Gene information

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LDs
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Gene summary (source: UniProtKB/Swiss-Prot)

Effects of gene knockdown on lipid droplet parameters (RZ score)

Number (MNCELLSFROMBDPCHILDLPOBCT_RZ)

LD number (LDs per cell), which is the mean number of LDs per cell.

Dispersion (MNLPOBNEIGHPCTTOUCHING2_RZ)

LD dispersion (percent touching), which is the percent of the object's boundary pixels that touch neighbors, after the objects have been expanded by two pixels.

Shape (MNLPOBARSHECCENTRICITY_RZ)

LD shape (eccentricity), where the eccentricity is the ratio of the distance between the foci of the ellipse and its major axis length. The value is between 0 and 1. (0 and 1 are degenerate cases; an ellipse whose eccentricity is 0 is actually a circle, while an ellipse whose eccentricity is 1 is a line segment.)

Size (MNLPOBARSHMEDIANRAD_RZ)

LD size (median radius) which is the median distance of any pixel in the object to the closest pixel outside of the object.

Intensity (MNLPOBINTINTGRINTBDP_RZ)

LD intensity (integrated intensity), which is the sum of the pixel intensities within an object.


Show 21 parameters plot

Number
Dispersion
Shape
Size
Intensity

Lipid droplet imaging in gene knockdown THP-1 human macrophages

In these images, lipid droplets appear white and nuclei are blue.

Control cells

Gene

View additional RISC-free control images
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Regulation by acetylated LDL

RNA sequencing

Total RNA was isolated and sequenced from THP-1 cells incubated with/without acetylated apolipoprotein B-containing lipoproteins (inducing triacylglycerol and cholesterol ester storage). Raw sequence data were converted to FASTQ format using bcl2fastq software (Illumina), and transcript abundance was quantified using Salmon (Patro et al., 2017). Results were imported into RStudio using tximport (Soneson et al., 2015), and differentially expressed genes were identified using DESeq2 (Love et al., 2014). In brief, the dataset was filtered to remove rows with only a single count across all samples and differentially expressed genes were identified using alpha < 0.1.

Enrichment in lipid droplet fraction ()

Mouse proteomes

Protein and Phosphopeptide Correlation Profiling of Mouse Liver in Steatosis

To map protein and phosphopeptide localization in the liver of mice under a low fat control diet (LFD) or 3 and 12 weeks of high fat diet (HFD), organelles of total liver homogenates were separated by density centrifugation. All fractions of the gradients were analyzed by label-free proteomics and EasyPhos. To generate profiles that reflect subcellular distributions, the intensities for identified protein or phosphopeptide were scaled from 0-1 and plotted over all fractions. Normalized intensities of average profiles from 3-4 biological replicates for each condition are shown. For organelle assignment, a set of organelle marker proteins was used for parameter optimization and training of the SVM based supervised learning approach implemented in Perseus software (Sigma=0.2 and C=4). With SVM classification the main subcellular localization was assigned to every identified protein and phosphopeptide for each condition separately, or for all conditions combined. For the assignment of a second organelle contribution, a separate algorithm determined the highest correlation between the experimentally determined protein or phosphopeptide profile with in silico generated combination profiles. The correlation value between the experimentally determined protein or phosphopeptide profile and the assigned in silico generated combination profile is given as measure for the quality of secondary organelle assignment. The alpha value (0-1) is a quantitative measure for the second organelle contribution. Proteins significantly changing their subcellular localization between the LFD condition and the HFD conditions were identified by a correlation based outlier test (FDR<0.2). The analysis was performed pairwise between the HFD3 or HFD12 vs LFD control.
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Murine liver protein correlation profile

Phosphosites

Associated phenotypes (Phenotype : p-value)

MAGMA algorithm

Associated phenotypes are those predicted by the MAGMA algorithm (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004219) to be associated with this gene. MAGMA (Multi-marker Analysis of GenoMic Annotation) analyzes genetic association and linkage disequilibrium data using a multiple regression approach in order to quantify the association of a gene with a particular phenotype. Phenotypes highlighted in darker orange have p-values less than 2.5e-6 for association with the gene, while those highlighted in lighter orange have association p-values between 2.5e-6 and 5e-3.

Corresponding fly genes (sorted by confidence) Drosophila melanogaster orthologs were identified via DIOPT v7.1

External resources