Modeling Modulation of the Tick Regulome in Response to Anaplasma phagocytophilum for the Identification of New Control Targets

Abstract

Ticks act as vectors of pathogens affecting human and animal health worldwide, and recent research has focused on the characterization of tick-pathogen interactions using omics technologies to identify new targets for developing novel control interventions. The regulome (transcription factors-target genes interactions) plays a critical role in cell response to pathogen infection. Therefore, the application of regulomics to tick-pathogen interactions would advance our understanding of these molecular interactions and contribute to the identification of novel control targets for the prevention and control of tick infestations and tick-borne diseases. However, limited information is available on the role of tick regulome in response to pathogen infection. In this study, we applied complementary in silico approaches to modeling how Anaplasma phagocytophilum infection modulates tick vector regulome. This proof-of-concept research provided support for the use of network analysis in the study of regulome response to infection, resulting in new information on tick-pathogen interactions and potential targets for developing interventions for the control of tick infestations and pathogen transmission. Deciphering the precise nature of circuits that shape the tick regulome in response to pathogen infection is an area of research that in the future will advance our knowledge of tick-pathogen interactions, and the identification of new antigens for the control of tick infestations and pathogen infection/transmission.

Keywords: Anaplasma phagocytophilum; ISE6 cells; Ixodes scapularis; regulome; tick; transcription; vaccine.

FIGURE 1

Changes in the expression of TF in tick ISE6 cells, salivary gland and midgut in response to A. phagocytophilum infection. (A) The percentage change of the index Betweenness Centrality (BNC) infection among uninfected and infected target organs in the 13 BP GO annotations. (B) Values of BNC of the 8 TF that showed the highest changes between uninfected BNC (u) and infected BNC (i) ISE6 cells. (C) Values of BNC of the 21 TF that showed the highest changes between uninfected BNC (u) and infected BNC (i) salivary glands. (D) Values of BNC of the 17 TF that showed the highest changes between uninfected BNC (u) and infected BNC (i) midgut. Abbreviations: reg., regulation; (u), uninfected; (i), infected.

FIGURE 2

Co-correspondence analysis (CoCA) of TF and TG in uninfected and A. phagocytophilum-infected samples. CoCA was conducted in I. scapularis (A) ISE6 cells, (B) salivary glands and (C) midgut. The charts show the position of TF (black symbol and label) and TG (blue symbol and red label) after the CoCA of the indexes of centrality. The TF and associated TG with highest values of centrality in the network of infected cells appear together at negative values of the Axis 1 (n = 4, 4, and 9 in ISE6 cells, salivary glands and midgut, respectively). The TF and the associated TG with highest values of centrality in the network of uninfected cells appear together at positive values of the Axis 1 (n = 4, 17, and 8 in ISE6 cells, salivary glands and midgut, respectively). High-resolution images are shown in Supplementary Figures 2A–C.

FIGURE 3

Biological processes affected by the tick ISE6 cells regulome in response to A. phagocytophilum infection. (A) Upregulated and downregulated target genes in the in silico predicted tick ISE6 cells regulome in response to A. phagocytophilum infection were grouped according to their BP. The BP with higher representation in the upregulated than in downregulated regulome in response to infection (arrows) were selected for characterization of TF-TG interactions. (B) Predicted regulatory DNA motifs according to regulatory factors identified by RNAseq in infected cells only and involved in the control of upregulated target genes annotated in the peptidase inhibitor and stress response BP with higher representation in the upregulated than in downregulated regulome.

FIGURE 4

Functional characterization of selected TF-TG components of the tick ISE6 cells regulome. (A) The predictive results of network analysis and in silico prediction of TF-TG interactions were compared in infected ISE6 cells. The TF-TG interactions predicted by both methods (squared in black letter for TF and red letter for TG) were then functionally characterized by RNAi in tick ISE6 cells. (B) Percentage of TF gene knockdown with respect to Rs86 siRNA control in ISE6 cells. Normalized against tick rps4 Ct values were compared between test siRNAs-treated tick cells and controls treated with Rs86 siRNA by Chi²-test (p < 0.05; n = 4 biological replicates). (C) The TG mRNA levels were determined by qRT-PCR in ISE6 cells after TF gene knockdown or treatment with control Rs86 siRNA. Normalized against tick rps4 Ct values (average + S.E.) were compared between test siRNAs-treated tick cells and controls treated with Rs86 siRNA by Chi²-test (^∗p < 0.001; n = 4 biological replicates). (D) The A. phagocytophilum DNA levels were determined by qPCR in ISE6 cells after TF gene knockdown or treatment with control Rs86 siRNA. Normalized against tick rps4 Ct values (average + S.D.) were compared between test siRNAs-treated tick cells and controls treated with Rs86 siRNA by Chi²-test (^∗p < 0.001; n = 2–4 biological replicates).