Biology subjects

Gradowski, M.

Publications and source records attributed to Gradowski, M..

4 recordsLinked to original sources

bioRxiv2026

Systematic discovery of protein kinase-like domains reveals diverse evolutionary strategies in the human oral microbiome

The protein kinase-like (PKL) superfamily regulates metabolism, biofilm formation, host interactions, and antimicrobial resistance in bacteria, although much of its diversity remains unknown. We used a structure-based pipeline that combined protein structure prediction with structural similarity searches to search 5.1 million protein sequences in the Human Oral Microbiome Database (HOMD). Together with 30 families that had been previously characterized, we identified 20 PKL families new to this genome collection: 15 entirely novel and 5 previously reported as preliminary findings. Our taxonomic analysis revealed that the families were distributed either across multiple bacterial phyla or restricted to a single species; hits spanning domains suggest either ancient origins or horizontal transfer. Three families were examined in detail and show different evolutionary pathways: SEAE1 from Segetibacter aerophilus, which is a putative lipid kinase; POGI1 from Porphyromonas gingivalis, a putative ethanolamine kinase with the PKL fold that is limited to pathogens; and SrfA-N from Haemophilus parainfluenzae, a non-catalytic scaffold that has been convergently co-opted as a tripartite toxin platform. Taken together, these examples demonstrate enzymatic specialization, adaptation specific to pathogens and structural co-option. The families identified are candidates for further mechanistic study.

bioinformatics↗

bioRxiv2024

A survey of ADP-ribosyltransferase families in the pathogenic Legionella

BackgroundADP-ribosyltransferases (ARTs) are a superfamily of enzymes implicated in various cellular processes, including pathogenic mechanisms. The Legionella genus, known for causing Legionnaires disease, possesses diverse ART-like effectors. This study explores the proteomes of 41 Legionella species to bioinformatically identify and characterise novel ART-like families, providing insights into their potential roles in pathogenesis and host interactions. MethodsWe conducted a comprehensive bioinformatic survey of 41 Legionella species to identify proteins with significant sequence or structural similarity to known ARTs. Sensitive sequence searches were performed to detect candidate ART-like families. Subsequent validation, including structure prediction of such families, was achieved using artificial intelligence-driven tools, such as AlphaFold. Comparative analyses were performed to assess sequence and structural similarities between the novel ART-like families and known ARTs. ResultsOur analysis identified 63 proteins with convincing similarity to ARTs, organised into 39 ART-like families, including 26 novel families. Key findings include: O_LIDUF2971 family: exhibits sequence similarity to DarT toxins and other DNA-acting ARTs. C_LIO_LIDUF4291 family: the largest newly identified family shows structural and sequence similarity to the diphtheria toxin, suggesting the ability to modify proteins. C_LI Most members of the novel ART families are predicted effectors. Although experimental validation of the predicted ART effector functions is necessary, the novel ART-like families identified present promising targets for understanding Legionella pathogenicity and developing therapeutic strategies. We publish a complete catalogue of our results in the astARTe database: http://bioinfo.sggw.edu.pl/astarte/.

bioinformatics↗

bioRxiv2020

A Legionella effector ADP-ribosyltransferase inactivates glutamate dehydrogenase

ADP-ribosyltransferases (ARTs) are a widespread superfamily of enzymes frequently employed in pathogenic strategies of bacteria. Legionella pneumophila, the causative agent of Legionnaires disease, has acquired over 330 translocated effectors that showcase remarkable biochemical and structural diversity. Here we took a bioinformatic approach to search the Legionella effector repertoire for additional divergent members of the ART superfamily and identified an ART domain in Lpg0181. We show that L. pneumophila Lpg0181 targets a specific class of 120-kDa NAD+-dependent glutamate dehydrogenase (GDH) enzymes found in fungi and protists, including many natural hosts of Legionella. Lpg0181 targets a conserved arginine residue in the NAD+ -binding pocket of GDH, thereby blocking oxidative deamination of glutamate. While intracellular pathogens employ diverse virulence mechanisms to overcome host-limited nutrient availability, Lpg0181 is--to the best of our knowledge--the first example of a Legionella effector which directly targets a host metabolic enzyme.

biochemistry↗

bioRxiv2020

A novel predicted ADP-ribosyltransferase family conserved in eukaryotic evolution

The presence of many completely uncharacterized proteins, even in well-studied organisms such as humans, seriously hampers full understanding of the functioning of the living cells. ADP-ribosylation is a common post-translational modification of proteins; also nucleic acids and small molecules can be modified by the covalent attachment of ADP-ribose. This modification, important in cellular signalling and infection processes, is usually executed by enzymes from the large superfamily of ADP-ribosyltransferases (ARTs) Here, using bioinformatics approaches, we identify a novel putative ADP-ribosyltransferase family, conserved in eukaryotic evolution, with a divergent active site. The hallmark of these proteins is the ART domain nestled between flanking leucine-rich repeat (LRR) domains. LRRs are involved in innate immune surveillance. The novel family appears as likely novel ADP-ribosylation "writers", previously unnoticed new players in cell signaling by this emerging post-translational modification. We propose that this family, including its human member LRRC9, may be involved in an ancient defense mechanism, with analogies to the innate immune system, and coupling pathogen detection to ADP-ribosyltransfer signalling.

bioinformatics↗