Scholarly Works, Biological Systems Engineering

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Research articles, presentations, and other scholarship

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13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production
Guo, Weihua; Sheng, Jiayuan; Feng, Xueyang (MDPI, 2015-12-25)
Metabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been continuously developed and widely applied to rigorously investigate cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, many ¹³C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganisms
Accelerating structure-function mapping using the ViVa webtool to mine natural variation
Hamm, Morgan; Moss, Britney; Leydon, Alexander; Gala, Hardik; Lanctot, Amy; Ramos, Román; Klaeser, Hannah; Lemmex, Andrew; Zahler, Mollye; Nemhauser, Jennifer L.; Wright, R. Clay (Wiley, 2018-12-05)
Thousands of sequenced genomes are now publicly available capturing a significant amount of natural variation within plant species; yet, much of this data remains inaccessible to researchers without significant bioinformatics experience. Here, we present a webtool called ViVa (Visualizing Variation) which aims to empower any researcher to take advantage of the amazing genetic resource collected in the Arabidopsis thaliana 1001 Genomes Project (http://1001genomes.org). ViVa facilitates data mining on the gene, gene family or gene network level. To test the utility and accessibility of ViVa, we assembled a team with a range of expertise within biology and bioinformatics to analyze the natural variation within the well-studied nuclear auxin signaling pathway. Our analysis has provided further confirmation of existing knowledge and has also helped generate new hypotheses regarding this well studied pathway. These results highlight how natural variation could be used to generate and test hypotheses about less studied gene families and networks, especially when paired with biochemical and genetic characterization. ViVa is also readily extensible to databases of interspecific genetic variation in plants as well as other organisms, such as the 3,000 Rice Genomes Project (http://snp-seek.irri.org/) and human genetic variation (https://www.ncbi.nlm.nih.gov/clinvar/).
ACWA: An AI-driven Cyber-Physical Testbed for Intelligent Water Systems
Batarseh, Feras; Kulkarni, Ajay; Sreng, Chhayly; Lin, Justice; Maksud, Siam (2023-10-05)
This manuscript presents a novel state-of-the-art cyber-physical water testbed, namely: The AI and Cyber for Water and Agriculture testbed (ACWA). ACWA is motivated by the need to advance water resources’ management using AI and Cybersecurity experimentation. The main goal of ACWA is to address pressing challenges in the water and agricultural domains by utilising cutting-edge AI and data-driven technologies. These challenges include Cyberbiosecurity, resources’ management, access to water, sustainability, and data-driven decision-making, among others. To address such issues, ACWA consists of multiple topologies, sensors, computational nodes, pumps, tanks, smart water devices, as well as databases and AI models that control the system. Moreover, we present ACWA simulator, which is a software-based water digital twin. The simulator runs on fluid and constituent transport principles that produce theoretical time series of a water distribution system. This creates a good validation point for comparing the theoretical approach with real-life results via the physical ACWA testbed. ACWA data are available to AI and water domain researchers and are hosted in an online public repository. In this paper, the system is introduced in detail and compared with existing water testbeds; additionally, example use-cases are described along with novel outcomes such as datasets, software, and AI-related scenarios.
