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RNA-Seq - membrane-type 1 matrix metalloproteinase

Cite this dataset

Mignatti, Paolo (2020). RNA-Seq - membrane-type 1 matrix metalloproteinase [Dataset]. Dryad.


Membrane-type 1 matrix metalloproteinase (MT1-MMP, MMP-14), a transmembrane proteinase with a short cytoplasmic tail, is a major effector of extracellular matrix (ECM) remodeling. Genetic silencing of MT1-MMP in mouse (Mmp14-/-) and man causes dwarfism, osteopenia, arthritis and lipodystrophy, abnormalities ascribed to defective collagen turnover. We have previously shown non-proteolytic functions of MT1-MMP mediated by its cytoplasmic tail, where the unique tyrosine (Y573) controls intracellular signaling. The Y573D mutation blocks TIMP2/MT1-MMP-induced Erk and Akt signaling without affecting proteolytic activity. Here we report that a mouse with the MT1-MMP Y573D mutation (Mmp14Y573D/Y573D) shows abnormalities similar to, but also different from those of Mmp14-/- mice. Skeletal stem cells (SSC) of Mmp14Y573D/Y573D mice show defective differentiation consistent with the mouse phenotype, which is rescued by wild-type SSC transplant. These results provide the first in vivo demonstration that MT1-MMP modulates bone, cartilage and fat homeostasis by controlling SSC differentiation through a mechanism independent of proteolysis.


The indicated tissues and SSC were isolated from Mmp14wt/wt and Mmp14Y573D/Y573 mice (3 mice/tissue/genotype) and total RNA was extracted using the RNeasy kit (Qiagen). The subsequent steps were carried out by personnel of the Genome Technology Center of NYU School of Medicine. RNA-Seq libraries were prepared with the TruSeq sample preparation kit (Illumina, San Diego, CA). Sequencing reads were mapped to the mouse genome using the STAR aligner (v2.5.0c) (Dobin et al., 2013). Alignments were guided by a Gene Transfer Format file (Ensembl GTF version GRCh37.70). The mean read insert sizes and their standard deviations were calculated using Picard tools (v.1.126). Read count tables were generated using HTSeq (v0.6.0) (Anders et al., 2015), normalized to their library size factors with DESeq (v3.7) (Anders & Huber, 2010), and differential expression analysis was performed. Read Per Million (RPM) normalized BigWig files were generated using BEDTools (v2.17.0) (Quinlan & Hall, 2010) and bedGraphToBigWig tool (v4). Statistical analyses were performed with R (v3.1.1), GO analysis with David Bioinformatics Resources 6.8 (Huang da et al., 2009a, 2009b) and GSEA with the hallmarks (h.all.v6.1.symbols.gmt) gene sets of the Molecular Signature Database (MSigDB) v6.1 (Mootha et al., 2003; Subramanian et al., 2005).

Anders, S., & Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biol, 11(10), R106. doi:10.1186/gb-2010-11-10-r106

Anders, S., Pyl, P. T., & Huber, W. (2015). HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics, 31(2), 166-169. doi:10.1093/bioinformatics/btu638

Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15-21. doi:10.1093/bioinformatics/bts635

Huang da, W., Sherman, B. T., & Lempicki, R. A. (2009a). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res, 37(1), 1-13. doi:10.1093/nar/gkn923

Huang da, W., Sherman, B. T., & Lempicki, R. A. (2009b). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 4(1), 44-57. doi:10.1038/nprot.2008.211

Mootha, V. K., Lindgren, C. M., Eriksson, K. F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstrale, M., Laurila, E., Houstis, N., Daly, M. J., Patterson, N., Mesirov, J. P., Golub, T. R., Tamayo, P., Spiegelman, B., Lander, E. S., Hirschhorn, J. N., Altshuler, D., & Groop, L. C. (2003). PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet, 34(3), 267-273. doi:10.1038/ng1180

Quinlan, A. R., & Hall, I. M. (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6), 841-842. doi:10.1093/bioinformatics/btq033

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A, 102(43), 15545-15550. doi:10.1073/pnas.0506580102

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Office of the Director, Award: R01 CA136715

Office of the Director, Award: R01CA136715-05S1

National Institute on Aging, Award: R21 AG033735