Biology subjects

Dziurzynski, M.

Publications and source records attributed to Dziurzynski, M..

3 recordsLinked to original sources

bioRxiv2024

Fast and accurate detection of metal resistance genes using MetHMMDb

Heavy metal pollution poses a major environmental challenge, with microbial resistance to heavy metals offering potential solutions through bioremediation. Additionally, the presence and diversity of microbial metal resistance genes (MMRGs) could contribute to an ecosystems ability to adapt and recover from heavy metal contamination by maintaining essential microbial functions and promoting the cycling of nutrients under stress conditions. Thus, MMRGs may serve not only as markers of contamination but also as indicators of an ecosystems self-purification capacity and resilience to environmental disturbances. Here we present MetHMMDB, a database containing 254 profile Hidden Markov Models representing 121 MMRGs. Unlike traditional sequence-based resources, MetHMMDB relies on HMMs to improve detection sensitivity and functional specificity across microbial communities. Created through iterative database searches, sequence clustering, structural prediction, and manual annotation, MetHMMDB emphasizes functional annotation rather than gene classification. The database outperforms sequence-based approaches, identifying over twice as many MMRGs in metagenomic datasets, including those from extreme environments. Analysis of agricultural soil revealed distinct resistance profiles correlating with soil quality. MetHMMDB advances our understanding of microbial adaptation to heavy metal contamination while supporting environmental management strategies through improved identification and characterization of metal resistance mechanisms. Database URL: https://github.com/Haelmorn/MetHMMDB.

bioinformatics↗

bioRxiv2024

Insights into optimization of oleaginous fungi - genome-scale metabolic reconstruction and analysis of Umbelopsis sp. WA50703

Oleaginous fungi, known for their high lipid content--up to 80% of their dry mass--are of significant interest for biotechnological applications, particularly in biofuel and fatty acid production. Among these, the genus Umbelopsis, a common soil saprotroph of the Mucoromycota phylum, stands out for its rapid growth, low nutritional requirements, and ability to produce substantial amounts of lipids, especially polyunsaturated fatty acids (PUFAs). Despite previous studies on lipid production in Umbelopsis, metabolic engineering has been underexplored. This study fills that gap by presenting the first comprehensive metabolic model for Umbelopsis sp. WA50703, encompassing 2413 metabolites, 2216 reactions, and 1629 genes (iMD1629). The model demonstrated strong predictive accuracy, correctly predicting metabolic capabilities in 82.1% of cases when evaluated against experimental data. Using the Flux Scanning based on Enforced Objective Flux (FSEOF) algorithm, the study identified 33 genes linked to 23 metabolic reactions. Notably, reactions catalysed by acetyl-CoA carboxylase and carbonic anhydrase emerged as prime candidates for up-regulation. These findings provide a solid framework for future metabolic engineering efforts to optimize PUFA production in Umbelopsis strains. ImportanceUmbelopsis strains are capable of producing valuable compounds like polyunsaturated fatty acids (PUFAs). These compounds are essential for human health, found in various foods and supplements that support heart and brain function. In this study we developed a computer model to better understand how this fungus works at a metabolic level, guiding further research investigations towards optimization of PUFAs production in a cost-effective way. This research lays the groundwork for future innovations in metabolic engineering of Umbelopsis PUFA production leading to healthier food options and a more sustainable food system, directly impacting everyday life.

systems biology↗

bioRxiv2021

Modelling hCDKL5 heterologous expression in bacteria

hCDKL5 refers to the human cyclin-dependent kinase that is primarily expressed in the brain where it exerts its function in several neuron districts. Mutations in its coding sequence are often causative of hCDKL5 deficiency disorder. The large-scale recombinant production of hCDKL5 is desirable to boost the translation of current therapeutic approaches into the clinic. However, this is hampered by the following features: i) almost two-thirds of hCDKL5 sequence are predicted to be intrinsically disordered, making this region more susceptible to proteolytic attack; ii) the cytoplasmic accumulation of the enzyme in eukaryotic host cells is associated to toxicity. The bacterium Pseudoalteromonas haloplanktis TAC125 (PhTAC125) is the only prokaryotic host in which the full-length production of hCDKL5 has been demonstrated. To date, a system-level understanding of the metabolic burden imposed by hCDKL5 production is missing, although it would be crucial for the upscaling of the production process. Here, we have combined experimental data on protein production and nutrients assimilation with metabolic modelling to infer the global consequences of hCDKL5 production in PhTAC125 and to identify potential overproduction targets. Our analyses showed a remarkable accuracy of the model in simulating the recombinant strain phenotype and also identified priority targets for optimized protein production.

systems biology↗