Compositional Graphical Lasso Resolves the Impact of Parasitic Infection on Gut Microbial Interaction Networks in a...

Area of Science:

• Microbiology
• Computational Biology
• Bioinformatics

Background:

• Understanding microbial interactions is crucial for host-environment dynamics and disease. Existing methods for inferring microbial interactions from microbiome data struggle with data characteristics like discreteness, compositionality, and heterogeneity.
• Parasitic infections can be influenced by host-associated microbial communities, highlighting the need for accurate methods to study these interactions.

Purpose of the Study:

• To develop a novel computational approach, compositional graphical lasso, that explicitly accounts for the unique characteristics of microbiome data to infer microbial interactions.
• To demonstrate the advantages of compositional graphical lasso over existing methods using simulations and benchmark datasets.
• To apply the new method to a zebrafish parasite infection study to uncover microbial interactions associated with infection and identify potential therapeutic strategies.

Main Methods:

• Developed the compositional graphical lasso method, extending existing graphical models to incorporate discreteness, compositionality, and heterogeneity of microbiome data.
• Validated the method through various simulation scenarios and analysis of the Tara Oceans Project dataset.
• Applied compositional graphical lasso to analyze gut microbiome data from a Zebrafish Parasite Infection Study.

Main Results:

• Compositional graphical lasso outperformed current methods in analyzing microbiome data under diverse simulation conditions.
• The method identified significant changes in interaction degrees for specific taxa (Photobacterium, Gemmobacter, Paucibacter) between infected and uninfected zebrafish, which were not detected by other approaches.
• Analysis revealed potential pathobiotic roles for Photobacterium and Gemmobacter, and a probiotic role for Paucibacter in the zebrafish gut during infection.

Conclusions:

• Compositional graphical lasso is a powerful and accurate tool for resolving complex microbial interactions within host-associated microbiomes.
• The method facilitates novel biological discoveries, such as identifying specific microbial taxa with potential roles in modulating parasitic infection success.
• This approach can drive the discovery of new diagnostic and therapeutic strategies for infectious diseases by elucidating host-microbe interactions.