DOI: 10.5061/dryad.g1jwstqt6
title: Lin28a induces SOX9 and chondrocyte reprogramming via HMGA2 and blunts cartilage loss in mice

description: Among tissues undergoing permanent stress, cartilage has low regenerative
 capacity. Irreversible cartilage lesions characterize osteoarthritis (OA),
 in which cartilage loss is not followed by tissue repair. We investigated
 the physiopatological role of LIN28a, an RNA binding protein, in cartilage
 in adults. Lin28a was detected in only damaged cartilage of humans and
 mice. Here, inducible conditional cartilage deletion of Lin28a upregulates
 Mmp13 expression in sham mice and exacerbates cartilage destruction in OA
 mice. Lin28a-specific cartilage overexpression protects mice against
 cartilage breakdown, stimulates chondrocyte proliferation and the
 expression of Prg4 and Sox9, but reduces the expression of Mmp13. Lin28a
 overexpression inhibits Let-7b and Let-7c miRNA levels, facilitates the
 expression of HMGA2, and thereby activates the transcription of Sox9, a
 factor required for chondrocyte reprogramming. Finally, administration of
 siRNA against HMGA2 inhibits the cartilage protective effect in Lin28a
 overexpressing mice. This study provides insights into a new pathway to
 promote chondrocyte anabolism in injured cartilage involving the
 Lin28a–Let7 axis.
description: <strong>RNAseq
 </strong> RNAseq analysis involved using duplicates for
 the following conditions: primary murine chondrocyte were transduced with
 empty vector (CMV500 empty
 vector Addgene plasmid #33348) for control condition and with
 pMSCV-mLin28A (Addgene plasmid #26357) for Lin28 over expression. Total
 RNA were extracted and sent to <strong>IntegraGen</strong>
 <strong>SA</strong> Company (evry France). Libraries are prepared
 with NEBNext Ultra II Directional RNA Library Prep Kit for Illumina
 protocol according supplier recommendations. Briefly the key stages of
 this protocol are successively, the purification of PolyA containing mRNA
 molecules using poly-T oligo attached magnetic beads from 1µg total RNA
 (with the Magnetic mRNA Isolation Kit from NEB), a fragmentation using
 divalent cations under elevated temperature to obtain approximately 300bp
 pieces, double strand cDNA synthesis and finally Illumina adapters
 ligation and cDNA library amplification by PCR for sequencing. Sequencing
 is then carried out on Paired End 100b reads of Illumina NovaSeq. Image
 analysis and base calling is performed using Illumina Real Time Analysis
 (3.4.4) with default parameters. <strong>Quantification of gene
 expression</strong> STAR was used to obtain the number of reads
 associated to each gene in the Gencode vM24 annotation (restricted to
 protein-coding genes, antisense and lincRNAs). Raw counts for each sample
 were imported into R statistical software. Extracted count matrix was
 normalized for library size and coding length of genes to compute FPKM
 expression levels. <strong>Unsupervised
 analysis</strong> The Bioconductor edgeR package was used to
 import raw counts into R statistical software, and compute vnormalized
 log2 CPM (counts per millions of mapped reads) using the TMM (weighted
 trimmed mean of M-values) as normalization procedure. The normalized
 expression matrix from the 1000 most variant genes (based on standard
 deviation) was used to classify the samples according to their gene
 expression patterns using principal component analysis (PCA), hierarchical
 clustering and consensus clustering. PCA vwas performed by FactoMineR::PCA
 function with “ncp = 10, scale.unit = FALSE” parameters. Hierarchical
 clustering was performed by stats::hclust function (with euclidean
 distance and ward.D method). Consensus clustering was performed by
 Consensus ClusterPlus::ConsensusClusterPlus function to examine the
 stability of the clusters. We established consensus partitions of the data
 set in K clusters (for K = 2, 3, . . . , 8), on the basis of 1,000
 resampling iterations (80% of genes, 80% of sample) of hierarchical
 clustering, with euclidean distance and ward.D method. Then, the
 cumulative distribution functions (CDFs) of the consensus matrices were
 used to determine the optimal number of clusters (K = 3 for instance),
 considering both the shape of the functions and the area under the CDF
 curves. tSNE analysis was performed with the Bioconductor Rtsne package
 applied to the PCA object (theta=0.0, perplexity=, max_iter=1000).