Inflammatory diseases often involve complex and dynamic responses of monocytes, crucial cells of the innate immune system. Understanding these responses, particularly to lipopolysaccharide (LPS), a key inflammatory stimulus, is vital yet remains challenging due to their heterogeneity and plasticity. Upon analyzing available single-cell RNA sequencing data sets, we defined key patterns of monocyte inflammatory responses challenged with varying LPS dosages. We found that high-dose LPS induced the generation of exhausted monocytes with elevated expression of genes associated with pathogenic inflammation and immune suppression.. In contrast, super-low-dose LPS led to a state of low-grade inflammation, characterized by enhanced chemotaxis; immune-enhancement; and adhesion.. Pseudo-time analysis revealed a potential bifurcation of monocytes, starting from a proliferative, less-differentiated and premature state into either the exhausted state (under prolonged high dose LPS challenge) or the low-grade inflammatory state (under the prolonged super-low dose LPS treatment). Complementing our analyses with in vitro cultured murine monocytes, we observed similar exhaustion of monocytes collected from septic murine hearts published in an independent study. Furthermore, we analyzed publicly available scRNAseq datasets regarding monocytes from septic and severe COVID human patients and revealed a similar exhaustion phenotype as we documented in murine exhausted monocytes. In contrast, our analyses of newly published scRNAseq data regarding monocytes from chronic autoimmune patients reveal key distinct low-grade inflammation features. With translational potential, we analyzed the scRNAseq datasets of monocytes trained with 4-PBA, a potent anti-inflammatory compound, and observed that 4-PBA can effectively arrest monocytes in an anti-inflammatory state. Together, our comparative analyses reveal a systems landscape of monocyte memory dynamics with distinct dosage and history of LPS challenges, and offer novel insights for potential therapeutic strategies for modulating both acute sepsis and chronic inflammatory diseases. Our studies also provide a foundation for guiding future mechanistic and translational studies regarding monocyte dynamics and their involvements in health and disease pathogenesis. / Doctor of Philosophy / Inflammation is the body's natural response to injury or infection. A key player in this process is a type of immune cell called monocyte. Monocytes are our body's first line of defense, rushing to the site of injury or infection. However, the way these cells respond can vary greatly, depending on the dosage and duration of external challenges.
In our research, we analyzed data collected through an advanced technique called single-cell RNA sequencing, in order to take a detailed look at how an individual monocyte responds to different amounts of LPS, a key substance found in most bacteria. We found that when exposed to a prolonged challenge of higher dose LPS, monocytes become exhausted with pathogenic inflammation and immune suppression, as seen in sepsis. However, when the LPS dose is low, these cells enter a state of low-grade inflammation, responsible for chronic inflammation as seen in autoimmune diseases, atherosclerosis and other chronic diseases.
We found that this paradigm of exhaustion and low-grade inflammation can be seen in data analyzed from either patients with severe infections such as sepsis or severe COVID-19, or patients with long-term autoimmune diseases.
In simpler terms, our study provides a detailed road map regarding how our body's first responders, namely the monocytes, react under different levels of threat, and how we might be able to guide their responses toward a beneficial direction. Understanding these processes more clearly may lead to new ways to treat a range of infectious or inflammatory diseases.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/115881 |
| Date | 27 July 2023 |
| Creators | Yi, Ziyue |
| Contributors | Genetics, Bioinformatics, and Computational Biology, Li, Liwu, Zhang, Liqing, Tong, Rong, Geng, Shuo |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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