Temperature variability increases Trypanosoma cruzi load but not the extrinsic incubation period in Triatoma infestans
BárbaraÁlvarez-Duhart1,2Emailbalvarezd@docente.uss.cl
SabrinaClavijo-Baquet3,4✉Emailsclavijo@bio.puc.clEmailsabrinaclavijo@fcien.edu.uy
LucíaValenzuela-Perez5Emaillucia.valenzuela.perez@gmail.com
JuanDiegoMaya6Emailjdmaya@uchile.cl
MiguelSaavedra1Emailmiguel.saavedra@uss.cl
SofíaOrtiz2Emailsofia.ortiz@ug.uchile.cl
CatalinaMuñoz-San1Emailcatalina.munoz@uss.cl
Martín1
AntonellaBacigalupo7Emailanto.e.bacigalupo@gmail.com
A
PedroE.Cattan2✉1Facultad de Medicina VeterinariaUniversidad San SebastiánCampus BellavistaSantiagoChile
2Departamento de Ciencias Biológicas Animales, Facultad de Ciencias Veterinarias y PecuariasUniversidad de ChileSanta Rosa 11725, La PintanaSantiagoChile
3Sección Etología, Facultad de CienciasUniversidad de la RepúblicaMontevideoUruguay
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Departamento de Ecología, Facultad de Ciencias BiológicasPontificia Universidad Católica de Chile5Laboratorio de Biología Celular y Molecular, ICBM. Facultad de MedicinaUniversidad de ChileSantiagoChile
6Instituto de Ciencias Biomédicas Facultad de MedicinaICBM, Universidad de Chile
7School of Biodiversity, One Health and Veterinary MedicineUniversity of GlasgowGlasgowScotland, United Kingdom
Bárbara Álvarez-Duhart1,2, Sabrina Clavijo-Baquet3,4*, Lucía Valenzuela-Perez5, Juan Diego Maya6, Miguel Saavedra1, Sofía Ortiz2, Catalina Muñoz-San Martín1, Antonella Bacigalupo7 and Pedro Cattan2*
1 Facultad de Medicina Veterinaria, Universidad San Sebastián,Campus Bellavista, Santiago, Chile.
2 Departamento de Ciencias Biológicas Animales, Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santa Rosa 11725, La Pintana, Santiago, Chile. Postal code: 8820808.
3 Sección Etología, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay.
4 Departamento de Ecología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile
5 Laboratorio de Biología Celular y Molecular, ICBM. Facultad de Medicina, Universidad de Chile, Santiago, Chile.
6 ICBM. Instituto de Ciencias Biomédicas Facultad de Medicina - Universidad de Chile
7School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, Scotland, United Kingdom. CP G12 8QQ.
*Correspondence: sabrinaclavijo@fcien.edu.uy; sclavijo@bio.puc.cl ORCID ID: 0000-0002-9784-9144 and pcattan@uchile.cl ORCID ID: 0000-0002-7830-654X
Sabrina Clavijo-Baquet and Pedro E. Cattan should be considered joint corresponding authors.
BA-D: balvarezd@docente.uss.cl, balvarezd@docente.uss.cl; ORCID ID: 0000-0001-5039-9468
LV-P: lucia.valenzuela.perez@gmail.com; ORCID ID: 0000-0002-5145-303X
JDM: jdmaya@uchile.cl; ORCID ID: 0000-0003-3934-6319
MS: miguel.saavedra@uss.cl; ORCID ID: 0000-0002-3982-9153
SO: sofia.ortiz@ug.uchile.cl; ORCID ID: 0009-0002-2687-3128
CM-S: catalina.munoz@uss.cl; ORCID ID: 0000-0001-8087-512X
AB: anto.e.bacigalupo@gmail.com; ORCID ID: 0000-0003-1661-8916
Abstract and keywords
Background
Trypanosoma cruzi, the etiologic agent of Chagas disease, is transmitted via the dejections of triatomine insects such as Triatoma infestans. The extrinsic incubation period (EIP), parasite load, and infectivity of the vector are sensitive to environmental temperatures. Global warming is expected to increase both mean temperatures and their variability, potentially altering vector competence.
Methods
We experimentally infected T. infestans with the T. cruzi Dm28c strain and exposed them to four temperature regimes: two constant (18°C and 27°C) and two variables (18 ± 5°C and 27 ± 5°C). Over 42 days, we collected dejection samples for parasite quantification via qPCR and recorded the time of the first positive detection to estimate the EIP. Dejection samples were collected every two days throughout the study to quantify the parasite load using qPCR, enabling us to assess infection dynamics over time.
Results
Higher temperatures significantly shortened the EIP and increased the overall parasite load. However, temperature variability alone did not significantly alter the EIP. In contrast, variability increased the peak parasite load in the cold treatments without affecting the probability of positive dejections. The parasite load exhibited a bell-shaped curve over time, peaking earlier and higher under warmer conditions. A larger volume of ingested blood also reduced the EIP, especially under cold treatments.
Conclusions
Temperature increases accelerate T. cruzi development in T. infestans, potentially enhancing vector competence under climate change scenarios. Although variability in temperature did not affect EIP, it did influence parasite load, suggesting that both mean temperature and variability must be considered to understand the impact of climate change on Chagas disease transmission.
Keywords:
Climate change
infectious
Chagas disease
Vector-borne diseases
A
Background
The relationship between climate change and infectious diseases is a pressing ecological [1–3] and global health concern [4]. Vector-borne diseases are especially susceptible to temperature because their transmission rate and incidence are related to the biological traits of vectors, which are ectothermic arthropods [3]. Climate change is expected to affect multiple vector-borne diseases [5], such as dengue fever [6], chikungunya virus [7], Zika virus [8], West Nile virus [9], Bluetongue virus [10], leishmaniasis [11] malaria [12], and Chagas disease [1]. The effect of temperature on biting rate, reproduction, development, survival, and probability of becoming infectious after biting an infectious host (i.e., vector competence) has been reported for several disease vectors [13–15]. In summary, temperature influences pathogen transmission by promoting its transmission at optimal constant temperatures and stopping it beyond the lower and upper thermal activity limits [3, 16].
