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Bacterial reprogramming of tick metabolism impacts vector fitness and susceptibility to infection

Nature Microbiology volume 9, pages 2278–2291 (2024)Cite this article

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Abstract

Arthropod-borne pathogens are responsible for hundreds of millions of infections in humans each year. The blacklegged tick, Ixodes scapularis, is the predominant arthropod vector in the United States and is responsible for transmitting several human pathogens, including the Lyme disease spirochete Borrelia burgdorferi and the obligate intracellular rickettsial bacterium Anaplasma phagocytophilum, which causes human granulocytic anaplasmosis. However, tick metabolic response to microbes and whether metabolite allocation occurs upon infection remain unknown. Here we investigated metabolic reprogramming in the tick ectoparasite I. scapularis and determined that the rickettsial bacterium A. phagocytophilum and the spirochete B. burgdorferi induced glycolysis in tick cells. Surprisingly, the endosymbiont Rickettsia buchneri had a minimal effect on bioenergetics. An unbiased metabolomics approach following A. phagocytophilum infection of tick cells showed alterations in carbohydrate, lipid, nucleotide and protein metabolism, including elevated levels of the pleiotropic metabolite β-aminoisobutyric acid. We manipulated the expression of genes associated with β-aminoisobutyric acid metabolism in I. scapularis, resulting in feeding impairment, diminished survival and reduced bacterial acquisition post haematophagy. Collectively, we discovered that metabolic reprogramming affects interspecies relationships and fitness in the clinically relevant tick I. scapularis.

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Fig. 1: Alterations in glycolysis and OxPhos affects I. scapularis fitness.
Fig. 2: A. phagocytophilum and B. burgdorferi induce glycolysis upon infection of tick cells.
Fig. 3: A. phagocytophilum and B. burgdorferi enhance the glycolytic flux from glucose to lactate in tick cells.
Fig. 4: Global changes to tick cellular metabolism in response to A. phagocytophilum or R. buchneri infection.
Fig. 5: BAIBA metabolism affects tick fitness.
Fig. 6: BAIBA metabolism is involved in A. phagocytophilum infection in I. scapularis nymphs.

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Data availability

Metabolomics data are available at MetaboLights (identifier MTBLS686), a database from the European Bioinformatics Institute. Synthetic nucleotide information and reagents/resources are included in Supplementary Tables 1 and 2, respectively. Source data are provided with this paper.

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Acknowledgements

We thank J. Gillespie (University of Maryland, Baltimore) and T. Driscoll (West Virginia University) for sharing primer details to measure R. buchneri, E. E. McClure Carroll (University of Maryland School of Medicine) for schematics, M. Noto and A. Tyler (University of Maryland School of Medicine) for providing instrumentation, H. Hammond for administrative support, A. S. Oliva Chavez (University of Wisconsin) for technical insights, and the Biopolymer/Genomics core facility for Sanger sequencing and the Seahorse metabolic flux assay (S10OD025101). This work was supported by grants from the NIH to A.J.O’N. (F31AI152215); H.J.L.-Y. (T32AI162579); L.R.B. (F31AI167471); D.K.S. (R01AI162819); J.H.F.P. (R01AI134696, R01AI116523, R01AI049424); J.H.F.P., E.F. and U.P. (P01AI138949); and U.P. (R01AI080615). J.H.F.P. was also supported in kind by the Fairbairn Family Lyme Research Initiative. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, the Department of Health and Human Services or the US Government.

Author information

Author notes

  1. Anya J. O’Neal

    Present address: Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

  2. Nisha Singh

    Present address: Department of Biotechnology, School of Energy Technology, Pandit Deendayal Energy University; Knowledge Corridor, Gandhinagar, India

  3. Xiaowei Wang

    Present address: MP Biomedicals, Solon, OH, USA

  4. L. Rainer Butler

    Present address: Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA

  5. These authors contributed equally: Agustin Rolandelli, Anya J. O’Neal.

Authors and Affiliations

  1. Department of Microbiology and Immunology, School of Medicine, University of Maryland, Baltimore, MD, USA

    Sourabh Samaddar, Agustin Rolandelli, Anya J. O’Neal, Hanna J. Laukaitis-Yousey, Liron Marnin, Nisha Singh, Xiaowei Wang, L. Rainer Butler, Francy E. Cabrera Paz, Luisa Valencia, Camila R. Ferraz & Joao H. F. Pedra

