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Kim, A. R.

Publications and source records attributed to Kim, A. R..

3 recordsLinked to original sources

Early-life adversity alters the adult blood proteome primarily in female mice

Background: Early-life adversity (ELA) is one of the strongest predictors of poor mental and physical health later in life, increasing an individual's risk for disorders ranging from depression to metabolic syndrome. However, whether and how ELA shapes immune memory through adulthood, subsequently affecting susceptibility to immune-associated diseases such as autoimmunity, remains elusive. Methods: To address this question, we employed a translationally and ethologically relevant mouse model of resource deprivation, the limited bedding and nesting model, to study the long-term effects of ELA on the immune system. ELA was induced by limited bedding and nesting material in the cage during postnatal days (P)2-9, which provokes fragmented and unpredictable maternal behavior in the dams, and results in chronic stress in the pups. Nucleic acid Linked ImmunoSandwich Assay (NULISA) was used to survey the proteome of adult (~P90) mice of both sexes that experienced ELA vs. control conditions. Results: Using a novel proteomic method, we detected significant upregulation in the levels of IL-17A in the adult blood, primarily in females. Within females, ELA also upregulated IL-13 and CCL3. Three proteins, HGF, PDGFA, and WNT16, were significantly downregulated in ELA mice of both sexes. Conclusions: Being a hallmark proinflammatory cytokine, IL-17A is found to increase in patients with systemic lupus erythematosus (SLE), as well as in animals with lupus-like diseases in virtually all reports. Importantly, autoimmune diseases like SLE have a higher prevalence in women than men, and our data support this sex bias. Together, our data suggest that ELA shapes long-term immune function in a way that may increase risk for autoimmune diseases, with promise for the development of more targeted and personalized therapies for autoimmunity.

immunology↗

Meta-analysis using new methods for three-stressor combinations reveal substantial higher-order interactions and emergent properties

Although natural populations are typically subjected to multiple stressors, most past research has focused on single stressors and two-stressor interactions, with little attention paid to higher-order interactions among three or more stressors. However, higher-order interactions increasingly appear to be widespread. Consequently, we used a recently introduced and improved framework to re-analyze higher-order ecological interactions. We conducted a literature review of the last 100 years (1920-2020) and reanalyzed 151 ecological three-stressor interactions from 45 published papers. We found that 89% (n=134) of the three-stressor combinations resulted in new or different interactions than previously reported. We also found substantial levels of emergent properties-- interactions that are only revealed when all three stressors are present. Antagonism was the most prevalent net interaction whereas synergy was the most prevalent emergent interaction. Understanding multiple stressor interactions is crucial for fundamental questions in ecology and also has implications for conservation biology and population management.

ecology↗

Cardio PyMEA: A user-friendly, open-source Python application for cardiomyocyte microelectrode array analysis

Open source analytical software for the analysis of electrophysiological cardiomyocyte data offers a variety of new functionalities to complement closed-source, proprietary tools. Here, we present the Cardio PyMEA application, a free, modifiable, and open source program for the analysis of microelectrode array (MEA) data obtained from cardiomyocyte cultures. Cardio PyMEA was written entirely in Python 3 to provide an accessible, integrated workflow that possesses a user-friendly graphical user interface (GUI) written in PyQt5 to allow for performant, cross-platform utilization. This application makes use of object-oriented programming (OOP) principles to facilitate the relatively straightforward incorporation of custom functionalities, e.g. power law analysis, that suit the needs of the user. Cardio PyMEA is available as an open source application under the terms of the GNU General Public License (GPL). The source code for Cardio PyMEA can be downloaded from Github at the following repository: https://github.com/csdunhamUC/cardio_pymea.

cell biology↗