Advancements in the development of HIF-1α-activated protein switches for use in enzyme prodrug therapy
Wright, R. Clay; Khakhar, Arjun; Eshleman, James R.; Ostermeier, Marc (2014-01)
While gene-directed enzyme prodrug therapy has shown potential as a cancer therapeutic in animal and clinical trials, concerns over the efficacy, selectivity, and safety of gene delivery vehicles have restricted its advance. In an attempt to relieve some of the demands on targeted gene delivery vehicles and achieve the full potential of enzyme prodrug therapy, cancer-targeted activity can be engineered into the enzyme itself. We previously engineered a switchable prodrug-activating enzyme that selectively kills human cancer cells accumulating the cancer marker hypoxia-inducible factor-1α (HIF-1α). This HIF-1α-activated protein switch (Haps59) is designed to increase its ability to convert the prodrug 5-fluorocytosine into the chemotherapeutic 5-fluorouracil in a HIF-1α-dependent manner. However, in cancer cell lines expressing Haps59 the 5FC sensitivity difference between the presence and absence of HIF-1α was not as large as desired. In this work, we aimed to improve the cancer specificity of this switch via a directed evolution approach utilizing random mutagenesis, linker mutagenesis, and random insertion and circular permutation. We identified improved HIF-1α-activated protein switches that confer E. coli with modest increases in HIF-1α-dependent 5FC toxicity. Additionally, the current bottleneck in the development of improved HIF-1α-activated protein switches is screening switch candidates in mammalian cells. To accommodate higher throughput and reduce experimental variability, we explored the use of Flp recombinase-mediated isogenic integration in 293 cells. These experiments raised the possibility that Haps59 can be activated by other interactors of the CH1 domain, and experiments in E. coli indicated that CITED2 can also activate Haps59. Although many CH1 binding partners are also oncogenes, CH1's promiscuous binding and subsequent off-target activation of Haps59 needs to be examined under normal physiological conditions to identify off-target activators. With aberrant activating molecules identified, further directed evolution can be performed to improve the cancer specificity of HIF-1α-activated protein switches.
Advances in Biochemical Engineering-Biotechnology
Zhang, Y. H. Percival; Rollin, Joseph A.; Ye, Xinhao; Del Campo, Julia S. Martin; Adams, Michael W. W. (Springer, 2014-07-15)
In vitro hydrogen generation represents a clear opportunity for novel bioreactor and system design. Hydrogen, already a globally important commodity chemical, has the potential to become the dominant transportation fuel of the future. Technologies such as in vitro synthetic pathway biotransformation (SyPaB)—the use of more than 10 purified enzymes to catalyze unnatural catabolic pathways—enable the storage of hydrogen in the form of carbohydrates. Biohydrogen production from local carbohydrate resources offers a solution to the most pressing challenges to vehicular and bioenergy uses: small-size distributed production, minimization of CO2 emissions, and potential low cost, driven by high yield and volumetric productivity. In this study, we introduce a novel bioreactor that provides the oxygen-free gas phase necessary for enzymatic hydrogen generation while regulating temperature and reactor volume. A variety of techniques are currently used for laboratory detection of biohydrogen, but the most information is provided by a continuous low-cost hydrogen sensor. Most such systems currently use electrolysis for calibration; here an alternative method, flow calibration, is introduced. This system is further demonstrated here with the conversion of glucose to hydrogen at a high rate, and the production of hydrogen from glucose 6-phosphate at a greatly increased reaction rate, 157 mmol/L/h at 60 [degrees] C.
Advances in Watershed Management: Modeling, Monitoring, and Assessment
Benham, Brian L.; Yagow, Eugene R.; Chaubey, I.; Douglas-Mankin, K. R. (American Society of Agricultural and Biological Engineers, 2011)
This article introduces a special collection of nine articles that address a wide range of topics all related to improving the application of watershed management planning. The articles are grouped into two broadly defined categories.. modeling applications, and monitoring and assessment. The modeling application articles focus on one of two widely used watershed-scale water quality modeling packages: HSPF or SWAT The HSPF article assesses the model's robustness when applied to watersheds across a range of topographic settings and climatic conditions. In the SWAT-related articles, researchers used the model to inform watershed management efforts in a variety of ways, including subwatershed prioritization in the context of achieving broader watershed management goals, examining the utility of applying SWAT in a watershed receiving groundwater inputs from outside the topographic watershed boundaries, and estimating the uncertainty and risk associated with meeting TMDL target loads. The monitoring and assessment articles cover such diverse topics as an examination of how best management practice effectiveness is assessed, examination of estimated nutrient loads to a reservoir where a nutrient TMDL has been developed, examination of the sources of fecal indicator bacteria in an urban watershed, and detailed accounting of issues related to flow measurements in small watersheds. The articles in this collection contribute to the body of literature that seeks to inform and advance sound watershed management planning and execution.
