BioGPS has become the valuable resource that it is because of the contributions from our wonderful user community. Thank you for contributing plugins, suggestions, and ideas–all of which have improved BioGPS for everyone. In order to celebrate the contributions of BioGPS users to the scientific research community, this series will feature publications and articles generated by BioGPS users. We sincerely hope you will join us in celebrating the fascinating work that YOU do.

This week, we will feature an article that was published by a very productive research group:
Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density by Larry D. Mesner, Brianne Ray, Yi-Hsiang Hsu, Ani Manichaikul, Eric Lum, Elizabeth C. Bryda, Stephen S. Rich, Clifford J. Rosen, Michael H. Criqui, Matthew Allison, Matthew J. Budoff, Thomas L. Clemens, and Charles R. Farber.(doi:10.1172/JCI73072)

Dr. Charles R. Farber kindly answered our inquiries for this series.

  1. Who is the team behind the work that was published in Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density?
    The team is covered in the author list.

  3. Please provide a brief summary of your research findings from Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density.
    My lab’s primary interest is in unraveling the genetics of complex skeletal phenotypes that influence bone strength, such as bone mineral density (BMD). We do both mouse and human genetics, but much of our gene discovery is done using the mouse. We primarily use “system genetics” strategies, which integrate global gene expression data into genetic studies, to identify novel genes. Systems genetics studies seek to identify genetic variation that impacts gene expression and then utilize this information to more rapidly identify the subset of variation that contributes to disease.

    In the article we used multiple sets of approaches. The discovery of Bicc1 as a regulator of BMD was accomplished using a “systems genetics” approach. First, linkage analysis of BMD in a cross between strains divergent for BMD identified a locus on Chromosome 10. This locus contained several hundred genes and to pinpoint which one was responsible, microarray expression data was generated in the cross. Genes were then identified whose expression correlated with BMD and was controlled by the same genetic variation impacting BMD. Through this approach a difference in the expression of Bicc1 was predicted to be responsible for the change in BMD. We confirmed this prediction by demonstrating that mice carrying one copy of a defective version of Bicc1 had lower BMD.

    In the second part of the paper, we used a network-based approach to determine how Bicc1 was influencing BMD. We asked a simple question; in a bone co-expression network what genes of known function are co-expressed with Bicc1. It turned out that Bicc1 was highly co-expressed with genes that play a role in the differentiation and function of bone-forming osteoblasts, suggesting it also played a role in this process. In the network, Bicc1 was most strongly co-expressed with Pkd2. We went on to show that siRNA knockdown of both Bicc1 and Pkd2 impaired osteoblast function. Moreover, overexpression of Pkd2 rescued the impaired osteoblast function due to the absence of Bicc1, suggesting that the effect of Bicc1 is mediated through Pkd2. Thus, we were able to determine the cell-type in which it was active and its function by defining what other genes it was co-expressed with.

    A third and important part of the paper was to show that genetic variation in both human BICC1 and PKD2 are associated with BMD, demonstrating their relevance to human bone biology.

  5. How did the team learn about and/or utilize BioGPS for this research?
    My lab has been using BioGPS for a number of years. We’ve used it in at least seven publications (see list at end). There are many times in which we begin working with a novel gene and we want to know where it is expressed and BioGPS is almost always how we answer that question. We particularly love the bone cell (osteoblast and osteoclast) expression data. In addition to individual gene queries we also use it evaluate the overall expression of a group of genes, e.g. Are members of a specific co-expression network module enriched for expression in particular tissues or cell-types?

  7. What are some future directions for the team behind this research?
    With regards to Bicc1 and Pkd2 we are actively working on the exact nature of their interaction as well as more clearly defining the mechanism through which they influence osteoblast differentiation.

  9. Is there anyone else the team would like to acknowledge that may have gotten skipped in this exciting publication?
    I hope not!

More excellent research from Dr. Farber

Thanks again to Dr. Charles R. Farber for answering our questions. Click here to read their fascinating article. It’s worth a look because these awesome researchers have made their work open access, so you can read the whole exciting article for free!

Used BioGPS and cited it in your publication? Let us know! We would love to feature YOUR work, no matter how long ago it was published. BioGPS Featured Article Series only started recently, but we know your contributions to science is ongoing.

-2016.06.07 edit: All PMC links updated to match original publication