Understanding the micro-coevolution of the human gut microbiome with host genetics is challenging but essential in both evolutionary and medical studies.To gain insight into the interactions between host genetic varia...Understanding the micro-coevolution of the human gut microbiome with host genetics is challenging but essential in both evolutionary and medical studies.To gain insight into the interactions between host genetic variation and the gut microbiome,we analyzed both the human genome and gut microbiome collected from a cohort of 190 students in the same boarding college and representing 3 ethnic groups,Uyghur,Kazakh,and Han Chinese.We found that differences in gut microbiome were greater between genetically distinct ethnic groups than those genetically closely related ones in taxonomic composition,functional composition,enterotype stratification,and microbiome genetic differentiation.We also observed considerable correlations between host genetic variants and the abundance of a subset of gut microbial species.Notably,interactions between gut microbiome species and host genetic variants might have coordinated effects on specific human phenotypes.Bacteroides ovatus,previously reported to modulate intestinal immunity,is significantly correlated with the host genetic variant rs12899811(meta-P=5.55×10^(-5)),which regulates the VPS33B expression in the colon,acting as a tumor suppressor of colorectal cancer.These results advance our understanding of the micro-coevolution of the human gut microbiome and their interactive effects with host genetic variation on phenotypic diversity.展开更多
The gut microbiota of intensive care unit(ICU)patients displays extreme dysbiosis associated with increased susceptibility to organ failure,sepsis,and septic shock.However,such dysbiosis is difficult to characterize o...The gut microbiota of intensive care unit(ICU)patients displays extreme dysbiosis associated with increased susceptibility to organ failure,sepsis,and septic shock.However,such dysbiosis is difficult to characterize owing to the high dimensional complexity of the gut microbiota.We tested whether the concept of enterotype can be applied to the gut microbiota of ICU patients to describe the dysbiosis.We collected 131 fecal samples from 64 ICU patients diagnosed with sepsis or septic shock and performed 16S rRNA gene sequencing to dissect their gut microbiota compositions.During the development of sepsis or septic shock and during various medical treatments,the ICU patients always exhibited two dysbiotic microbiota patterns,or ICU-enterotypes,which could not be explained by host properties such as age,sex,and body mass index,or external stressors such as infection site and antibiotic use.ICU-enterotype I(ICU E1)comprised predominantly Bacteroides and an unclassified genus of Enterobacteriaceae,while ICU-enterotype II(ICU E2)comprised predominantly Enterococcus.Among more critically ill patients with Acute Physiology and Chronic Health Evaluation II(APACHE II)scores>18,septic shock was more likely to occur with ICU E1(P=0.041).Additionally,ICU E1 was correlated with high serum lactate levels(P=0.007).Therefore,different patterns of dysbiosis were correlated with different clinical outcomes,suggesting that ICU-enterotypes should be diagnosed as independent clinical indices.Thus,the microbial-based human index classifier we propose is precise and effective for timely monitoring of ICU-enterotypes of individual patients.This work is a first step toward precision medicine for septic patients based on their gut microbiota profiles.展开更多
In 2011, the term ‘‘enterotype" first appeared to the general public in Nature, which refers to stratification of human gut microbiota. However, with more studies on enterotypes conducted nowadays, doubts about...In 2011, the term ‘‘enterotype" first appeared to the general public in Nature, which refers to stratification of human gut microbiota. However, with more studies on enterotypes conducted nowadays, doubts about the existence and robustness of enterotypes have also emerged. Here we reviewed current opinions about enterotypes from both conceptual and analytical points of view.We firstly illustrated the definition of the enterotype and various factors influencing enterotypes,such as diet, administration of antibiotics, and age. Then we summarized lines of evidence that pose the concept against the enterotype, and described the current methods for enterotype analysis.Finally, we showed that the concept of enterotype has been extended to other ecological niches.Based on current studies on enterotypes, it has been clear that more studies with larger sample sizes are needed to characterize the enterotypes. Improved computational methods are also required to build sophisticated models, reflecting the dynamics and resilience of enterotypes.展开更多
