Biology subjects

Blank, L. M.

Publications and source records attributed to Blank, L. M..

42 records · Page 3Linked to original sources

bioRxiv2019

Insights into an alternative pathway for glycerol metabolism in a glycerol kinase deficient Pseudomonas putida KT2440

Pseudomonas putida KT2440 is known to metabolise glycerol via glycerol-3-phosphate using glycerol kinase an enzyme previously described as critical for glycerol metabolism (1). However, when glycerol kinase was knocked out in P. putida KT2440 it retained the ability to use glycerol as the sole carbon source, albeit with a much-extended lag period and 2 fold lower final biomass compared to the wild type strain. A metabolomic study identified glycerate as a major and the most abundant intermediate in glycerol metabolism in this mutated strain with levels 21-fold higher than wild type. Erythrose-4-phosphate was detected in the mutant strain, but not in the wild type strain. Glyceraldehyde and glycraldehyde-3-phosphate were detected at similar levels in the mutant strain and the wild type. Transcriptomic studies identified 191 genes that were more than 2-fold upregulated in the mutant compared to the wild type and 175 that were down regulated. The genes involved in short chain length fatty acid metabolism were highly upregulated in the mutant strain. The genes encoding 3-hydroxybutyrate dehydrogenase were 5.8-fold upregulated and thus the gene was cloned, expressed and purified to reveal it can act on glyceraldehyde but not glycerol as a substrate.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC=\"FIGDIR/small/567230_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (21K):\norg.highwire.dtl.DTLVardef@1b5298eorg.highwire.dtl.DTLVardef@bb4841org.highwire.dtl.DTLVardef@184de3forg.highwire.dtl.DTLVardef@1b2e2a_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2019

Investigating metabolic interactions in a microbial co-culture through integrated modelling and experiments

Microbial co-cultures have been used in several biotechnological applications. Within these co-cultures, the micro-organisms tend to interact with each other and perform complex actions vis-a-vis a single organism. Investigating metabolic interactions in microbial co-cultures is crucial in designing microbial consortia tailored for specific applications. In this study, we present a pipeline integrating modelling and experimental approaches to understand metabolic interactions between organisms in a community. We define a new index named Metabolic Support Index (MSI), which quantifies the benefits derived by each organism in the presence of the other when grown as a co-culture. We computed MSI for several experimentally demonstrated co-culture systems and showed that MSI, as a metric, accurately identifies the organism that derives the maximum benefit. We also computed MSI for a commonly used yeast co-culture consisting of Saccharomyces cerevisiae and Pichia stipitis and observed that the latter derives higher benefit from the interaction. Further, we designed two-stage experiments to study mutual interactions and showed that P. stipitis indeed derives the maximum benefit from the interaction, as shown from our computational predictions. Also, using our previously developed computational tool MetQuest, we identified all the metabolic exchanges happening between these organisms by analysing the pathways spanning the two organisms. By analysing the HPLC profiles and studying the isotope labelling, we show that P. stipitis consumes the ethanol produced by S. cerevisiae when grown on glucose-rich medium under aerobic conditions, as also indicated by our in silico pathway analyses. Our approach represents an important step in understanding metabolic interactions in microbial communities through an integrating framework of modelling and experiments.

systems biology↗

bioRxiv2018

A physiology-based model of bile acid metabolism shows altered tissue concentrations after drug administration and in specific genotype subgroups

