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4 results on '"Congmin Zhu"'

1. iMeta Conference 2024: Building an innovative scientific research ecosystem for microbiome and One Health

Author

Yao Wang, Huiyu Hou, Hao Luo, Jiani Xun, Chuang Ma, Haifei Yang, Defeng Bai, Salsabeel Yousuf, Hujie Lyu, Tianyuan Zhang, Xiulin Wan, Xiaofang Yao, Tengfei Ma, Yuanping Zhou, Zhihao Zhu, Meiyin Zeng, Sanqi An, Qing Bai, Yao Bai, Guodong Cao, Tingting Cao, Yongkai Cao, Chihmin Chang, Lijia Chang, Bo Chen, Dai Chen, Dijun Chen, Hanqing Chen, Jiali Chen, Jinfeng Chen, Wei‐Hua Chen, Xinhai Chen, Yue Chen, Zhangran Chen, Cheng Cheng, Quan Cheng, Xi‐Jian Dai, Chaowen Deng, Feilong Deng, Jingwen Deng, Chang‐Sheng Dong, Lei Dong, Lianhui Duan, Yi Duan, Qingjie Fan, Chao Fang, Tingyu Fang, Wensheng Fang, Zhencheng Fang, Min Fu, Minjie Fu, Cong Gao, Hao Gao, Weiwei Gao, Xinrui Gao, Yi‐Zhou Gao, Yan Geng, Wenping Gong, Shaohua Gu, Xia Gu, Zhengquan Gu, Jian‐Wei Guo, Junjie Guo, Qiuyan Guo, Xiang Guo, Xiaoqian Guo, Dongfei Han, Ziyi Han, Yanan Hao, Jiale He, Jianquan He, Jianyu He, Ruolin He, Guosen Hou, Bin Hu, Haibo Hu, Yi Hu, Yongfei Hu, Yucan Hu, Guanyin Huang, Haiyun Huang, Jiaomei Huang, Shenghui Huang, Baolei Jia, Xingxing Jian, Chao Jiang, Kun Jiang, Lanyan Jiang, Shuaiming Jiang, Jian‐Yu Jiao, Hao Jin, Jiajia Jin, Siyuan Kong, Xinxing Lai, Yuxin Leng, Bang Li, Bing Li, Fang Li, Hao Li, Huanjie Li, Jing Li, Kai Li, Lanqi Li, Leyuan Li, Minghan Li, Pengsong Li, Wei Li, Xianyu Li, Li Xuemeng, Yafei Li, Yuantao Li, Zhi Li, Liqin Liang, Rong Liang, Zhuobin Liang, Qingya Liu, Dejian Liu, Huiheng Liu, Jinchao Liu, Li Liu, Lihui Liu, Moyang Liu, Ran Liu, Shuai Liu, Tianyang Liu, Wei Liu, Wenjuan Liu, Xiaomin Liu, Yang Liu, Yichen Liu, Yina Liu, Yuan Liu, Zhe Liu, Zhipeng Liu, Zhiquan Liu, Chunhao Long, Yun Long, Changying Lu, Chao Lu, Cheng Lu, Qi Lu, Yaning Luan, Peng Luo, Sheng Luo, Ning Ma, Xiao‐Ya Ma, Yan Ma, Wenjun Mao, Yuanfa Meng, Yan Ni, Yawen Ni, Kang Ning, Dongze Niu, Kai Peng, Zhengwu Peng, Xubo Qian, Zhiguang Qiu, Hui Qu, Zepeng Qu, Yan Ren, Zhigang Ren, Youming Shen, Lin Shi, Linlin Shi, Wenxuan Shi, Yongpeng Shi, Tianyuan Song, Xiaohui Song, Xiaoming Song, Xiaowei Song, Qi Su, Yufan Su, Lifang Sun, Qiang Sun, Tiefeng Sun, Yunke Sun, Hua Tang, Wenjing Tang, Tao Yu, Simon Tian, Shuo Wang, Bowen Wang, Cheng Wang, Wang Jin, Leli Wang, Liangliang Wang, Lixiao Wang, Mingbang Wang, Ming‐Ke Wang, Pingyi Wang, Shaolin Wang, Shaopu Wang, Xinxia Wang, Xueqiang Wang, Mi Wei, Yan Wei, Yanxia Wei, Yongjun Wei, Chaoliang Wen, Xin Wen, Linkun Wu, Shengru Wu, Yuting Wu, Shuting Xia, Xiaodong Xia, Yu Xia, Xionggen Xiang, Chuanxing Xiao, Weihua Xiao, Yingping Xiao, Ruohan Xie, Rui Xing, Hui Xu, Wei Xu, Zhimin Xu, Hongliang Xue, Chao Yan, Qiu‐Long Yan, Shaofei Yan, Xiuchuan Yan, Mengli Yang, Yufan Yang, Zhipeng Yang, Ziyuan Yang, Guixiang Yao, Yanlai Yao, Xianfu Yi, Chong Yin, Mingliang Yin, Shicheng Yu, Ying Yu, Yongyao Yu, Fusong Yuan, Shao‐Lun Zhai, Bo Zhang, Chen Zhang, Fang Zhang, Feng‐Li Zhang, Hengguo Zhang, Jinping Zhang, Junya Zhang, Kun Zhang, Li Zhang, Lin Zhang, Lingxuan Zhang, Meng Zhang, Qian Zhang, Runan Zhang, Tongtong Zhang, Tongxue Zhang, Weipeng Zhang, Yong Zhang, Yuchao Zhang, Yujun Zhang, Zeng Zhang, Zhengxiao Zhang, Zhi‐Feng Zhang, Boxi Zhao, Yanyan Zhao, Yibing Zhao, Ziwei Zhao, Diwei Zheng, Ying Zheng, Wenqiang Zhi, Jixin Zhong, Xiangjian Zhong, Wei Zhou, Xin Zhou, Zhemin Zhou, Zhichao Zhou, Congmin Zhu, Feiying Zhu, Xiaodie Zhu, Yutian Zou, Hongling Zhou, Lei Lei, Yanliang Bi, Hubing Shi, Hui‐Zeng Sun, Shuangxia Jin, Wenkai Ren, Lei Dai, Xin Wang, Canhui Lan, Hongwei Liu, Shuang‐Jiang Liu, Yulong Yin, Chun‐Lin Shi, Ren‐You Gan, Fangqing Zhao, Jun Yu, Tong Chen, Xin Hong, Hua Yang, Bangzhou Zhang, Shifu Chen, Xiaodong Li, Yunyun Gao, and Yong‐Xin Liu

