Table 2.
Chemical composition of total mixed ration and feedstuffs used in vitro (dry matter basis).
Item
1
TMR
Corn Silage
Oat Hay
Alfalfa Hay
DM, %
94.6
±
0.3
93.5
±
0.2
94.1
±
0.2
93.4
±
0.3
CP, %
16.0
±
0.3
8.6
±
0.1
6.2
±
0.2
21.4
±
0.5
NDF, %
38.2
±
0.6
54.0
±
0.9
56.9
±
1.1
41.1
±
0.6
ADF, %
25.3
±
0.4
34.3
±
0.6
33.9
±
0.4
26.6
±
0.3
Starch, %
26.8
±
0.3
28.6
±
0.2
-
-
1
DM: dry matter; CP: crude protein; NDF: neutral detergent fiber; ADF: acid detergent fiber. Chemical composi-
tion was obtained from chemical analysis and shown as mean
±
standard deviation. ‘-’ indicates no data.
2.3. Sample Collection and Measurement
After 7 h, 24 h, 30 h, and 48 h of incubation, the content of each sample was filtered
through a nylon bag (80 mm
×
150 mm size with 42 µm pores), and then dried at 60
◦
C
for 48 h in a forced-air oven (Wujiang Zhongda Electrical Technology Co., Ltd., Wujiang,
Jiangsu, China) to analyze and calculate nutrient degradability. The content of dry matter
(DM), neutral detergent fiber (NDF), acid detergent fiber (ADF), and crude protein (CP) in
the original sample, and residues at 24 h, 30 h, and 48 h were determined according to the
previously described method [
27
]. The starch content of residues of TMR and corn silage
at 7 h was determined using a commercial starch assay kit (BioVision, Inc., San Francisco
Bay Area, the United States of America). Starch is hydrolyzed to glucose, which is oxidized
to color at 570 nm and can be read by a microplate reader (Multiskan Sky Microplate
spectrophotometer, Thermo Fisher Scientific-CN, Shanghai, China).
The culture fluid at 48 h was sampled from four of the six samples in each group into
2.5 mL microtubes and stored in liquid N for DNA extraction. The 15 mL fluid subsample
was stored at
−
80
◦
C for ammonia nitrogen (NH
3
-N) and volatile fatty acid (VFA) anal-
ysis [
28
]. Briefly, the culture fluid at 48 h for VFA was centrifuged at 4731
×
g (5400 rpm,
14.5 cm) for 10 min, and the supernatant (1 mL) mixed with 200 µL metaphosphoric acid
solution (25%, w/v). After shaking in an ice-water bath for 30 min, samples were cen-
trifuged at 5595
×
g (10,000 rpm, 5 cm) for 10 min. The supernatant was injected into gas
chromatography (Agilent 6890N, Agilent Technologies, Inc., Beijing, China) to determine
the concentrations of acetate, propionate, butyrate, and branched fatty acids. The content
of individual VFAs is shown as a molar proportion. The subsamples for NH
3
-N analysis
were centrifuged at 2000
×
g for 20 min at 4
◦
C, and the supernatant (2 mL) was acidified
with 8 mL of 0.2 N hydrochloric acid. The pH at 48 h was detected immediately using
a portable pH meter (S2-Meter, Mettler Toledo International Co., Ltd., Shanghai, Beijing,
China). The experimental design is shown in Supplemental Figure S1.
2.4. DNA Extraction and Sequencing
Bacterial DNA was extracted from 48 h samples using an Omega Stool DNA kit
(Omega Bio-Tek, Norcross, GA, USA). The quality and quantity of the DNA were de-
termined using NanoDrop 2000 UV–vis spectrophotometer (Thermo Scientific, Wilm-
ington, DE, USA). Amplicon library preparation was performed by polymerase chain
reaction (PCR) of the V3-V4 region of the 16s rRNA gene using universal primers 338F (5
0
-
ACTCCTACGGGAGGCAGCAG-3
0
), and reverse primers 306R (5
0
-GGACTACHVGGGTWT
CTAAT-3
0
) [
28
]. The 20 µL reaction system included 10 ng of template DNA, 4 µL of FasPfu
buffer, 2 µL of 2.5 mmol/L dNTPs, 0.8 µL of each primer, 0.4 µL of FasPfu polymerase,
0.2 µL bovine serum albumin, and double-distilled H
2
O to 20 µL. The amplicons were
electrophoresed on 2% agarose gel, purified using an Agencourt AM Pure XP kit (Beckman
Coulter Genomics, Indianapolis, IN, USA), and quantified using the Quanti-FluorTM-
ST system (Promega, Madison, WI, USA). Finally, the purified amplicons were pooled in
equimolar concentrations and pair-end sequenced on an Illumina MiSeq platform (Illumina,
Inc., San Diego, CA, USA).