Recently advancements in deep learning models have significantly facilitated the development of sequential recommender systems(SRS).However,the current deep model structures are limited in their ability to learn high-...Recently advancements in deep learning models have significantly facilitated the development of sequential recommender systems(SRS).However,the current deep model structures are limited in their ability to learn high-quality embeddings with insufficient data.Meanwhile,highly skewed long-tail distribution is very common in recommender systems.Therefore,in this paper,we focus on enhancing the representation of tail items to improve sequential recommendation performance.Through empirical studies on benchmarks,we surprisingly observe that both the ranking performance and training procedure are greatly hindered by the poorly optimized tail item embeddings.To address this issue,we propose a sequential recommendation framework named TailRec that enables contextual information of tail item well-leveraged and greatly improves its corresponding representation.Given the characteristics of the sequential recommendation task,the surrounding interaction records of each tail item are regarded as contextual information without leveraging any additional side information.This approach allows for the mining of contextual information from cross-sequence behaviors to boost the performance of sequential recommendations.Such a light contextual filtering component is plug-and-play for a series of SRS models.To verify the effectiveness of the proposed TailRec,we conduct extensive experiments over several popular benchmark recommenders.The experimental results demonstrate that TailRec can greatly improve the recommendation results and speed up the training process.The codes of our methods have been available.展开更多
Despite the skin microbiome has been linked to skin health and diseases,its role in modulating human skin appearance remains understudied.Using a total of 1244 face imaging phenomes and 246 cheek metagenomes,we first ...Despite the skin microbiome has been linked to skin health and diseases,its role in modulating human skin appearance remains understudied.Using a total of 1244 face imaging phenomes and 246 cheek metagenomes,we first established three skin age indices by machine learning,including skin phenotype age(SPA),skin microbiota age(SMA),and skin integration age(SIA)as surrogates of phenotypic aging,microbial aging,and their combination,respectively.Moreover,we found that besides aging and gender as intrinsic factors,skin microbiome might also play a role in shaping skin imaging phenotypes(SIPs).Skin taxonomic and functionalαdiversity was positively linked to melanin,pore,pigment,and ultraviolet spot levels,but negatively linked to sebum,lightening,and porphyrin levels.Furthermore,certain species were correlated with specific SIPs,such as sebum and lightening levels negatively correlated with Corynebacterium matruchotii,Staphylococcus capitis,and Streptococcus sanguinis.Notably,we demonstrated skin microbial potential in predicting SIPs,among which the lightening level presented the least error of 1.8%.Lastly,we provided a reservoir of potential mechanisms through which skin microbiome adjusted the SIPs,including the modulation of pore,wrinkle,and sebum levels by cobalamin and heme synthesis pathways,predominantly driven by Cutibacterium acnes.This pioneering study unveils the paradigm for the hidden links between skin microbiome and skin imaging phenome,providing novel insights into how skin microbiome shapes skin appearance and its healthy aging.展开更多
基金the National Natural Science Foundation of China(NSFC)(31771388,32030020,31525014,32071465,31871334,31671374,91731303,31961130380,and 32041008)the Strategic Priority Research Program(XDPB17,XDB38000000)+2 种基金the Chinese Academy of Sciences,National Key Research and Development Program of China(2018YFC0910502,2016YFC0906403)the UK Royal Society-Newton Advanced Fellowship(NAF\R1\191094)the Shanghai Municipal Science and Technology Major Project(2017SHZDZX01).
文摘Understanding the micro-coevolution of the human gut microbiome with host genetics is challenging but essential in both evolutionary and medical studies.To gain insight into the interactions between host genetic variation and the gut microbiome,we analyzed both the human genome and gut microbiome collected from a cohort of 190 students in the same boarding college and representing 3 ethnic groups,Uyghur,Kazakh,and Han Chinese.We found that differences in gut microbiome were greater between genetically distinct ethnic groups than those genetically closely related ones in taxonomic composition,functional composition,enterotype stratification,and microbiome genetic differentiation.We also observed considerable correlations between host genetic variants and the abundance of a subset of gut microbial species.Notably,interactions between gut microbiome species and host genetic variants might have coordinated effects on specific human phenotypes.Bacteroides ovatus,previously reported to modulate intestinal immunity,is significantly correlated with the host genetic variant rs12899811(meta-P=5.55×10^(-5)),which regulates the VPS33B expression in the colon,acting as a tumor suppressor of colorectal cancer.These results advance our understanding of the micro-coevolution of the human gut microbiome and their interactive effects with host genetic variation on phenotypic diversity.
基金This work was partially supported by special Fund for Clinical Research of Wu Jieping Medical Foundation,China(Grant No.320.6750.18422)the Ministry of Science and Technology of the People’s Republic of China(Grant No.2018YFC0910502)the National Natural Science Foundation of China(Grant Nos.31871334 and 31671374).