AgroSeek: a system for computational analysis of environmental metagenomic data and associated metadata
Liang, Xiao; Akers, Kyle; Keenum, Ishi M.; Wind, Lauren L.; Gupta, Suraj; Chen, Chaoqi; Aldaihani, Reem; Pruden, Amy; Zhang, Liqing; Knowlton, Katharine F.; Xia, Kang; Heath, Lenwood S. (2021-03-10)
Background Metagenomics is gaining attention as a powerful tool for identifying how agricultural management practices influence human and animal health, especially in terms of potential to contribute to the spread of antibiotic resistance. However, the ability to compare the distribution and prevalence of antibiotic resistance genes (ARGs) across multiple studies and environments is currently impossible without a complete re-analysis of published datasets. This challenge must be addressed for metagenomics to realize its potential for helping guide effective policy and practice measures relevant to agricultural ecosystems, for example, identifying critical control points for mitigating the spread of antibiotic resistance. Results Here we introduce AgroSeek, a centralized web-based system that provides computational tools for analysis and comparison of metagenomic data sets tailored specifically to researchers and other users in the agricultural sector interested in tracking and mitigating the spread of ARGs. AgroSeek draws from rich, user-provided metagenomic data and metadata to facilitate analysis, comparison, and prediction in a user-friendly fashion. Further, AgroSeek draws from publicly-contributed data sets to provide a point of comparison and context for data analysis. To incorporate metadata into our analysis and comparison procedures, we provide flexible metadata templates, including user-customized metadata attributes to facilitate data sharing, while maintaining the metadata in a comparable fashion for the broader user community and to support large-scale comparative and predictive analysis. Conclusion AgroSeek provides an easy-to-use tool for environmental metagenomic analysis and comparison, based on both gene annotations and associated metadata, with this initial demonstration focusing on control of antibiotic resistance in agricultural ecosystems. Agroseek creates a space for metagenomic data sharing and collaboration to assist policy makers, stakeholders, and the public in decision-making. AgroSeek is publicly-available at https://agroseek.cs.vt.edu/ .
Ajna: A Wearable Shared Perception System for Extreme Sensemaking
Wilchek, Matthew; Luther, Kurt; Batarseh, Feras A. (ACM, 2024)
This paper introduces the design and prototype of Ajna, a wearable shared perception system for supporting extreme sensemaking in emergency scenarios. Ajna addresses technical challenges in Augmented Reality (AR) devices, specifically the limitations of depth sensors and cameras. These limitations confine object detection to close proximity and hinder perception beyond immediate surroundings, through obstructions, or across different structural levels, impacting collaborative use. It harnesses the Inertial Measurement Unit (IMU) in AR devices to measure users? relative distances from a set physical point, enabling object detection sharing among multiple users across obstacles like walls and over distances. We tested Ajna's effectiveness in a controlled study with 15 participants simulating emergency situations in a multi-story building. We found that Ajna improved object detection, location awareness, and situational awareness, and reduced search times by 15%. Ajna's performance in simulated environments highlights the potential of artificial intelligence (AI) to enhance sensemaking in critical situations, offering insights for law enforcement, search and rescue, and infrastructure management.
