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- All Subjects: Biochemistry
The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.
In order to further compare porcine and human-derived enzymes, a determination of the enzyme effectiveness was done via digestion simulation. The digestion for both the human and porcine-derived enzymes consisted of three steps: oral, gastric, and intestinal. After the digestion, the absorbance for each enzyme class as well as a dilution curve of the formula used was read and recorded. Using the standard dilution curve and the absorbance values for each unknown, the formula and thus enzyme concentration that was lost through the reaction was able to be calculated.
The effectiveness of both the human and porcine enzymes, determined by the percent of formula lost, was 18.2% and 19.7%, respectively, with an error of 0.6% from the spectrophotometer, and an error of about 10% from the scale used for measuring the enzymes. This error was likely due to the small mass required of the enzymes and can be prevented in the future by performing the experiment at a larger scale.
This dissertation aims to 1) develop new strategy to identify high affinity nucleic acid aptamers against biological ligand; and 2) explore highly orthogonal RNA riboregulators in vivo for constructing multi-input gene circuits with NOT logic. With the aid of a DNA nanoscaffold, pairs of hetero-bivalent aptamers for human alpha thrombin were identified with ultra-high binding affinity in femtomolar range with displaying potent biological modulations for the enzyme activity. The newly identified bivalent aptamers enriched the aptamer tool box for future therapeutic applications in hemostasis, and also the strategy can be potentially developed for other target molecules. Secondly, by employing a three-way junction structure in the riboregulator structure through de-novo design, we identified a family of high-performance RNA-sensing translational repressors that down-regulates gene translation in response to cognate RNAs with remarkable dynamic range and orthogonality. Harnessing the 3WJ repressors as modular parts, we integrate them into biological circuits that execute universal NAND and NOR logic with up to four independent RNA inputs in Escherichia coli.
The diagnostic utility of this approach as applied to lung cancer patients across all stages as well as prostate, serous ovarian, and pancreatic cancer patients compared to certifiably healthy individuals, nominally healthy individuals and/or risk-matched controls is reported. Markers for terminal fucosylation, α2-6 sialylation, β1-4 branching, β1-6 branching and outer-arm fucosylation were most able to differentiate cases from controls. These markers behaved in a stage-dependent manner in lung cancer as well as other types of cancer. Using a Cox proportional hazards regression model, the ability of these markers to predict progression and survival in lung cancer patients was assessed. In addition, the potential mechanistic role of aberrant P/S glycans in cancer progression is discussed.
Plasma samples from former bladder cancer patients with currently no evidence of disease (NED), non-muscle invasive bladder cancer (NMIBC), and muscle invasive bladder cancer (MIBC) along with certifiably healthy controls were analyzed. Markers for α2-6 sialylation, β1-4 branching, β1-6 branching, and outer-arm fucosylation were able to separate current and former (NED) cases from controls; but NED, NMIBC, and MIBC were not distinguished from one another. Markers for α2-6 sialylation and β1-6 branching were able to predict recurrence from the NED state using a Cox proportional hazards regression model adjusted for age, gender, and time from cancer. These two glycan features were found to be correlated to the concentration of C-reactive protein, a known prognostic marker for bladder cancer, further strengthening the link between inflammation and abnormal plasma protein glycosylation.
Several strategies were investigated to address the three previously mentioned limitations. The first attempt was to study the effect length and conformation of polyethylene glycol (PEG) on DN stability. DNs were also coated with PEG-lipid and human serum albumin (HSA) and their stealth efficiencies were compared. The findings reveal that both PEGylation and albumin coating enhance low salt stability, increase resistance towards nuclease action and reduce uptake of DNs by macrophages. Any protective coating around a DN increases its hydrodynamic radius, which is a crucial parameter influencing their clearance. Keeping this in mind, intrinsically stable DNs that can survive low salt concentration without any polymer coating were built. Several DNA compaction agents and DNA binders were screened to stabilize DNs in low magnesium conditions. Among them arginine, lysine, bis-lysine and hexamine cobalt showed the potential to enhance DN stability.
This thesis also presents a sensitive assay, the Proximity Ligation Assay (PLA), for the estimation of DN stability with time. It requires very simple modifications on the DNs and it can yield precise results from a very small amount of sample. The applicability of PLA was successfully tested on several DNs ranging from a simple wireframe tetrahedron to a 3D origami and the protocol to collect in vivo samples, isolate the DNs and measure their stability was developed.