  2. Department of Anesthesiology and Center for Shock, Trauma and Anesthesiology Research, School of Medicine, University of Maryland, Baltimore, MD, USA

    Parisa Rangghran, Gary M. Fiskum & Brian M. Polster

  3. Department of Veterinary Medicine, University of Maryland, College Park, MD, USA

    Chrysoula Kitsou & Utpal Pal

  4. Department of Entomology, University of Minnesota, Saint Paul, MN, USA

    Ulrike G. Munderloh & Benjamin Cull

  5. Division of Environmental Health Sciences, University of Minnesota, Minneapolis, MN, USA

    Benedict Khoo & Jonathan Oliver

  6. Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA, USA

    Kristin L. Rosche & Dana K. Shaw

  7. Department of Internal Medicine, Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT, USA

    Sukanya Narasimhan & Erol Fikrig

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Contributions

S.S., A.R., A.J.O'N. and J.H.F.P. conceptualized the study. S.S., A.R., A.J.O'N., H.J.L.-Y., L.M., N.S., X.W., B.K., B.C., K.L.R., J.O. and S.N. performed the experiments. P.R., C.K., F.E.C.P., L.V., L.R.B. and C.R.F. aided in experiments. E.F., U.G.M., U.P., G.M.F., D.K.S. and B.M.P. supervised experiments. S.S., A.R., A.J.O'N., H.J.L.-Y. and J.H.F.P. wrote the manuscript. All authors analysed the data, provided intellectual input into the study and contributed to editing of the manuscript. J.H.F.P. supervised and managed the entire project.

Corresponding author

Correspondence to Joao H. F. Pedra.

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Extended data

Extended Data Fig. 1 Drug susceptibility of tick cell lines.

Cell viability (%) of (a-e) I. scapularis (IDE12), (f-j) Amblyomma americanum (AAE2) and (k-o) Dermacentor andersoni (DAE100) cell lines treated with different inhibitor concentrations for 48 hours (n = 5 for each condition; n represents individual culture wells). Purple bars indicate non-significant, whereas red bars denote significant differences in cell viability compared to untreated control (grey). Data are representative of two independent experiments with mean ± SEM. Statistical significance was calculated by one-way ANOVA followed by Dunnett’s multiple comparison test. Significant p values (<0.05) are displayed in the figure.

Source data

Extended Data Fig. 2 Growth kinetics of A. phagocytophilum and R. buchneri in ISE6 cells under Seahorse conditions.

(a) Schematic of the infection assay. ISE6 cells were cultured in L15C300 complete medium. Upon infection with A. phagocytophilum or R. buchneri at multiplicity of infection (MOI) 50 or 100, the L15C300 medium was replaced with the modified L15C (mL15C) medium on day 0. Bacterial infection was assessed at 1, 24 and 48 hours post-infection by RT-qPCR. (b-c) A. phagocytophilum infection for (b) MOI 50 or (c) MOI 100, quantified by amplification of 16 s rRNA gene (n = 10 for each condition; n represents individual culture wells). (d-e) R. buchneri infection for (d) MOI 50 or (e) MOI 100, measured using the citrate synthase (gltA) gene (n = 10 for each condition; n represents individual culture wells). Data were normalized to 1 hr, and tick actin was used as a housekeeping gene. Data are representative of two independent experiments with mean ± SEM. Statistical significance was evaluated by (b, d-e) Kruskal Wallis followed by Dunn’s multiple comparison test or (c) one-way ANOVA followed by Dunnett’s multiple comparison test. Significant p values (<0.05) are displayed in the figure.

Source data

Extended Data Fig. 3 A. phagocytophilum infection in response to bioenergetic alterations.

