Fungal and ciliate protozoa are the main rumen microbes associated with methane emissions in dairy cattle

Author:

López-García Adrián1ORCID,Saborío-Montero Alejandro12ORCID,Gutiérrez-Rivas Mónica1ORCID,Atxaerandio Raquel3ORCID,Goiri Idoia3ORCID,García-Rodríguez Aser3ORCID,Jiménez-Montero Jose A4ORCID,González Carmen1ORCID,Tamames Javier5ORCID,Puente-Sánchez Fernando5ORCID,Serrano Magdalena1ORCID,Carrasco Rafael6ORCID,Óvilo Cristina1ORCID,González-Recio Oscar17ORCID

Affiliation:

1. Departamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Crta. de la Coruña km 7.5, 28040 Madrid, Spain

2. Escuela de Zootecnia y Centro de Investigación en Nutrición Animal, Universidad de Costa Rica, 11501 San José, Costa Rica

3. NEIKER – Instituto Vasco de Investigación y Desarrollo Agrario. Basque Research and Technology Alliance (BRTA), Campus Agroalimentario de Arkaute s/n, 01192 Arkaute, Spain

4. Confederación de Asociaciones de Frisona Española (CONAFE), Ctra. de Andalucía km 23600 Valdemoro, 28340 Madrid, Spain

5. Departamento de Biología de Sistemas, Centro Nacional de Biotecnología, CSIC, Madrid, 28049 Madrid, Spain

6. Departamento de Periodismo y Nuevos Medios, Universidad Complutense de Madrid, Ciudad Universitaria s/n, 28040 Madrid, Spain

7. Departamento de Producción Agraria, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Ciudad Universitaria s/n, 28040 Madrid, Spain

Abstract

Abstract Background Mitigating the effects of global warming has become the main challenge for humanity in recent decades. Livestock farming contributes to greenhouse gas emissions, with an important output of methane from enteric fermentation processes, mostly in ruminants. Because ruminal microbiota is directly involved in digestive fermentation processes and methane biosynthesis, understanding the ecological relationships between rumen microorganisms and their active metabolic pathways is essential for reducing emissions. This study analysed whole rumen metagenome using long reads and considering its compositional nature in order to disentangle the role of rumen microbes in methane emissions. Results The β-diversity analyses suggested a subtle association between methane production and overall microbiota composition (0.01 < R2 < 0.02). Differential abundance analysis identified 36 genera and 279 KEGGs as significantly associated with methane production (Padj < 0.05). Those genera associated with high methane production were Eukaryota from Alveolata and Fungi clades, while Bacteria were associated with low methane emissions. The genus-level association network showed 2 clusters grouping Eukaryota and Bacteria, respectively. Regarding microbial gene functions, 41 KEGGs were found to be differentially abundant between low- and high-emission animals and were mainly involved in metabolic pathways. No KEGGs included in the methane metabolism pathway (ko00680) were detected as associated with high methane emissions. The KEGG network showed 3 clusters grouping KEGGs associated with high emissions, low emissions, and not differentially abundant in either. A deeper analysis of the differentially abundant KEGGs revealed that genes related with anaerobic respiration through nitrate degradation were more abundant in low-emission animals. Conclusions Methane emissions are largely associated with the relative abundance of ciliates and fungi. The role of nitrate electron acceptors can be particularly important because this respiration mechanism directly competes with methanogenesis. Whole metagenome sequencing is necessary to jointly consider the relative abundance of Bacteria, Archaea, and Eukaryota in the statistical analyses. Nutritional and genetic strategies to reduce CH4 emissions should focus on reducing the relative abundance of Alveolata and Fungi in the rumen. This experiment has generated the largest ONT ruminal metagenomic dataset currently available.

Publisher

Oxford University Press (OUP)

Subject

Computer Science Applications,Health Informatics

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