Undergraduate Papers
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Browsing Undergraduate Papers by Content Type "Article - Refereed"
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- AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA NeoantigensSamudrala, Madhav; Dhaveji, Sindhusri; Savsani, Kush; Dakshanamurthy, Sivanesan (MDPI, 2024-03-27)Previous epitope-based cancer vaccines have focused on analyzing a limited number of mutated epitopes and clinical variables preliminarily to experimental trials. As a result, relatively few positive clinical outcomes have been observed in epitope-based cancer vaccines. Further efforts are required to diversify the selection of mutated epitopes tailored to cancers with different genetic signatures. To address this, we developed the first version of AutoEpiCollect, a user-friendly GUI software, capable of generating safe and immunogenic epitopes from missense mutations in any oncogene of interest. This software incorporates a novel, machine learning-driven epitope ranking method, leveraging a probabilistic logistic regression model that is trained on experimental T-cell assay data. Users can freely download AutoEpiCollectGUI with its user guide for installing and running the software on GitHub. We used AutoEpiCollect to design a pan-cancer vaccine targeting missense mutations found in the proto-oncogene PIK3CA, which encodes the p110ɑ catalytic subunit of the PI3K kinase protein. We selected PIK3CA as our gene target due to its widespread prevalence as an oncokinase across various cancer types and its lack of presence as a gene target in clinical trials. After entering 49 distinct point mutations into AutoEpiCollect, we acquired 361 MHC Class I epitope/HLA pairs and 219 MHC Class II epitope/HLA pairs. From the 49 input point mutations, we identified MHC Class I epitopes targeting 34 of these mutations and MHC Class II epitopes targeting 11 mutations. Furthermore, to assess the potential impact of our pan-cancer vaccine, we employed PCOptim and PCOptim-CD to streamline our epitope list and attain optimized vaccine population coverage. We achieved a world population coverage of 98.09% for MHC Class I data and 81.81% for MHC Class II data. We used three of our predicted immunogenic epitopes to further construct 3D models of peptide-HLA and peptide-HLA-TCR complexes to analyze the epitope binding potential and TCR interactions. Future studies could aim to validate AutoEpiCollect’s vaccine design in murine models affected by PIK3CA-mutated or other mutated tumor cells located in various tissue types. AutoEpiCollect streamlines the preclinical vaccine development process, saving time for thorough testing of vaccinations in experimental trials.
- Effects of a Low-Fat Vegan Diet on Gut Microbiota in Overweight Individuals and Relationships with Body Weight, Body Composition, and Insulin Sensitivity. A Randomized Clinical TrialKahleova, Hana; Rembert, Emilie; Alwarith, Jihad; Yonas, Willy N.; Tura, Andrea; Holubkov, Richard; Agnello, Melissa; Chutkan, Robynne; Barnard, Neal D. (MDPI, 2020-09-24)Diet modulates gut microbiota and plays an important role in human health. The aim of this study was to test the effect of a low-fat vegan diet on gut microbiota and its association with weight, body composition, and insulin resistance in overweight men and women. We enrolled 168 participants and randomly assigned them to a vegan (n = 84) or a control group (n = 84) for 16 weeks. Of these, 115 returned all gut microbiome samples. Gut microbiota composition was assessed using uBiome Explorer™ kits. Body composition was measured using dual energy X-ray absorptiometry. Insulin sensitivity was quantified with the predicted clamp-derived insulin sensitivity index from a standard meal test. Repeated measure ANOVA was used for statistical analysis. Body weight decreased in the vegan group (treatment effect −5.9 kg [95% CI, −7.0 to −4.9 kg]; p < 0.001), mainly due to a reduction in fat mass (−3.9 kg [95% CI, −4.6 to −3.1 kg]; p < 0.001) and in visceral fat (−240 cm3 [95% CI, −345 to −135 kg]; p < 0.001). PREDIcted M, insulin sensitivity index (PREDIM) increased in the vegan group (treatment effect +0.83 [95% CI, +0.48 to +1.2]; p < 0.001). The relative abundance of Faecalibacterium prausnitzii increased in the vegan group (+5.1% [95% CI, +2.4 to +7.9%]; p < 0.001) and correlated negatively with changes in weight (r = −0.24; p = 0.01), fat mass (r = −0.22; p = 0.02), and visceral fat (r = −0.20; p = 0.03). The relative abundance of Bacteroides fragilis decreased in both groups, but less in the vegan group, making the treatment effect positive (+18.9% [95% CI, +14.2 to +23.7%]; p < 0.001), which correlated negatively with changes in weight (r = −0.44; p < 0.001), fat mass (r = −0.43; p < 0.001), and visceral fat (r = −0.28; p = 0.003) and positively with PREDIM (r = 0.36; p < 0.001), so a smaller reduction in Bacteroides fragilis was associated with a greater loss of body weight, fat mass, visceral fat, and a greater increase in insulin sensitivity. A low-fat vegan diet induced significant changes in gut microbiota, which were related to changes in weight, body composition, and insulin sensitivity in overweight adults, suggesting a potential use in clinical practice.