Climate change projections include an increase in the average environmental temperature in the long term, as well as more frequent periods of temperature variability, such as daily, monthly, or seasonal variation [17]. The survival, reproduction, and thermal performance of ectotherms are affected by temperature variability in a nonlinear manner, which can positively affect individuals who are below their thermal optimum and negatively affect individuals when they are above their optimum temperature [18] [19, 20] Therefore, it is difficult to elucidate how global warming change will modify the distribution and incidence of vector-borne diseases [3, 21]. Thus, to generate accurate predictions, the precise effects of temperature and mechanisms underlying these effects must be fully understood [8].
The extrinsic incubation period (EIP, hereafter) is the time required for a pathogen or parasite to develop from its entry into the vector until the first forms capable of infecting another host appear [22]. EIP influences the incidence of vector-borne diseases [23–25] as a lower EIP generally indicates a faster capacity to transmit the pathogen, which leads to an increase in the incidence of the disease. This parameter determines the basic reproductive number R0 for vector-borne diseases [26]. The EIP is also included in the vectorial capacity equation, which is a measure of the transmission potential of a vector-borne pathogen within a susceptible population [24, 25, 27]. Therefore, even small changes in the EIP can have a large impact on the results of mechanistic models based on these equations [28–30]. The practical relevance of this problem has been demonstrated for dengue [25], malaria [31], and bluetongue [32] and the EIP of various species of parasites and viruses has been proven to be susceptible to temperature and its variability [23, 33, 34].
Chagas disease is a neglected tropical disease caused by the flagellate protozoan Trypanosoma cruzi, with 6 million people estimated to be infected worldwide, predominantly in areas endemic to Latin America [35, 36]. The primary mode of transmission for this protozoan involves contact with contaminated feces or urine from hematophagous insect vectors belonging to the subfamily Triatominae (Hemiptera: Reduviidae) with mucous membranes or wounds in mammals [37, 38]. Additionally, transmission can occur through other routes such as blood transfusions, organ transplants, congenital means, laboratory accidents, and orally in natural reservoirs [39]. Recent human epidemiological outbreaks have highlighted oral transmission as a significant route of T. cruzi transmission [40–42]. Triatoma infestans is the most important vector in southern South America [43]. The wild foci of T. infestans have been documented in Bolivia [44], Argentina [45], Paraguay [46], and Chile [47]. The presence of wild foci hinders disease control initiatives because these foci play a crucial role in the recolonization of peridomestic and domestic habitats [47–49]. In addition, the projected rise in temperature due to climate change could potentially exacerbate the source of individuals from wild foci, as it may lead to an increase in their activity or abundance. Winter temperature has been proposed as the most limiting factor in terms of population growth [50–52].
T. infestans inhabits peridomestic habitats that can buffer temperature fluctuations, and its thermal preference lies between 25°C and 29°C [53], which is easily reached inside houses, but its activity ranges between 18°C and 42°C [54]. Currently, the minimum mean winter temperature in the central region of Chile, where wild T. infestans foci have been reported, is 10.7°C but the minimum mean temperature is 4°C in the coldest month, below the T. infestans activity range [2]. However, climate change projections estimate a temperature increase ranging from 2°C to 5°C in the worst-case scenario [2]. Considering the buffering effect of peridomestic structures and houses (i.e., up to ± 5°C to ± 8°C), the minimum temperature from the coldest month could fall within the range of T. infestans activity as a consequence of climate change. Since increases in mean temperature due to climate change are expected in southern South America, these shifts in temperatures could potentially expand habitat suitability for T. infestans beyond current expectations. However, it is crucial to consider the complex interplay between the factors that influence the distribution and presence of T. infestans [55].
Temperature is a major abiotic factor in T. cruzi-triatomine interactions [56, 57]. The development of T. cruzi and its relationship with temperature have been studied under field and laboratory conditions [57–59], revealing a positive relationship between environmental temperature and the concentration of infectious forms of Triatoma proctata [60], T. infestans [57], and Rhodnius prolixus [58]. An increase in temperature reduces the incubation time of parasites within the vector [57]. Field studies in Northwestern Argentina have recorded higher numbers of infectious forms of T. infestans during warm months [59]. However, the effect of temperature variability on T. infestans parasite development has never been tested under controlled conditions, despite its relevance in the context of climate change. Therefore, in this study, we aimed to evaluate the influence of temperature and its variability on the EIP and parasite load of T. cruzi in T. infestans and the probability of T. cruzi-positive dejections samples over time.
