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Associate Professor TAKAHASHI Hideyuki and his research team have predicted the gut microbiota that influences livestock reproductive efficiency
Computational scientific verification of causal structures negatively affecting reproductive performance.
Point
- The relationship between gut microbiota and reproductive performance has become a prominent research topic in recent years.
- In cattle, which are industrial animals, results from machine learning (※1) and causal inference (※2) indicated that the family Erysipelotrichaceae, the genus Clostridium sensu stricto 1, the genus Family XIII AD3011 group, and the creatinine degradation pathway (PWY-4722) may influence the increase in the number of artificial inseminations.
- Furthermore, it was estimated that future artificial insemination frequency could be predicted from the microbiota present more than five months before artificial insemination.
Abstract
Graduate student TAGUCHI Yutaka (at the time of research), graduate student YAMANO Haruki (co-first author), and Associate Professor TAKAHASHI Hideyuki from the Faculty of Agriculture at Kyushu University, in collaboration with MIYAMOTO Hirokuni, Visiting Chief Researcher, and OHNO Hiroshi, Team Director from the RIKEN Center for Integrative Medical Sciences, KIKUCHI Jun, Team Director, and KUROTANI Atsushi, Researcher (at the time of research) from the RIKEN Center for Sustainable Resource Science (currently at the National Agriculture and Food Research Organization, Agricultural Information Research Center), MORIYA Shigeharu, Senior Researcher, and WADA Satoshi, Team Director from the RIKEN Center for Advanced Photonics, conducted an industry-academia collaborative research with Mirai Global Farm Co., Ltd., Itoham Foods Inc., Nippon Formula Feed Mfg. Co., Ltd., Nikkankagaku Co., Ltd., Thermus Co., Ltd., and Keiyo Gas Energy Solution Co., Ltd. They evaluated the causal structures of gut microbiota and reproductive performance in Japanese Black breeding cows using computational scientific methods, demonstrating the possibility of predicting future reproductive performance from gut microbiota.
The relationship between gut microbiota and reproductive performance has become a prominent research topic in recent years. In humans and mice, it is known that disruptions in gut microbiota can negatively affect the reproductive system through obesity and immune system abnormalities. However, in industrial animals such as cattle, the relationship between pre-pregnancy gut microbiota and reproductive performance has not been fully elucidated.
In this study, Japanese Black virgin heifers were used to comprehensively analyze the fecal microbiota at 150 days of age (more than five months before artificial insemination) and 300 days of age (just before artificial insemination) in groups with fewer and more artificial inseminations required for pregnancy. The results indicated that the family Erysipelotrichaceae, the genus Clostridium sensu stricto 1, and the genus Family XIII AD3011 group at 150 days of age (more than five months before artificial insemination) may influence the increase in the number of artificial inseminations (delayed conception) more than at 300 days of age (just before artificial insemination). Additionally, pathway analysis (※3) predicting metabolic pathways from fecal microbiota data showed that the creatinine degradation pathway PWY-4722 also influences the increase in the number of artificial inseminations at 150 days of age. Furthermore, the impact of thermophilic probiotic administration for a short period up to 90 days of age was evaluated, computationally demonstrating the importance of diagnosis at 150 days of age.
From these results, it was suggested that the number of artificial inseminations, which is extremely important in beef cattle production, can be inferred from the fecal microbiota more than five months before artificial insemination, rather than just before artificial insemination.
These research findings were published on Friday, March 28, 2025, in Springer Nature's journal "Animal Microbiome," which is ranked second in the field of veterinary science by Scopus.
Glossary
(※1) Machine Learning: Computer algorithms that learn from data to automatically discover certain patterns or rules. It is classified into unsupervised machine learning and supervised machine learning. In this study, it is used for extracting characteristic factors.
(※2) Causal Inference: A method for statistically estimating causal effects between data based on information obtained from experimental and observational data.
(※3) Pathway Analysis: An analytical method for predicting bacterial metabolic pathways from 16S RNA sequence data.
Paper Information
Journal: Animal Microbiome
Title: Causal estimation of the relationship between reproductive performance and the fecal bacteriome in cattle
Authors: Yutaka Taguchi, Haruki Yamano, Yudai Inabu, Hirokuni Miyamoto, Koki Hayasaki, Noriyuki Maeda, Yoshiro Kanmera, Seiji Yamasaki, Noboru Ota, Kenji Mukawa, Atsushi Kurotani, Shigeharu Moriya, Teruno Nakaguma, Chitose Ishii, Makiko Matsuura, Tetsuji Etoh, Yuji Shiotsuka, Ryoichi Fujino, Motoaki Udagawa, Satoshi Wada, Jun Kikuchi, Hiroshi Ohno, Hideyuki Takahashi
DOI：10.1186/s42523-025-00396-x
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