Exploiting Bioprocessing Fluctuations to Elicit the Mechanistics of De Novo Lipogenesis in Yarrowia lipolytica

Despite substantial achievements in elucidating the metabolic pathways of lipogenesis, a mechanistic representation of lipid accumulation and degradation has not been fully attained to-date. Recent evidence suggests that lipid accumulation can occur through increases of either the cytosolic copy-number of lipid droplets (LDs), or the LDs size. However, the prevailing phenotype, or how such mechanisms pertain to lipid degradation remain poorly understood. To address this shortcoming, we employed the–recently discovered–innate bioprocessing fluctuations in Yarrowia lipolytica, and performed single-cell fluctuation analysis using optical microscopy and microfluidics that generate a quasi-time invariant microenvironment. We report that lipid accumulation at early stationary phase in rich medium is substantially more likely to occur through variations in the LDs copy-number, rather than the LDs size. Critically, these mechanistics are also preserved during lipid degradation, as well as upon exposure to a protein translation inhibitor. The latter condition additionally induced a lipid accumulation phase, accompanied by the downregulation of lipid catabolism. Our results enable an in-depth mechanistic understanding of lipid biogenesis, and expand longitudinal single-cell fluctuation analyses from gene regulation to metabolism.


I. Lipid Accumulation Flux
Generally two broad cascade elements are involved in lipid biogenesis, namely carbon internalization and intracellular metabolic reactions (highlighted in green in the block diagram below). Intracellular carbon is denoted as Ci, neutral lipid product as Si and the related enzyme manifolds for lipid synthesis and degradation as E + and Erespectively (with k + and kbeing their reaction rates).
Under the assumption that intracellular carbon is at equilibrium with the quasi-timeinvariant extracellular supply (i.e. Ci: constant, while Si, E + , E -, k + and kbeing time dependent), lipid biogenesis may be further simplified as a single enzymic step reaction, where synthesis and degradation enzyme manifolds are characterized by the k + and kreaction rates [1]: Using the law of mass action, this reaction scheme can be re-written as: This differential equation represents the abovementioned reaction scheme, with blue denoting lipid synthesis and red lipid degradation. This rate equation indicates that temporal fluctuations of the lipid bioprocessing rate (dSi/dt) generally emanate from changes in the concentration (E) and reaction rates (κ) of related enzyme manifolds, as well as the instantaneous product concentration (Si).

II. Materials and Methods
Strains and Culture Conditions: All experiments were performed using the oleaginous yeast Yarrowia lipolytica. Specifically, we employed the Po1g strain (Yeastern Biotech Company, Taipei, Taiwan), the details and extensive characterization of which have been previously presented in reference [2]. The cells were grown in YPD medium, with glucose as the carbon source (10g/L Yeast Extract -Difco Laboratories, 20g/L Bacto-Peptone -Difco and 20g/L glucose -Sigma Aldrich). Where indicated, cycloheximide (CHX -Sigma Aldrich) was added to the medium flowing in the microfluidics following immobilization at concentrations of 16 µg/ml and 33 µg/ml [3,4]. CHX was dissolved in DMSO (Molecular Probes) at a 330 mg/ml concentration, and divided in 20 µL aliquots for storage at -20 o C until further use. DMSO was chosen for dissolving CHX due to the high solubility of the lipid stain in the same solvent (see below). The total DMSO concentration in the medium was kept at 0.02%, well below the 1% concentration rendered as having no impact on yeast physiology [5,6]. In comparing the lipogenesis effects of protein translation inhibition, care was taken so that the total DMSO concentration in the medium was the same in all cases (0.02%).
Cell Growth and Sampling: Cells were grown at room temperature (regulated at Each cell required approximately 1 min for optical sampling at all three spectral channels, which imposed an upper temporal resolution limit. Under such imaging and staining conditions, the smallest detectable lipid droplet was of an area equal to 0.09 µm 2 . This limit was set by requiring a signal to noise > 2. In the context of spatial resolution (~200 nm according to the manufacturer), a single dark pixel criterion was used to distinguish two lipid droplets.
Due to the lower axial resolution (i.e. along the z-axis) of the microscope than the lateral one (xy-plane), we quantified the size of the lipid droplets using the maximum intensity projection analysis [7,12]. Following acquisition, the images were stored for processing and analysis using ImageJ. For processing, the 3D confocal images were converted to 2D through the maximum intensity projection analysis, followed by convolution with a Gaussian (radius of decay = standard deviation -σ) to attenuate its low spatial-frequency components. Subsequently a bandpass filter was applied to the images (filter size equal to maximum feature size), followed by subtracting the filtered image from the original one (ImageJ). The lipid droplets were then identified by applying thresholding based on histogram entropy [13]. In this way, the number and area of individual lipid droplets was determined and normalized over the cell area as determined by bright field imaging.
Data Analysis: Due to the presence of outliers in our observations, robust statistics were employed in data analysis (see for example [14]). To this end, a custom script was written 6 in Matlab for performing the statistical analysis for the longitudinal fluctuations and noise determination. Linear fits and correlation analyses were performed in Origin Pro (OriginLab, 64Bit, 2015).