Integrating RNA-Seq with GWAS reveals novel insights into the molecular mechanism underpinning ketosis in cattle

- Integrating RNA-Seq with GWAS reveals novel insights into the molecular.
- By employing a weighted gene co-expression network analysis (WGCNA), we detected that 4 out of 16 gene-modules, which were significantly engaged in lipid metabolism and immune responses, were transcriptionally (FDR <.
- 0.05) associated with ketosis, among which three were correlated with postpartum ketosis based on WGCNA.
- Our phenome-wide association analysis (Phe-WAS) demonstrated that human orthologues of these candidate genes were also significantly associated with many metabolic, endocrine, and immune traits in humans.
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- The transition period, known as 3 weeks pre- until 3 weeks post-calving, is a critical time for dairy cows since many metabolic and infectious diseases occur due to dramatic physiological challenges faced by cows (e.g., the negative energy balance, NEB) [1].
- The incidence of ketosis is as high as 15–30% in the dairy industry, and cows with high milk yield predis- pose to ketosis [2], leading to huge economic losses worldwide.
- For instance, each case of ketosis costs and ¥ 3200 in the U.S.
- 1), to explore the genetic architec- ture underlying ketosis, we generated RNA-Seq of blood.
- We then integrated RNA-Seq with large-scale GWAS ( n ≈ 10 K) of ketosis and other five health traits, includ- ing livability, DSAB, hypocalcemia (CALC), MAST and metritis (METR).
- Summary of RNA-Seq data.
- In total, we generated 24 RNA-seq data from 12 Holstein cows, including 4 healthy and 8 ketosis-diagnosed ones, be- fore (2 weeks) and after (5 days) calving, respectively.
- By aligning clean data to the cattle reference genome (UMD3.1.1), we obtained an averaged mapping rate of 94.76% (ranging from 93.86 to 95.73%) among all of the 24 samples.
- Gene co-expression modules associated with ketosis and biochemical indicators.
- 0.05) and specifically associated with post-partum ketosis (Fig.
- which tended to be ( P = 0.008, FDR = 0.10) associated with post-partum ketosis.
- Gene Ontology enrichment analysis showed that genes in the Royalblue module were signifi- cantly (FDR <.
- 0.05) involved in the microtubule-based and macromolecule biosynthetic processes, while genes in the remaining three modules were significantly engaged in im- mune responses (Fig.
- 0.05) enriched for gene with specific expression in digestive and immune systems (e.g., diaphragm and gall bladder), while genes in the remaining three modules were significantly enriched for genes with specific expression in the blood and immune system (Fig.
- 0.1) associated with pre-partum ketosis.
- Genes in this module were significantly engaged in the nervous system (Additional file 3: Table S3), which might reflect the cross-talk between the nervous system and digestive/im- mune systems (i.e., the so-called gut-brain axis) [20–23]..
- We also observed that two modules, Darkorange and Midnightblue, were associated with HDL, while Steelblue and Skyblue modules were.
- associated with LDL and INS, respectively.
- The pre-partum ketosis-associated module, Lightcyan, tended to be ( P = 0.02, FDR = 0.13) associated with INS (Fig.
- Furthermore, we observed distinct expression patterns of these genes in the post-partum ketosis group compared to others (Fig.
- For instance, C14H8orf82 and ACSS1 had lower expression levels in the post-partum ketosis group than in others, lead- ing to a lower HDL level.
- In contrast, EPB2 and PLK1 exhib- ited higher expression levels in the post-partum ketosis group, resulting in lower levels of LDL and INS, respectively..
- Gene co-expression modules enriched with GWAS signals of health traits.
- To investigate whether gene co-expression modules were enriched with GWAS signals of ketosis and other.
- 1 Global framework of the study.
- The green box (left) represents the experimental design of RNA-Seq study.
- The brown box shows major bioinformatics and statistical analyses involved in the study.
- we found that these five candidate genes were also as- sociated ( P <.
- Phenome-wide association analysis (Phe-WAS) for ketosis candidate genes in humans.
- 0.05) with many meta- bolic traits and other health-relevant traits in humans, such as endocrine and immunological traits, suggesting their conserved roles in the regulation of metabolism.
- 2 The weighted gene correlation network analysis (WGCNA) for 24 RNA-Seq datasets.
- b Gene modules associated with four physiological stages (Post-partum Healthy, H_Post.
- Post-partum Ketosis, K_Post;.
- The values in the brackets are the numbers of genes in corresponding modules.
- c The top significantly enriched biological processes for genes in the top four modules associated with the K_Post group.
- d The top significantly enriched tissue/cell types for genes in the top four modules associated with the K_Post group.
- It was also significantly associated with many endocrine traits (e.g., Insulin sensitivity index, FDR = 0.042.