Alterations in the molecular composition of COVID-19 patient urine, detected using Raman spectroscopic/computational analysis
Robertson, John L.; Senger, Ryan S.; Talty, Janine; Du, Pang; Sayed-Issa, Amr; Avellar, Maggie L.; Ngo, Lacy T.; Gomez de la Espriella, Mariana; Fazili, Tasaduq N.; Jackson-Akers, Jasmine Y.; Guruli, Georgi; Orlando, Giuseppe (PLOS, 2022-07-01)
We developed and tested a method to detect COVID-19 disease, using urine specimens. The technology is based on Raman spectroscopy and computational analysis. It does not detect SARS-CoV-2 virus or viral components, but rather a urine ‘molecular fingerprint’, representing systemic metabolic, inflammatory, and immunologic reactions to infection. We analyzed voided urine specimens from 46 symptomatic COVID-19 patients with positive real time-polymerase chain reaction (RT-PCR) tests for infection or household contact with test-positive patients. We compared their urine Raman spectra with urine Raman spectra from healthy individuals (n = 185), peritoneal dialysis patients (n = 20), and patients with active bladder cancer (n = 17), collected between 2016–2018 (i.e., pre-COVID-19). We also compared all urine Raman spectra with urine specimens collected from healthy, fully vaccinated volunteers (n = 19) from July to September 2021. Disease severity (primarily respiratory) ranged among mild (n = 25), moderate (n = 14), and severe (n = 7). Seventy percent of patients sought evaluation within 14 days of onset. One severely affected patient was hospitalized, the remainder being managed with home/ambulatory care. Twenty patients had clinical pathology profiling. Seven of 20 patients had mildly elevated serum creatinine values (>0.9 mg/dl; range 0.9–1.34 mg/dl) and 6/7 of these patients also had estimated glomerular filtration rates (eGFR) <90 mL/min/1.73m2 (range 59–84 mL/min/1.73m2). We could not determine if any of these patients had antecedent clinical pathology abnormalities. Our technology (Raman Chemometric Urinalysis—Rametrix®) had an overall prediction accuracy of 97.6% for detecting complex, multimolecular fingerprints in urine associated with COVID-19 disease. The sensitivity of this model for detecting COVID-19 was 90.9%. The specificity was 98.8%, the positive predictive value was 93.0%, and the negative predictive value was 98.4%. In assessing severity, the method showed to be accurate in identifying symptoms as mild, moderate, or severe (random chance = 33%) based on the urine multimolecular fingerprint. Finally, a fingerprint of ‘Long COVID-19’ symptoms (defined as lasting longer than 30 days) was located in urine. Our methods were able to locate the presence of this fingerprint with 70.0% sensitivity and 98.7% specificity in leave-one-out cross-validation analysis. Further validation testing will include sampling more patients, examining correlations of disease severity and/or duration, and employing metabolomic analysis (Gas Chromatography–Mass Spectrometry [GC-MS], High Performance Liquid Chromatography [HPLC]) to identify individual components contributing to COVID-19 molecular fingerprints.
Analysis of crab meat volatiles as possible spoilage indicators for blue crab (Callinectes sapidus) meat by gas chromatography-mass spectrometry
Sarnoski, Paul J.; O'Keefe, Sean F.; Jahncke, Michael L.; Mallikarjunan, Parameswarakumar; Flick, George J. Jr. (Elsevier, 2010-10-01)
Traditionally crab meat spoilage has been evaluated using sensory panels. A method was developed using solid-phase microextraction–gas chromatography–mass spectrometry (SPME–GC–MS) to examine the aroma profile of blue crab (Callinectes sapidus) for chemical indicators of spoilage. The chemicals found to correlate best with spoilage were trimethylamine (TMA), ammonia, and indole over a period of 7 days. In addition, chemicals previously not identified in the aroma profile of blue crab were tentatively detected. Scan mode of the mass spectrometer was used to qualitatively determine compounds extracted from the volatile profile of spoiling blue crab by the SPME fiber. Selected ion monitoring (SIM) mode of the mass spectrometer improved resolution, identified compounds at low concentrations, and allowed spoilage related compounds to be detected in one chromatographic run without sample heating. TMA increased linearly. A significant difference in TMA concentrations were found for day 0 and day 4 samples. Indole concentrations corresponded well with sensory and microbial evaluations, in early, mid, and highly spoiled crab meat samples.
Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India
Loukika, Kotapati Narayana; Keesara, Venkata Reddy; Sridhar, Venkataramana (MDPI, 2021-12-13)
The growing human population accelerates alterations in land use and land cover (LULC) over time, putting tremendous strain on natural resources. Monitoring and assessing LULC change over large areas is critical in a variety of fields, including natural resource management and climate change research. LULC change has emerged as a critical concern for policymakers and environmentalists. As the need for the reliable estimation of LULC maps from remote sensing data grows, it is critical to comprehend how different machine learning classifiers perform. The primary goal of the present study was to classify LULC on the Google Earth Engine platform using three different machine learning algorithms—namely, support vector machine (SVM), random forest (RF), and classification and regression trees (CART)—and to compare their performance using accuracy assessments. The LULC of the study area was classified via supervised classification. For improved classification accuracy, NDVI (normalized difference vegetation index) and NDWI (normalized difference water index) indices were also derived and included. For the years 2016, 2018, and 2020, multitemporal Sentinel-2 and Landsat-8 data with spatial resolutions of 10 m and 30 m were used for the LULC classification. ‘Water bodies’, ‘forest’, ‘barren land’, ‘vegetation’, and ‘built-up’ were the major land use classes. The average overall accuracy of SVM, RF, and CART classifiers for Landsat-8 images was 90.88%, 94.85%, and 82.88%, respectively, and 93.8%, 95.8%, and 86.4% for Sentinel-2 images. These results indicate that RF classifiers outperform both SVM and CART classifiers in terms of accuracy.
Analysis of the causes of extreme precipitation in major cities of Peninsular India using remotely sensed data
Kotrike, Tharani; Keesara, Venkata Reddy; Sridhar, Venkataramana (Elsevier, 2024-01)
Anticipating and adapting to the future impacts of climate change on the health, security and welfare of low elevation coastal zone (LECZ) communities in Southeastern USA
Allen, Thomas; Behr, Joshua; Bukvic, Anamaria; Calder, Ryan S. D.; Caruson, Kiki; Connor, Charles; D'Elia, Christopher; Dismukes, David; Ersing, Robin; Franklin, Rima; Goldstein, Jesse; Goodall, Jonathon; Hemmerling, Scott; Irish, Jennifer L.; Lazarus, Steven; Loftis, Derek; Luther, Mark; McCallister, Leigh; McGlathery, Karen; Mitchell, Molly; Moore, William B.; Nichols, C. Reid; Nunez, Karinna; Reidenbach, Matthew; Shortridge, Julie; Weisberg, Robert; Weiss, Robert; Donelson Wright, Lynn; Xia, Meng; Xu, Kehui; Young, Donald; Zarillo, Gary; Zinnert, Julie C. (MDPI, 2021-10-29)
Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.
Application of a New Species-Richness Based Flow Ecology Framework for Assessing Flow Reduction Effects on Aquatic Communities
Rapp, Jennifer L.; Burgholzer, Robert; Kleiner, Joseph; Scott, Durelle T.; Passero, Elaina M. (2020)
Water-resources managers are challenged with maintaining a balance among beneficial uses throughout river networks and need robust means of assessing potential risks to aquatic life resulting from flow alterations. This study generated ecological limit functions from species-streamflow relations to quantify potential fish richness response to flow alteration and compared results to currently accepted streamflow management guidelines. Modeled responses of absolute richness change were watershed specific and varied among sample sets derived from hydrologic unit classifications of different sizes (large HUC 6 basins to regional scale HUC 8). With a 20% flow reduction, 10% of HUC 8 predicted a richness decrease in one or more taxa. While absolute richness change was consistent across streams within a HUC, percent richness change was stream size dependent. Comparisons with Instream Flow Incremental Methodology habitat models predicted habitat loss greater than percent richness change; however, predictions for habitat and richness decreased similarly as stream size decreased. Watershed-specific responses from flow reductions could allow water-resources management decisions to be made locally based on the predicted richness change for certain sized streams. Quantitative results highlight the utility of a richness-based framework for generating watershed-specific risk assessments that validate and inform currently employed water-resources management practices.
