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Investigating the Role of metR on E. coli Growth and Viral Replication
No full textUnderstanding how bacterial metabolic pathways influence viral infections is
fundamental to host-pathogen biology. In Escherichia coli, the transcription factor metR
regulates genes required for methionine biosynthesis, an essential pathway for protein synthesis,
methylation, and cellular metabolism. Although metR has been studied in relation to how it
induces methionine production, it has not been studied whether it is needed for methionine
production. This study investigates how deleting metR affects E. coli growth and susceptibility to
T4 and T2 bacteriophages.
Wild-type E. coli and ΔmetR knockout strains were grown in nutrient-rich LB and
nutrient-limited M9 minimal media, and growth was monitored by OD600 measurements. Phage
replication was quantified using standard plaque assays, two-point tier measurements, and lysis
curves. Additional experiments assessed whether providing exogenous methionine could rescue
knockout growth.
Across media types, the ΔmetR strain exhibited consistently reduced growth compared to
the wild type, with the largest impairment in methionine-deficient M9 minimal media. Upon
introducing the T4 bacteriophage, the ΔmetR strain showed increased lysis relative to the parent
strain, demonstrating heightened susceptibility to viral replication. Plaque assays with T2
bacteriophage revealed significantly lower infectious particle counts in the knockout strain,
indicating reduced phage propagation efficiency. Two-point tier assays confirmed this trend, with
the ΔmetR mutant producing fewer virions at both early and late time points. Together, these
findings suggest that metR contributes to bacterial growth and to supporting efficient phage
replication. Our inconsistent results pose a need for further experiments for better clarity.
This work provides insight into how bacterial metabolic regulation intersects with viral
infection dynamics. Future studies will focus on introducing methionine back into the knockout
phenotype in different dilutions and identifying specific metabolic pathways altered by the
ΔmetR deletion that influence phage productivity
Are we safe? Seeking information and knowledge sharing by individuals with hidden disabilities
No full textThis study explores how individuals with invisible disabilities seek, access, and utilize information across medical and online platforms. This article uses research and studies conducted that examined the life-world of people living with or who have lived with invisible disabilities to examine the motivations, barriers, and strategies that shape their information behavior. Data were analyzed to identify key patterns of information needs, value, and trust. Findings suggest that individuals prefer informal sources over formal sources and report to social media to seek new information rather than their General Practitioners. The study conducted should be beneficial to information and medical professionals to better understand the needs of those with invisible disabilities. However, future research should be conducted to better understand those who avoid or are unsure of how to seek out their information needs
The Effect of the purA gene on E. Coli Growth & Bacteriophage Replication
No full textWe investigated how the purA gene influences bacterial growth and phage replication in E.coli. purA is a gene involved in purine biosynthesis responsible for the synthesis of adenine, a crucial nucleotide required for many biological functions, including DNA replication, transcription, translation, and ATP synthesis. Deletion of purA has been shown to increase bacteria's susceptibility to bacteriophages, viruses specific to bacteria, and to enhance phage therapy, an alternative to antibiotics. Through our research, we aim to assess the growth and phage susceptibility of purA-deficient E. coli to better understand the role of purA in E. coli metabolic processes and to gauge the effectiveness of purA deletion in improving phage therapy. A series of experiments was conducted using microbiological techniques, including growth curves, plaque assays, and lysis curves, in both LB and M9 media, to compare bacterial growth patterns and phage susceptibility between the wild-type and knockout strains under different nutrient conditions. Growth curve data on LB media showed that the wild-type strain grew faster than the knockout strain. However, the growth curve on M9 media suggested that the purA knockout strain grew superiorly to the wildtype strain, indicating that the lack of purines in M9 media may prove beneficial for growth and replication. Plaque assay data revealed that the wild-type strain E. coli is more susceptible to phage attack compared to its knockout counterpart, as greater PFU were observed in all three dilution plates of the wild type compared to the knockout strain. Lysis data suggest both strains follow similar lysis patterns, as the stationary phase and death phase durations are similar. The log-phase concentrations pre-lysis differ between strains, with the wild-type strain concentration at the end of the log phase being double that of the knockout strain. At the two measured time points, we observed that the E. coli purA knockout strain had lower OD values than the parent strain, indicating lower bacterial growth. This trend remains consistent between both time points, demonstrating that, as hypothesized, although purA is a non-essential gene, bacterial growth decreases under these conditions. These results are significant because they show that the effect of purA knockout can be environment-dependent, affecting growth, metabolism, and susceptibility to phage. This data reveals that although purA is non-essential, it can still play a significant role in how E. coli responds to various environmental factors. Our findings show that deleting the purA gene alters E. coli growth and phage interactions, with effects that depend heavily on nutrient availability. Overall, this indicates that although purA is non-essential, our data suggest it still influences how well the bacteria grow and how they handle phage exposure across different environments. Future studies should examine how the knockout strain behaves across a broader range of nutrient conditions and stressors, which would help determine under what circumstances purA truly becomes essential for growth and phage resistance
