A network-based comparative framework to study conservation and divergence of proteomes in plant phylogenies

We present a novel multi-species proteomic dataset and a computational pipeline to systematically compare the protein levels across multiple plant species

Junha Shin; Harald Marx; Alicia Richards; Dries Vaneechoutte; Dhileepkumar Jayaraman; Junko Maeda; Sanhita Chakraborty; Michael Sussman; Klaas Vandepoele; Jean-Michel Ané; Joshua Coon; Sushmita Roy

2020

Scholarcy highlights

  • Comparative functional genomics offers a powerful lens to study the evolution of complex traits by measuring and comparing large-scale molecular profiles, such as transcriptomes, epigenomes, proteomes, across multiple species
  • To find additional genes and pathways associated with nodulation, we examined the M. truncatula Expectation Maximization enriched for these curated gene sets and nodulation related Gene Ontology processes, such as ‘nodulation’ and ‘response to bacterium’
  • Comparative functional genomics offers a powerful lens into the molecular changes associated with diverse speciesspecific traits and has been used to study transcriptome evolution in different yeast and mammalian species
  • Systematic comparison of proteins levels across numerous species and interpretation of patterns of conservation and divergence in the context of specific biological processes is a significant challenge in plant phylogenies, which have substantial duplication events and are not as well annotated as mammalian species
  • We optimized an mass spectrometry-based assay to measure the proteomes of six plant species and developed a computational pipeline to compare and interpret these proteomic measurements across the species
  • The protoeme is more constrained than the transcriptome from different tissues, which is consistent with studies in mammals
  • To investigate the large-scale patterns in the enriched processes of the clade-specific gene sets, we applied Non-negative Matrix Factorization-based bi-clustering to group both gene sets and the associated process terms into 10 groups and characterized the process group by highest scoring term for the cluster

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