A correction method for systematic error in (1)H-NMR time-course data validated through stochastic cell culture simulation

A Comprehensive Model for Separating Systematic Bias and Noise in Metabolomic Timecourse Data-A Nonlinear B-Spline Mixed-Effects Approach

The simultaneous detection of tens to hundreds of metabolites in a single metabolomic timecourse sample offers a unique but often unrealized opportunity for quantification validation. An individual timecourse fit for each metabolite fundamentally convolutes measurement noise with systematic sample bias (stemming from, for example, variable sample dilution, extraction, and normalization). However, since systematic bias, by its definition, influences all metabolites within a sample in a similar fashion, it can be identified and corrected through the simultaneous fit of all detected metabolites in a single timecourse model. This study presents a nonlinear B-spline mixed-effects model as a convenient formulation capable of estimating and correcting such bias. The proposed model was successfully applied to real cell culture data and validated using simulated timecourse data perturbed with varying degrees of random noise and systematic bias. The model was able to accurately correct systematic bias of 3%-10% to within 0.5% on average for typical data. An R package for the correction model has been developed to facilitate model adoption and use. The proposed nonlinear B-spline mixed-effects formulation is general enough for application to a broad range of research areas beyond just cell culture metabolomics.

NMR metabolomics of plant and yeast-based hydrolysates for cell culture media applications - A comprehensive assessment

Cultivated meat products, generated by growing isolated skeletal muscle and fat tissue, offer the promise of a more sustainable and ethical alternative to traditional meat production. However, with cell culture media used to grow the cells accounting for 55-95% of the overall production cost, achieving true sustainability requires significant media optimization. One means of dealing with these high costs is the use of low-cost complex additives such as hydrolysates to provide a wide range of nutrients, from small molecules (metabolites) to growth factors and peptides. Despite their potential, most hydrolysate products remain poorly characterized and many are thought to suffer from persistent issues of high batch-to-batch variability. Although there have been a number of isolated efforts to determine metabolic profiles for a handful of hydrolysate products, we present the first attempt at a more comprehensive metabolomic characterization of nine different products (four plant and five yeast-based) from two to four different lots each. NMR analysis identified 90 unique metabolites, with only 15 metabolites common to all hydrolysate products (including eight of the nine essential amino acids), and 16 metabolites found in only a single hydrolysate product. The different hydrolysate products were found to have substantial differences in metabolite concentrations (as a fraction of overall mass), ranging from a high of 43% in yeast extract to a low of 14% in soy hydrolysates. The proportion of various metabolites also varied between products, with carbohydrate concentrations particularly high in soy hydrolysates and nucleosides more prominent in two of the yeast products. Overall, yeast extract generally had higher metabolite concentrations than all the other products, whereas both yeast extract and cotton had the largest variety of metabolites. A direct calculation of batch-to-batch variability revealed although there are significant differences between lots, these are largely driven by a relatively small fraction of compounds. This report will hopefully serve as a useful starting point for a more nuanced consideration of hydrolysate products in cell culture media optimization, both in the context of cultivated meat and beyond.

Effect of an Amyloidogenic SARS-COV-2 Protein Fragment on α-Synuclein Monomers and Fibrils

Aggregates of α-synuclein are thought to be the disease-causing agent in Parkinson's disease. Various case studies have hinted at a correlation between COVID-19 and the onset of Parkinson's disease. For this reason, we use molecular dynamics simulations to study whether amyloidogenic regions in SARS-COV-2 proteins can initiate and modulate aggregation of α-synuclein. As an example, we choose the nine-residue fragment SFYVYSRVK (SK9), located on the C-terminal of the envelope protein of SARS-COV-2. We probe how the presence of SK9 affects the conformational ensemble of α-synuclein monomers and the stability of two resolved fibril polymorphs. We find that the viral protein fragment SK9 may alter α-synuclein amyloid formation by shifting the ensemble toward aggregation-prone and preferentially rod-like fibril seeding conformations. However, SK9 has only a small effect on the stability of pre-existing or newly formed fibrils. A potential mechanism and key residues for potential virus-induced amyloid formation are described.

Effect of an amyloidogenic SARS-COV-2 protein fragment on α-synuclein monomers and fibrils

Using molecular dynamic simulations we study whether amyloidogenic regions in viral proteins can initiate and modulate formation of α-synuclein aggregates, thought to be the disease-causing agent in Parkinson's Disease. As an example we choose the nine-residue fragment SFYVYSRVK (SK9), located on the C-terminal of the Envelope protein of SARS-COV-2. We probe how the presence of SK9 affects the conformational ensemble of α-synuclein monomers and the stability of two resolved fibril polymorphs. We find that the viral protein fragment SK9 may alter α-synuclein amyloid formation by shifting the ensemble toward aggregation-prone and preferentially rod-like fibril seeding conformations. However, SK9 has only little effect of the stability of pre-existing or newly-formed fibrils.

