binding kinetic constants of single live bacteria with ligands, and quantify heterogeneity in a microbial population.
Background: Healthy individuals on the lower end of the insulin sensitivity spectrum also have a reduced gene expression response to exercise for specific genes. The goal of this study was to determine the relationship between insulin sensitivity and exercise-induced gene expression in an unbiased, global manner.
Methods and Findings: Euglycemic clamps were used to measure insulin sensitivity and muscle biopsies were done at rest and 30 minutes after a single acute exercise bout in 14 healthy participants. Changes in mRNA expression were assessed using microarrays, and miRNA analysis was performed in a subset of 6 of the participants using sequencing techniques. Following exercise, 215 mRNAs were changed at the probe level (Bonferroni-corrected P<0.00000115). Pathway and Gene Ontology analysis showed enrichment in MAP kinase signaling, transcriptional regulation and DNA binding. Changes in several transcription factor mRNAs were correlated with insulin sensitivity, including MYC, r=0.71; SNF1LK, r=0.69; and ATF3, r= 0.61 (5 corrected for false discovery rate). Enrichment in the 5’-UTRs of exercise-responsive genes suggested regulation by common transcription factors, especially EGR1. miRNA species of interest that changed after exercise included miR-378, which is located in an intron of the PPARGC1B gene.
Conclusions: These results indicate that transcription factor gene expression responses to exercise depend highly on insulin sensitivity in healthy people. The overall pattern suggests a coordinated cycle by which exercise and insulin sensitivity regulate gene expression in muscle.
Although insulin resistance in skeletal muscle is well-characterized, the role of circulating whole blood in the metabolic syndrome phenotype is not well understood. We set out to test the hypothesis that genes involved in inflammation, insulin signaling and mitochondrial function would be altered in expression in the whole blood of individuals with metabolic syndrome. We further wanted to examine whether similar relationships that we have found previously in skeletal muscle exist in peripheral whole blood cells. All subjects (n=184) were Latino descent from the Arizona Insulin Resistance registry. Subjects were classified based on the metabolic syndrome phenotype according to the National Cholesterol Education Program’s Adult Treatment Panel III. Of the 184 Latino subjects in the study, 74 were classified with the metabolic syndrome and 110 were without. Whole blood gene expression profiling was performed using the Agilent 4x44K Whole Human Genome Microarray. Whole blood microarray analysis identified 1,432 probes that were altered in expression ≥1.2 fold and P<0.05 after Benjamini-Hochberg in the metabolic syndrome subjects. KEGG pathway analysis revealed significant enrichment for pathways including ribosome, oxidative phosphorylation and MAPK signaling (all Benjamini-Hochberg P<0.05). Whole blood mRNA expression changes observed in the microarray data were confirmed by quantitative RT-PCR. Transcription factor binding motif enrichment analysis revealed E2F1, ELK1, NF-kappaB, STAT1 and STAT3 significantly enriched after Bonferroni correction (all P<0.05). The results of the present study demonstrate that whole blood is a useful tissue for studying the metabolic syndrome and its underlying insulin resistance although the relationship between blood and skeletal muscle differs.