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Genetic factors underlying host resistance to Rhipicephalus microplus tick infestation in Braford cattle: a systems biology perspective

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Abstract

Approximately 80% of the world’s cattle are raised in regions with a high risk of tick-borne diseases, resulting in significant economic losses due to parasitism by Rhipicephalus (Boophilus) microplus. However, the lack of a systemic biology approach hampers a comprehensive understanding of tick–host interactions that mediate tick resistance phenotypes. Here, we conducted a genome-wide association study (GWAS) of 2933 Braford cattle and found 340 single-nucleotide polymorphisms (SNPs) associated with tick counts. Gene expression analyses were performed on skin samples obtained from previously tick-exposed heifers with extremely high or low estimated breeding values for R. microplus counts. Evaluations were performed both before and after artificial infestation with ticks. Differentially expressed genes were found within 1-Mb windows centered at significant SNPs from GWAS. A total of 330 genes were related to the breakdown of homeostasis that was induced by larval attachment to bovine skin. Enrichment analysis pointed to a key role of proteolysis and signal transduction via JAK/STAT, NFKB and WNT/beta catenin signaling pathways. Integrative analysis on matrixEQTL revealed two cis-eQTLs and four significant SNPs in the genes peptidyl arginine deiminase type IV (PADI4) and LOC11449251. The integration of genomic data from QTL maps and transcriptome analyses has identified a set of twelve key genes that show significant associations with tick loads. These genes could be key candidates to improve the accuracy of genomic predictions for tick resistance in Braford cattle.

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Data availability

The datasets generated/analyzed for this study can be found in the GEO repository database (https://www.ncbi.nlm.nih.gov/geo; accession number GSE 224634). We have restrictions on sharing the bovine phenotypes as they are licensed for commercial exploitation by GenSys® (https://gensys.com.br/) and Embrapa through a genomic predictions service. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

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Acknowledgements

The authors acknowledge the Delta G Connection/Gensys breeding program for providing this research’s animals and performance data. We thank our colleagues from Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ-USP) Functional Genomics Center, especially Aline Silva Mello Cesar, for releasing the use of the Metacore® software key and helping with understanding some of the algorithms used in the analysis. We also thank Michael D. MacNeil, our colleague from Delta Genetics, for his support and improvement in the manuscript’s English language. We also appreciate the dedication and support of all field technicians involved in animal husbandry and tick infestations during the experiments.

Funding

This research was financially supported by Embrapa (Brazilian Agricultural Research Corporation) grants, which provided infrastructure, consumables, and personnel for the field experiment and laboratory analyses. Personnel and consumables were also supported by CAPES (Coordination for the Improvement of Higher Level Personnel) grant PNPD 02645/09–2. FFC also acknowledges funding from CNPq (National Council for Scientific and Technological Development) grant 305102/2018–4.

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Authors and Affiliations

  1. Embrapa Dairy Cattle, Juiz de Fora, Brazil

    Wanessa A. Carvalho

  2. Embrapa South Livestock, Bagé, Brazil

    Emanuelle B. Gaspar, Robert Domingues & Fernando F. Cardoso

  3. Embrapa Southeast Livestock, São Carlos, Brazil

    Luciana C. A. Regitano

Authors
  1. Wanessa A. Carvalho
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  2. Emanuelle B. Gaspar
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  3. Robert Domingues
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  4. Luciana C. A. Regitano
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  5. Fernando F. Cardoso
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Contributions

EBG involved in investigation, methodology, project administration, writing—original draft; FFC in conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, supervision, validation, visualization, writing—review & editing; LCAR in conceptualization, data curation, funding acquisition, investigation, methodology, project administration, resources, supervision, visualization, writing—review & editing; RD in data curation, formal analysis, investigation, methodology, project administration, validation, visualization, writing—review & editing; WAC in conceptualization, formal analysis, investigation, methodology, supervision, validation, visualization, writing—original draft, writing—review & editing.

Corresponding author

Correspondence to Wanessa A. Carvalho.

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Supplementary Information

Below is the link to the electronic supplementary material.