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Published
2024
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2. Determine independent gut microbiota-diseases association by eliminating the effects of human lifestyle factors

Author

Congmin Zhu, Xin Wang, Jianchu Li, Rui Jiang, Hui Chen, Ting Chen, and Yuqing Yang

Subjects
Gut microbiota, Human variables, Disease classification, Machine learning, Microbiology, QR1-502
Abstract

Abstract Lifestyle and physiological variables on human disease risk have been revealed to be mediated by gut microbiota. Low concordance between case-control studies for detecting disease-associated microbe existed due to limited sample size and population-wide bias in lifestyle and physiological variables. To infer gut microbiota-disease associations accurately, we propose to build machine learning models by including both human variables and gut microbiota. When the model’s performance with both gut microbiota and human variables is better than the model with just human variables, the independent gut microbiota -disease associations will be confirmed. By building models on the American Gut Project dataset, we found that gut microbiota showed distinct association strengths with different diseases. Adding gut microbiota into human variables enhanced the classification performance of IBD significantly; independent associations between occurrence information of gut microbiota and irritable bowel syndrome, C. difficile infection, and unhealthy status were found; adding gut microbiota showed no improvement on models’ performance for diabetes, small intestinal bacterial overgrowth, lactose intolerance, cardiovascular disease. Our results suggested that although gut microbiota was reported to be associated with many diseases, a considerable proportion of these associations may be very weak. We proposed a list of microbes as biomarkers to classify IBD and unhealthy status. Further functional investigations of these microbes will improve understanding of the molecular mechanism of human diseases.

