Integrated Metabolomics, Transcriptomics and Proteomics Identifies Metabolic Pathways Affected by Anaplasma...

Anaplasma phagocytophilum is an emerging zoonotic pathogen that causes human granulocytic anaplasmosis. These intracellular bacteria establish infection by affecting cell function in both the vertebrate host and the tick vector, Ixodes scapularis. Previous studies have characterized the tick transcriptome and proteome in response to A. phagocytophilum infection. However, in the postgenomic era, the integration of omics datasets through a systems biology approach allows network-based analyses to describe the complexity and functionality of biological systems such as host-pathogen interactions and the discovery of new targets for prevention and control of infectious diseases. This study reports the first systems biology integration of metabolomics, transcriptomics, and proteomics data to characterize essential metabolic pathways involved in the tick response to A. phagocytophilum infection. The ISE6 tick cells used in this study constitute a model for hemocytes involved in pathogen infection and immune response. The results showed that infection affected protein processing in endoplasmic reticulum and glucose metabolic pathways in tick cells. These results supported tick-Anaplasma co-evolution by providing new evidence of how tick cells limit pathogen infection, while the pathogen benefits from the tick cell response to establish infection. Additionally, ticks benefit from A. phagocytophilum infection by increasing survival while pathogens guarantee transmission. The results suggested that A. phagocytophilum induces protein misfolding to limit the tick cell response and facilitate infection but requires protein degradation to prevent ER stress and cell apoptosis to survive in infected cells. Additionally, A. phagocytophilum may benefit from the tick cell's ability to limit bacterial infection through PEPCK inhibition leading to decreased glucose metabolism, which also results in the inhibition of cell apoptosis that increases infection of tick cells. These results support the use of this experimental approach to systematically identify cell pathways and molecular mechanisms involved in tick-pathogen interactions. Data are available via ProteomeXchange with identifier PXD002181.