- Identifying Candidate Biomarkers of Ionizing Radiation in Human Pulmonary Microvascular Lumens Using Microfluidics—A Pilot StudyMillet, Larry J.; Giannone, Richard J.; Greenwood, Michael S.; Foster, Carmen M.; O’Neil, Kathleen M.; Braatz, Alexander D.; Davern, Sandra M. (MDPI, 2021-07-29)The microvasculature system is critical for the delivery and removal of key nutrients and waste products and is significantly damaged by ionizing radiation. Single-cell capillaries and microvasculature structures are the primary cause of circulatory dysfunction, one that results in morbidities leading to progressive tissue and organ failure and premature death. Identifying tissue-specific biomarkers that are predictive of the extent of tissue and organ damage will aid in developing medical countermeasures for treating individuals exposed to ionizing radiation. In this pilot study, we developed and tested a 17 µL human-derived microvascular microfluidic lumen for identifying candidate biomarkers of ionizing radiation exposure. Through mass-spectrometry-based proteomics, we detected 35 proteins that may be candidate early biomarkers of ionizing radiation exposure. This pilot study demonstrates the feasibility of using humanized microfluidic and organ-on-a-chip systems for biomarker discovery studies. A more elaborate study of sufficient statistical power is needed to identify candidate biomarkers and test medical countermeasures of ionizing radiation.
- Transfusion with Blood Plasma from Young Mice Affects rTg4510 Transgenic Tau Mice Modeling of Alzheimer's DiseaseHernandez, Carlos M.; Barkey, Rachel E.; Craven, Kristen M.; Pedemonte, Karin A.; Alisantosa, Bernadette; Sanchez, Jonathan O.; Flinn, Jane M. (MDPI, 2023-05-23)Alzheimer’s disease (AD) is characterized by the buildup of plaques and tangles in the brain. Tangles are formed when the stabilizing protein, tau, becomes hyperphosphorylated and clumps together. There are limited treatments for AD; therefore, the exploration of new treatments is warranted. Previous research showed that plasma transfusion from young donor mice improved spatial memory and increased synaptic proteins in old transgenic APP/PS1 mice, suggesting a remediation of memory and synaptic function. In the current study, plasma was transfused from 2–3-month-old young wildtype mice (WT) to 8-month-old rTg4510 mice expressing human tau (Tau). One week after the transfusions, behavior and tau pathology were examined. We found that Tau mice injected with plasma had lower expression of phosphorylated tau (ptau) in the brain, accompanied by fewer tau tangles in the cortex and CA1 region of the hippocampus and smaller tau tangles in the cortex, when compared to Tau mice injected with saline. Despite no improvement in behavior, the decreased level of ptau and tangles open the door to future studies involving plasma transfusions.
- Weather Research and Forecasting — Fire Simulated Burned Area and Propagation Direction Sensitivity to Initiation Point Location and TimeDeCastro, Amy; Siems-Anderson, Amanda; Smith, Ebone; Knievel, Jason C.; Kosović, Branko; Brown, Barbara G.; Balch, Jennifer K. (MDPI, 2022-04-28)Wildland fire behavior models are often initiated using the detection information listed in incident reports. This information carries an unknown amount of uncertainty, though it is often the most readily available ignition data. To determine the extent to which the use of detection information affects wildland fire forecasts, this research examines the range of burned area values and propagation directions resulting from different initiation point locations and times. We examined the forecasts for ten Colorado 2018 wildland fire case studies, each initiated from a set of 17 different point locations, and three different starting times (a total of 520 case study simulations). The results show that the range of forecast burned area and propagation direction values is strongly affected by the location of the initiation location, and to a lesser degree by the time of initiation.