Microscopy movies used for Electric field driven dynamic assembly of active colloidal aggregates
No full textMonodisperse silica particles (4 μm and 2 μm diameter, Polysciences Inc.) were used in all experiments. Fresh stock solutions of para-benzoquinone (p-BQ, 99.5%, Sigma Aldrich) and potassium chloride (KCl, Sigma Aldrich) were prepared prior to each experiment in 18.2 MΩ deionized water and used immediately. Samples were prepared in an electrolyte solution of 1 mM KCl, with BQ added to reach a final concentration of 10 mM. A liquid cell was created by placing two indium tin oxide (ITO) coated glass slides together with a non-conducting spacer of ~ 300 µm thickness between them. An external electric field was applied using a Keysight Trueform function generator. Particle dynamics were observed with a Zeiss Axio Observer 7 inverted microscope operating in bright field imaging mode. Colloids were subjected to ‘multimode’ electric potentials consisting of a 400 Hz sinusoidal oscillatory potential superimposed with a steady offset potential.Active colloids self-propel and dynamically assemble in response to external fields and chemical reactions. Previous work has focused on single, monomeric active colloids. Here we show that electric fields drive binary mixtures of passive spherical colloids to aggregate into active clusters that self-propel, reshape, merge, and split. Self-propulsion arises from imbalanced electrohydrodynamic (EHD) flows, with a few large (4 μm) dielectric particles leading groups of smaller (2 μm) ones. Propulsion velocity decreases as large particles occupy more aggregate boundary, forming more symmetric, less active aggregates. Sustained self-propulsion and dynamic assembly occurs at sufficiently large particle concentration and when small particles outnumber large ones by 20–40 times. Splitting follows elongation and formation of hydrodynamically coupled clusters of large particles at aggregate poles. We present evidence that phoretic attraction (EHD flow) between aggregates drives mergers, while splitting occurs when tensile forces created by divergent self-propulsion of surface laden large particles overcomes cohesive EHD flows. Scaling analysis demonstrates the aggregate area dependence of merger and splitting rates to be consistent with these mechanisms. These results reveal how passive colloidal mixtures can be activated by electric fields to form self-organizing, reconfigurable microscale assemblies
Selecting a DNA Aptamer Against CTLA-4 for Immune Checkpoint Therapy
No full textImmune checkpoint therapies for cancer treatment leverage the body’s immune system by preventing its inhibition through cancer cell signaling. Currently available immune checkpoint therapies are antibody-based, leading to adverse side effects in the majority of patients. Aptamers—short, single stranded nucleic acids—have been explored as alternatives to antibodies for immune checkpoint therapy. In this project, we utilized bead-based SELEX to begin selection for aptamers with a specific affinity to the immune checkpoint protein CTLA-4. CTLA-4 with a histidine tag was immobilized on Ni-NTA magnetic beads and incubated with a GC-rich DNA library. Bound sequences were amplified with PCR, cleaned, and quantified before digestion with lambda exonuclease to generate ssDNA to be used in further rounds of selection. Ideal lambda exonuclease concentrations and incubation times for the generation of ssDNA after symmetric PCR were determined for use within the project, and potential aptamer candidates were amplified after the first round of selection. In the future, we will continue this project by running more rounds of selection, completing sequencing of our aptamers, and performing binding affinity assays for aptamer candidates
ROLE OF TRPV4 MECHANOSENSING IN DIFFERENTIATION OF VALVULAR INTERSTITIAL CELLS AND AORTIC ENDOTHELIAL CELLS
No full textCardiovascular diseases, such as atherosclerosis and aortic valve stenosis, have continued to remain the leading cause of death for the past several decades. Although, due to the improvement in medical science, the mortality rates have decreased over time, the number of cases diagnosed in recent years has increased at a significant rate. While patients from higher socio-economic backgrounds can afford the treatment procedures, there has been a pressing need for preventative therapies to support the greater population, compelling us to study the fundamental process of the disease initiation and progression. Aortic valve stenosis (AVS), one of the most complicated disease conditions, leads to increased stiffness (rigidity) of the heart valve tissue, causing valvular interstitial cells (VICs) to differentiate into myofibroblasts. Endothelial-to-mesenchymal transition (Endo-MT) is a process where endothelial cells (ECs) differentiate into