Methods
A
A total of 144 fourth-instar T. infestans nymphs were obtained from a colony that had been maintained for two years at the Laboratorio de Ecología, Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile. This colony was originally established in 1950 at the Faculty of Medicine at the same university. The fourth instar is easily manageable and can transmit T. cruzi to vertebrate hosts [61]. The specimens were maintained individually in plastic flasks (3.8 cm x 6.8 cm) inside climatic chambers at 27°C with a photoperiod of 12 L:12D and 50% to 70% humidity. Prior to feeding on infected mice, the triatomines were fasted for 15 days.Triatomine infection with Trypanosoma cruzi
A
Triatomines were infected by feeding on mice previously inoculated with T. cruzi clone Dm28c (DTU-TcI) under controlled laboratory conditions. The Trypomastigote forms of T. cruzi were cultivated in Vero cells (60–70% confluence), 5 ml of RPMI 1640 culture medium (Biological Industries) with 10% fetal bovine serum, 2 ml of trypsin, 1 ml of penicillin, and 100 µg/mL of streptomycin (Biological Industries), following the protocol from Valenzuela et al. [62]. Fourteen 8-week-old female BALB/c mice were inoculated only once by intraperitoneal injection with 1,000 parasites suspended in 1 mL of RPMI 1640 medium, and the parasite concentration was determined by light microscopy using a Neubauer chamber. After inoculation, mice were housed in cages at 20–25°C and 40–70% relative humidity, with free access to water and food, following the approved bioethics protocol (Nº: 19262-MED-UCH). Mouse parasitemia was evaluated, 0.1 ml blood sample was extracted from the tail vein to measure parasite concentration and viability using light microscopy (40X) with a Neubauer chamber every 3 days from day 7 post-infection until reaching 80,000-500,000 trypomastigotes/ml to ensure triatomine infection. Once reached, the insects were fed on these T. cruzi inoculated mice. Mice were sedated following the bioethics protocol (Nº: 19262-MED-UCH) with a dose of ketamine 100 mg/ml (mouse dose 100 mg/kg) in association with xylazine 5 mg/ml (mouse dose 10 mg/kg) and then placed inside a plastic box (32 × 21 × 14 cm) over a heating pad. Triatomines were fed during the dark phase and placed in the same plastic box as the mouse for 30 minutes in the dark. Each triatomine was weighed before and immediately after feeding using a BOECO Model BAS-31 (precision = 0.0001 g) to check for blood ingestion during the experimental procedure. All individuals were included in the treatments regardless of the volume of blood ingested. Each mouse was fed with a group of 12 T. infestans individuals. Insects were individualized with colored paint according to the protocol [63].Temperature treatments
After feeding on infected mice, all triatomines were randomly allocated to one of the four thermal treatments (i.e., 18 ± 0°C (n = 36), 18 ± 5°C (n = 36), 27 ± 0°C (n = 36), and 27 ± 5°C (n = 36)). The total maintenance time under these treatments was 42 days post infection (p. i.). After feeding on infected mice, triatomines were randomly assigned to one of four thermal treatments: 18 ± 0°C (n = 36), 18 ± 5°C (n = 36), 27 ± 0°C (n = 36), and 27 ± 5°C (n = 36). Each treatment group comprised 36 individuals, with each insect housed separately in a plastic vial (3.8 × 6.8 cm). Environmental chambers were set to the target temperatures with an accuracy of ± 1°C and a precision of 0.2°C (PITEC), under identical photoperiod and humidity conditions, as previously described. Each vial was labeled with a unique identification code indicating the individual insect, its assigned thermal treatment, and source mouse. The individual identification system was maintained throughout the experiment. Temperature treatments were chosen based on the average temperature of the central zone of Chile [2], where populations of the species have historically been detected, both in domestic and wild environments [47, 48], and the temperature of the insect microsite habitat [64]. Therefore, low winter temperatures have been proposed as the most limiting factor in T. infestans population growth [50, 51, 65]. The lowest acclimation temperature considered the mean winter temperature (10.7°C), plus a 5°C of microhabitat buffer, and an additional 2°C reflecting projected temperature increases under climate change. Individuals were acclimated to these low temperatures with and without the inclusion of temperature variability (i.e., 18°C ± 5°C and 18°C ± 0°C, respectively). We also acclimated individuals to their optimal temperature, with and without temperature variability (i.e., 27°C ± 5°C and 27°C ± 0°C, respectively), which is the inside-house temperature [54]. In the variable temperature treatments, the rise in temperature occurred during the light hours and started to increase linearly at 7:00 h, reaching a maximum at 8:00 h (32°C and 23°C for warm and cold treatments, respectively), then remained constant, and began to decrease at 19:00 h, reaching its minimum at 20:00 h (22°C and 13°C for warm and cold treatments, respectively). Additionally, the range of temperatures used in our experiments was well within the thermotolerance range of the species, avoiding individual mortality [18]. The diurnal variation range selected was conservative with respect to the diurnal daily variation of Chile's Central Region [66] but was chosen because the animals inside the climatic chambers cannot use behavioral thermoregulation. In addition, these temperatures were previously used what will allow to integrate information for future studies on the effects of climate change on T. infestans [18, 67]. Temperature and humidity were monitored daily using climatic chamber sensors. The photoperiod lights were switched on at 7:00 and turned off at 19:00.
Dejection sampling
Every two days, each individual was transferred to a new, dated flask labeled with its corresponding identification code. Simultaneously, the nymphal state was recorded for all individuals in the experiment. Excretion samples were placed in Eppendorf tubes, labeled, and recorded according to the identification code, date of collection, and number of fecal samples collected in the flask. Each fecal sample was diluted with nuclease-free water (40 µL per excretion). The contents of the solution were subsequently transferred to 1.5 ml Eppendorf tubes labeled with the insect ID and collection date. These tubes were then stored at -20°C until DNA extraction. Kollien & Shaub [68] observed that a short-term starvation period of 30 days led to the initial appearance of deceased flagellates in the rectum of Triatomines, whereas a starvation period of 90 days resulted in a mortality rate of 99.5% of T. cruzi in the rectum. Thus, triatomines were fed on non-infected laboratory mice (Mus musculus) at day 30 post-infection using the same protocol used for triatomine infection. We measured the weight before and after feeding, and the nymphal state. After the experimental period (42 days), the insects were euthanized by freezing for a minimum of 48 h following the bioethical protocol (Nº 19262-MED-UCH).
Trypanosoma cruzi detection in dejections
We extracted T. cruzi DNA from T. infestans dejections using the innuPREP Blood DNA Mini Kit (Analityk Jena AG), following the manufacturer’s instructions. All samples were co-extracted with 1 pg/µL of a 183 bp synthetic sequence from Arabidopsis thaliana (Brassicales: Brassicaceae), synthesized by gBlocks Gene Fragments (IDT), and used as an exogenous internal amplification control to evaluate the carryover of inhibitors and DNA loss in the extraction process.
The qPCR assays amplified the satellite nuclear conserved region of T. cruzi with the primers Cruzi 1 (5 'ASTCGGCTGATCGTTTTCGA 3') and Cruzi 2 (5 'AATTCCTCCAAGCAGCGGATA 3') [69] in a final volume of 20 µL, containing 5 µL of template DNA, 5X HOT FIREPol EvaGreen qPCR Mix Plus (Solis BioDyne), 0.3 µM of each primer, and nuclease-free water. The cycling conditions were a pre-incubation for 15 min at 95°C, followed by 40 cycles of denaturation step of 95°C for 15 s, a hybridization step of 60°C for 20 s, and an extension step of 72°C for 20 s in a Rotor-Gene Q (QIAGEN) thermal cycler. The emitted fluorescence was recorded at the end of each cycle, and a melting curve was generated at the end of the program, with a ramp from 72 ° C to 95 °, increasing by 1°C in each step, waiting for 90 s of pre-melting conditioning in the first step, and 5 s for each subsequent step. Each reaction was carried out with a negative control (infection-free triatomine dejection’s DNA), a positive control (T. cruzi DNA quantification standard), and a no-template control (nuclease-free water). All samples were analyzed in duplicate.