文摘The gut microbiota of intensive care unit(ICU)patients displays extreme dysbiosis associated with increased susceptibility to organ failure,sepsis,and septic shock.However,such dysbiosis is difficult to characterize owing to the high dimensional complexity of the gut microbiota.We tested whether the concept of enterotype can be applied to the gut microbiota of ICU patients to describe the dysbiosis.We collected 131 fecal samples from 64 ICU patients diagnosed with sepsis or septic shock and performed 16S rRNA gene sequencing to dissect their gut microbiota compositions.During the development of sepsis or septic shock and during various medical treatments,the ICU patients always exhibited two dysbiotic microbiota patterns,or ICU-enterotypes,which could not be explained by host properties such as age,sex,and body mass index,or external stressors such as infection site and antibiotic use.ICU-enterotype I(ICU E1)comprised predominantly Bacteroides and an unclassified genus of Enterobacteriaceae,while ICU-enterotype II(ICU E2)comprised predominantly Enterococcus.Among more critically ill patients with Acute Physiology and Chronic Health Evaluation II(APACHE II)scores>18,septic shock was more likely to occur with ICU E1(P=0.041).Additionally,ICU E1 was correlated with high serum lactate levels(P=0.007).Therefore,different patterns of dysbiosis were correlated with different clinical outcomes,suggesting that ICU-enterotypes should be diagnosed as independent clinical indices.Thus,the microbial-based human index classifier we propose is precise and effective for timely monitoring of ICU-enterotypes of individual patients.This work is a first step toward precision medicine for septic patients based on their gut microbiota profiles.
基金supported by the National Key R&D Program of China (Grant No. 2018YFC0910502)the National Natural Science Foundation of China (Grant Nos. 61103167, 31271410, and 31671374)
文摘In 2011, the term ‘‘enterotype" first appeared to the general public in Nature, which refers to stratification of human gut microbiota. However, with more studies on enterotypes conducted nowadays, doubts about the existence and robustness of enterotypes have also emerged. Here we reviewed current opinions about enterotypes from both conceptual and analytical points of view.We firstly illustrated the definition of the enterotype and various factors influencing enterotypes,such as diet, administration of antibiotics, and age. Then we summarized lines of evidence that pose the concept against the enterotype, and described the current methods for enterotype analysis.Finally, we showed that the concept of enterotype has been extended to other ecological niches.Based on current studies on enterotypes, it has been clear that more studies with larger sample sizes are needed to characterize the enterotypes. Improved computational methods are also required to build sophisticated models, reflecting the dynamics and resilience of enterotypes.
基金the National Key R&D Program of China(No.2021YFF0901003)。
文摘Recently advancements in deep learning models have significantly facilitated the development of sequential recommender systems(SRS).However,the current deep model structures are limited in their ability to learn high-quality embeddings with insufficient data.Meanwhile,highly skewed long-tail distribution is very common in recommender systems.Therefore,in this paper,we focus on enhancing the representation of tail items to improve sequential recommendation performance.Through empirical studies on benchmarks,we surprisingly observe that both the ranking performance and training procedure are greatly hindered by the poorly optimized tail item embeddings.To address this issue,we propose a sequential recommendation framework named TailRec that enables contextual information of tail item well-leveraged and greatly improves its corresponding representation.Given the characteristics of the sequential recommendation task,the surrounding interaction records of each tail item are regarded as contextual information without leveraging any additional side information.This approach allows for the mining of contextual information from cross-sequence behaviors to boost the performance of sequential recommendations.Such a light contextual filtering component is plug-and-play for a series of SRS models.To verify the effectiveness of the proposed TailRec,we conduct extensive experiments over several popular benchmark recommenders.The experimental results demonstrate that TailRec can greatly improve the recommendation results and speed up the training process.The codes of our methods have been available.
基金supported by the National Natural Science Foundation of China(Grant Nos.32071465,31871334,and 31671374)the National Key R&D Program of China(Grant No.2018YFC0910502)Yuhao Zhang(Huazhong University of Science and Technology,China)to improve the analysis of this study。
文摘Despite the skin microbiome has been linked to skin health and diseases,its role in modulating human skin appearance remains understudied.Using a total of 1244 face imaging phenomes and 246 cheek metagenomes,we first established three skin age indices by machine learning,including skin phenotype age(SPA),skin microbiota age(SMA),and skin integration age(SIA)as surrogates of phenotypic aging,microbial aging,and their combination,respectively.Moreover,we found that besides aging and gender as intrinsic factors,skin microbiome might also play a role in shaping skin imaging phenotypes(SIPs).Skin taxonomic and functionalαdiversity was positively linked to melanin,pore,pigment,and ultraviolet spot levels,but negatively linked to sebum,lightening,and porphyrin levels.Furthermore,certain species were correlated with specific SIPs,such as sebum and lightening levels negatively correlated with Corynebacterium matruchotii,Staphylococcus capitis,and Streptococcus sanguinis.Notably,we demonstrated skin microbial potential in predicting SIPs,among which the lightening level presented the least error of 1.8%.Lastly,we provided a reservoir of potential mechanisms through which skin microbiome adjusted the SIPs,including the modulation of pore,wrinkle,and sebum levels by cobalamin and heme synthesis pathways,predominantly driven by Cutibacterium acnes.This pioneering study unveils the paradigm for the hidden links between skin microbiome and skin imaging phenome,providing novel insights into how skin microbiome shapes skin appearance and its healthy aging.
