Ecoclimate drivers shape virome diversity in a globally invasive tick species

Abstract

Spillovers of viruses from animals to humans occur more frequently under warmer conditions, particularly arboviruses. The invasive tick species Haemaphysalis longicornis, the Asian longhorned tick, poses a significant public health threat due to its global expansion and its potential to carry a wide range of pathogens. We analyzed meta-transcriptomic data from 3595 adult H. longicornis ticks collected between 2016 and 2019 in 22 provinces across China encompassing diverse ecological conditions. Generalized additive modeling revealed that climate factors exerted a stronger influence on the virome of H. longicornis than other ecological factors, such as ecotypes, distance to coastline, animal host, tick gender, and antiviral immunity. To understand how climate changes drive the tick virome, we performed a mechanistic investigation using causality inference with emphasis on the significance of this process for public health. Our findings demonstrated that higher temperatures and lower relative humidity/precipitation contribute to variations in animal host diversity, leading to increased diversity of the tick virome, particularly the evenness of vertebrate-associated viruses. These findings may explain the evolution of tick-borne viruses into generalists across multiple hosts, thereby increasing the probability of spillover events involving tick-borne pathogens. Deep learning projections have indicated that the diversity of the H. longicornis virome is expected to increase in 81.9% of regions under the SSP8.5 scenario from 2019 to 2030. Extension of surveillance should be implemented to avert the spread of tick-borne diseases.

Keywords: climate changes; deep learning network; ecological modeling; meta-transcriptomics; tick virome; tick-borne viruses.

Figure 1

Vertebrate-associated virome of Haemaphysalis longicornis in different ecotypes. (A) Mean relative abundance of each vertebrate-associated viral family in different ecotypes. (B) Mean relative abundance of each vertebrate-associated viral species in different ecotypes. Human pathogenic viruses are indicated by asterisk. The mean relative abundance has been normalized by reads per million (viral reads number × 10⁶/total reads number) and taken to Log10 for better visualization. Yellow color represents higher mean viral abundance, whereas green represents lower abundance. (C) Diversity of vertebrate-associated viromes (Shannon index) in different ecotypes. ^***P < .001; ^**P < .01; ^*P < .05; ns indicates P > .05. (D) Prevalence of each human pathogenic virus in different ecotypes. Severe fever with thrombocytopenia syndrome virus (SFTSV), Songling virus (SLV), Tacheng tick virus (TGTV), Jingmen tick virus (JMTV), Beiji nairovirus (BJNV), and Nairobi sheep disease virus (NSDV).

Figure 2

(A) Geographic map of sampling sites and the diversity of the vertebrate-associated virome (Shannon index) at that site.-(B) Vertebrate-associated virome diversity (Shannon index) grouped by geographic closeness to the coastline. ^***P < .001; ^**P < .01; ^*P < .05; ns P > .05.

Figure 3

(A) Explained variation of ecoclimate factors by GAM under six index measurements of virome diversity. All ecoclimate factors were included to fit a joint model for six virome diversity index values, successively. (B) Proportions of the 10 virus species with the largest abundance in different virome diversity groupings (by Shannon index). Pathogenic virus is indicated by asterisk. ^***P < .001; ^**P < .01; ^*P < .05.

Figure 4

The effects of ecoclimate factors that explain the vertebrate-associated virome diversity and viral abundance of human pathogenic viruses under the GAM. (A) Explained variation of vertebrate-associated virome diversity and human pathogenic viral abundance for all ecoclimate factors. Each ecoclimate factor was individually fitted to the model for vertebrate-associated virome diversity or pathogenic viral abundance. (B-G) Partial effect plots showing the relative effect of each variable for vertebrate-associated virome diversity (Shannon index). All ecoclimate factors were included to fit the best-fit GAM model. Shaded area is the 95% confidence interval of the mean partial effect. (H) Actual Shannon index and predicted Shannon index using the GAM model. (I) Actual Shannon index and predicted Shannon index using the deep learning model.

Figure 5

The causality mechanism of ecoclimate factors that have an impact on the vertebrate-associated virome diversity. (A) Causality effect of ecoclimatic factors on vertebrate-associated virome diversity. The number on the arrow indicates the power of the causality effect of humidity and minimum temperature on mammals, and mammals on the tick virome. Causality power for 90% subset dataset: 95% CI: [0.50, 0.59] for humidity; [1.00, 1.06] for minimum temperature; [3.06, 3.16] for mammals. (B) Comparing virome diversity in regions with varying tick-borne pathogenic virus occurrences (multiple occurrences, single occurrence, and non–tick-borne pathogenic virus) within a 50-, 100-, and 150-km radius.

Figure 6

Global prediction of vertebrate-associated virome diversity under SSP4.5 in 2019.