About the project

Contribute to the LD-KP project!

We welcome submissions of datasets, methods, or visualizations relevant to lipid droplet biology for inclusion in the LD-KP. Data submissions could include but are not limited to genome perturbation data, gene expression data and proteomics datasets highly relevant to LD biology. Initially submissions should include all relevant primary data and statistics with a brief description of the dataset and its relevance to LD biology, as well as references to relevant publications.

Submissions will be reviewed bi-monthly by the directors and scientific advisory board of the LD-KP project and recommendations for inclusion will be followed with a discussion on details of data visualization, data integration and cost sharing. Please contact us for more information.

Knowledge Portal Team

Project directors Noël Burtt
Robert Farese, Jr.
Jason Flannick
Niklas Mejhert
Tobias Walther
Scientific Advisory Board Joel Goodman, UT Southwestern Medical Center, Dallas, TX
Natalie Krahmer, Helmholz Research Center, Munich, Germany
Mikael Rydén, Karolinska Institutet, Huddinge, Sweden
James Olzmann, UC Berkeley, Berkeley, CA
Creative lead Maria Costanzo
Software engineers Marc Duby
Clint Gilbert
Quy Hoang
Dong-Keun Jang
Oliver Ruebenacker
Preeti Singh
Computational biologists Ryan Koesterer
Trang Nguyen
Project manager MacKenzie Brandes

Contact the team to make suggestions or get individual help.


Results on RNAi screening for LD protein targeting in Drosophila cells:

Song J, et al.
Identification of two pathways mediating protein targeting from ER to lipid droplets.
Submitted for publication, 2021.
bioRxiv preprint

Description of the LD-Portal:

Mejhert N, Gabriel KR, et al.
The Lipid Droplet Knowledge Portal: A resource for systematic analyses of lipid droplet biology.
Submitted for publication, 2021.
bioRxiv preprint

Results on RNAi screening, RNA-seq, and proteomics in THP-1 and SUM159 cells:

Mejhert N et al.
Partitioning of MLX-Family Transcription Factors to Lipid Droplets Regulates Metabolic Gene Expression.
Mol Cell. 2020 Mar 19;77(6):1251-1264.e9. doi: 10.1016/j.molcel.2020.01.014.
PMID: 32023484

Results on mouse liver proteomics and phosphoproteomics:

Krahmer N et al.
Organellar Proteomics and Phospho-Proteomics Reveal Subcellular Reorganization in Diet-Induced Hepatic Steatosis.
Dev Cell. 2018 Oct 22;47(2):205-221.e7. doi: 10.1016/j.devcel.2018.09.017.
PMID: 30352176

Multi-marker Analysis of GenoMic Annotation (MAGMA) method:

de Leeuw C et al.
MAGMA: generalized gene-set analysis of GWAS data.
PLoS Comput Biol. 2015 Apr 17;11(4):e1004219. doi: 10.1371/journal.pcbi.1004219..
PMID: 25885710

MAGMA software

Results on Huh7 and U2OS LD proteomics:

Bersuker K et al.
A Proximity Labeling Strategy Provides Insights into the Composition and Dynamics of Lipid Droplet Proteomes.
Dev Cell. 2018 Jan 8;44(1):97-112.e7. doi: 10.1016/j.devcel.2017.11.020.
PMID: 29275994