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Quantifying (non)parallelism of gut microbial community change using multivariate vector analysis.

Andreas Härer1, Diana J Rennison1

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Microbiota parallelism, or similar microbial community changes in related hosts, can be quantified using multivariate vector analysis. This method reveals insights into host-microbe co-evolution and adaptive processes.

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Area of Science:

  • Evolutionary Biology
  • Microbial Ecology
  • Bioinformatics

Background:

  • Parallel evolution of host traits suggests natural selection, but parallel changes in associated microbial communities (microbiotas) are less understood.
  • Microbiotas are vital for host ecology and evolution, yet quantitative methods to assess their parallel evolution are scarce.
  • Existing approaches often treat parallelism as binary, overlooking its quantitative nature.

Purpose of the Study:

  • To introduce and validate multivariate vector analysis for quantifying microbiota parallelism.
  • To assess the extent of microbiota community changes accompanying parallel host evolution.
  • To provide an R package ('multivarvector') for analyzing microbial community dynamics.

Main Methods:

  • Applied multivariate vector analysis (phenotypic change vector analysis) to quantify microbiota shifts.
  • Reanalyzed gut microbiota data from fish species with parallel trophic ecology shifts.
  • Compared results with existing statistical methods and evaluated effects of taxonomic level.

Main Results:

  • Multivariate vector analysis provided results consistent with other statistical methods.
  • Microbiota parallelism estimates were robust across different taxonomic levels.
  • Gut microbiota function may exhibit stronger parallelism than taxonomic composition.

Conclusions:

  • Multivariate vector analysis offers a quantitative framework for comparing microbiota parallelism across host lineages.
  • This approach can enhance predictions of microbial community shifts during host adaptive evolution.
  • Further exploration of quantitative measures is crucial for understanding microbiota dynamics in parallel evolution.