POTW: Uncovering the function of an intergenic SNP

My choice for Paper Of The Week this week is a report from a few weeks back (digging through the pile...) in which a polymorphism conferring increased risk of renal cell carcinoma is investigated for allele-specific functions. The paper is "Common genetic variants at the 11q13.3 renal cancer susceptibility locus influence binding of HIF to an enhancer of cyclin D1 expression" by Schödel, et al. (Nature Genetics 44:420-425).

Although the authors had several clues that the risk SNPs would (likely) affect expression of CCND1 (cyclin D1) in a manner regulated by hypoxia-induced factors - namely, that HIFs were known to regulate CCND1 but from an unknown binding site and that CCND1 is an established oncogene, among others - they accumulated much new data to nail down the role of EPAS1 (HIF-2) in regulating CCND1 expression.

One nice aspect of this work is the authors' taking advantage of signals seen in a renal carcinoma cell line and not in a breast cancer cell line (serving then as control). For example, they looked at the epigenetic enhancer marks at the 11q13.3 susceptibility locus with FAIRE (ormaldehyde-assisted isolation of regulatory elements to identify regions of nucleosome occupancy), and EPAS1 binding as assessed by ChIP-qPCR. The use of pVHL-defective RCC cell lines verified the role of VHL (von Hippel–Lindau tumor suppressor) in this cancer and consequence of allele-specific expression of CCND1.

Taken together, the data presented show that the haplotype associating with reduced renal cell cancer risk hinders EPAS1 binding, "resulting in an allelic imbalance in cyclin D1 expression, thus affecting a link between hypoxia pathways and cell cycle control." This is nice work and a fine example of the approaches needed to develop a clear understanding of polymorphism and disease risk from a functional perspective.

MicroRNAs and glucose metabolism

A noteworthy article released yesterday in Nature Cell Biology reports that in mice, in liver, microRNA miR-143 induces the down-regulation of oxysterol binding protein-like 8 (OSBPL8, ORP8). This leads to an impaired ability of insulin to induce AKT activation. AKT is a protein kinase. What is most interesting about this work is the presentation of evidence that microRNA-based regulation of gene activity is important in glucose homeostasis and perhaps onset of type 2 diabetes.

Before I get into what else is known about miR-143/MIRN143, it is interesting to note that OSBPL8 suppresses ABCA1 expression and cholesterol efflux from macrophages, as reported by Yan et al. (2008). ABCA1 is itself regulated, in part, by microRNA MIR33A encoded within SREBF2 to regulate both HDL biogenesis in the liver and cellular cholesterol efflux.

MIRN143:
MIRN143 is frequently observed to be downregulated in colorectal (Ng Sung 2009 Br J Cancer 101:699) and gastric cancers (Takagi Akao 2009 Oncology 77:12)

MIRN143 is frequently downregulated in pancreatic cancer cells (Kent Mendell 2009 Cancer Biol Ther 8:2013)

MIRN143 was a transcriptional target of myocardin and other transcriptional factors involved in smooth muscle cell fate (Cordes Srivastava 2009 Nature 460:705)

MIRN143 has also been found to play a role in adipocyte differentiation (Xie Lodish 2009 Dibetes 58:1050, Walden Cannon 2009 J Cell Physiol 218:444, Takanabe Hasegawa 2008 Biochem Biophys Res Commun 376:728, Esau Griffey 2004 J Biol Chem 279:52361))

Expression of MIRN143 was elevated in differentiating adipocytes and inhibition of MIRN143 could suppress differentiation of adipocytes (Esau Griffey 2004 J Biol Chem 279:52361)

Ectopically expressed MIRN143 in preadipocyte 3T3-L1 cells has been found to accelerate adipogenesis (Xie Lodish 2009 Diabetes 58:1050)

In addition, MIRN145, neighboring MIRN143 in the human genome is also a participant to this regulatory network:

IRS1 translation is downregulated by MIRN145 (Shi B, Baserga R, et al J. Biol. Chem. 282:32582-32590, 2007)

MIRN145 regulates actin cytoskeletal dynamics (Xin 2009 Genes Dev 23:2166)

stem cell pluripotency is regulated by MIRN145 (Xu 2009 Cell 137:647)

Transcription factor databases

The following is a guest-post by my colleague Jacqueline Lane (with some editing by me). She has been interested in identifying novel transcription factors (TF) involved in obesity and genetic variants in their binding sites as well as in the TF genes themselves.

Jackie has put together a list of TF-gene interaction databases she is willing to share here. There are three types of data:

1) TF-gene interaction
This is a compilation of databases with TF-gene interaction data. This might be of the most interest because it lists many databases. See http://www.pazar.info/. There is also the oRegAnno database, which is easy to view if you click on the tfview link on the right-hand side; see http://www.oreganno.org/oregano/. Lastly, TF-gene binding data can also be found at http://www.tfcat.ca/.

2) TF-TF interactions
This is a database of TF-TF co-activators and co-repressors (TFs that direct transcription of a gene in concert). This helps with determining tissue/temporal specific combinatorial regulation. See http://www.cell.com/retrieve/pii/S0092867410000796.

3) TF co-activators
The TF co-factor database lists proteins that bind to TFs, but not directly to DNA. These protein interactions can give a better picture of the full interaction. Find the data at http://nar.oxfordjournals.org/content/early/2010/10/20/nar.gkq945.full.