Role of microRNA-4458 in patients with non-small-cell lung cancer

Incidence and progression of non-small-cell lung cancer (NSCLC) is a multi-factor, multi-step process. The present study investigated the association between the expression level of microRNA (miR)-4458 in NSCLC and paracarcinoma liver tissues and survival rates, and studied the biological functions of miR-4458 at the cellular and protein level. NSCLC and paracarcinoma tissues were sequenced using a miR expression chip. The association between miR-4458 expression and tumor-node-metastasis staging, total survival rate and relapse-free survival rate was analyzed. miR-4458 was subjected to target gene prediction. The target protein of cyclin D1 (CCND1) was verified with western blot analysis, immunohistochemistry and a luciferase reporter assay. The relative level of miR-4458 in paracarcinoma tissues of 9 NSCLC patients decreased from 2.38 to 0.65 (P<0.001). Total five-year survival rates of the high-expression miR-4458 group (29.21%) significantly exceeded that of the low-expression group (14.37%) (P=0.025). The viability of human lung carcinoma A549 and H460 cells transfected with miR-4458 decreased significantly compared with cells transfected with a normal control (blank control plasmid) within 72 h (P<0.001). The percentage of A549 and H460 cells transfected with a miR-4458 mimic at the cell cycle stage G0/G1 was 69.94±8.05 and 68.15±7.75%, respectively. The percentages increased significantly compared with the control group (46.06±6.93 for A549 cells; 45.22±7.24 for H640 cells; P<0.001). CCND1 mRNA was downregulated significantly in H460 cells 72 h subsequent to the addition of miR-4458 mimics (P<0.001). The activity of mutant-CCND1 altered slightly, while the fluorescence intensity of the wild-type-CCND1 group decreased significantly following the addition of miR-4458 mimics. In conclusion, miR-4458 was expressed at low levels in lung cancer tissues, and it arrested cells in vitro at stage G0/G1 and inhibited cell proliferation. Therefore, miR-4458 may participate in the onset of lung cancer as a suppressor gene by inhibiting CCND1.

Identifying model error in metabolic flux analysis - a generalized least squares approach

BACKGROUND

The estimation of intracellular flux through traditional metabolic flux analysis (MFA) using an overdetermined system of equations is a well established practice in metabolic engineering. Despite the continued evolution of the methodology since its introduction, there has been little focus on validation and identification of poor model fit outside of identifying "gross measurement error". The growing complexity of metabolic models, which are increasingly generated from genome-level data, has necessitated robust validation that can directly assess model fit.

RESULTS

In this work, MFA calculation is framed as a generalized least squares (GLS) problem, highlighting the applicability of the common t-test for model validation. To differentiate between measurement and model error, we simulate ideal flux profiles directly from the model, perturb them with estimated measurement error, and compare their validation to real data. Application of this strategy to an established Chinese Hamster Ovary (CHO) cell model shows how fluxes validated by traditional means may be largely non-significant due to a lack of model fit. With further simulation, we explore how t-test significance relates to calculation error and show that fluxes found to be non-significant have 2-4 fold larger error (if measurement uncertainty is in the 5-10 % range).

CONCLUSIONS

The proposed validation method goes beyond traditional detection of "gross measurement error" to identify lack of fit between model and data. Although the focus of this work is on t-test validation and traditional MFA, the presented framework is readily applicable to other regression analysis methods and MFA formulations.

Disruption of the Reductive 1,3-Propanediol Pathway Triggers Production of 1,2-Propanediol for Sustained Glycerol Fermentation by Clostridium pasteurianum

Crude glycerol, the major by-product of biodiesel production, is an attractive bioprocessing feedstock owing to its abundance, low cost, and high degree of reduction. In line with the advent of the biodiesel industry, Clostridium pasteurianum has gained prominence as a result of its unique capacity to convert waste glycerol into n-butanol, a high-energy biofuel. However, no efforts have been directed at abolishing the production of 1,3-propanediol (1,3-PDO), the chief competing product of C. pasteurianum glycerol fermentation. Here, we report rational metabolic engineering of C. pasteurianum for enhanced n-butanol production through inactivation of the gene encoding 1,3-PDO dehydrogenase (dhaT). In spite of current models of anaerobic glycerol dissimilation, culture growth and glycerol utilization were unaffected in the dhaT disruption mutant (dhaT::Ll.LtrB). Metabolite characterization of the dhaT::Ll.LtrB mutant revealed an 83% decrease in 1,3-PDO production, encompassing the lowest C. pasteurianum 1,3-PDO titer reported to date (0.58 g liter(-1)). With 1,3-PDO formation nearly abolished, glycerol was converted almost exclusively to n-butanol (8.6 g liter(-1)), yielding a high n-butanol selectivity of 0.83 g n-butanol g(-1) of solvents compared to 0.51 g n-butanol g(-1) of solvents for the wild-type strain. Unexpectedly, high-performance liquid chromatography (HPLC) analysis of dhaT::Ll.LtrB mutant culture supernatants identified a metabolite peak consistent with 1,2-propanediol (1,2-PDO), which was confirmed by nuclear magnetic resonance (NMR). Based on these findings, we propose a new model for glycerol dissimilation by C. pasteurianum, whereby the production of 1,3-PDO by the wild-type strain and low levels of both 1,3-PDO and 1,2-PDO by the engineered mutant balance the reducing equivalents generated during cell mass synthesis from glycerol.

IMPORTANCE

Organisms from the genus Clostridium are perhaps the most notable native cellular factories, owing to their vast substrate utilization range and equally diverse variety of metabolites produced. The ability of C. pasteurianum to sustain redox balance and glycerol fermentation despite inactivation of the 1,3-PDO pathway is a testament to the exceptional metabolic flexibility exhibited by clostridia. Moreover, identification of a previously unknown 1,2-PDO-formation pathway, as detailed herein, provides a deeper understanding of fermentative glycerol utilization in clostridia and will inform future metabolic engineering endeavors involving C. pasteurianum To our knowledge, the C. pasteurianum dhaT disruption mutant derived in this study is the only organism that produces both 1,2- and 1,3-PDOs. Most importantly, the engineered strain provides an excellent platform for highly selective production of n-butanol from waste glycerol.