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Resilience of human gut microbial communities for the long stay with multiple dietary shifts
  1. Hong Liu1,2,
  2. Maozhen Han1,
  3. Shuai Cheng Li3,
  4. Guangming Tan4,5,
  5. Shiwei Sun4,5,
  6. Zhiqiang Hu2,
  7. Pengshuo Yang1,
  8. Rui Wang2,
  9. Yawen Liu2,
  10. Feng Chen2,
  11. Jianjun Peng2,
  12. Hong Peng2,
  13. Hongxing Song2,
  14. Yang Xia2,
  15. Liqun Chu2,
  16. Quan Zhou2,
  17. Feng Guan2,
  18. Jing Wu2,
  19. Dongbo Bu4,5,
  20. Kang Ning1
  1. 1 Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
  2. 2 Beijing Shijitan Hospital, Capital Medical University, Beijing, China
  3. 3 Department of Computer Science, City University of Hong Kong, Hong Kong, China
  4. 4 Key Lab of Intelligent Information Processing, State Lab of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
  5. 5 School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing, China
  1. Correspondence to Dr Jing Wu, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China; wujingsjt{at}gmail.com, Professor Dongbo Bu, Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China; dbu{at}ict.ac.cn and Professor Kang Ning, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; ningkang{at}hust.edu.cn

https://doi.org/10.1136/gutjnl-2018-317298

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    We read with great interest the article by Bennet et al 1 that investigated the impact of dietary shifts on gut microbiota. Together with several recent studies,2–5 they have shown that short-term dietary shifts can alter the composition of gut microbiota for both patients and healthy humans. However, these works drawn the conclusion with only a few time points and the subjects under investigation had undergone just one dietary shift. The dynamic patterns of microbial communities across longer time scales with multiple dietary shifts remain unclear.

    In this study, we recruited a Chinese volunteer team (VT) composing of 10 people who departed from Beijing, conducted a long stay of 6 months in Trinidad and Tobago (TAT) and returned to Beijing. Their faecal samples (188 samples), together with detailed dietary information, were collected using a high-density longitudinal sampling strategy (19 time points on average for VT members). We partitioned the whole longitudinal study into six phases: T1 represents pre-travel phase (20 samples), T2 (28 samples), T3 (60 samples) and T4 (21 samples) represent three time-slots when VT stayed in TAT, and T5 (35 samples) and T6 (20 samples) represent two time-slots after VT returned to Beijing. Meanwhile, we also collected samples from healthy natives of Beijing (BJN, 57 samples), TAT healthy natives (TTN, 28 samples), TAT patients (TTP, six samples) and TAT Chinese (TTC, eight samples) as control datasets. Finally, we sequenced the V4 …

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