Paper reading (七十九)：A subset of the core rumen microbiome dictates cow productivity and emissions

论文题目：A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions

scholar 引用：3

页数：13

发表时间：2019.07

发表刊物：Science Advances

作者：R. John Wallace, Goor Sasson,...,Itzhak Mizrahi

摘要：

A 1000-cow study across four European countries was undertaken to understand to what extent ruminant microbiomes can be controlled by the host animal and to identify characteristics of the host rumen microbiome axis that determine productivity and methane emissions. A core rumen microbiome, phylogenetically linked and with a preserved hierarchical structure, was identified. A 39-member subset of the core formed hubs in co-occurrence networks linking microbiome structure to host genetics and phenotype (methane emissions, rumen and blood metabolites, and milk production efficiency). These phenotypes can be predicted from the core microbiome using machine learning algorithms. The heritable core microbes, therefore, present primary targets for rumen manipulation toward sustainable and environmentally friendly agriculture.

正文组织架构：

1. Introduction

2. Results

3. Discussion and Conclusions

4. Materials and Methods

4.1 Experimental design and subject details

4.2 Method details

正文部分内容摘录：

1. Biological Problem: What biological problems have been solved in this paper?

phenotypes prediction
predict rumen metabolism diet and host traits

2. Main discoveries: What is the main discoveries in this paper?

A core rumen microbiome, phylogenetically linked and with a preserved hierarchical structure, was identified.
A 39-member subset of the core formed hubs in co-occurrence networks linking microbiome structure to host genetics and phenotype (methane emissions, rumen and blood metabolites, and milk production efficiency).
These phenotypes can be predicted from the core microbiome using machine learning algorithms.
The heritable core microbes, therefore, present primary targets for rumen manipulation toward sustainable and environmentally friendly agriculture.
These tools highlighted the core microbiome as highly explanatory for dietary components and rumen metabolites, with propionate approaching an agreement of r2 = 0.9 in some farms.
Our findings also show that core microbiome has higher prediction power than host animals’ genotype (based on the GRM), as has dietary composition
Overall, in both machine learning algorithms, the heritable microbes exhibited, on average, a significantly higher explanatory power for host phenotypes and other experimental variables compared to other core microbes

3. ML(Machine Learning) Methods: What are the ML methods applied in this paper?

Two distinctive machine learning algorithms were applied to predict rumen metabolism diet and host traits, based on core microbiome composition, Ridge regression and random forest (RF) , using linear regression and decision tree–based approaches, respectively.
Ridge regression：
Prediction of phenotypes and other experimental variables by core microbiome： The abundances of the core microbes within each farm were used as features fed into a Ridge regression to predict each of the traits (separately).
Prediction of phenotypes by core microbiome while correcting for diet：To estimate the phenotypic variability explained by core microbes with omission of diet components effect
Prediction of phenotypes by diet components：Diet components within each farm were used as features fed into a Ridge regression to predict each of the phenotypes (separately).
Comparing heritable microbes to other core miocrobes’ ability to explain experimental variables
Explained variation (r2) of different host traits as function of core microbiome composition. r2 estimates were derived from a machine learning approach where a trait value was predicted for a given animal using the Ridge regression that was constructed from other animals in the farm (leave-one-out k-fold regression). Thereafter, prediction r2 value was calculated between the vectors of observed and predicted trait values. Indicated host traits were significantly explained (via prediction) by core microbe (OTU) abundance profiles.
random forest (RF) ：
Prediction of phenotypes and other experimental variables by core microbiome using RF：As an additional analysis to further verify our findings of core microbiome explainability (by prediction) of host phenotypes and experimental variables, we repeated that analysis using RF regression. The abundances of the core microbes within each farm were used as features fed into a RF regression model to predict each of the traits (separately).

4. ML Advantages: Why are these ML methods better than the traditional methods in these biological problems?

This allowed us to investigate the degree of agreement between predicted and actual values.

5. Biological Significance: What is the biological significance of these ML methods’ results?

although having lower explainability, many of the host traits, including host plasma metabolites and milk composition, could be explained to an extent by the core microbiome composition

6. Prospect: What are the potential applications of these machine learning methods in biological science?

The heritable core microbes, therefore, present primary targets for rumen manipulation toward sustainable and environmentally friendly agriculture.

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Paper reading (七十九)：A subset of the core rumen microbiome dictates cow productivity and emissions

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