335_2024_10030_MOESM1_ESM.csv

Supplementary Table 1. List of 340 significative SNPs ( − log10 (P) >  3.0) evaluated for bovine resistance to R. microplus ticks. This list of SNPs was derived from a genome-wide association study (GWAS) in a population of Braford cattle evaluated for bovine resistance to R. microplus ticks. Braford cattle (n =  2933) were genotyped by BovineHD DNA chip (Illumina®) and artificially challenged repeatedly with R. microplus for engorged female tick counts (number of records including repeated measures: 9203). A total of 480,948 SNPs were analyzed by the single-step GBLUP method of which 340 present  − log10 (P) >  3.0 (CSV 28 kb)

335_2024_10030_MOESM2_ESM.xlsx

Supplementary Table 2. The ten most relevant biological processes highlighted by enrichment analysis of DEGs from Braford animals previously phenotyped for R. microplus resistance for R vs S comparison. The list of 121 annotated DEGs (FDR < 0.05; |Log2FC| ≥ 1) derived from R vs S comparison (n = 20 and = 19, respectively) were used for gene ontology enrichment analysis on Metacore® software (Clarivate Analytics, CA, USA). The table displays the ten most relevant GO processes, the total number of genes on the GO database and the corresponding entry to our DEG database (in data), statistics (P and False Discovery Rate), and the abbreviation of the DEGs involved in each process (XLSX 13 kb)

335_2024_10030_MOESM3_ESM.xlsx

Supplementary Table 3. The ten most relevant process networks highlighted by enrichment analysis of DEGs from Braford animals previously phenotyped for R. microplus resistance for R vs S comparison. The list of 121 annotated DEGs (FDR < 0.05; |Log2FC| ≥ 1) derived from R vs S comparison (R = 20 and S = 19 animals) were used for gene ontology enrichment analysis on Metacore® software (Clarivate Analytics, CA, USA). The table displays the ten most relevant process networks, the total numbers of genes on the GO database and the correspondent entry to our DEG database (in data), statistics (P and False Discovery Rate), and the network objects from the DEG database (XLSX 13 kb)

335_2024_10030_MOESM4_ESM.xlsx

Supplementary Table 4. The ten most relevant process networks highlighted by enrichment analysis of 330 genes from BI vs AI comparison within the genomic 1Mb windows centered at each one of the 227 SNPs potentially associated with tick counts. From the 4704 DEGs found in BI vs AI comparison, 330 genes were within the genomic 1Mb windows centered at each of the 227 SNPs potentially associated with tick counts. These 330 genes were used for gene ontology enrichment analysis on Metacore® software (Clarivate Analytics, CA, USA). The table displays the ten most relevant process networks, the total numbers of genes on the GO database and the correspondent entry to our DEG database (in data), statistics (P and False Discovery Rate), and the network objects from the DEG database (XLSX 13 kb)

335_2024_10030_MOESM5_ESM.xlsx

Supplementary Table 5. The ten most relevant pathway maps highlighted by enrichment analysis of 330 genes at BI vs AI comparison within the genomic 1Mb windows centered at each of the 227 SNPs potentially associated with tick counts. From the 4704 DEGs found in BI vs AI comparison, 330 genes were within the genomic 1Mb windows centered at each of the 227 SNPs potentially associated with tick counts. These 330 genes were used for enrichment analysis on Metacore® software (Clarivate Analytics, CA, USA). The table displays the ten most relevant pathway maps, the total numbers of genes on the Metacore® database and the correspondent entry to our DEG database (in data), statistics (P and False Discovery Rate), and the network objects from the DEG database (XLSX 13 kb)

335_2024_10030_MOESM6_ESM.xlsx

Supplementary Table 6. Genetic correlation between RNA abundance and tick counts. The normalized read count for each potential cis-eQTLs and DEGs of R vs S comparison were correlated with tick counts (mean of four consecutive tick counts from naturally infested animals). The cutoff > ǀ0.8ǀ was used. Correlation above 0.8 was highlighted in green, and below − 0.8 was highlighted in red (XLSX 30 kb)

335_2024_10030_MOESM7_ESM.xls

Supplementary Table 7. The ten most relevant process networks highlighted by enrichment analysis of genes with significant correlation to tick counts displayed Multi-trait AIREML analyses. The 313 DEGs exclusively found at BI x AI comparison, which were within a 1Mb window of a GWAS, plus all the 121 DEGs of R vs S comparison. Genes associated with tick counts (r > |0.8|) before infestation (BI; n = 61) and after infestation (AI; n = 84) were aligned and enriched on Metacore® software (Clarivate Analytics). The intersection set of genes for both experiments defined as “common” (gray columns) and “unique” for BI (blue columns) and AI (orange columns) were enriched for the ten most relevant network process functional ontology comparing dataset gene lists. The probability of a random intersection between a set of dataset genes and the size of the target list with ontology entities was estimated in the p-value of hypergeometric intersection. The lower P (displayed on the top of the representative graph) means higher relevance of the entity to the dataset, which shows in a higher rating for the entity (XLS 42 kb)

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Carvalho, W.A., Gaspar, E.B., Domingues, R. et al. Genetic factors underlying host resistance to Rhipicephalus microplus tick infestation in Braford cattle: a systems biology perspective. Mamm Genome (2024). https://doi.org/10.1007/s00335-024-10030-x

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