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Multi-View Integrative Approach For Imputing Short-Chain Fatty Acids and Identifying Key factors predicting Blood

Anqi Liu1, Bo Tian2, Chuan Qiu1

  • 1Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, USA.

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Summary

This study introduces M²AE, a novel method to predict blood short-chain fatty acids (SCFAs) using gut microbiome data. It integrates gut microbiome, diet, and host factors for a comprehensive understanding of SCFA metabolism.

Keywords:
Deep learningGut MicrobiotasImputationMetagenomeShort-Chain Fatty Acids

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Area of Science:

  • Microbiome research
  • Metabolomics
  • Computational biology

Background:

  • Short-chain fatty acids (SCFAs) are crucial metabolites produced by gut bacteria through dietary fiber fermentation.
  • SCFAs influence host physiology, but incomplete blood SCFA data limits understanding of gut microbiome-host metabolic interactions.
  • Existing studies often lack comprehensive blood SCFA profiles due to instrumental limitations.

Purpose of the Study:

  • To develop an innovative computational approach for imputing blood SCFA levels from gut metagenomic sequencing (MGS) data.
  • To integrate gut microbiome, dietary, and host characteristics for a holistic analysis of SCFA dynamics.
  • To overcome limitations of incomplete blood SCFA data in current microbiome research.

Main Methods:

  • Developed M²AE (Multi-task Multi-View Attentive Encoders), an integrative approach using multiple data views (microbiome, diet, host characteristics).
  • Employed multi-task learning and attention mechanisms to capture view-specific and cross-view correlations for SCFA prediction.
  • Applied the M²AE model to two large datasets comprising MGS, blood SCFAs, host, and dietary information.

Main Results:

  • M²AE significantly outperformed traditional regression and neural network models in imputing blood SCFA levels.
  • Identified key gut bacterial species (e.g., *Bacteroides thetaiotaomicron*), host factors (race, gender), and dietary elements (fruit, pickle intake) influencing SCFA imputation.
  • Demonstrated the significant contribution of gut microbiome, diet, and host characteristics to blood SCFA production.

Conclusions:

  • The M²AE approach provides a robust method for estimating blood SCFA levels, enhancing our understanding of host-microbiome metabolism.
  • Findings highlight the complex interplay between gut microbiota, diet, and host factors in regulating SCFA production and host health.
  • This work offers a foundation for deeper insights into how these factors collectively impact human physiology and health outcomes.