Integrated Metabolomics, Transcriptomics and Proteomics Identifies Metabolic Pathways Affected by Anaplasma phagocytophilum Infection in Tick Cells*

Abstract

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. Molecular & Cellular

Tick-Pathogen Interactions and Vector Competence: Identification of Molecular Drivers for Tick-Borne Diseases

“…Although our understanding of tick-pathogen interactions is still limited, advances in this field are facilitated by the increasing number of available genomic resources, including metabolomics, transcriptomics, and proteomics datasets of various ticks and tick-borne pathogens (TBPs) (Nene et al, 2004; Ayllón et al, 2015a; Cramaro et al, 2015; Kotsyfakis et al, 2015; Villar et al, 2015a; Gulia-Nuss et al, 2016; de Castro et al, 2016), and the recently published genome from Ixodes scapularis , a vector of Borrelia burgdorferi and Anaplasma phagocytophilum in North America (Gulia-Nuss et al, 2016). Together with tools such as tick cell lines and the widespread adaptation of RNA interference (RNAi) to study tick gene function (Bell-Sakyi et al, 2007; de la Fuente et al, 2007), this has opened exciting possibilities to identify determinants affecting tick vector competence.…”

Microfluidics meets metabolomics to reveal the impact of Campylobacter jejuni infection on biochemical pathways

“…Another study of HepG2/C3A cells combining metabolomics profiling and metabolic flux analysis showed that cells cultured in µF had similar glycolytic activity as cells cultured in petri dishes, while exhibiting higher TCA cycle activity which were supported by transcriptional activity of hypoxia-regulating factor-1 (Ouattara et al, 2012). Metabolomics analysis in the field of infectious diseases have been used to uncover host-pathogen interactions (Milner et al, 2015, Jung et al, 2015, Sanchez and Lagunoff, 2015, Villar et al, 2015), antibiotic susceptibility (Lobritz et al, 2015), and find potential strategies and targets for prevention, control, and therapeutics (McGarvey et al, 2009). Metabolite profiling of the liver, spleen, and serum from mice infected with the bacterial pathogen Orientia tsutsugamushi showed that infection caused decreased energy production and deficiencies in both remethylation sources and glutathione, as well as acceleration of uncommon energy production pathways (i.e.…”

“…excess fatty acid and protein oxidation in the host) (Jung et al, 2015). A systems biology approach to understand the host-pathogen interaction between the bacteria Anaplasma phagocytophilum and tick cells integrated metabolomics, transcriptomics and proteomics data and demonstrated novel aspects of the co-evolution between host and pathogen (Villar et al, 2015). These studies illustrate the strength of integrating metabolomics analysis in understanding host-pathogen interaction.…”

Anopheles gambiae: Metabolomic Profiles in Sugar-Fed, Blood-Fed, and Plasmodium falciparum-Infected Midgut

“…In mosquito studies, transcriptomic responses to blood feeding and malaria parasite infection have been well characterized [4][5][6][7][8][9]. However, comprehensive metabolomic data are limited for medically important arthropods, except for a few reports [10][11][12][13][14]. Metabolomics allows for the identification and quantification of a range of metabolites in a system [15].…”