Mono-mix strategy enables comparative proteomics of a cross-kingdom microbial symbiosis

作者：Sunnyjoy Dupuis, Usha Lingappa, Samuel Purvine, Lauren Chiang, Sean D. Gallaher, Nicora Carrie D., Mary S. Lipton, Sabeeha S. Merchant · 发表于：PLoS ONE · 年份：2026 · DOI：10.1371/journal.pone.0340253 · 被引用次数：1 · 研究领域：Microbial Community Ecology and Physiology、Bacteriophages and microbial interactions、Legume Nitrogen Fixing Symbiosis

Cross-kingdom microbial symbioses, such as those between algae and bacteria, are key players in biogeochemical cycles. The molecular changes during initiation and establishment of symbiosis are of great interest, but quantitatively monitoring such changes can be challenging, particularly when the microorganisms differ greatly in size or are intimately associated. Here, we analyze output from label-free, data-dependent acquisition (DDA) LC-MS/MS proteomics experiments investigating the well-studied interaction between the alga Chlamydomonas reinhardtii and the heterotrophic bacterium Mesorhizobium japonicum. We found that detection of bacterial proteins decreased in coculture by 50% proteome-wide due to the abundance of algal proteins. As a result, standard differential expression analysis led to numerous false-positive reports of significantly downregulated proteins, where it was not possible to distinguish meaningful biological responses to symbiosis from artifacts of the reduced protein detection in coculture relative to monoculture. We show that data normalization alone does not eliminate the impact of altered detection on differential expression analysis of the cross-kingdom symbiosis. We assessed two additional strategies to overcome this methodological artifact inherent to DDA proteomics. In the first, we combined algal and bacterial monocultures at a relative abundance that mimicked the coculture, creating a "mono-mix" control to which the coculture could be compared. ...