The DNA standard curve for absolute quantification was obtained from the genomic DNA of T. cruzi strain DM28c. The calculation was performed considering that a parasite cell contains approximately 200 fg of DNA [70, 71]. Serial 1/10 dilutions were made with nuclease-free water to cover a range of 106 to 0.1 parasite equivalents/mL. All samples were co-extracted with 1 pg/µl of a sequence of 183 bp from the tonoplast intrinsic protein 5;1 (TIP 5;1) of A. thaliana to normalize the parasite load, as previously described [72]. Quantification of the parasite equivalents from the DNA samples was performed by considering the amplification of the T. cruzi DNA standard curve, and the results were normalized according to the results of the exogenous IAC.
Statistical analysis
Extrinsic incubation period (EIP)
Only the first positive dejection sample for each individual was used to estimate the effect of temperature on the EIP of T. cruzi in T. infestans (total n = 108; 18 ± 0°C n = 23; 18 ± 5°C n = 28; 27 ± 0°C n = 29; 27 ± 5°C n = 28). Generalized Linear Models (GLM) with a gamma function were fitted with EIP as a response variable and temperature treatment (T), individual body mass before infection (mb), ingested blood during infection (bi), estimated by subtracting the weight before and after feeding, and mouse parasitemia (Pm) as predictors. Model adequacy was evaluated using the likelihood ratio test (LRT) [73], while model selection was conducted based on the Akaike information criterion (AIC) [74]. To compare the treatment groups, post hoc analyses were performed using Tukey’s test with Shaffer-adjusted p-values.
Parasite load in T. infestans dejections
All positive dejection samples were included in this analysis (n = 307), with a total of 50 samples for 18 ± 0°C treatment, 74 for 18 ± 5°C, 97 for 27 ± 0°C, and 86 for the 27 ± 5°C treatment. Generalized Additive Models (GAM) with a lognormal distribution were fitted for the parasite load (Par-eqml) as the response variable. In these models, the temperature treatment (T), body mass before infection (mb), ingested blood during infection (bi), time in days of the collected sample post-infection (Days), and mouse parasitemia (Pm) were predictors. Cubic splines were used to capture the nonlinear relationships between each of the following variables and parasite load over time: temperature treatment (T), ingested blood (bi), and body mass before infection (mb). We also included a random effect for triatomine individuals (ID) to correct for the pseudo-replication of data. The model was selected according to the Akaike information criterion (AIC) [74].
Probability of positive dejections
We analyzed the qPCR test results from all dejection samples (n = 438) to determine their positive or negative T. cruzi qPCR test results, generating a binary variable. In this analysis, all insects that fed on infected blood were included regardless of their infection status. To test the probability of positive dejections over time according to the different temperature treatments of individuals, we performed a Generalized Additive Model for Location, Scale, and Shape (GAMLSS) method for statistical analyses [75, 76]. This method does not require a priori assumptions regarding the shape of the relationship between the response variable and time (i.e., the probability of positive dejections in time), fitting a cubic spline (cs). Thus, the qPCR test results were the response variable, while temperature treatment (T), mouse parasitemia (Pm), body mass (mb), ingested blood (bi), a cubic spline of the time (Days), and their interactions were the explanatory variables. We also included a random effect for individuals (ID) to correct the pseudo-replication of the data given by the effect of repeated measurements. In GAMLSS, the functions of the variables are unknown smooth functions, providing additional flexibility in the modeling process for a binary response variable. We performed models assuming a binomial distribution with ‘RS’ algorithm [76]. All analyses were performed in R version 2.13.0 [77] using RSTUDIO and ggplot2, lmtest, multcomp, mgcv, pROC, and gamlss packages.
Results
A total of 144 individuals were included in the study: 108 were positive and 36 negative, resulting in 75% positive individuals and 25% negative individuals. A total of 499 dejections were obtained, of which 326 were positive and 173 were negative.
Extrinsic incubation period (EIP)
A generalized linear model (GLM) with gamma distribution and inverse link function was used to evaluate the effects of thermal treatment (T), ingested blood during infection (bi), body mass before infection (mb), and their interaction on the extrinsic incubation period (EIP) of Trypanosoma cruzi in Triatoma infestans. The analysis included only individuals who tested positive at least once during the 42-day post-infection period (n = 108). The model revealed that the thermal treatment significantly affected the EIP (Table 1 and Fig. 1). Compared with the reference group (18 ± 0 ºC), both the warm constant (27 ± 0 ºC) and warm variable (27 ± 5°C) treatments were associated with significantly shorter EIP values (P < 0.005 for both). The generalized linear model showed a substantial improvement in fit compared to the null model (null deviance = 39.26, df = 107; residual deviance = 22.39, df = 101; AIC = 698.34), indicating that the selected predictors significantly contributed to explaining the variation in the extrinsic incubation period of T. cruzi in T. infestans.
Estimate | Size effect (days) | Standard Error | t value | p value | ||
|---|---|---|---|---|---|---|
Intercept (18 ± 0 ºC) | -0.007 | 30 | 0.012 | -0.534 | 0.594 | |
18 ± 5 ºC | 0.004 | 27 | 0.007 | 0.632 | 0.529 | |
27 ± 0 ºC | 0.050 | 12 | 0.011 | 4.525 | < 0.05 | |
27 ± 5 ºC | 0.037 | 14 | 0.010 | 3.819 | < 0.05 | |
bi | 0.786 | 30 | 0.189 | 4.157 | < 0.05 | |
mb | 0.975 | 1.28 | 0.294 | 3.320 | < 0.05 | |
bi * mb | -12.918 | - | 4.044 | -3.195 | < 0.05 | |
| The groups were defined as follows: 18 ± 0°C, cold constant treatment; 18 ± 5ºC: cold variable treatment; 27 ± 0°C, warm constant treatment; 27 ± 5ºC: warm variable treatment. Abbreviations are as follows: bi, ingested blood; mb, body mass before infection. The size effect was calculated as 1 / (intercept estimate + estimate of the category). The volume of blood ingested decreased EIP in all treatments. | ||||||
Fig. 1
Boxplot of extrinsic incubation period (EIP measured as Days p.i) of Trypanosoma cruzi on Triatoma infestans exposed to four temperature treatments 18 ± 0 ºC (n = 23); 18 ± 5 ºC (n = 28); 27 ± 0 ºC (n = 29); 27 ± 5 ºC (n = 28). The letters show the results from the posteriori test (Tukey’s test). The boxplot shows no significant differences between the groups at 18 ± 0 ºC and 18 ± 5 ºC, indicated by the same letter 'a' on top, and between the groups 27 ± 0 ºC and 27 ± 5 ºC, indicated by the same letter 'b' on top.
