Root Hairs (RHs) growth is influenced by endogenous and by external environmental signals that coordinately regulate its final cell size. We have recently determined that RH growth was unexpectedly boosted whenArabidopsis thalianaseedlings are cultivated at low temperatures. It was proposed that RH growth plasticity in response to low temperature was linked to a reduced nutrient availability in the media. Here, we explore the molecular basis of this RH growth response by using a Genome Wide Association Study (GWAS) approach usingArabidopsis thaliananatural accessions. We identify the poorly characterized PEROXIDASE 62 (PRX62) and a related protein PRX69 as key proteins under moderate low temperature stress. Strikingly, a cell wall protein extensin (EXT) reporter reveals the effect of peroxidase activity on EXT cell wall association at 10 °C in the RH apical zone. Collectively, our results indicate that PRX62, and to a lesser extent PRX69, are key apoplastic PRXs that modulate ROS-homeostasis and cell wall EXT-insolubilization linked to RH elongation at low temperature.

The majority of microbial genomes have yet to be cultured, and most proteins identified in microbial genomes or environmental sequences cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely on incomplete reference databases that cannot adequately capture the functional diversity of the microbial tree of life, limiting our ability to model high-level features of biological sequences. Here we present LookingGlass, a deep learning model encoding contextually-aware, functionally and evolutionarily relevant representations of short DNA reads, that distinguishes reads of disparate function, homology, and environmental origin. We demonstrate the ability of LookingGlass to be fine-tuned via transfer learning to perform a range of diverse tasks: to identify novel oxidoreductases, to predict enzyme optimal temperature, and to recognize the reading frames of DNA sequence fragments. LookingGlass enables functionally relevant representations of otherwise unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth.

Metal-binding proteins have a central role in maintaining life processes. Nearly one-third of known protein structures contain metal ions that are used for a variety of needs, such as catalysis, DNA/RNA binding, protein structure stability, etc. Identifying metal-binding proteins is thus crucial for understanding the mechanisms of cellular activity. However, experimental annotation of protein metal-binding potential is severely lacking, while computational techniques are often imprecise and of limited applicability. we developed a novel machine learning-based method, mebipred, for identifying metal-binding proteins from sequence-derived features. This method is over 80% accurate in recognizing proteins that bind metal ion-containing ligands; the specific identity of 11 ubiquitously present metal ions can also be annotated. mebipred is reference-free, i.e. no sequence alignments are involved, and is thus faster than alignment-based methods; it is also more accurate than other sequence-based prediction methods. Additionally, mebipred can identify protein metal-binding capabilities from short sequence stretches, e.g. translated sequencing reads, and, thus, may be useful for the annotation of metal requirements of metagenomic samples. We performed an analysis of available microbiome data and found that ocean, hot spring sediments and soil microbiomes use a more diverse set of metals than human host-related ones. For human microbiomes, physiological conditions explain the observed metal preferences. Similarly, subtle changes in ocean sample ion concentration affect the abundance of relevant metal-binding proteins. These results highlight mebipred’s utility in analyzing microbiome metal requirements. mebipred is available as a web server at services.bromberglab.org/mebipred and as a standalone package at https://pypi.org/project/mymetal/. Supplementary data are available at Bioinformatics online.

Root hair cells are important sensors of soil conditions. They grow towards and absorb water-soluble nutrients. This fast and oscillatory growth is mediated by continuous remodeling of the cell wall. Root hair cell walls contain polysaccharides and hydroxyproline-rich glycoproteins, including extensins (EXTs). Class-III peroxidases (PRXs) are secreted into the apoplastic space and are thought to trigger either cell wall loosening or polymerization of cell wall components, such as Tyr-mediated assembly of EXT networks (EXT-PRXs). The precise role of these EXT-PRXs is unknown. Using genetic, biochemical, and modeling approaches, we identified and characterized three root-hair-specific putative EXT-PRXs, PRX01, PRX44, and PRX73. prx01,44,73 triple mutation and PRX44 and PRX73 overexpression had opposite effects on root hair growth, peroxidase activity, and ROS production, with a clear impact on cell wall thickness. We use an EXT fluorescent reporter with contrasting levels of cell wall insolubilization in prx01,44,73 and PRX44-overexpressing background plants. In this study, we propose that PRX01, PRX44, and PRX73 control EXT-mediated cell wall properties during polar expansion of root hair cells.

Computational exploration of similarities among metal-binding protein structural motifs elucidates the origins of life.

Background: Aedes aegypti (L.) is an urban mosquito, vector of several arboviruses that cause severe diseases in hundreds of million people each year. The resistance to synthetic insecticides developed by Ae. aegypti populations worldwide has contributed to failures in vector control campaigns, increasing the impact of arbovirus diseases. In this context, plant-derived essential oils with larvicidal activity could be an attractive alternative for vector control. However, the mode of action and the detoxificant response of mosquitoes to plant derived compounds have not been established, impairing the optimization of their use. Methods and findings: Here we compare gene expression in Ae. aegypti larvae after 14 hrs of exposure to Eucalyptus camaldulensis essential oil with a control group exposed to vehicle (acetone) for the same lapse, by using RNA-Seq. We found differentially expressed genes encoding for cuticle proteins, fatty-acid synthesis, membrane transporters and detoxificant related gene families (i.e. heat shock proteins, cytochromes P450, glutathione transferases, UDP-glycosyltransferases and ABC transporters). Finally, our RNA-Seq and molecular docking results provide evidence pointing to a central involvement of chemosensory proteins in the detoxificant response in mosquitoes. Conclusions and significance: Our work contributes to the understanding of the physiological response of Ae. aegypti larvae to an intoxication with a natural toxic distilled from Eucalyptus leafs. The results suggest an involvement of most of the gene families associated to detoxification of xenobiotics in insects. Noteworthy, this work provides important information regarding the implication of chemosensory proteins in the detoxification of a natural larvicide. Understanding the mode of detoxification of Eucalyptus distilled compounds could contribute to their implementation as a tool in mosquito control.