TISSUES

Tissue expression database

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CD300LF tissues

CD300LF [ENSP00000462309]

CMRF35-like molecule 1; Acts as an inhibitory receptor for myeloid cells and mast cells . Positively regulates the phagocytosis of apoptotic cells (efferocytosis) via phosphatidylserine (PS) recognition; recognizes and binds PS as a ligand which is expressed on the surface of apoptotic cells. Plays an important role in the maintenance of immune homeostasis, by promoting macrophage-mediated efferocytosis and by inhibiting dendritic cell-mediated efferocytosis (By similarity). Negatively regulates Fc epsilon receptor-dependent mast cell activation and allergic responses via binding to ceramide and sphingomyelin which act as ligands . May act as a coreceptor for interleukin 4 (IL-4). Associates with and regulates IL-4 receptor alpha-mediated responses by augmenting IL-4- and IL-13-induced signaling (By similarity). Negatively regulates the Toll-like receptor (TLR) signaling mediated by MYD88 and TRIF through activation of PTPN6/SHP-1 and PTPN11/SHP-2 . Inhibits osteoclast formation. Induces macrophage cell death upon engagement (By similarity). ECO:0000269|PubMed:15549731, ECO:0000269|PubMed:22043923,; Belongs to the CD300 family.

Synonyms:  CD300LF,  CD300LFp,  hCD300LF,  J3KS52,  Q8TDQ1 ...

Linkouts:  STRING  Pharos  UniProt

0 1 2 3 4 5 Confidence

The tissue associations are derived from manually curated knowledge in UniProtKB and via automatic text mining of the biomedical literature, which has not been manually verified. The confidence of each association is signified by stars, where ★★★★★ is the highest confidence and ★☆☆☆☆ is the lowest. Download files from earlier versions are archived on figshare.

Each tissue–gene association is based on a text-mining score, which is proportional to 1) the absolute number of comentionings and 2) the ratio of observed to expected comentionings (i.e. the enrichment). These scores are normalized to z-scores by comparing them to a random background. This is represented by stars, each star corresponding to two standard deviations above the mean of the background distribution.

Developed by Alberto Santos, Oana Palasca, Christian Stolte, Kalliopi Tsafou, Sune Frankild, Janos Binder, Sean O'Donoghue, Jan Gorodkin, and Lars Juhl Jensen from the Novo Nordisk Foundation Center for Protein Research, Center for non-coding RNA in Technology and Health, and the Commonwealth Scientific and Industrial Research Organisation (CSIRO).