Biology subjects

Pentjuss, A.

Publications and source records attributed to Pentjuss, A..

3 recordsLinked to original sources

bioRxiv2023

Pichia pastoris growth - coupled heme biosynthesis analysis using metabolic modelling

Soy legHemoglobin is one of the most important and key ingredients in plant-based meat substitutes that can imitate the colour and flavour of the meat. In order to improve the high-yield production of legHemoglobin protein and its main component - heme in the yeast Pichia pastoris, glycerol and methanol cultivation conditions were studied. Additionally, in-silico metabolic modelling analysis of growth-coupled enzyme quantity, suggests metabolic gene up/down-regulation strategies for heme production. First, cultivations and metabolic modelling analysis of P. pastoris were performed on glycerol and methanol in different growth media. Glycerol cultivation uptake and production rates can be increased by 50 % according to metabolic modelling results, but methanol cultivation - is near the theoretical maximum. Growth-coupled metabolic optimisation results revealed the best feasible upregulation (33 reactions) (1.47 % of total reactions) and 67 downregulation/deletion (2.98 % of total) reaction suggestions. Finally, we describe reaction regulation suggestions with the highest potential to increase heme production yields.

systems biology↗

bioRxiv2022

Metabolic modeling of Hermetia illucens larvae resource allocation for high-value fatty acid production

All plant and animal kingdom organisms use highly connected biochemical networks to facilitate sustaining, proliferation and growth functions. While biochemical network details are well known, the understanding of intense regulation principles is still limited. We chose to investigate Hermetia illucens fly at the larval stage as it is crucial for successful resource accumulation and allocation for the consequential organisms developmental stages. We combined the iterative wet lab experiments and innovative metabolic modeling design approaches, to simulate and explain the H. illucens larval stage resource allocation processes and biotechnology potential. We performed time-based growth and high-value chemical compound accumulation wet lab chemical analysis experiments in larvae and Gainesville diet composition. To predict diet-based alterations on fatty acid allocation potential, we built and validated the first H. illucens medium-size stoichiometric metabolic model. Using optimization methods like Flux balance and Flux variability analysis on the novel insect metabolic model, it predicted that doubled essential amino acid consumption increased the growth rate by 32%, but pure glucose consumption had no positive impact on growth. In the case of doubled pure valine consumption, the model predicted a 2% higher growth rate. In this study, we describe a new framework to research the impact of dietary alterations on the metabolism of multi-cellular organisms at different developmental stages for improved, sustainable and directed high-value chemicals. Significance StatementMetabolic modeling serves as a platform for researchers to investigate and study in depth the possible states of the system based on the existing knowledgebase (e.g. metabolic reactions, substrates, products and their stoichiometry). These models can be applied for different industrial applications, to simulate resource allocation potential and growth conditions. Moreover, these models predict the required diet for living organisms and insects to improve survival and growth rates and accumulate higher-value products, like fatty acids.

systems biology↗

bioRxiv2021

Integrative Gene Expression and Metabolic Analysis tool IgemRNA

Genome scale metabolic modelling is widely used technique to research metabolism impacts on organisms properties. Additional omics data integration enables a more precise genotype-phenotype analysis for biotechnology, medicine and life sciences. Transcriptome data amounts rapidly increase each year. Many transcriptome analysis tools with integrated genome scale metabolic modelling are proposed. But these tools have own restrictions, compatibility issues and the necessity of previous experience and advanced user skills. We have analysed and classified published tools, summarized possible transcriptome pre-processing, and analysis methods and implemented them in the new transcriptome analysis tool IgemRNA. Tool novelty is the possibility of transcriptomics data pre-processing approach, analysis of transcriptome with or without genome scale metabolic models and different thresholding and gene mapping approach availability. In comparison with usual Gene set enrichment analysis methods, IgemRNA options provide additional transcriptome data validation, where minimal metabolic network connectivity and flux requirements are met. IgemRNA allows to process transcriptome datasets, compare data between different phenotypes, execute multiple analysis and data filtering functions. All this is done via graphical user interface. IgemRNA is compatible with Cobra Toolbox 3.0 and uses some of its functions for genome scale metabolic model optimization tasks. IgemRNA is open access software available at https://github.com/BigDataInSilicoBiologyGroup/IgemRNA.

systems biology↗