Biology subjects

Chodasiewicz, M.

Publications and source records attributed to Chodasiewicz, M..

3 recordsLinked to original sources

bioRxiv2022

A novel role of 3,5-cAMP in the regulation of actin cytoskeleton in Arabidopsis.

The role of cyclic adenosine monophosphate (3,5-cAMP) in plants is not well understood, and here, we report a novel role of 3,5-cAMP in regulating the actin cytoskeleton. The 3,5-cAMP treatment affects the thermal stability of 51 proteins, including a vegetative actin isoform, ACTIN2. Consistent with the above results, the increase in 3,5-cAMP levels, obtained either by feeding or by chemical modulation of 3,5-cAMP metabolism, is sufficient to partially rescue the short hypocotyl phenotype of the actin2 actin7 mutant, severely compromised in actin level. No such complementation was measured for a positional isomer of 3,5-cAMP, 2,3-cAMP, attesting to the specificity of 3,5-cAMP treatment. Moreover, supplementation of 3,5-cAMP partly counters the activity of an actin-depolymerizing drug latrunculin B. In vitro characterization of the 3,5-cAMP - actin interaction argues against the direct binding. Instead, based on the proteomics characterization of the act2act7 mutant supplemented with 3,5-cAMP, we hypothesize that 3,5-cAMP affects cytoskeleton dynamic by modulation of calcium signaling, and actin binding proteins.

plant biology↗

bioRxiv2021

2',3'-cAMP treatment mimics abiotic stress response

The role of the RNA degradation product 2,3-cyclic adenosine monophosphate (2,3-cAMP) is poorly understood. Recent studies have identified 2,3-cAMP in plant material and determined its role in stress signaling. The level of 2,3-cAMP increases upon wounding, dark, and heat, and 2,3-cAMP by binding to an RNA-binding protein, Rbp47b, promotes stress granule (SG) assembly. To gain further mechanistic insight into 2,3-cAMP function, we used a multi-omics approach combining transcriptomics, metabolomics, and proteomics to dissect Arabidopsis response to 2,3-cAMP treatment. We demonstrated that 2,3-cAMP is metabolized into adenosine, suggesting that the well-known cyclic nucleotide-adenosine pathway from human cells might also exist in plants. Transcriptomic analysis revealed only minor overlap between 2,3-cAMP-and adenosine-treated plants, suggesting that these molecules act through independent mechanisms. Treatment with 2,3-cAMP changed the levels of hundreds of transcripts, proteins, and metabolites, many previously associated with plant stress responses including protein and RNA degradation products, glucosinolates, chaperones and SG components. Finally, we demonstrated that 2,3-cAMP treatment influences the movement of processing bodies, supporting the role of 2,3-cAMP in the formation and motility of membraneless organelles.

plant biology↗

bioRxiv2021

SLIMP: Supervised learning of metabolite-protein interactions from co-fractionation mass spectrometry data

Metabolite-protein interactions affect and shape diverse cellular processes. Yet, despite advances, approaches for identifying metabolite-protein interactions at a genome-wide scale are lacking. Here we present an approach termed SLIMP that predicts metabolite-protein interactions using supervised machine learning on features engineered from metabolic and proteomic profiles from a co-fractionation mass spectrometry-based technique. By applying SLIMP with gold standards, assembled from public databases, along with metabolic and proteomic data sets from multiple conditions and growth stages we predicted over 9,000 and 20,000 metabolite-protein interactions for Saccharomyces cerevisiae and Arabidopsis thaliana, respectively. Extensive comparative analyses corroborated the quality of the predictions from SLIMP with respect to widely-used performance measures (e.g. F1-score exceeding 0.8). SLIMP predicted novel targets of 2, 3 cyclic nucleotides and dipeptides, which we analysed comparatively between the two organisms. Finally, predicted interactions for the dipeptide Tyr-Asp in Arabidopsis and the dipeptide Ser-Leu in yeast were independently validated, opening the possibility for future applications of supervised machine learning approaches in this area of systems biology.

systems biology↗