DOI: 10.5061/dryad.x3ffbg80h
title: Data from: HIF1α gates tendon response to overload and drives tendinopathy independently of vascular recruitment

description: Tendons are sparsely vascularized connective tissues that link muscles to
 bones, withstanding some of the highest mechanical stresses in the body.
 Mechanical overloading and tissue hypervascularity are implicated in
 tendinopathy, a common musculoskeletal disorder, yet their mechanistic
 roles remain unclear. Here, we identify HIF1α not only as a marker but as
 a driver of tendinopathy. Initial histological and multi-omics evaluation
 of human tendinopathic samples revealed extensive extracellular matrix
 remodeling, including pathological collagen crosslinking coinciding with
 active hypoxic signaling. Hypothesizing a causal contribution of hypoxia
 signaling, we generated mice with tenocyte-targeted deletions of the Von
 Hippel-Lindau (VHL) gene, which controls hypoxia signaling by regulating
 HIFα degradation. We demonstrated that VHL inactivation suffices to induce
 pathological hallmarks of tendinopathy, such as collagen matrix
 disorganization, crosslinking, altered mechanics, and neuro-vascular
 ingrowth. This phenotype was HIF1α-dependent, since co-deleting HIF1α
 rescued tendon morphology and mechanics. Moreover, deleting vascular
 endothelial growth factor A (VEGFA) alongside VHL effectively suppressed
 neovascularization, but failed to rescue extracellular matrix
 abnormalities or restore mechanical function, emphasizing a direct role of
 HIF1α in driving tendon disease independently of angiogenesis.
 Mechanistically, we found that HIF1α activation was strain-dependent in
 primary cultured human tendon cells and induced by mechanical overload in
 murine tendon explants. Furthermore, genetically removing HIF1α from
 tenocytes prevented aberrant tendon remodeling in response to chronic
 overload. These findings position HIF1α signaling as a central driver of
 tendinopathy that acts through a maladaptive tissue response to chronic
 overload, providing mechanistic insights that could be leveraged for
 improved therapeutic approaches.
description: # Data from: HIF1α gates tendon response to overload and drives
 tendinopathy independently of vascular recruitment Dataset DOI:
 [10.5061/dryad.x3ffbg80h](https://doi.org/10.5061/dryad.x3ffbg80h) ##
 Description of the data and file structure Bulk RNA sequencing of two
 separate datasets was performed to investigate transcriptional responses
 of human long head of the biceps tendon (LHBT) tissue and tendon-derived
 cells under different mechanical loading conditions. The first dataset
 (*Kallisto_LHBT_Prox_Dist*) includes RNA-seq data from frozen proximal and
 distal LHBT tissue samples collected during shoulder surgery. These
 samples were examined by a pathologist, histopathologically scored, and
 characterized for matrix composition and collagen crosslink content. The
 second dataset (*Kallisto_TendonSheets*) comprises RNA-seq data from
 primary human tendon-derived cells cultured in silicone chambers. After
 three days of culture with ascorbic acid supplementation, the cells were
 subjected to cyclic tensile stretching on day four using two distinct
 strain regimes (low-load and high-load). ### Files and variables ####
 File: Kallisto_LHBT_Prox_Dist.zip **Description:** Folder containing
 transcript quantification results generated with the **Kallisto** package
 in R. The folder includes a total of **61 files**: * 1 metadata.xlsx
 containing samples metadata * 30 adundance.txt files with transcript-level
 abundance estimates (**15 proximal** and **15 distal** samples) * 30
 adundance.h5 files, which are binary versions of the same quantification
 data for use in downstream analysis (e.g. *tximport* ) #### File:
 Kallisto_TendonSheets.zip **Description:** Folder containing transcript
 quantification results generated with the **Kallisto** package in R. The
 folder includes a total of **43 files**: * 1 metadata.xlsx containing
 samples metadata * 21 adundance.txt files with transcript-level abundance
 estimates (representing **7 patients** across **3 conditions** —
 *Control*, *High load*, and *Low load*) * 21 adundance.h5 files, which are
 binary versions of the same quantification data for use in downstream
 analysis (e.g. *tximport* ) **Kallisto** is a tool for quantifying
 transcript abundances from RNA-Seq data. Instead of performing full read
 alignment to the genome, Kallisto uses a **pseudoalignment** **approach**:
 it rapidly determines which transcripts each read is *compatible* with,
 without computing a base-by-base alignment. To do this, Kallisto relies on
 a **prebuilt transcriptome index**, which contains the k-mer–based
 representation of all reference transcripts (reference paper
 ***[https://www.nature.com/articles/nbt.3519](https://www.nature.com/articles/nbt.3519)***).  **Input files** As input files Kallisto accepts: * **paired-end reads**: two FASTQ files per sample (`sample_R1.fastq.gz`, `sample_R2.fastq.gz`) * **single-end reads**: one FASTQ file per sample plus fragment length information. **Output files** Kallisto produces several files in the output directory for each sample. These files contain transcript-level abundance estimates and metadata needed for reproducible analysis. #### **1.** **`abundance.tsv`** **— main quantification file** A tab-separated text file containing one row per transcript and the following columns: * **target_id**: transcript identifier * **length**: actual transcript length * **eff_length**: “effective length” used to model fragment mapping * **est_counts**: estimated number of fragments originating from this transcript * **tpm**: transcripts per million (normalized expression) **How to reuse:** * Use **est_counts** for downstream differential expression (e.g., with DESeq2 via tximport). * Use **tpm** for comparing expression levels across samples. --- #### **2.** **`abundance.h5`** **— structured HDF5 version of the quantification** Binary file containing: * the same values as `abundance.tsv`, * plus additional information used by tools like **Sleuth**, including bootstrap estimates (if performed). **How to reuse:**\ Load directly in R using **Sleuth** (recommended for Kallisto-specific workflows). --- #### **3.** **`run_info.json`** **— metadata for reproducibility** This file records: * Kallisto version * Transcriptome index used * Number of processed reads * Fragment length parameters * Number of threads * Whether strand-specific or single-end settings were used **How to reuse:**\ This file allows users to replicate the exact Kallisto run. ## Code/software Library preparation and sequencing was performed by GENEWIZ® (Leipzig, Germany). In short, RNA integrity and yield were assessed with Agilent Fragment Analyzer (Agilent Technologies, USA). Total RNA was used to prepare the RNA-seq libraries with ribosomal depletion enrichment. Libraries were then sequenced on a NovaSeq 6000 sequencer (Illumina Inc, USA) and sequenced data were processed using Kallisto package in R  to generate a count file matrix for each individual sample. ## Access information Other publicly accessible locations of the data: * NA Data was derived from the following sources: * NA - all data were generated in this study.