Animals and Experimental Design
The study was carried out in a commercial dairy farm of Holstein cows (herd size, 600) located in General Belgrano, Buenos Aires, Argentina (35°46′00″S 58°30′00″O). The feed intake of dairy cows complied with the Nutrient Requirements of Dairy Cattle recommendations [19]. Two hundred Holstein cows meeting the following inclusion criteria were selected: number of calvings (2 to 5), no clinical alterations, not receiving parenteral supplementation with minerals 90 days before calving and not receiving pharmacological treatment 60 days before calving. Cows were randomly separated into two groups: supplemented (SG, n = 100) and control (CG, n = 100). Animals from SG received 5 ml of a mineral suspension containing Cu (10 mg/mL as edetate; Sigma-Aldrich®, Missouri, US), Zn (60 mg/mL as edetate; Sigma-Aldrich®), Se (5 mg/mL as sodium selenite; Sigma-Aldrich®) and Mn (10 mg/mL as edetate; Surfactan®) 20 days before calving (day -20) and on the day of calving (day 0). At the same time, CG cows received 5 ml of sterile saline solution.
Seven days after calving, blood samples were obtained from the jugular vein of randomly selected animals (n= 25 from each group) to determine SOD and GSHpx activity, AC and thiobarbituric acid reactive substances (TBARS). Blood samples were collected in Vacutainer® EDTA tubes for hematocrit and GSHpx determination and in tubes without anticoagulant for colorimetric, SOD activity, AC and TBARS determinations.
Six randomly selected blood samples from each group were extracted, preserved in RNAlater (Thermo Fisher Scientific, USA) and stored at -80 °C until processing by RNA-sequencing analysis. The study design is presented in Figure 1.
Biochemical Determinations
Hematocrit and GSHpx Activity
After blood collection, samples were divided into two aliquots, one for hematocrit determination and the other for erythrocyte hemolysis with high-performance liquid chromatography (HPLC)-grade water (1:4). Samples were frozen at -70 °C until complete sample collection (n = 50). After centrifugation to separate plasma from red blood cells, plasma was discarded and red blood cells were used to measure GSHpx activity by ultraviolet visible (UV-vis) spectrophotometry with the Glutathione Peroxidase Assay Kit (Cayman Chemical Company, MI, USA) (340 nm wavelength), whose rate of decrease in A340 is directly proportional to GSHpx activity.
SOD Activity, AC and TBARS
After centrifugation, samples were aliquoted into two tubes, one for SOD activity and AC assessment and the other for TBARS determination. SOD activity was determined by UV-vis spectrophotometry using the Superoxide Dismutase Assay Kit® (Cayman Chemical). One SOD unit was the necessary amount of enzyme to produce a 50% reduction of the superoxide radical. Absorbance was measured at 440-460 nm. The determination of AC was done by UV spectrophotometry with the Total Antioxidant Assay Kit (Cayman Chemical) at 750 nm. TBARS were determined with the Total Antioxidant Assay Kit (Cayman Chemical) and measured at 530-540 nm.
RNA-seq and Differential Gene Expression (DGE) Analysis
The blood samples preserved in RNAlater were delivered to the Novogene Sequencing Facility (Sacramento, CA, USA) for RNA extraction with assay and hemoglobin depletion. RNA quality was verified with an Agilent bioanalyzer (Agilent, CA, USA). After quality control procedures, the samples were sequenced in a NovaSeq 6000 sequencing platform (Illumina Inc, CA, USA) and the paired-end 150bp (PE150) strategy. Raw data were filtered to remove low-quality and adapter sequences. Then, the nf-core/RNAseq project pipeline [20] was used to perform RNA-seq analysis using the ARS-UCD1.2 version as the reference bovine genome. In brief, this workflow includes the following analyses: quality control of reads with FastQC [21], STAR software for alignment to reference genome [22] and featureCounts software for counting reads [23]. Readings not having in average 10 total reads in SG and CG were filtered to delete non-expressed genes and transcripts with low expression levels. To identify differentially expressed genes (DEG), the DESeq2 software in R was applied [24].
Statistical analysis
The biochemical determinations in both groups were compared using the Student´s t-test. Statistical significance was set at p < 0.05. DEG were identified using the Benjamini and Hochberg method implemented in the DESeq2 software in R [24]. Transcripts with an adjusted p value < 0.05 were considered to have DGE, as identified by the Ensembl database.