DCiPatho: deep cross-fusion networks for genome scale identification of pathogens

Jiang, Gaofei; Zhang, Jiaxuan; Zhang, Yaozhong; Yang, Xinrun; Li, Tingting; Wang, Ningqi; Chen, Xingjian; Zhao, Fang‐Jie; Wei, Zhong; Xu, Yangchun; Shen, Qirong; Wei, Xin

doi:10.1093/bib/bbad194

Cited by 10 publications

(1 citation statement)

References 50 publications

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“…Deep cross-fusion networks for genome-scale identification of pathogens (DCiPatho) for toxicity prediction of bacteria were used to assess the pathogenicity of differentially relative abundant microbes [57]. DCiPatho is a tool for predicting pathogenic microbial genes within the microbiota by analyzing gene toxicity, thus assisting in identifying potential microbial biomarkers (https://github.com/LorMeBioAI/DCiPatho, accessed on 20 September 2023).…”

Section: Core Microbial Biomarker and Enterotype Analysismentioning

confidence: 99%

Microbial Dysbiosis Linked to Metabolic Dysfunction-Associated Fatty Liver Disease in Asians: Prevotella copri Promotes Lipopolysaccharide Biosynthesis and Network Instability in the Prevotella Enterotype

Yuan,

Wu,

Wang

et al. 2024

IJMS

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Metabolic dysfunction-associated fatty liver disease (MAFLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is characterized by hepatic fat accumulation by metabolic dysfunction. The rising prevalence of MAFLD, especially among Asians, may be associated with changes in gut microbiota. We investigated gut microbiota characteristics and potential mechanisms leading to MAFLD development according to enterotypes. Case-control studies examining the gut microbiota composition between MAFLD and non-MAFLD participants were searched in public databases until July 2023. Gut microbiota was categorized into two enterotypes by principal component analysis. According to the enterotypes, LEfSe, ALDEx2, XGBoost, and DCiPatho were utilized to identify differential abundances and pathogenic microbes in the gut between the MAFLD and non-MAFLD groups. We analyzed microbial community networks with the SprCC module and predicted microbial functions. In the Prevotella enterotype (ET-P), 98.6% of Asians and 65.1% of Caucasians were associated with MAFLD (p = 0.049). MAFLD incidence was correlated with enterotype, age, obesity, and ethnicity (p < 0.05). Asian MAFLD patients exhibited decreased Firmicutes and Akkermansia muciniphila and increased Bacteroidetes and P. copri. The pathogenicity scores were 0.006 for A. muciniphila and 0.868 for P. copri. The Asian MAFLD group showed decreased stability and complexity in the gut microbiota network. Metagenome function analysis revealed higher fructose metabolism and lipopolysaccharide (LPS) biosynthesis and lower animal proteins and α-linolenic acid metabolism in Asians with MAFLD compared with the non-MAFLD group. LPS biosynthesis was positively correlated with P. copri (p < 0.05). In conclusion, P. copri emerged as a potential microbial biomarker for MAFLD. These findings enhance our understanding of the pathological mechanisms of MAFLD mediated through the gut microbiota, providing insights for future interventions.

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Section: Core Microbial Biomarker and Enterotype Analysismentioning

confidence: 99%

Microbial Dysbiosis Linked to Metabolic Dysfunction-Associated Fatty Liver Disease in Asians: Prevotella copri Promotes Lipopolysaccharide Biosynthesis and Network Instability in the Prevotella Enterotype

Yuan,

Wu,

Wang

et al. 2024

IJMS

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Threat of Human Pathogens in Farmlands: A One Health Perspective

Jiang,

Yang,

Pommier

et al. 2024

J of Sust Agri & Env

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A key aspect of “One Health” is to recognize that environmental health and human health are inseparable. In this interconnected network, farmland plays a crucial role, as it serves as a link connecting various ecosystems. However, the threat of human pathogens in farmlands is often overlooked despite growing evidence highlighting its importance as a pressing public health concern via direct and indirect pathways. To address this issue, we advocate the adoption of a “One Health” approach, which relies on strengthening the development of detection tools and emphasizing the importance of sustainable agricultural practices aimed at enhancing soil biodiversity. By doing so, we can effectively disrupt the transmission source and mitigate the risks posed by human pathogens in agricultural ecosystems, ultimately safeguarding both human health and agricultural sustainability.

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Transfer learning enables predictions in soil-borne diseases

Xin,

Xie,

Wen

et al. 2024

Soil Ecol. Lett.

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The Transformer model precisely predicts soil health status from high-throughput sequencing data.• The SMOTE algorithm addresses data imbalance issues, improving model accuracy.• Transfer learning validates the model on small samples, strengthening its generalization capabilities.Inhibiting the occurrence of soil-borne diseases is considered as the most favorable approach for promoting sustainable agricultural development. Constructing soil disease prediction models can serve precision agriculture. However, the analysis results of the metaframework often contradict each other, causing inconsistency in the important features of machine learning results. Therefore, it is necessary to compare the classification accuracy of various machine learning models and further optimize the features of the models to enhance their classification accuracy. Here, we conducted a comparison of eight common machine learning algorithms (XGBoost, CatBoost, Decision Tree, LGBM, Naïve Byes, Perceptron, Logistic, and Random Forest) at the levels of family, genus, and class. The important features of the model were extracted based on the differences in model accuracy and important features, followed by an interpretable analysis of these important features using feature importance. Subsequently, the data underwent resampling using the SMOTE algorithm, and the results show that the SMOTE-Transformer model performs well, surpassing the training results of the voting and stacking strategies, with an accuracy reaching 90%. We have also deployed the SMOTE-Transformer model on sequencing data, which has an accuracy of over 80%. The construction of SMOTE-Transformer model provides a new idea for soil microbial data analysis by greatly improving the accuracy and robustness of soil microbial data processing tools.

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DCiPatho: deep cross-fusion networks for genome scale identification of pathogens

Cited by 10 publications

References 50 publications

Microbial Dysbiosis Linked to Metabolic Dysfunction-Associated Fatty Liver Disease in Asians: Prevotella copri Promotes Lipopolysaccharide Biosynthesis and Network Instability in the Prevotella Enterotype

Microbial Dysbiosis Linked to Metabolic Dysfunction-Associated Fatty Liver Disease in Asians: Prevotella copri Promotes Lipopolysaccharide Biosynthesis and Network Instability in the Prevotella Enterotype

Threat of Human Pathogens in Farmlands: A One Health Perspective

Transfer learning enables predictions in soil-borne diseases

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