(a) Schematic of microbial infection upon glycolysis or OxPhos inhibitor treatment in vitro. 1 × 106 ISE6 cells were treated with 50 mM 2-deoxy-D-glucose (2-DG), 0.1 µM rotenone (Rot), 0.5 µM antimycin (Anti), 0.5 µM oligomycin (Oligo) or 20 µM 2,4-dinitrophenol (2,4-DNP) for 1 hour prior to infection with A. phagocytophilum at MOI 50. Cells were collected 2 days post-infection for bacterial quantification. Infected cells without inhibitor treatment (-) served as a control. (b) A. phagocytophilum infection quantified by amplification of 16 S rRNA gene (n = 5 for each condition; n represents individual culture wells). (c) Experimental design of inhibitor treatment on A. phagocytophilum infection in vivo. Created with BioRender.com. (d-e) Ticks were injected with either PBS (-, grey) or 2-DG (purple) at indicated amounts prior to feeding on A. phagocytophilum-infected C57BL/6 mice. (d) Tick weight (n = 17, 29, 14 and 21) and (e) A. phagocytophilum infection (n = 7, 17, 7 and 12) were recorded. (f-g) Ticks were injected with either PBS (-, grey) or 0.8 pmol oligomycin (orange) prior to feeding on A. phagocytophilum-infected C57BL/6 mice. (f) Tick weight (n = 27 and 31) and (g) A. phagocytophilum infection (n = 10 and 12) were measured. (b, d-g) Data are representative of two independent experiments with mean ± SEM. Statistical significance was evaluated by (b, d-e) Kruskal–Wallis test followed by Dunn’s multiple comparisons test or (f-g) two-sided Mann-Whitney test. NS=not significant. Significant p values (<0.05) are displayed in the figure.

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Extended Data Fig. 4 R. buchneri infection in response to bioenergetic alterations.

(a) Schematic of microbial infection upon glycolysis or OxPhos inhibitor treatment. 1 × 106 ISE6 cells were treated with 50 mM 2-deoxy D-glucose (2-DG), 0.1 µM rotenone (Rot), 0.5 µM antimycin (Anti), 0.5 µM oligomycin (Oligo) or 20 µM 2,4-dinitrophenol (2,4-DNP) for 1 hour prior to infection with R. buchneri at MOI 50. Cells were collected 2 days post-infection for bacterial quantification via RT-qPCR. Infected cells without inhibitor treatment (-) served as controls. (b) R. buchneri infection was measured using the citrate synthase (gltA) gene by RT-qPCR (n = 6 for each condition, n represents individual culture wells). Data are representative of two independent experiments with mean ± SEM. Statistical significance was evaluated by Kruskal Wallis test followed by Dunn’s multiple comparisons test. Significant p values (<0.05) are displayed in the figure.

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Extended Data Fig. 5 Attachment of ticks silenced for genes associated with the D-BAIBA metabolism.

I. scapularis nymphs were injected with siRNA or scrambled control for beta-ureidopropionase 1 (upb1) or alanine-glyoxylate aminotransferase 2 (agxt2) and allowed to feed on uninfected C57BL/6 mice for 3 days. The attachment of silenced ticks compared to scrambled controls was recorded for (a) upb1 (n = 69 and 83) or (b) agxt2 (n = 76 and 110). Data are representative of two independent experiments where n = an individual tick. Statistical significance was evaluated by the Fisher’s exact test. NS=not significant. Significant p values (<0.05) are displayed in the figure.

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Extended Data Fig. 6 Effect of abat silencing on tick fitness.

(a-d) I. scapularis nymphs were injected with siRNA (blue) or scrambled control (grey) for 4-aminobutyrate aminotransferase (abat) and allowed to feed on uninfected C57BL/6 mice for 3 days. Fitness parameters were recorded. (a) Silencing efficiency (n = 11 and 10); (b) tick attachment (n = 76 and 81); (c) weight (n = 37 and 34); and (d) survival (n = 30 and 31). (a-d) Data are representative of two independent experiments with mean ± SEM where n = an individual tick. Statistical significance was evaluated by (a, c) two-sided t-test with Welch’s correction for unequal variances (b) Fisher’s exact test (d) Log-rank (Mantel-Cox). NS = not significant. Significant p values (<0.05) are displayed in the figure.