mesenchymal cells, which may lead to the endothelial dysfunctions which are involved in major cardiovascular diseases such as atherosclerosis. Matrix stiffness is recognized as a risk factor in both AVS and atherosclerosis development and progression. This study investigated the role of Transient Receptor Potential Vanilloid 4 (Trpv4), a mechanosensitive ion channel in VIC-myofibroblast activation and Endo-MT in response to both matrix stiffness and TGFβ, a major promoter of tissue fibrosis. We confirmed Trpv4 functionality in primary mouse VICs and aortic ECs (aECs) and found that its genetic and pharmacologic deletion/antagonism blocked VIC to myofibroblast and Endo-MT induced by matrix stiffness and TGFβ1, as indicated by changes in cell morphology, α-smooth muscle actin, and F-actin expression. Key findings revealed that residues 30-130 in Trpv4 were essential in stiffness-mediated VIC to myofibroblast differentiation and Endo-MT. Furthermore, Trpv4 was shown to regulate PI3K-AKT activity necessary for myofibroblast differentiation and cellular traction force generation. Moreover, we found that Trpv4 regulates the stiffness-mediated phosphorylation of MLC2, A major regulator which in turn regulates Endo-MT in aECs. These results highlight Trpv4's novel role in VIC- myofibroblast activation and mechanotransduction in regulating Endo-MT. Altogether our results suggest that Trpv4-based targeted therapeutic strategies may have the potential to prevent or suppress cardiovascular diseases
Dataset for Constraining Wetland and Landfill Methane Emission Signatures Through Atmospheric Methane Clumped Isotopologue Measurements" [Paper #2024JG008249-T]
No full textDescribed in methods section of primary paperFrom Primary Paper: Microbial methane emissions are associated with a wide range of isotopic signatures, providing information about the sources and sinks of methane. Methods of directly sampling methane from environments such as wetlands may fail to capture the temporal and spatial variations in emissions at a specific site and time. The Keeling plot method is commonly used to infer the overarching isotopic signatures of methane sources. In this study, we have expanded the application of the Keeling plot from conventional stable isotope ratios to include novel clumped isotopologue compositions of methane. This advancement aims to provide more robust constraints on regional methane emission signatures. We analyzed methane isotopologue compositions from air samples collected above wetlands and landfills across Maryland, USA, and determined the endmember compositions for background air, wetland, and landfill sources. Our findings indicate that the isotopologue compositions of methane from regional wetland emissions exhibit seasonal variations — δ13C and δD values become less positive as winter approaches, reflecting changes in methane oxidation and production rates. The continuous monitoring of air methane isotopologue signatures will deepen our understanding of the seasonal patterns in methane emissions and contribute to refining the global methane budget, as valuable insights can be extracted from these measurements.JS was supported by NOAA grant NA19NES4320002 (Cooperative Institute for Satellite Earth System Studies -CISESS) at UMD. Funding to support MH was provided by U.S. National Science Foundation grant (EAR-PF: 2052834)
Social Support and Trans Communities in Maryland: Implications for Social Support and Bereavement Resources, Policies, and Practices
No full textThe Maryland Trans Survey is a community-based research project conducted by Trans Maryland and the Queer/Trans Collective for Research on Equity and Wellness examining experiences of trans people in the State of Maryland in areas such as health and healthcare, employment and economic wellbeing, and legal and policy experiences. To date, it is the largest survey of trans people in the State, with 750 trans people representing all 23 counties in Maryland and Baltimore City. Data were collected from June to December 2023 through in-person and online community outreach. The project was approved by Towson University’s Institutional Review Board (Protocol #1897) and used Transgender Research Informed Consent (TRICON) Disclosures to provide trans community members with additional transparency on the project, recognizing long histories of harmful practices in trans research from scientific institutions. This brief contains data on experiences of social support among trans communities from the Maryland Trans Survey. This brief also discusses research implications for social support and bereavement resources, policies, and practices so that advocates, policymakers, and community organizations can better understand and address the current needs of trans people in Maryland.University System of Maryland - Wilson H. Elkins Professorship (2021-2023); Washington University in St. Louis - Audre Lorde Distinguished Professorship (2023-present
Animated Sequences Showing the Ejecta Produced in the DART Impact of Asteroid (65803) Didymos
No full textThe data consist of 11 animated sequences, each with a leading panel that provides a summary of what is portrayed in that sequence. Additional information is given in the accompanying text for each file. Identical sequences are provided in both MOV and MP4 formats. Displaying with Quicktime or a similar app allows the user to play the movie at different speeds, and also to step through the sequence a frame at a time, which is helpful for following the details as the ejecta evolves. Time tags (seconds since the time of impact) are incorporated to help identify the images in the sequence.
