Influence of Maternal BLV Infection on miRNA and tRF Expression in Calves

Abstract

Small non-coding RNAs, such as microRNAs (miRNA) and tRNA-derived fragments (tRF), are known to be involved in post-transcriptional gene regulation. Research has provided evidence that small RNAs may influence immune development in calves. Bovine leukosis is a disease in cattle caused by Bovine Leukemia Virus (BLV) that leads to increased susceptibility to opportunistic pathogens. No research has addressed the potential influence that a maternal BLV infection may have on gene regulation through the differential expression of miRNAs or tRFs in progeny. Blood samples from 14-day old Holstein calves born to BLV-infected dams were collected. Antibodies for BLV were assessed using ELISA and levels of BLV provirus were assessed using qPCR. Total RNA was extracted from whole blood samples for small RNA sequencing. Five miRNAs (bta-miR-1, bta-miR-206, bta-miR-133a, bta-miR-133b, and bta-miR-2450d) and five tRFs (tRF-36-8JZ8RN58X2NF79E, tRF-20-0PF05B2I, tRF-27-W4R951KHZKK, tRF-22-S3M8309NF, and tRF-26-M87SFR2W9J0) were dysregulated in calves born to BLV-infected dams. The miRNAs appear to be involved in the gene regulation of immunological responses and muscle development. The tRF subtypes and parental tRNA profiles in calves born to infected dams appear to be consistent with previous publications in adult cattle with BLV infection. These findings offer insight into how maternal BLV infection status may impact the development of offspring.

Keywords: BLV; bovine leukemia virus; cattle; microRNAs; tRNA-derived fragments.

Figure 1

Principal component analysis (PCA) shows variation in miRNA and tRF expression between progeny born to BLV negative (black text) and BLV positive (purple text) animals. The x-axis represents the first principal component (PC1), which shows the individuals with the most variability with regards to miRNA and tRF expression. The y-axis represents the second principal component (PC2), which reveals additional relationships in the data that are not explained by PC1. PC1 and PC2 represent linear combinations based on the gene expression data and are used to calculate maximum variance in the dataset. PC1 explains the most variation and PC2 explains the second most variation.

Figure 2

Distribution of tRF subtypes between progeny born to BLV positive and BLV negative dams. Bar graph showing the percentage of reads assigned to 5′ tRF, 3′ tRF, i-tRF, 5′ half and 3′ half subtypes. All reads were counts per million (CPM) normalized and percentages contributing to the total tRF profile were calculated. The y-axis sums to 100% for each BLV status and standard error bars are shown in black.

Figure 3

Nuclear and Mitochondrial tRF distribution between progeny born to BLV negative and BLV positive dams. Reads were logCPM normalized and the number of reads derived from each parent tRNA are shown. The SummarySE function of the Rmisc package was used to calculate summary statistics by treatment group and the standard error bars for each parent tRNA within each treatment group are shown.