- RECQL4 was sig- nificantly associated with many endocrine (e.g., Type 2 Diabetes, FDR = 4.53e-06), immunological (e.g., Mean corpuscular hemoglobin concentration, FDR = 2.61e- 11) and metabolic traits (e.g., Estimated glomerular filtration rate, FDR = 9.86e-06).
- associated with nucleic acid binding and annealing helicase activity [24, 25].
- C14H8orf82 was also significantly associated with many metabolic (e.g., Cholesterol esters in large LDL, FDR = 0.032.
- 3 Gene examples in the gene co-expression modules associated with post-partum ketosis and blood biochemical indicators.
- To our best knowledge, this is the first study to explore the genetic and biological basis of ketosis in dairy cattle by systematically integrating RNA-Seq and large-scale GWAS data.
- Here, we applied the typical WGCNA strategy - single co-expression network analysis.
- By using samples of multiple status, a single co-expression net- work could identify common co-expression modules across status [27].
- This analysis strategy has been widely used to detect genes that were associated with develop- mental stages of diseases, sex and tissues at a system- level [28–31].
- Compared to differential expression analyses at individ- ual gene-level, WGCNA considers the relationship between altering genes as a whole, and reduces the mul- tiple testing burden by focusing on tens of co-expression modules rather than thousands of individual genes..
- However, it is of note that the status/condition-specific co-expression modules may not be detected in the co- expression networks constructed from samples under multiple conditions, because the correlation signal of the condition-specific modules might be diluted by a lack of correlation in other conditions [27].
- To identify modules unique to a specific condition, an alternative strategy, namely differential weighted gene co-expression network analysis (DWGCNA), could be used when sample size is.
- Table 1 Summary of five candidate genes for ketosis.
- 4 Gene co-expression modules enriched with GWAS signals of ketosis and other five health traits in cattle.
- Four modules marked in red are significantly associated with ketosis.
- b Correlation between GWAS enrichment of ketosis and module-states associations from WGCNA across all 16 modules in the ketosis post-partum group, where r means Pearson ’ s correlation and P reflects the statistical significance.
- The DWGCNA approach constructs co- expression networks separately for different datasets to uncover the differences in modules .
- Compared to ketosis, the plasma bio-indicators serve as intermediate phenotypes, which are more directly associated with alterations of gene expression induced by ketosis.
- which had lower expression levels in the post-partum ketosis group compared to others, were positively correlated with HDL, leading to a lower HDL level in animals with post-partum ketosis (Fig.
- For instance, in our study, we observed that gene co-expression modules, which were significantly correlated with post-partum ke- tosis rather than other status (e.g., pre-partum ketosis), were significantly enriched for GWAS signals of ketosis..
- It is thus of great inter- est to collect more RNA-Seq data from multiple time points in the transition period to further explore the causal genes for ketosis in future studies..
- 5 Phenome-wide association analysis (Phe-WAS) for ketosis candidate genes in humans.
- also proposed that MAFA was implicated in the regulation of immuno- modulatory cytokines such as interferon-β (IFNβ1) [47]..
- In this study, we integrated RNA-seq of blood leukocytes with large-scale GWAS results to detect genes/pathways underlying ketosis in cattle.
- These cows were housed in the same pen and fed the same diet.
- and insulin, INS), while leukocytes were stored in the liquid nitrogen for further RNA se- quencing.
- Bioinformatics analysis of RNA-seq.
- Weighted gene correlation network analysis (WGCNA) We employed the WGCNA (v1.12.0), implemented in R, to construct gene co-expression network [56].
- DSAB, highly associated with KETO, occurs when the abomasum fills with gas and moves from the floor to the top of the abdomen.
- We applied a sum-based method, implemented in the R package for Quantitative Genetic and Genomic ana- lyses (QGG package.
- http://psoerensen.github.io/qgg/) [58], for GWAS signal enrichment analyses across all 16 gene co-expression modules detected by WGCNA.
- Briefly, we calculated the following summary statistics for each gene co- expression module:.
- where T sum is the summary statistics for a tested gene module, b is the estimate of marker effect obtained in the single-marker GWAS, and n is the number of SNPs located in the genes (20 kb up and downstream) within the module being tested.
- To investigate functions of genes in our significant gene co- expression modules, we conducted functional enrichment analysis using the hypergeometric test based on Gene Ontology database and performed protein-protein inter- action analysis using STRING v11 database with default set- tings (https://string-db.org.
- The details of the tissue/.
- To test whether human orthologues of ketosis-candidate genes are associated with similar traits in humans.
- Summary of RNA-Seq data mapping information..
- RNA-Seq: RNA Sequencing.
- WGCNA: Weighted gene co-expression network analysis.
- DWGCNA: Differential weighted gene co-expressed network analysis.
- in the collection, analysis, and interpretation of data.
- in the writing of the manuscript.
- and in the decision to submit the manuscript for publication..
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