Estimate | Size effect (days) | Standard Error | t value | p value | ||
|---|---|---|---|---|---|---|
Intercept (18 ± 0 ºC) | -0.007 | 30 | 0.012 | -0.534 | 0.594 | |
18 ± 5 ºC | 0.004 | 27 | 0.007 | 0.632 | 0.529 | |
27 ± 0 ºC | 0.050 | 12 | 0.011 | 4.525 | < 0.05 | |
27 ± 5 ºC | 0.037 | 14 | 0.010 | 3.819 | < 0.05 | |
bi | 0.786 | 30 | 0.189 | 4.157 | < 0.05 | |
mb | 0.975 | 1.28 | 0.294 | 3.320 | < 0.05 | |
bi * mb | -12.918 | 2.3 | 4.044 | -3.195 | < 0.05 | |
| The groups are defined as follows: 18 ± 0ºC: cold constant treatment; 18 ± 5ºC: cold variable treatment; 27 ± 0ºC: warm constant treatment; 27 ± 5ºC: warm variable treatment. The abbreviations are as follows: bi: ingested blood; mb: body mass before infection. The size effect is calculated as 1 / (intercept estimate + estimate of the category). The volume of blood ingested decreased the EIP in all treatments. | ||||||
A
Tukey test showed a significant difference between the 27 and 18 ºC treatments, either being constant or variable (27 ± 0 ºC − 18 ± 0 ºC, z = 4.525, P < 0.001; 27 ± 5 ºC -18 ± 0 ºC z = 3.819, P < 0.001; 27 ± 0 ºC − 18 ± 5 ºC z = 3.994, P < 0.001; 27 ± 5 ºC − 18 ± 5 ºC z = 3.214, P < 0.005). However, the Tukey test did not show significant differences between the constant and thermally variable treatments within the same mean temperature (18 ± 0°C–18 ± 5°C, z = 0.632, P = 0.580 and 27 ± 0°C–27 ± 5°C z = -1.05, P = 0.580) (Fig. 1, Supplementary material: Table S2). The predicted values for the EIP were 30 days (± 3.27) for the cold constant treatment, 27 days (± 2.86) for the cold variable treatment, 12 days (± 1.28) for the warm constant treatment, and 14 days (± 1.49) for the warm variable treatment (Table 1). An increase in ingested blood (bi) reduced EIP, and the effect was greater for cold treatments (Supplementary material: Fig. S1).Parasite load in dejection samples of T. infestans
A
The parasite load varied with temperature (T), time post-infection (Days), body mass (mb), and the interaction term between body mass (mb) and ingested blood at the time of infection (bi) (Table 2). The smooth term selected (Days, bs = "cs,” by = T) implies that the parasite load varied among temperature treatments (T) as time went by, and it was significant for all four treatments (P < 0.005). Thus, the parasite load followed a bell-shaped curve: it started low, peaked between 20 and 30 days, and then declined (Fig. 3). In addition, the effect of temperature treatment on parasite loads varied across groups. Compared to the 18 ± 0°C group, insects maintained at 27 ± 0°C and 27 ± 5°C exhibited significantly higher parasite loads (Estimate = 2.57 and 2.53, respectively; P < 0.001), whereas no significant difference was observed for the 18 ± 5°C group (P = 0.964) (Table 2 and Fig. 3). The interaction between body mass and the amount of blood ingested was significant in all treatments (P < 0.005), except for 27 ± 5°C (P = 0.229), and described different bell-shaped curves for each treatment. The interaction between blood ingested (bi) and insect body mass (mb) over parasite load was most evident in insects with low body mass. Hence, smaller individuals who ingested lower blood volumes at the time of infection exhibited lower parasite loads than those who ingested intermediate volumes. This effect gradually diminished as body mass increased and was not marked in larger insects. In fact, the relationship was reversed in the heaviest individuals (Supplementary material: Fig. S2). Altogether, according to the best-competing model (Supplementary Material: Table S3), the parasite load trend changed with time after infection (Day) and was affected by the amount of ingested blood (bi), temperature treatment (T), and body mass (mb) (Table 2). The deviance of this model explained 64.3% of the variance, indicating a good fit for the parasite load in T. infestans.Parametric coefficients: | ||||
|---|---|---|---|---|
Variable | Estimate | Standard Error | t value | p value |
Intercept (18 ± 0 ºC) | 5.210 | 0.582 | 8.956 | < 0.05 |
18 ± 5 ºC | 0.031 | 0.704 | 0.044 | 0.9647 |
27 ± 0 ºC | 2.575 | 0.770 | 3.343 | < 0.05 |
27 ± 5 ºC | 2.527 | 0.751 | 3.365 | < 0.05 |
Approximate significance of smooth terms: | ||||
Edf | Ref.df | F | p value | |
s(ID) | 0.977 | 1.000 | 7.173 | < 0.05 |
s(Day): 18 ± 0 ºC | 3.002 | 9.000 | 2.986 | < 0.05 |
s(Day): 18 ± 5 ºC | 6.489 | 9.000 | 6.806 | < 0.05 |
s(Day): 27 ± 0 ºC | 2.952 | 9.000 | 5.969 | < 0.05 |
s(Day): 27 ± 5 ºC | 4.049 | 9 | 8.036 | < 0.05 |
te(mb,bi): 18 ± 0 ºC | 8.157 | 19 | 2.755 | < 0.05 |
te(mb,bi): 18 ± 5 ºC | 8.711 | 20 | 2.082 | < 0.05 |
te(mb,bi): 27 ± 0 ºC | 4.575 | 21 | 1.041 | < 0.05 |
te(mb,bi): 27 ± 5 ºC | 1.945 | 20 | 0.138 | 0.230 |
R-sq.(adj) = | 0.583 | Deviance explained = | 64.3% | n = 307 |
| Estimate of the selected model; standard error; t-value; p-value; Edf: effective degrees of freedom; Ref. df: Reference degrees of freedom; F: F-tests on smooth terms; p-value: p-value of smooth terms; R-sq.(adj): Adjusted R-squared value; 18 ± 0°C: cold constant treatment; 18 ± 5°C: cold variable treatment; 27 ± 0°C: warm constant treatment; 27 ± 5°C warm variable treatment; s: spline; ID: triatomine individuals; Days: time measured in days p.i; mb: body mass before infection; bi: ingested blood. | ||||
Fig. 3
Probability of positive dejections to T. cruzi in T. infestans by temperature treatment. Probability is represented by a gradient palette from turquoise: minimum to orange: maximum probability of positivity. The dotted vertical lines mark the time of refeeding with an uninfected blood meal (day 30). All insects that fed on the infected blood were included, regardless of their infection status, and each point represents a dejection sample obtained from 36 insects by treatment. Sample size per treatment 18 ± 0 ºC (n = 78), 18 ± 5 ºC (n = 117), 27 ± 0 ºC (n = 117) and 27 ± 5 ºC (n = 126).
