Supplemental Tables for the Manuscript : Effect of an intramammary lipopolysaccharide challenge on the hindgut microbial composition and fermentation of dairy cattle experiencing intermittent subacute ruminal acidosis
Our hypothesis is that a transient subacute ruminal acidosis (SARA) challenge will result in microbial changes due to large undigested carbohydrates bypassing the rumen and being fermented in the hindgut, and these changes will be more prominent after an LPS challenge due to the systemic effects of the LPS. To test this 18 early-lactating Simmental cows were divided into three groups (n = 6), two were fed a SARA-inducing feeding regime, one was fed a control (CON) diet, with either 60% or 40% concentrate, respectively. On d 30, one SARA group (SARA-LPS) and the CON group (CON-LPS) were intramammarily challenged with a single dose of 50 µg LPS from E. coli (O26:B6), while the remaining six SARA (SARA-PLA) received a placebo. Feces from the cows were collected and analyzed for pH and short chain fatty acids SCFA, as well as DNA was extracted for 16S rRNA analysis. Microbial analysis was performed using QIIME2. The data here is a statistical analysis of the operational taxonomic units (OTUs) that were measured in the feces between dairy cows fed either a control or a SARA challenge diet (Supplementary Table 1), the statistical analysis of the OTUs measured in feces of dairy cows fed control diet and receiving a LPS infusion (CON-LPS) or a SARA diet receiving either a LPS (SARA-LPS) or placebo infusion (SARA-PLA; Supplementary Table 2). As well this repository includes a Spearman's correlation coefficient matrix between statistically significant identified genera of fecal microbes and the fermentation characteristics of feces (short chain fatty acids SCFA; pH).
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For pH measurement, fecal samples were diluted (1:10) using double-distilled water. The mixtures were homogenized with vortex machine (Biosan Sia, Latvia) and subsequently measured using pH meter (Metler Toledo Gmbh, Schwezenbach, Switzerland). For SCFA analysis, 1 gram of feces from each sample was mixed with 1 mL distilled water, 0.3 mL internal standard (4-methyl-valeric acid; Sigma-Aldrich, Vienna, Austria), and 0.2 mL H3PO4. Those mixtures were centrifuged at 4°C and 20,000 × g for 10 min at to remove solid substances (5810 R; Eppendorf, Hamburg, Germany). Subsequently, the supernatant was centrifuged at 20,000 × g for 20 minutes at 4°C (centrifuge 5424 R; Eppendorf, Hamburg, Germany) and the subsequent supernatant analyzed for SCFA concentration using gas chromatography (GC model 8060 MS DPFC, number 950713; Fisons, Rodena, Italy). Injector and detector had temperatures of 170 °C and 190 °C, respectively. Helium was used as the carrier gas with a flow rate of 1 mL/min. The individual SCFA (propionate, acetate, n-butyrate, isobutyrate, caproate, isovalerate, and n-valerate) proportions were computed following the protocols described by Humer et al., (2018c). Fecal samples were collected at the same time as the sampling for fecal pH and SCFA for analysis of the microbial community. DNA extraction, sequencing, and quality control followed the previous protocols (Petri et al., 2019). One 20 μL aliquot of obtained DNA from each sample was used for 16S rRNA amplicon sequencing using a MiSeq Illumina sequencing platform and paired-end technology. The sequencing targeted the V3-V5 region of bacterial 16S rRNA gene with the primer set 357F (5’-CCTACGGGAGGCAGCAG-3’) and 926R (5’-CCGTCAATTCMTTTRAGT-3’) to produce amplicon size around ~570 bp (Peterson et al., 2009). Further processing of sequences was done using the open-source software QIIME (version 1.9.1 http://qiime.org/; accessed 2017; Caporaso et al., 2010), according to previously published workflows (Petri et al., 2019). The experimental design was a longitudinal randomized controlled design (RCD) with a common baseline and experimental duration of 32 days. All data were analyzed by ANOVA using the MIXED procedure of SAS (version 9.4; SAS Institute Inc, NC, USA). To assess the SARA challenge on the fecal microbiota composition, pH, and SCFA profile statistical analysis were evaluated using a model with fixed effects of treatment (CON (n = 6), SARA (n = 12)), feeding phase (SARA I, SARA Break, SARA II), and their interaction. Measurements carried out in the baseline were considered as covariates in the analysis. Associations between microbial genera, LPS treatment and fecal fermentation variables were investigated by performing a Spearman`s rank correlation analysis and a heatmap graphic were produced using the ggcorrplot package in R (Kassambara, 2019: accessed 2019).