Zip files containing all 11 animated sequences are included for both MOV and MP4 versions.This data collection contains animated sequences showing different aspects of the ejecta that were observed after the Double Asteroid Impact Test (DART) spacecraft crashed into Dimorphos, the moon of asteroid (65803) Didymos on September 26, 2022. The images comprising the sequences were obtained with the LICIACube Unit Key Explorer (LUKE) instrument on board the LICIACube spacecraft that flew by the Didymos system about 3 minutes after the impact event. Although the sequences are comprised of the same observations they are presented in different ways to emphasize various aspects of the ejecta field. These animations are intended as a supplement to the individual LUKE images, to provide insight and to help in the interpretation of the data in support of studies that address spatial and temporal changes in the DART ejecta field. Note that in some of the sequences, black sections may encroach in from the sides. These are gaps in the data where the asteroids moved to the edge of the camera's detector.This study was supported in part by the DART mission, National Aeronautics and Space Administration (NASA) contract No. 80MSFC20D0004 to JHU/APL
Exploring the Power of Machine Learning in Medical Research: A Focus on Movement Disorder Diagnosis and Age-Related Hearing Loss
No full textThe medical research field is experiencing a remarkable evolution due to the application of data science and machine learning techniques and new developments in these fields. The accessibility of large datasets, enhanced computing capabilities, and advanced algorithms have opened up new possibilities to extract valuable insights, identify patterns, and develop predictive models from complex biomedical data. This integration has the potential to revolutionize medical research, resulting in enhanced diagnostic capabilities, personalized treatment approaches, and ultimately, improved patient care.
In this dissertation, I explore the impact of data science and machine learning in medical research, with a specific focus on the diagnosis of movement disorders and age-related hearing loss. In the first of these domain areas, I use data from a wearable sensor to accurately identify individuals with Parkinson’s disease based on their movements during several motor tasks. I demonstrate that applying machine learning to wearable sensor data can achieve diagnostic accuracy surpassing that of movement disorder experts in routine clinical settings for differentiating Parkinson’s disease from controls and is comparable to expert clinicians for distinguishing Parkinson’s disease from other parkinsonian disorders. I also found that repeating mobility tasks is unnecessary for improving diagnostic accuracy. I propose several steps to simplify mobility test protocols, which can save time and effort for both clinicians and participants without compromising accuracy. Specifically, using a single sensor, a single mobility task, and just one trial of each task for the classification tasks explored in this study can streamline the process. This approach facilitates the practical application of wearable sensors as a diagnostic tool in clinical settings.
In the second domain area, I study age-related hearing loss by constructing ensemble models to examine data from participants with diverse ages and varying degrees of hearing loss. By integrating audiometric, perceptual, electrophysiological, and cognitive data, I predict speech perception in challenging auditory conditions like noise, reverberation, and time compression. Leveraging machine learning techniques, my objective is to identify the variables that are highly predictive of demanding speech-perception conditions, thereby confirming existing associations and potentially uncovering novel ones. The findings underscore the critical role of audiometric thresholds, particularly within the 1–4 kHz range, and emphasize the utility of composite variables spanning multiple frequencies in accurately predicting speech perception. Furthermore, basic temporal processing ability demonstrates a moderate influence, whereas cognitive factors and extended high-frequency thresholds exhibit limited to negligible predictive capability in this context. Continued research and exploration of associations will contribute to a deeper understanding of the complex interplay between speech perception, aging, hearing loss, and cognition