Variable | Estimate | Standard Error | t value | p value |
|---|---|---|---|---|
Intercept (18 ± 0 ºC) | 5.209 | 0.5816 | 8.956 | < 0.05 |
18 ± 5 ºC | 0.031 | 0.7036 | 0.044 | 0.9647 |
27 ± 0 ºC | 2.575 | 0.7703 | 3.343 | < 0.05 |
27 ± 5 ºC | 2.527 | 0.7510 | 3.365 | < 0.05 |
Approximate significance of smooth terms: | ||||
Edf | Ref.df | F | p value | |
s(ID) | 0.977 | 1.000 | 7.173 | < 0.05 |
s(Day): 18 ± 0 ºC | 3.002 | 9.000 | 2.986 | < 0.05 |
s(Day): 18 ± 5 ºC | 6.489 | 9.000 | 6.806 | < 0.05 |
s(Day): 27 ± 0 ºC | 2.952 | 9.000 | 5.969 | < 0.05 |
s(Day): 27 ± 5 ºC | 4.049 | 9 | 8.036 | < 0.05 |
te(mb,bi): 18 ± 0 ºC | 8.157 | 19 | 2.755 | < 0.05 |
te(mb,bi): 18 ± 5 ºC | 8.711 | 20 | 2.082 | < 0.05 |
te(mb,bi): 27 ± 0 ºC | 4.575 | 21 | 1.041 | < 0.05 |
te(mb,bi): 27 ± 5 ºC | 1.945 | 20 | 0.138 | 0.2297 |
R-sq.(adj) = | 0.583 | Deviance explained = | 64.3% | n = 307 |
| Estimate of the selected model; Standard error; t value; p value; Edf: effective degrees of freedom; Ref. df: Reference degrees of freedom; F: F-tests on smooth terms; p-value: p-value of the smooth terms; R-sq.(adj): Adjusted R squared value; 18 ± 0 ºC: Cold constant treatment; 18 ± 5 ºC: cold variable treatment; 27 ± 0 ºC: warm constant treatment; 27 ± 5 ºC warm variable treatment; s: spline; ID: triatomine individuals; Days: time measured in days; mb: body mass before infection; bi: ingested blood. | ||||
Probability of positive dejections
We collected 438 dejection samples from 144 T. infestans individuals; 131 samples were negative and 307 were positive (30% and 70%, respectively). At 18 ± 0°C, 28 out of 36 insects provided 78 samples; at 18 ± 5°C, 33 out of 36 insects provided 117 samples; at 27 ± 0°C, 31 out of 36 individuals provided 117 samples; and finally, at 27 ± 5°C, all 36 individuals provided samples, with a total of 126 samples. We found significant differences in the probability of a positive result between warm and cold treatments. However, we did not find significant differences between constant and thermally variable treatments within the same temperature range (27 ± 0°C and 27 ± 5°C; 18 ± 0°C and 18 ± 5ºC) (Table 3, Fig. 3).
Estimate | Standard Error | t value | p value | |
|---|---|---|---|---|
Intercept (18 ± 0 ºC) | -3.136 | 0.477 | -4.038 | < 0.05 |
18 ± 5 ºC | 0.285 | 0.667 | 0.293 | 0.769 |
27 ± 0 ºC | -5.474 | 2.573 | -2.317 | < 0.05 |
27 ± 5 ºC | -8.275 | 2.187 | -3.984 | < 0.05 |
cs(Days) | 0.151 | 0.020 | 4.682 | < 0.05 |
18 ± 5 ºC:cs(Day) | -0.001 | 0.029 | -0.040 | 0.967 |
27 ± 0 ºC:cs(Day) | 1.317 | 0.335 | 3.894 | < 0.05 |
27 ± 5 ºC:cs(Day) | 1.612 | 0.272 | 5.756 | < 0.05 |
| Estimate of the selected model; standard error; t value; p value; 27 ± 0°C: warm constant treatment; 18 ± 0°C: cold constant treatment; 18 ± 5°C: cold variable treatment; 27 ± 5°C warm variable treatment; cs: cubic spline; d: time in days; ID: triatomine individuals. | ||||
The best model fit for the probability of T. cruzi-positive dejections included a cubic spline on time (Days) with an interaction between temperature treatments (T) (Supplementary material: Table S3), showing differences between warm and cold treatments (P < 0.05) (Fig. 3 and Table 3). There was no significant difference in the probability of positive dejections between the treatments with the same mean temperature (Table 3 and Fig. 3). The GAMLSS model indicated that time (Days) did not significantly affect the probability of positive results among the cold treatments (P = 0.967). However, for the warm treatments, the time was significant (P < 0.05) (Table 3), and as time passed (Days), the probability of positive samples increased (Fig. 3), as represented by the interaction between time (Days) and temperature treatment (T) (Table 3). For these warm treatments, the probability of obtaining negative samples decreased with time and became practically improbable after day 10. In contrast, for individuals acclimated to low temperatures, the probability of finding negative samples in these treatments was maintained throughout the study period (Fig. 3).
