MetaGraph enables scalable indexing of large sets of DNA, RNA or protein sequences using annotated de Bruijn graphs.

The fossil record demonstrates that filamentous microbes invaded ancient plant cells with intracellular hyphal structures over 450 million years ago. To this day, a rich diversity of extant land plant...

Microbes are ubiquitous in the rhizosphere and play crucial roles in plant health, yet the metabolisms and physiologies of individual species in planta remain poorly understood. In this study, we examined microbial gene expression in response to the maize root environment for seven bacterial species originally isolated from maize roots. We grew each species individually, both in vitro in a minimal medium and in planta, and used differential metaproteomics to identify functions upregulated specifically when bacteria are grown on maize roots. We identified between 1,500 and 2,100 proteins from each species, with approximately 30-70% of these proteins being differentially abundant between the two conditions. While we found that transporter proteins were upregulated in all species in planta, all other differentially abundant functions varied greatly between species, suggesting niche specialization in root-associated microbes. Indeed, in vitro assays confirmed that Curtobacterium pusillum likely degrades plant hemicellulose, Enterobacter ludwigii may benefit the plant by phosphate solubilization, and Herbaspirillum robiniae colonizes maize roots more effectively when both of its Type VI Secretion Systems are functional. Together, our findings highlight both conserved and species-specific bacterial strategies for growth in the root environment and lay a foundation for future work investigating the mechanisms underlying plant-microbiota interactions.

Microtubule severing is essential for proper eukaryotic cell elongation and division. Here we show that the microtubule severing protein, KATANIN, is encoded by two genes in Zea mays (maize) called Di...

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Mass spectrometry-based lipidomics and metabolomics generate large, complex datasets requiring effective analysis. Here, authors review key statistical and visualization methods alongside widely used R and Python tools, and provide a GitBook with step-by-step code for accessible, reproducible data analysis.

A new approach sheds light on the biological features learned by protein language models, promising greater interpretability for unsupervised sequence learning.

Coffee growers are facing climate change, labor shortages and incomes below the poverty line. On International Coffee Day, we take stock of the industry behind the beverage.