Biology subjects

Stahlberg, J.

Publications and source records attributed to Stahlberg, J..

3 recordsLinked to original sources

bioRxiv2023

Thermoprotection by a cell membrane-localized metacaspase in a green alga

Caspases are restricted to animals, while other organisms, including plants possess metacaspases (MCAs), a more ancient and broader class of structurally-related yet biochemically distinct proteases. Our current understanding of plant MCAs is derived from studies in streptophytes, and mostly in Arabidopsis expressing nine MCAs with partly redundant activities. In contrast to streptophytes, most chlorophytes contain only one or two hitherto uncharacterized MCAs, providing an excellent platform for MCA research. Here we investigate CrMCA-II, a single type II MCA from a model chlorophyte Chlamydomonas reinhardtii. Surprisingly, unlike other studied MCAs and similar to caspases, CrMCA-II dimerizes both in vitro and in vivo. Furthermore, activation of CrMCA-II in vivo correlates with the dimerization. Most of CrMCA-II in the cell is present as a zymogen attached to the plasma membrane (PM). Deletion of CrMCA-II by CRISPR/Cas9 compromises thermotolerance leading to increased cell death under heat stress. Adding back either wild-type or catalytically dead CrMCA-II restores thermoprotection, suggesting that its proteolytic activity is dispensable for this effect. Finally, we link the non-proteolytic role of CrMCA-II in thermotolerance to the ability to modulate PM fluidity. Our study reveals an ancient, MCA-dependent thermotolerance mechanism retained by Chlamydomonas and probably lost during the evolution of multicellularity.

plant biology↗

bioRxiv2023

Structure-function analysis of a calcium-independent metacaspase reveals a novel proteolytic pathway for lateral root emergence

Metacaspases are part of an evolutionarily broad family of multifunctional cysteine proteases, involved in disease and normal development. Despite the extensive study of metacaspases in the two decades since their discovery, the structure-function relationship of metacaspases remains poorly understood. Furthermore, previous studies on their function have been thwarted by the redundancy in gene copy number and potential phenotypic suppression of genetic mutations, especially in plants. Here, we have solved the X-ray crystal structure of an Arabidopsis thaliana type II metacaspase (AtMCA-IIf) that belongs to a particular sub-group that does not require calcium ions for activation. Compared to crystal structures of other metacaspases and caspases, the AtMCA-IIf active site is structurally similar and poses a conundrum for the catalytic mechanism of the cysteine-histidine dyad. To study metacaspase activity in plants, we developed an in vitro chemical screen to identify small molecule metacaspase inhibitors. Several hits with a minimal thioxodihydropyrimidine-dione (TDP) structure were identified, some being specific inhibitors of AtMCA-IIf. We provide a mechanistic basis for inhibition by the TDP-containing compounds through molecular docking onto the AtMCA-IIf crystal structure. Finally, a TDP-containing compound (TDP6) was effective at inhibiting lateral root emergence in vivo, likely through the inhibition of metacaspases that are specifically expressed in the endodermal cells overlaying developing lateral root primordia. In the future, the small compound inhibitors and crystal structure of AtMCA-IIf can be used to study metacaspases in various other species, such as important human pathogens including those causing neglected diseases.

plant biology↗

bioRxiv2020

Machine learning reveals sequence-function relationships in family 7 glycoside hydrolases

Family 7 glycoside hydrolases (GH7) are among the principal enzymes for cellulose degradation in nature and industrially. These important enzymes are often bimodular, comprised of a catalytic domain attached to a carbohydrate binding module (CBM) via a flexible linker, and exhibit a long active site that binds cello-oligomers of up to ten glucosyl moieties. GH7 cellulases consist of two major subtypes: cellobiohydrolases (CBH) and endoglucanases (EG). Despite the critical biological and industrial importance of GH7 enzymes, there remain gaps in our understanding of how GH7 sequence and structure relate to function. Here, we employed machine learning to gain insights into relationships between sequence, structure, and function across the GH7 family. Machine-learning models, using the number of residues in the active-site loops as features, were able discriminate GH7 CBHs and EGs with up to 99% accuracy. The lengths of the A4, B2, B3, and B4 loops were strongly correlated with functional subtype across the GH7 family. Position-specific classification rules were derived such that specific amino acids at 42 different sequence positions predicted the functional subtype with accuracies greater than 87%. A random forest model trained on residues at 19 positions in the catalytic domain predicted the presence of a CBM with 89.5% accuracy. We propose these positions play vital roles in the functional variation of GH7 cellulases. Taken together, our results complement numerous experimental findings and present functional relationships that can be applied when prospecting GH7 cellulases from nature, for sequence annotation, and to understand or manipulate function.

bioinformatics↗