During the refeeding procedure with uninfected mice (dotted black line), the mean blood ingested by individuals was 0.037 g (± 0.09) at 18 ± 5°C, 0.046 g (± 0.05) at 18 ± 0°C, 0.040 g (± 0.04) at 27 ± 0°C, and 0.068 g (± 0.07) at 27 ± 5°C. Feeding was considered when > 0.0001 g was consumed. Note that some individuals were removed from the experiment because of mortality; therefore, not all treatments included 36 individuals. Nymphal molts occurred primarily in the warm treatments at 27 ± 0°C (13 molts) and 27 ± 5°C (12 molts). All molts reached stage V and occurred 13 days post-infection. In the cold treatment, only one molt was observed in the 18 ± 0°C treatment.
Discussion
A
Our results showed that as the temperature increased, the time at which the first positive sample was detected decreased (Fig. 1). Therefore, temperature significantly changed the extrinsic incubation period (EIP) of Trypanosoma cruzi within Triatoma infestans, but temperature variability did not have a significant effect. Furthermore, the probability of positivity of the dejections changed with temperature but not with variability (Fig. 4). The results also showed different patterns in the parasite load over time between the different heat treatments, both constant and variable (Fig. 2). A reduction in EIP owing to high environmental temperatures has been observed in three different species of triatomines, including T. infestans [57–60, 78], which is also supported by our results. Trypanosoma cruzi parasites reared in vitro at four different temperatures (21°C, 24°C, 27°C and 30°C) increased in number in direct relation to temperature [79]. Unfortunately, in vitro experiments do not include triatomine immune factors or their microbiota among other factors that affect the actual development of T. cruzi in wild vectors [80, 81].Fig. 2
Parasite load (in T. infestans excreta samples over time (Days) by temperature treatment from the best-fitted model (n = 307). Cold constant temperature in blue (18 ± 0 ºC, n = 50), cold variable temperature in light blue (18 ± 5 ºC, n = 74), warm constant temperature in red (27 ± 0 ºC, n = 97), warm variable temperature in dark red (27 ± 5 ºC, n = 86). The dotted vertical lines indicate the time of refeeding with an uninfected blood meal (day 30). Shaded area shows the standard error of mean (s.e.m.) estimated from the best-fit model. Note that parasite load units are parasites equivalent to 200 femtograms of DNA.
We did not observe that daily thermal variation of 10°C affected the extrinsic incubation period of T. cruzi in T. infestans. Populations are expected to evolve physiological adaptations to local climatic conditions in heterogeneous environments [64], exhibiting plastic strategies that allow them to survive a broad range of temperatures [82]. This is the case for T. infestans and its parasite that inhabit environments subjected to major temperature variability [83], such as Chaco in Argentina [84] and others along its geographic distribution. In these regions, environmental temperature varies in a range that surpasses by more than 10°C those applied in our temperature variable treatments; therefore, both vectors and parasites are probably adapted to thermal variation. Moreover, T. cruzi is continuously exposed to temperature changes throughout its life cycle [85](Marliére et al., 2015), because insect vectors do not physiologically regulate their body temperature and ingest warm blood periodically [86]. Thus, the parasite must be adapted to grow at a wide range of temperatures. In addition, the parasite alternates between insect vectors and mammalian endothermic hosts [87].
EIP was also affected by the amount of blood ingested, with a negative relationship between the amount of infected blood ingested and the time of the first infectious excreta (Supplementary material: Fig. S1). Kissing bugs typically consume a large amount of blood in a single meal, resulting in a high consumption of hemoglobin [83]; a higher intake of this metalloprotein could explain the negative relationship between EIP and ingested blood, since hemoglobin participates in the replication and survival of the parasite [83]. Previous studies found that blood meal size was positively correlated with parasite concentration in the excreted urine of subsequent nymphal instars of Rhodnius prolixus [56]. However, at higher intake levels, the volume of ingested blood had a reduced impact on the extrinsic incubation period (EIP) (Supplementary material: Fig. S1). This is likely because the parasite has already accessed all the nutrients it requires, reaching its maximum development rate, indicating a threshold beyond which additional blood consumption does not further reduce the EIP.
Our study is the first to describe a bell-shaped pattern of parasite population dynamics during the first days of T. infestans infection. However, previous studies have demonstrated a positive linear relationship between parasite load and time [57, 58]. Nevertheless, these studies are not directly comparable to ours because one of them used optical microscopy and a different strain [57], whereas the other used another vector species, R. prolixus, with its tissue samples obtained instead of dejections [58]. Prolonged fasting can significantly reduce parasite loads in triatomines [62], which explains the decrease in parasite load across days. However, in our experiments, the reduction in parasite load continued even after the insects were refed uninfected blood on day 30 (Fig. 2). Another plausible explanation is the secretion of antibacterial proteins in response to microbial shifts following blood ingestion [37, 88]. Kollien and Schaub [68] proposed that the available surface area of the rectal epithelium is also a limiting factor for T. cruzi multiplication, and once the available space becomes saturated, further parasite development may be restricted. It is more likely that nutrient depletion initiated the decline of parasites within T. infestans, with the immune response subsequently contributing to a further reduction in parasite load after feeding [89]. However, T. cruzi dynamics within triatomines involves a complex interplay that also includes alterations in the gut microbiota of insects. Azambuja et al. [90] demonstrated that bacteria can exert a trypanolytic effect and that blood ingestion can modify the bacterial composition in the midgut of triatomines. Consequently, these interactions are highly intricate, necessitating further studies to elucidate these interactions.