Approximating Community Water System Service Areas to Explore the Demographics of SDWA Compliance in Virginia
Marcillo, Cristina; Krometis, Leigh-Anne H.; Krometis, Justin (MDPI, 2021-12-16)
Although the United States Safe Drinking Water Act (SDWA) theoretically ensures drinking water quality, recent studies have questioned the reliability and equity associated with community water system (CWS) service. This study aimed to identify SDWA violation differences (i.e., monitoring and reporting (MR) and health-based (HB)) between Virginia CWSs given associated service demographics, rurality, and system characteristics. A novel geospatial methodology delineated CWS service areas at the zip code scale to connect 2000 US Census demographics with 2006–2016 SDWA violations, with significant associations determined via negative binomial regression. The proportion of Black Americans within a service area was positively associated with the likelihood of HB violations. This effort supports the need for further investigation of racial and socioeconomic disparities in access to safe drinking water within the United States in particular and offers a geospatial strategy to explore demographics in other settings where data on infrastructure extents are limited. Further interdisciplinary efforts at multiple scales are necessary to identify the entwined causes for differential risks in adverse drinking water quality exposures and would be substantially strengthened by the mapping of official CWS service boundaries.
Armored kinorhynch-like scalidophoran animals from the early Cambrian
Zhang, H.; Xiao, S.; Liu, Y.; Yuan, X.; Wan, B.; Muscente, A. D.; Shao, T.; Gong, H.; Cao, G. (2015-11-26)
Morphology-based phylogenetic analyses support the monophyly of the Scalidophora (Kinorhyncha, Loricifera, Priapulida) and Nematoida (Nematoda, Nematomorpha), together constituting the monophyletic Cycloneuralia that is the sister group of the Panarthropoda. Kinorhynchs are unique among living cycloneuralians in having a segmented body with repeated cuticular plates, longitudinal muscles, dorsoventral muscles, and ganglia. Molecular clock estimates suggest that kinorhynchs may have diverged in the Ediacaran Period. Remarkably, no kinorhynch fossils have been discovered, in sharp contrast to priapulids and loriciferans that are represented by numerous Cambrian fossils. Here we describe several early Cambrian (~535 million years old) kinorhynch-like fossils, including the new species Eokinorhynchus rarus and two unnamed but related forms. E. rarus has characteristic scalidophoran features, including an introvert with pentaradially arranged hollow scalids. Its trunk bears at least 20 annuli each consisting of numerous small rectangular plates, and is armored with five pairs of large and bilaterally placed sclerites. Its trunk annuli are reminiscent of the epidermis segments of kinorhynchs. A phylogenetic analysis resolves E. rarus as a stem-group kinorhynch. Thus, the fossil record confirms that all three scalidophoran phyla diverged no later than the Cambrian Period.
Artificial sinks to treat legacy nutrients in agricultural landscapes
Bock, Emily; Stephenson, Stephen Kurt; Easton, Zachary M. (2019-06-05)
Legacy nutrients introduce a critical time lag between changes in nutrient application or implementation of best management practices (BMPs) and observable reductions in loads delivered to downstream waters. Nitrogen and phosphorus leached through soils into groundwater may take decades to eventually be discharged to surface waters and, consequently, often prevent the attainment of water quality improvement goals. For example, the National Resource Council has cautioned that in the Chesapeake Bay watershed legacy nutrients, particularly nitrogen (N), could delay achievement of nutrient load reductions needed to meet Total Maximum Daily Load (TMDL) requirements.. Groundwater discharge transporting legacy N has been identified specifically as a significant nutrient source to the Bay. Unfortunately, most existing BMPs cannot remediate these nutrient reservoirs and the Chesapeake Bay Program has not active policy to address legacy nutrients; better management options are needed...