Published
2022
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3. Distinct Gut Microbiota Induced by Different Fat-to-Sugar-Ratio High-Energy Diets Share Similar Pro-obesity Genetic and Metabolite Profiles in Prediabetic Mice

Author

Kai Shan, Hongyan Qu, Keru Zhou, Liangfang Wang, Congmin Zhu, Haiqin Chen, Zhennan Gu, Jing Cui, Guoling Fu, Jiaqi Li, Heyan Chen, Rong Wang, Yumin Qi, Wei Chen, and Yong Q. Chen

Subjects
high-energy diet, prediabetes, gut microbiota, metagenome, metabolome, Microbiology, QR1-502
Abstract

ABSTRACT Gut microbiota play important roles in host metabolism, especially in diabetes. However, why different diets lead to similar diabetic states despite being associated with different microbiota is not clear. Mice were fed two high-energy diets (HED) with the same energy density but different fat-to-sugar ratios to determine the associations between the microbiota and early-stage metabolic syndrome. The two diets resulted in different microbiota but similar diabetic states. Interestingly, the microbial gene profiles were not significantly different, and many common metabolites were identified, including l-aspartic acid, cholestan-3-ol (5β, 3α), and campesterol, which have been associated with lipogenesis and inflammation. Our study suggests that different metabolic-syndrome-inducing diets may result in different microbiota but similar microbiomes and metabolomes. This suggests that the metagenome and metabolome are crucial for the prognosis and pathogenesis of obesity and metabolic syndrome. IMPORTANCE Various types of diet can lead to type 2 diabetes. The gut microbiota in type 2 diabetic patients are also different. So, two questions arise: whether there are any commonalities between gut microbiota induced by different pro-obese diets and whether these commonalities lead to disease. Here we found that high-energy diets with two different fat-to-sugar ratios can both cause obesity and prediabetes but enrich different gut microbiota. Still, these different gut microbiota have similar genetic and metabolite compositions. The microbial metabolites in common between the diets modulate lipid accumulation and macrophage inflammation in vivo and in vitro. This work suggests that studies that only use 16S rRNA amplicon sequencing to determine how the microbes respond to diet and associate with diabetic state are missing vital information.

Published
2019
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4. Inferring Multiple Metagenomic Association Networks based on Variation of Environmental Factors

Author

Kaikun Xie, Xin Wang, Yuqing Yang, Ning Chen, Ting Chen, and Congmin Zhu

Subjects
0303 health sciences, 03 medical and health sciences, Microbial ecosystem, 0302 clinical medicine, Metagenomics, 030211 gastroenterology & hepatology, Irritable bowel disease, Variation (game tree), Computational biology, Biology, Association (psychology), 030304 developmental biology, Rank correlation
Abstract

Identifying significant biological relationships or patterns is central to many metagenomic studies. Methods that estimate association networks have been proposed for this purpose, but they assume that associations are static, neglecting the fact that relationships in a microbial ecosystem may vary with changes in environmental factors, which can result in inaccurate estimations. We propose a computational model, k-Lognormal-Dirichlet-Multinomial model (kLDM), which estimates multiple association networks that correspond to specific environmental conditions according to values of environmental factors (EFs), and simultaneously infers microbe-microbe and environmental factor-microbe associations for each network. We showed the effectiveness of kLDM on synthetic data, a colorectal cancer dataset, as well as the TARA Oceans and American Gut project datasets. The results showed that the widely used Spearman’s rank correlation coefficient (SCC) performed much worse than other methods, indicating the importance of separating samples by environmental conditions. We compared cancer fecal samples with cancer-free samples, and our estimation showed fewer associations among microbes but stronger associations between specific bacteria such as five colorectal cancer (CRC)-associated OTUs, indicating gut microbe translocation in cancer patients. Some environmental-factor-dependent associations were found within marine eukaryotic community, and gut microbial heterogeneity of irritable bowel disease (IBD) patients was detected. Results demonstrated that kLDM could successfully unravel the underlying biological associations. In summary, our study presents a computational framework that can elucidate the complex associations within microbial ecosystems. The kLDM program, R, and python scripts, together with all experimental datasets are all accessible at Github (https://github.com/tinglab/kLDM.git).

Published
2020
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