A
Remarkably, a bell-shaped curve occurred in all temperature treatments, but with different levels of parasite load. The warm-temperature treatments presented a higher parasite load than the cold treatments during the entire study period (Fig. 2). Within the cold treatments, the effect of variable temperatures on parasite populations was evident, showing an increase in the parasite load (Fig. 2). This was also seen in the malaria vector Anopheles stephensi infected with Plasmodium chabaudi since temperature variability influenced the number of sporozoites per oocyst and those circulating in the hemocele. [31]. This is probably due to the activation of insect metabolism [91–93] triggered by the higher temperature from the cold variable treatment (i.e., 23°C), which is close to its preferred range [53, 54]. This metabolic activation could lead to Trypanosoma cruzi activation because the number of parasites increases in direct relation to temperature [79]. Therefore, daily temperature increases near the preferred thermal range might activate both the vector and parasite. Furthermore, the warm-variable treatment showed an earlier appearance of the parasite load peak than the warm-constant temperature treatment (Fig. 3). It is likely that the maximum daily temperature within the variable treatment (32°C) exerts a more significant influence on the development and emergence of the parasite than the potential delaying effect of the minimum daily temperature (22°C). Consequently, the fluctuating temperature conditions in this treatment may have resulted in a more rapid onset of the parasite development. High temperatures weaken the immune system of triatomines, decreasing the activity of prophenoloxidase (proPO) and the enzymatic cascade of phenoloxidase (PO), which is involved in the defense mechanism against pathogens [83, 94]. This could influence the degree of infection within the vector early, and only in the warm variable treatment, since it reached 32°C during the day, the other thermal treatments did not reach temperature values that could affect the proPO response [83, 94]. In summary, environmental variability could potentially influence individual susceptibility to T. cruzi infection because coping with temperature fluctuations probably had a negative impact on the immune system of these insects. Previous studies have shown a decrease in the survival and fecundity of R. prolixus in a thermally variable environment [19] and a decreased low-temperature performance of T. infestans [18]. Thus, environmental temperatures play a crucial role in mediating the outcome of host–parasite interactions, and these interactions are multifactorial and complex.Finally, we found a significant effect of temperature on the probability of obtaining positive samples without a temperature variability effect, which was similar to that found on the EIP. We also observed that, for insects maintained at low temperatures, there was greater heterogeneity in the probability of finding positive dejections, showing an individual effect. Beyond day 5 post-infection in the warm treatments, all individuals tested positive, whereas individuals subjected to cold treatments exhibited more heterogeneous distributions over time, suggesting individual variation. Neither cold nor warm treatments exhibited significant differences when compared to their respective constant counterparts, suggesting a similar impact on the probability of infection. This pattern is consistent with observations from the extrinsic incubation period (EIP). It is possible that the interaction between temperature and parasite load influences the likelihood of obtaining positive samples and producing the observed heterogeneity.
Conclusions
In this study, we observed that different temperatures (warm and cold), along with their variability, led to an increase in the parasite load on Triatoma infestans, modifying its EIP, which can potentially alter the basic reproductive number (R0) of Chagas and the number of secondary cases [24, 25, 27]. In the context of climate change, this vector may become more effective in transmitting Chagas disease in the near future. Nevertheless, it is important to acknowledge that temperature fluctuations can affect the survival and vital rates of insects, especially when they are outside their optimal thermal thresholds [18, 19, 32, 94, 95](Carrington et al., 2013), and that other factors may play a role in climate change, such as changes in precipitation and extreme climatic events, which may alter population parameters. Nonetheless, we hope that these results will contribute to a more complete understanding of the impact of increased temperature and its variability on this Chagas disease vector.
Acknowledgments
We thank the Mauricio Canals for providing T. infestans individuals from the Universidad de Médicina colony of Universidad de Chile.
A
Funding
Agencia Nacional de Investigación y Desarrollo (ANID) Fondecyt Iniciación 11160839 to SCB; Fondecyt Regular 1180940 to PC and Fondecyt Regular 1210359 to JDM. ANID - Programa Becas-Beca Doctorado Nacional - grant number 21171202 to MS and Doctorado Becas Chile 2019 - grant number 72200391 to AB.
Availability of data and materials
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Author Contribution
BA-D: Conceptualization, Formal analysis, Investigation, Methodology and Writing - original draft; SCB: Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, and Writing - Review & Editing; LV-P: Methodology, Resources and Review & Editing; JDM: Methodology and Resources; MS: Methodology and Investigation; SO: Investigation; CM-S: Methodology and Investigation; AB: Formal analysis; PEC: Conceptualization and Resources, Validation, Supervision, Funding Acquisition - Review & Editing.
Ethics approval and consent to participate
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All animal procedures were conducted according to the Chilean Animal Protection Law (Ley N° 20.380) and the Guide for the Care and Use of Laboratory Animals (Albus, 2012). A
The Scientific Ethical Committee for Research Safety of the Universidad de Chile approved all animal protocols used in this study (Protocol Nº: 19262-MED-UCH for Biosecurity and Ethical protocols).Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Data access
Data will be held on Figshare after acceptance 10.1101/2023.11.08.566164, private link https://figshare.com/s/a0011e5254aab62dfc74
Electronic Supplementary Material
Below is the link to the electronic supplementary material
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Tables
Tables
Table 3. Parameters of the GAMLSS selected model.
Estimate | Standard Error | t value | p value | |
|---|---|---|---|---|
Intercept (18 ± 0 ºC) | -3.136 | 0.477 | -4.038 | < 0.05 |
18 ± 5 ºC | 0.285 | 0.667 | 0.293 | 0.769 |
27 ± 0 ºC | -5.474 | 2.573 | -2.317 | < 0.05 |
27 ± 5 ºC | -8.275 | 2.187 | -3.984 | < 0.05 |
cs(Day) | 0.151 | 0.020 | 4.682 | < 0.05 |
18 ± 5 ºC:cs(Day) | -0.001 | 0.029 | -0.040 | 0.967 |
27 ± 0 ºC:cs(Day) | 1.317 | 0.335 | 3.894 | < 0.05 |
27 ± 5 ºC:cs(Day) | 1.612 | 0.272 | 5.756 | < 0.05 |
Estimate of the selected model; Standard error; t value; p value; 27 ± 0 ºC: warm constant treatment; 18 ± 0 ºC: cold constant treatment; 18 ± 5 ºC: cold variable treatment; 27 ± 5 ºC warm variable treatment; cs: cubic spline; Day: time in days; ID: triatomine individuals.