Assembly of Bio-Nanoparticles for Double Controlled Drug Release
Huang, Wei; Zhang, Jianfei; Dorn, Harry C.; Zhang, Chenming (PLOS, 2013-09-06)
A critical limiting factor of chemotherapy is the unacceptably high toxicity. The use of nanoparticle based drug carriers has significantly reduced the side effects and facilitated the delivery of drugs. Source of the remaining side effect includes (1) the broad final in vivo distribution of the administrated nanoparticles, and (2) strong basal drug release from nanoparticles before they could reach the tumor. Despite the advances in pH-triggered release, undesirable basal drug release has been a constant challenge under in vivo conditions. In this study, functionalized single walled carbon nanohorn supported immunoliposomes were assembled for paclitaxel delivery. The immunoliposomes were formulated with polyethylene glycol, thermal stable and pH sensitive phospholipids. Each nanohorn was found to be encapsulated within one immunoliposome. Results showed a highly pH dependent release of paclitaxel in the presence of serum at body temperature with minimal basal release under physiological conditions. Upon acidification, paclitaxel was released at a steady rate over 30 days with a cumulative release of 90% of the loaded drug. The drug release results proved our hypothesized double controlled release mechanism from the nanoparticles. Other results showed the nanoparticles have doubled loading capacity compared to that of traditional liposomes and higher affinity to breast cancer cells overexpressing Her2 receptors. Internalized nanoparticles were found in lysosomes.
AssemblyTron: Flexible automation of DNA assembly with Opentrons OT-2 lab robots
Bryant, John A., Jr.; Kellinger, Mason; Longmire, Cameron; Miller, Ryan; Wright, R. Clay (Oxford University Press, 2022-12-22)
As one of the newest fields of engineering, synthetic biology relies upon a trial-and-error Design-Build-Test-Learn approach to simultaneously learn how function is encoded in biology and attempt to engineer it. Many software and hardware platforms have been developed to automate, optimize, and algorithmically perform each step of the Design-Build-Test-Learn cycle. However, there are many fewer options for automating the Build step. Build typically involves DNA assembly, which remains manual, low throughput, and unreliable in most cases and limits our ability to advance the science and engineering of biology. Here, we present AssemblyTron: an open-source python package to integrate j5 DNA assembly design software outputs with build implementation in Opentrons liquid handling robotics with minimal human intervention. We demonstrate the versatility of AssemblyTron through several scarless, multipart DNA assemblies beginning from fragment amplification. We show that AssemblyTron can perform PCRs across a range of fragment lengths and annealing temperatures by using an optimal annealing temperature gradient calculation algorithm. We then demonstrate that AssemblyTron can perform Golden Gate and homology-dependent in vivo assemblies with comparable fidelity to manual assemblies by simultaneously building four four-fragment assemblies of chromoprotein reporter expression plasmids. Finally, we used AssemblyTron to perform site-directed mutagenesis reactions via homology-dependent in vivo assembly also achieving comparable fidelity to manual assemblies as assessed by sequencing. AssemblyTron can reduce the time, training, costs, and wastes associated with synthetic biology, which along with open-source and affordable automation, will further foster the accessibility of synthetic biology and accelerate biological research and engineering.
Assessing Strontium and Vulnerability to Strontium in Private Drinking Water Systems in Virginia
Scott, Veronica; Juran, Luke; Ling, Erin; Benham, Brian L.; Spiller, Asa (MDPI, 2020-04-08)
A total of 1.7 million Virginians rely on private drinking water (PDW) systems and 1.3 million of those people do not know their water quality. Because most Virginians who use PDW do not know the quality of that water and since strontium poses a public health risk, this study investigates sources of strontium in PDW in Virginia and identifies the areas and populations most vulnerable. Physical factors such as rock type, rock age, and fertilizer use have been linked to elevated strontium concentrations in drinking water. Social factors such as poverty, poor diet, and adolescence also increase social vulnerability to health impacts of strontium. Using water quality data from the Virginia Household Water Quality Program (VAHWQP) and statistical and spatial analyses, physical vulnerability was found to be highest in the Ridge and Valley province of Virginia where agricultural land use and geologic formations with high strontium concentrations (e.g., limestone, dolomite, sandstone, shale) are the dominant aquifer rocks. In terms of social vulnerability, households with high levels of strontium are more likely than the average VAHWQP participant to live in a food desert. This study provides information to help 1.7 million residents of Virginia, as well as populations in neighboring states, understand their risk of exposure to strontium in PDW.

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