Quantifying the Differences in Binding Affinity of Reduced Glutathione for Glutathione S-Transferase at pH 6.5 and 8.5 Using Isothermal Titration Calorimetry

The binding affinity between an enzyme and its substrate is often dependent on the pH of the local environment. Knowing the pH at which reduced glutathione (GSH) binds with the highest affinity to the enzyme glutathione S-transferase (GST) is useful for determining the optimal pH for purification of GST-fusion proteins during GST-affinity chromatography. In this study, GST of the species Schistosoma japonicum was purified, quantified, and utilized to study its binding interaction with GSH at pH 6.5 and 8.5 via isothermal titration calorimetry (ITC). After protein expression, extraction, and purification, the GST concentration was quantified using QubitTM fluorometry. Thermodynamic properties and a dissociation constant (KD) for each experiment were obtained utilizing the MicroCal PEAQ-ITC Analysis Software for the binding of GSH to GST at pH 6.5 and 8.5. Statistical analysis of the technical replicate data was performed to obtain an average and standard deviation of the KD at each pH point. The results indicate a statistically significant difference (

Cell migration dynamics after alteration of cell-cell contacts in fibrosarcoma and glioblastoma cell lines

Cell migration is a vital component of metastasis. In this study, our intent was to study cell migration by alteration of the Wnt/GSK-3 Pathway. Since BeSO4 is a known GSK-3 kinase inhibitor, we hypothesized that this agent would cause cell migration to decrease as a result of β-catenin stabilization. Two human cell lines, HT-1080 (fibrosarcoma) and A172 (glioblastoma), were used to observe migration levels in the presence and absence of BeSO4. Our results show that cell migration is diminished for cells that were pre-treated with BeSO4, in comparison to the untreated (control) cells

The interaction site of Flap Endonuclease-1 with WRN helicase suggests a coordination of WRN and PCNA

Werner and Bloom syndromes are genetic RecQ helicase disorders characterized by genomic instability. Biochemical and genetic data indicate that an important protein interaction of WRN and Bloom syndrome (BLM) helicases is with the structure-specific nuclease Flap Endonuclease 1 (FEN-1), an enzyme that is implicated in the processing of DNA intermediates that arise during cellular DNA replication, repair and recombination. To acquire a better understanding of the interaction of WRN and BLM with FEN-1, we have mapped the FEN-1 binding site on the two RecQ helicases. Both WRN and BLM bind to the extreme C-terminal 18 amino acid tail of FEN-1 that is adjacent to the PCNA binding site of FEN-1. The importance of the WRN/BLM physical interaction with the FEN-1 C-terminal tail was confirmed by functional interaction studies with catalytically active purified recombinant FEN-1 deletion mutant proteins that lack either the WRN/BLM binding site or the PCNA interaction site. The distinct binding sites of WRN and PCNA and their combined effect on FEN-1 nuclease activity suggest that they may coordinately act with FEN-1. WRN was shown to facilitate FEN-1 binding to its preferred double-flap substrate through its protein interaction with the FEN-1 C-terminal binding site. WRN retained its ability to physically bind and stimulate acetylated FEN-1 cleavage activity to the same extent as unacetylated FEN-1. These studies provide new insights to the interaction of WRN and BLM helicases with FEN-1, and how these interactions might be regulated with the PCNA-FEN-1 interaction during DNA replication and repai

Social Preferences and the Efficiency of Bilateral Exchange

Under what conditions do social preferences, such as altruism or a concern for fair outcomes, generate efficient trade? I analyze theoretically a simple bilateral exchange game: Each player sequentially takes an action that reduces his own material payoff but increases the other player’s. Each player’s preferences may depend on both his/her own material payoff and the other player’s. I identify necessary conditions and sufficient conditions on the players’ preferences for the outcome of their interaction to be Pareto efficient. The results have implications for interpreting the rotten kid theorem, gift exchange in the laboratory, and gift exchange in the field

From Military to Healthcare: Adopting and Expanding Ethical Principles for Generative Artificial Intelligence

In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has captivated the research community, leading to debates about its application in healthcare, mainly due to concerns about transparency and related issues. Meanwhile, concerns about the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied, and decision-makers often fail to consider the significance of generative AI. In this paper, we propose GREAT PLEA ethical principles, encompassing governance, reliability, equity, accountability, traceability, privacy, lawfulness, empathy, and autonomy, for generative AI in healthcare. We aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI in healthcare

Enabling precision medicine in neonatology, an integrated repository for preterm birth research.

Preterm birth, or the delivery of an infant prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. In the last decade, the advent and continued development of molecular profiling technologies has enabled researchers to generate vast amount of 'omics' data, which together with integrative computational approaches, can help refine the current knowledge about disease mechanisms, diagnostics, and therapeutics. Here we describe the March of Dimes' Database for Preterm Birth Research (http://www.immport.org/resources/mod), a unique resource that contains a variety of 'omics' datasets related to preterm birth. The database is open publicly, and as of January 2018, links 13 molecular studies with data across tens of thousands of patients from 6 measurement modalities. The data in the repository are highly diverse and include genomic, transcriptomic, immunological, and microbiome data. Relevant datasets are augmented with additional molecular characterizations of almost 25,000 biological samples from public databases. We believe our data-sharing efforts will lead to enhanced research collaborations and coordination accelerating the overall pace of discovery in preterm birth research

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

An immune clock of human pregnancy

The maintenance of pregnancy relies on finely tuned immune adaptations. We demonstrate that these adaptations are precisely timed, reflecting an immune clock of pregnancy in women delivering at term. Using mass cytometry, the abundance and functional responses of all major immune cell subsets were quantified in serial blood samples collected throughout pregnancy. Cell signaling-based Elastic Net, a regularized regression method adapted from the elastic net algorithm, was developed to infer and prospectively validate a predictive model of interrelated immune events that accurately captures the chronology of pregnancy. Model components highlighted existing knowledge and revealed previously unreported biology, including a critical role for the interleukin-2-dependent STAT5ab signaling pathway in modulating T cell function during pregnancy. These findings unravel the precise timing of immunological events occurring during a term pregnancy and provide the analytical framework to identify immunological deviations implicated in pregnancy-related pathologies

Prospective associations of perceived unit cohesion with postdeployment mental health outcomes

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149506/1/da22884_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149506/2/da22884.pd