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Extended Data Fig. 7 Fitness parameters in ticks silenced for genes related to D-BAIBA metabolism during A. phagocytophilum acquisition.

(a-d) Nymphs were injected with siRNA or scrambled control for genes involved in D-BAIBA metabolism and allowed to feed on A. phagocytophilum-infected mice for 3 days. (a-b) Effect of beta-ureidopropionase 1 (upb1) silencing on (a) attachment (n = 100 and 150) and (b) weight (n = 33 and 22) of fed ticks compared to the scrambled control treatment. (c-d) Effect of alanine-glyoxylate aminotransferase 2 (agxt2) silencing on (c) attachment (n = 146 and 100) and (d) weight (n = 38 and 35) of fed ticks compared to the scrambled control treatment. (a-d) Data are representative of two independent experiments with mean ± SEM where n= an individual tick. Statistical significance was evaluated by (a, c) Fisher’s exact test or (b, d) two-sided unpaired t-test with Welch’s correction. NS=not significant. Significant p values (<0.05) are displayed in the figure.

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Extended Data Fig. 8 Fitness in ticks microinjected with BAIBA and chronically infected with A. phagocytophilum.

Uninfected (Un) or chronically infected (In) A. phagocytophium nymphs were injected with 40 pmol of β-aminoisobutyric acid (BAIBA) or isomer and allowed to feed on C57BL/6 mice for 3 days. (a) Tick attachment to the mammalian host (n = 93,108, 102 and 97); (b) tick weight (n = 62, 64, 45 and 38) and (c) A. phagocytophilum quantification by amplification of the 16 S rRNA gene (n = 12 and 11). (a-c) Data are representative of two independent experiments with mean ± SEM where n = an individual tick. Statistical significance was evaluated by (a) Fisher’s exact test, (b) two-sided Mann-Whitney test or (c) two-sided unpaired t-test with Welch’s correction. NS = not significant. Significant p values (<0.05) are displayed in the figure.

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Extended Data Fig. 9 Contribution of BAIBA to A. phagocytophilum infection in ticks.

Proposed crosstalk between the infection cycle of A. phagocytophilum (brown) and I. scapularis metabolic pathways. Infection induces an expensive host process, and energy is partially supplied by rapid ATP generation through increased glycolysis and conversion of pyruvate to lactate (carbohydrate metabolism, green). Infection with A. phagocytophilum increases fatty acid, cholesterol, and phospholipids levels in the host cell, which are utilized for membrane restructuring and inclusion membrane synthesis during bacterium replication (lipid metabolism, yellow). A. phagocytophilum enhances proteolysis in the host cell and acquires amino acids through ER-dependent trans-Golgi network (TGN) vesicles, scavenging essential amino acids that the bacterium uses for synthesizing its own virulence factors and structural proteins (protein metabolism, blue). A. phagocytophilum also acquires nucleotides and their derivatives from the host to synthesize DNA and RNA for replication (nucleotide metabolism, purple). Finally, A. phagocytophilum synthesizes vitamins and cofactors during the infection cycle which serves as a supplementary source of nutrients for the host (vitamins and cofactors, orange). β-aminoisobutyric acid (BAIBA) acts as a key metabolite, playing pleiotropic roles by connecting the four major metabolic pathways altered by A. phagocytophilum infection: nucleotide, protein, lipid and carbohydrate pathways. Fine-tuned regulation of BAIBA metabolism is required to balance infection and fitness costs in I. scapularis. Figure created with BioRender.com.

Extended Data Table 1 Composition of modified tick cell medium for the Seahorse assay
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Supplementary Table 1 Primers and siRNA sequences. Supplementary Table 2 Resources and reagents.

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Samaddar, S., Rolandelli, A., O’Neal, A.J. et al. Bacterial reprogramming of tick metabolism impacts vector fitness and susceptibility to infection. Nat Microbiol 9, 2278–2291 (2024). https://doi.org/10.1038/s41564-024-01756-0

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