Effect of different forage-to-concentrate ratios on the structure of rumen bacteria and its relationship with nutrition levels and real-time methane production in sheep

Emission from ruminants has become the largest source of anthropogenic emission of methane in China. The structure of the rumen flora has a significant effect on methane production. To establish a more accurate prediction model for methane production, the rumen flora should be one of the most important parameters. The objective of the present study was to investigate the relationship among changes in rumen flora, nutrient levels, and methane production in sheep fed with the diets of different forage-to-concentration ratios, as well as to screen for significantly different dominant genera. Nine rumen-cannulated hybrid sheep were separated into three groups and fed three diets with forage-to-concentration ratios of 50:50, 70:30, and 90:10. Three proportions of the diets were fed according to a 3 × 3 incomplete Latin square, design during three periods of 15 d each. The ruminal fluid was collected for real-time qPCR, high-throughput sequencing and in vitro rumen fermentation in a new real-time fermentation system wit. Twenty-two genera were screened, the abundance of which varied linearly with forage-to-concentration ratios and methane production. In addition, during the 12-hour in vitro fermentation, the appearance of peak concentration was delayed by 26-27 min with the different structure of rumen bacteria. The fiber-degrading bacteria were positively correlated with this phenomenon, but starch-degrading and protein-degrading bacteria were negative correlated. These results would facilitate macro-control of rumen microorganisms and better management of diets for improved nutrition in ruminants. In addition, our findings would help in screening bacterial genera that are highly correlated with methane production.


Introduction
142 DNA continued to be tested for real-time quantitative polymerase chain reaction (qPCR) and 143 high-throughput sequencing. A total of 9 samples for the three diets were collected and each 144 sample was tested twice in order to have six replicates for each diet. Real-time qPCR were tested on Applied Biosystems StepOne™ Real-time qPCR 148 System based on the methods of Denman and McSweeney [31]. The designed primers were 149 shown in the Table 2.  Academy of Agricultural Sciences, code Qtfxy-6), which was tested for the effluent gas  Sequences of good quality were deeply studied through its uploading to QIIME [39].  three groups were similar in diversity ( Fig 1B). The richness (P<0.01) of the rumen 271 microbiota was related to F:C ( Fig 1C).  bacterial richness in five out of 14 phyla were discovered in the three groups ( At the level of genus, the identification of 150 genera in all the samples was 314 conducted despite F:C (Fig 2A)    In terms of the RDA, our dataset changed, which was principally interpreted by the 344 increasing F:C (Fig 3). It suggested that 100% change in bacteria was explained by all the 345 nutrition indices whose order of contribution was CP > ADF > NDF > Starch > EE > ADL 346 (   In vitro rumen fermentation characteristics, real-time methane 372 production and its correlation with the rumen microbiota 373 After 12 h fermentation, the concentrations of pH, AA and A/P were decreased 374 greatly with the decreasing F:C. Simultaneously, the concentrations of PA, BA, NH 3 -N, and 375 IVDMD were increased with the decreasing F:C. The greatly growth of IVDMD had led to 376 the massive production of VFAs (Table 8). The C max (P<0.01, Table 9, Fig 4) and total 377 production (P<0.05, Table 9) of methane decreased with the increase in F:C, whereas T max 378 (P<0.01, Table 9 with the C max and total production, and a negative correlation with T max (Table 10).

381
Firmicutes and Saccharibacteria were positively correlated with T max , but negatively 382 correlated with C max and total production ( Olsenella was positively correlated with C max and total production in bacterial abundance, but 385 negatively correlated with T max (Table 11) and Ruminiclostridium was positively correlated with T max in bacterial abundance, but 388 negatively correlated with C max and total production (Table 11).   The C max (P<0.01, Table 9, Fig 4) and total production (P<0.05, Table 9) of methane 422 decreased with the increase in F:C, whereas T max (P<0.01, Table 9, Fig 4) (Table 10). Firmicutes and Saccharibacteria were positively 426 correlated with T max , but negatively correlated with C max and total production ( Succinivibrio, Syntrophococcus and Olsenella was positively correlated with C max and total 429 production in bacterial abundance, but negatively correlated with T max (Table 11).

465
The proportion of Eubacterium with the function of degrading structural carbohydrates was 466 similar to that of Butyrivibrio [58].

467
In this study, the third aim was to gain a preliminary understanding of the relationship With lower CP content in diet, bacteria required more time for protein decomposition to 499 provide materials for their reproduction and methane synthesis, which indicated that methane 500 synthesis needed to go through a "start-up" phase before the normal fermentation mode.

501
Previous studies only found that C max of methane occurred at about 2 h after food intake

518
The genera of bacterial, as the parameters for the prediction models, had been narrowed 519 down. There were significant correlations between specific bacterial at the starting of fermentation and real-time methane production in vitro. However, the dynamic changes of 521 bacterial at the moment such as T max during the fermentation need to be explored in the 522 following study. Additionally, the fermentation in vivo was more complex. For instance, 523 nitrogen cycling in ruminants might provide bacteria with initial nitrogenous material [69].

524
Thus, further studies are required to confirm the occurrence of this delay phenomenon in vivo 525 and illustrate the process.