DOI: 10.5061/dryad.s4mw6m950
title: RNA-Seq - membrane-type 1 matrix metalloproteinase

description: 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.
description: The indicated tissues and SSC were isolated from
 <i>Mmp14<sup>wt/wt</sup></i> and
 <i>Mmp14<sup>Y573D/Y573</sup></i> 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. <i>Genome Biol, 11</i>(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. <i>Bioinformatics, 31</i>(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. <i>Bioinformatics, 29</i>(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. <i>Nucleic Acids Res, 37</i>(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. <i>Nat Protoc, 4</i>(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.
 <i>Nat Genet, 34</i>(3), 267-273. doi:10.1038/ng1180
 Quinlan, A.
 R., & Hall, I. M. (2010). BEDTools: a flexible suite of utilities
 for comparing genomic features. <i>Bioinformatics, 26</i>(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. <i>Proc Natl Acad Sci U S A, 102</i>(43), 15545-15550. doi:10.1073/pnas.0506580102
description: There are no missing values.