Microbiome-driven breeding strategy potentially improves beef fatty acid profile benefiting human health and reduces methane emissions

Abstract

Background: Healthier ruminant products can be achieved by adequate manipulation of the rumen microbiota to increase the flux of beneficial fatty acids reaching host tissues. Genomic selection to modify the microbiome function provides a permanent and accumulative solution, which may have also favourable consequences in other traits of interest (e.g. methane emissions). Possibly due to a lack of data, this strategy has never been explored.

Results: This study provides a comprehensive identification of ruminal microbial mechanisms under host genomic influence that directly or indirectly affect the content of unsaturated fatty acids in beef associated with human dietary health benefits C18:3n-3, C20:5n-3, C22:5n-3, C22:6n-3 or cis-9, trans-11 C18:2 and trans-11 C18:1 in relation to hypercholesterolemic saturated fatty acids C12:0, C14:0 and C16:0, referred to as N3 and CLA indices. We first identified that ~27.6% (1002/3633) of the functional core additive log-ratio transformed microbial gene abundances (alr-MG) in the rumen were at least moderately host-genomically influenced (HGFC). Of these, 372 alr-MG were host-genomically correlated with the N3 index (n=290), CLA index (n=66) or with both (n=16), indicating that the HGFC influence on beef fatty acid composition is much more complex than the direct regulation of microbial lipolysis and biohydrogenation of dietary lipids and that N3 index variation is more strongly subjected to variations in the HGFC than CLA. Of these 372 alr-MG, 110 were correlated with the N3 and/or CLA index in the same direction, suggesting the opportunity for enhancement of both indices simultaneously through a microbiome-driven breeding strategy. These microbial genes were involved in microbial protein synthesis (aroF and serA), carbohydrate metabolism and transport (galT, msmX), lipopolysaccharide biosynthesis (kdsA, lpxD, lpxB), or flagellar synthesis (flgB, fliN) in certain genera within the Proteobacteria phyla (e.g. Serratia, Aeromonas). A microbiome-driven breeding strategy based on these microbial mechanisms as sole information criteria resulted in a positive selection response for both indices (1.36±0.24 and 0.79±0.21 sd of N3 and CLA indices, at 2.06 selection intensity). When evaluating the impact of our microbiome-driven breeding strategy to increase N3 and CLA indices on the environmental trait methane emissions (g/kg of dry matter intake), we obtained a correlated mitigation response of -0.41±0.12 sd.

Conclusion: This research provides insight on the possibility of using the ruminal functional microbiome as information for host genomic selection, which could simultaneously improve several microbiome-driven traits of interest, in this study exemplified with meat quality traits and methane emissions. Video Abstract.

Keywords: Beef; Conjugated linoleic acid; Genomic selection; Methane emissions; Microbial genes; Microbiome-driven breeding; Rumen microbiome; Very long-chain n-3 fatty acids.

Fig. 1

Host-genomically influenced functional core microbiome (HGFC) in the rumen of cattle identified as additive log-ratio transformed microbial gene abundances (alr-MGs) with ≥ 70% occupancy across animals and highly probable host genomic effects. A Number of alr-MGs and summed cumulative relative abundance of alr-MGs numerators comprehending the HGFC in our population. B Violin plots represent the distribution of heritability estimates for the 1002 HGFC alr-MGs, classified by COG functional modules of the numerators, represented by different colors. Full COG names are described in Table S11

Fig. 2

Microbial genomes of genera from the Proteobacteria phylum highly enriched in microbial genes genomically correlated with N3 and CLA indices. The number of microbial genes present in each microbial genus ranges from 70 (Desulfovibrio) to 96 (Vibrio) (see Table S4). Different colours represent different COG functional modules. Full COG names are described in Table S11

Fig. 3

Study of 110 additive log-ratio transformed microbial gene abundances (alr-MGs) host-genomically influenced and correlated with N3 and CLA indices in the same direction. A Genomic correlations (RG) between alr-MGs and N3 and CLA indices in beef, classified by COG functional modules of the alr-MG numerators. Host genomic correlation estimates, and the names of the microbial genes are provided in Table S3. Co-abundance network analysis of B corrected phenotypic values or C estimated genomic breeding values for microbial gene abundances. Different colours indicate different clusters: 1 (green), 2 (orange) and 3 (blue). Edges correspond to the absolute Pearson correlation value between alr-MGs > l0.30l, and the thickness of the edges increases with the correlation size. The nodes represent alr-MGs and their size corresponds to the node degree (number of incident edges per node). D Thirty-one out of the 110 alr-MGs selected for breeding purposes classified along clusters and functions. Colours represent their position in the genomic co-abundance network analysis.

Fig. 4

Responses to selection in A N3 and CLA indices and B methane emissions (CH_4, g/kg of dry matter intake) using the 31 additive log-ratio transformed microbial gene abundances (alr-MGs) as selection information (i.e., microbiome-driven breeding strategy). Responses to selection in CLA, N3 indices and CH₄ emissions are estimated by selecting animals for their aggregate estimated breeding value for CLA and N3 (assuming equal economical weights) predicted using the 31 alr-MGs. Response is expressed in units of phenotypic standard deviations of the trait (SD)

Fig. 5

Host genomic correlations (Rg) between the 31 additive log-ratio transformed microbial gene abundances (alr-MGs) and CH₄ (rg_CH4), N3 (rg_N3) and CLA (rg_CLA) indices. The vast majority are favourable across traits, which indicate that an increase of CLA and N3 indices by genomic selection of the microbial gene abundances reduces CH₄ emissions. Bars represent means and highest posterior density interval at 95% probability. For full names of alr-MGs, see Table S8