Lipid droplet formation and subsequent steatosis (the abnormal retention of lipids within a cell) has been reported to contribute to hepatotoxicity and is an adverse effect of many pharmacological agents including the antiepileptic drug valproic acid (VPA). In this study, we have developed a simple model system (Dictyostelium discoideum) to investigate the effects of VPA and related compounds in lipid droplet formation. In mammalian hepatocytes, VPA increases lipid droplet accumulation over a 24-hour period, giving rise to liver cell damage, and we show a similar effect in Dictyostelium following 30 minutes of VPA treatment. Using (3)H-labelled polyunsaturated (arachidonic) or saturated (palmitic) fatty acids, we shown that VPA treatment of Dictyostelium gives rise to an increased accumulation of both types of fatty acids in phosphatidylcholine, phosphatidylethanolamine and non-polar lipids in this time period, with a similar trend observed in human hepatocytes (Huh7 cells) labelled with [(3)H]arachidonic acid. In addition, pharmacological inhibition of β-oxidation in Dictyostelium phenocopies fatty acid accumulation, in agreement with data reported in mammalian systems. Using Dictyostelium, we then screened a range of VPA-related compounds to identify those with high and low lipid-accumulation potential, and validated these activities for effects on lipid droplet formation by using human hepatocytes. Structure-activity relationships for these VPA-related compounds suggest that lipid accumulation is independent of VPA-catalysed teratogenicity and inositol depletion. These results suggest that Dictyostelium could provide both a novel model system for the analysis of lipid droplet formation in human hepatocytes and a rapid method for identifying VPA-related compounds that show liver toxicology.
Pharmacology, Toxicology and Pharmaceutical Science
2
4%
Neuroscience
2
4%
Other
7
14%
Unknown
12
24%
Attention Score in Context
Attention Score in Context
This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 July 2025.
All research outputs
#7,318,328
of 34,457,357 outputs
Outputs from Disease Models and Mechanisms
#855
of 2,404 outputs
Outputs of similar age
#39,538
of 188,984 outputs
Outputs of similar age from Disease Models and Mechanisms
#12
of 30 outputs
Altmetric has tracked 34,457,357 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,404 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one has gotten more attention than average, scoring higher than 63% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 188,984 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