Animals 2021, 11, 1248
5 of 19
2.5. Sequencing Data Processing
The raw sequencing data were filtered and processed using the Quantitative Insights
into Ecology (QIIME) program (1.9.0) [
29
]. The sequences were classified into operational
taxonomic units (OTUs) following the threshold of 97% identity using USTRA-fast sequence
analysis (version 10.0.240) [
30
], and the OTU numbers were assigned based on unique
OTU reads. Taxonomy classifications were assigned against the Silva bacterial alignment
database [
31
], with a confidence threshold of 70%, using the Ribosomal database project
classifier [
32
].
Alpha diversity indices were calculated to indicate community diversity through
QIIME [
29
]. Differences in Chao1, Ace, numbers of OTUs, and Shannon indices were
analyzed with the Mann–Whitney U test and the p-value was calculated, corrected for the
false discovery rate, on the Microbiome Analyst platform [
33
]. The principal coordinates
analysis (PCoA) was conducted based on the Bray–Curtis distance on the Microbiome
Analyst platform [
33
]. Analysis of non-parametric multivariate of variance (PERMANOVA)
was calculated using the Bray-Curtis distance metric (permutation = 999). The linear
discriminant analysis effect size (LEfSe) in the Microbiome Analyst platform was used
to identify genera that showed significant differences in relative abundance [
34
]. The
raw reads were deposited at NCBI (under BioProject accession ID: PRJNA699978, RUN:
SRR13696322-SRR13696353,
https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA6
99978
, (accessed on 12 February 2021)
2.6. Statistical Analyses
The gas production data (gas production, mL/g, dry matter basis) exported from the
automated recording system in Excel were fitted according to an exponential model as
described by France et al. [
35
].
GP
=
A
× [
1
−
e
−C × (time − Lag)
i
(1)
where GP (mL) is the gas production, A (mL) is the ideal maximum gas production, C (h
−1
)
is the gas production rate, Lag (mL) is the lag phase before gas production commences. The
nonlinear regression procedure in statistical analysis system (SAS) 9.2 (SAS Institute Inc.,
Cary, NC, USA) was used in this process. The time taken to reach half of the ideal maximum
gas production (HT, h), and average gas production rate when half of the ideal maximum
gas production produced (AGPR, mL/h) were calculated as shown below according to the
A, C, and Lag values obtained above [
36
]:
HT
=
log
2
C
+
Lag
(2)
AGPR
=
A
×
C
2
× (
log
(
2
) +
C
×
Lag
)
(3)
The data, including starch digestibility, fermentation parameters, and gas production
kinetics parameters, were analyzed using the general linear model produce (GLM) of
SAS 9.2 to obtain the effects of feed type, AOAN, and feed type
×
AOAN. The DM, CP,
NDF, and ADF digestibility at 24 h, 30 h, and 48 h were analyzed using GLM produce as
described above to investigate the effects of feed type, AOAN, time, and AOAN
×
time.
All data are presented as least-squares means. All differences were declared significant at
p
≤
0.05, and tendencies at 0.05
≤
p
≤
0.10.
To assess the correlation between the phenotypic variables and the relative abundance
of microbial genera, the Spearman correlation test was performed using SPSS 20.0 (IBM,
Armonk, NY, USA), and plotted using GraphPad Prism 7 (GraphPad Software, San Diego,
CA, USA). For each correlation, the correlation coefficient value ranged from
−
1 to +1
with larger absolute values indicating a stronger relationship and positive/negative values
indicating the direction of the association.
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