Drug-induced liver injuries (DILI) are an important issue in drug development and patient safety and often lead to termination of drug-development programs or late withdrawals of drugs. Since DILI events are hard to diagnose in preclinical settings, a need for alternative prediction methods such as computational modeling emerges. Impairment of bile acid (BA) metabolism, known as cholestasis, is a frequent form of DILI. Being rather a systemic then a single organ related disease, whole-body physiology-based modeling is a predestined approach for cholestasis modeling. The objectives of the presented study were 1) the development of a physiology-based model for human bile acid metabolism, 2) model validation and characterization for a virtual population, and 3) prediction and quantification of the effects of genetic predispositions and drug interaction on bile acid metabolism. The developed physiology-based bile acid (PBBA) model is based on the standard PBPK model of PKSim(R) and describes the bile acid circulation in a healthy reference individual. Active processes such as the hepatic synthesis, gallbladder emptying upon meal intake, transition through the gastrointestinal tract, reabsorption into the liver, distribution within the body, and excretion are included. The kinetics of active processes for the surrogate BA glycochenodeoxycholic acid were fitted to time-concentration profiles of blood BA levels reported in literature. The robustness of our PBBA model is underlined by the comparison of simulated plasma BA concentrations in a virtual population of 1,000 healthy individuals with reported data. In addition to plasma concentrations, the PBBA model allows simulations of BA exposure in relevant tissues like the liver and can therefore enhance the mechanistic understanding of cholestasis. This feature was used to analyse the reported increased risk of cholestatic DILI in Benign Recurrent Intrahepatic Cholestasis type 2 (BRIC2) patients. Simulations of the PBBA model suggest a higher susceptibility of BRIC2 patients towards cholestatic DILI due to BA accumulation in hepatocytes. Apart from these intrinsic effects, drug-interactions and their effect on the systemic bile acid metabolism were simulated by combining the PBBA model with a drug PBPK model of cyclosporine A (CsA). The results of which confirmed the reported higher risk of developing DILI as a consequence of CsA intake. Altogether, the presented model enhances our mechanistic understanding of cholestasis, allows the identification of drug-interactions leading to altered BA levels in blood and organs, and could be used to prevent clinical cases of cholestasis and enhance patient safety.

systems biology↗

bioRxiv2018

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.\n\nHere, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

systems biology↗

bioRxiv2018

Determination of growth-coupling strategies and their underlying principles

Metabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. We have developed the computational tool gcOpt, which identifies knockout strategies leading to the best possible GC by maximizing the minimally guaranteed product yield. gcOpt implicitly favors solutions resulting in strict coupling of product synthesis to growth and metabolic activity while avoiding solutions inferring weak, conditional coupling.\n\nGC intervention strategies identified by gcOpt were examined for GC generating principles under diverse conditions. Curtailing the metabolism to render product formation an essential carbon drain was identified as one major strategy generating strong coupling of metabolic activity and target synthesis. Impeding the balancing of cofactors and protons in the absence of target production was the underlying principle of all other strategies and further increased the GC strength of the aforementioned strategies. Thus, generating a dependency between supply of global metabolic cofactors and product synthesis appears to be advantageous in enforcing strong GC.\n\nAbbreviations

systems biology↗

bioRxiv2017

Discovery and Evaluation of Biosynthetic Pathways for the Production of Five Methyl Ethyl Ketone Precursors

The limited supply of fossil fuels and the establishment of new environmental policies shifted research in industry and academia towards sustainable production of the 2nd generation of biofuels, with Methyl Ethyl Ketone (MEK) being one promising fuel candidate. MEK is a commercially valuable petrochemical with an extensive application as a solvent. However, as of today, a sustainable and economically viable production of MEK has not yet been achieved despite several attempts of introducing biosynthetic pathways in industrial microorganisms. We used BNICE.ch as a retrobiosynthesis tool to discover all novel pathways around MEK. Out of 1325 identified compounds connecting to MEK with one reaction step, we selected 3-oxopentanoate, but-3-en-2-one, but-1-en-2-olate, butylamine, and 2-hydroxy-2-methyl-butanenitrile for further study. We reconstructed 3679610 novel biosynthetic pathways towards these 5 compounds. We then embedded these pathways into the genome-scale model of E. coli, and a set of 18622 were found to be most biologically feasible ones based on thermodynamics and their yields. For each novel reaction in the viable pathways, we proposed the most similar KEGG reactions, with their gene and protein sequences, as candidates for either a direct experimental implementation or as a basis for enzyme engineering. Through pathway similarity analysis we classified the pathways and identified the enzymes and precursors that were indispensable for the production of the target molecules. These retrobiosynthesis studies demonstrate the potential of BNICE.ch for discovery, systematic evaluation, and analysis of novel pathways in synthetic biology and metabolic engineering studies.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC=\"FIGDIR/small/209569_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (27K):\norg.highwire.dtl.DTLVardef@15bb5corg.highwire.dtl.DTLVardef@bfc12borg.highwire.dtl.DTLVardef@1230500org.highwire.dtl.DTLVardef@1d74d2_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

synthetic biology↗