DOI: 10.5061/dryad.wstqjq2wg
title: Data from: Gal-1 promotes lung cancer cell survival by enhancing PARP1/H1.2 interaction to promote DNA repair upon DNA damage response

description: Galectin-1 (Gal-1), a member of the galectin family, has emerged as a
 regulator of tumor progression. Several studies have reported the
 up-regulation of Gal-1 expression in multiple cancer cells and its
 promotion on tumor proliferation. However, the mechanism by which Gal-1
 promotes tumor growth remains to be thoroughly understood. In this study,
 it was discovered that high expression of Gal-1 in viral cancers was
 inversely correlated with the overall survival of patients. Through
 constructing Gal-1-overexpressing cell lines, it was uncovered that cell
 proliferation and colony formation were significantly improved. The
 results of transcriptomic and proximity-labeling-based proteomic analyses
 indicated that Gal-1 interacted with PARP1 and histone H1.2 in lung cancer
 cells. In the case of etoposide treatment leading to DNA double-strand
 breaks, Gal-1 accelerated the degradation of H1.2 by enhancing its
 interaction with PARP1 and promoting its PARylation modification. It
 caused the activation of downstream DNA repair pathways such as the ATM
 and NBS1 signaling pathways, thus reducing apoptosis. Moreover, Gal-1
 inhibitors TDG and OTX008 could restore cell sensitivity to etoposide.
 This study provides new clues for the role of Gal-1 in the development of
 tumors and renders suggestions for the treatment of patients with high
 Gal-1 expression in the clinic.
description: <strong>Proximity-labeling MS and
 analysis</strong> The stable
 Galectin-1-TurboID-NLS-Flag A549 cells were incubated with 500μM biotin， 5
 mM MgCl2, and 1 mM ATP for 12h. The cells were harvested and washed, then
 lysed with the IP lysis buffer. The lysate was incubated with blocked
 streptavidin beads(Smart-Lifesciences,#SM017K01) with a rotator at 4℃
 overnight. Then, wash the beads following the kit`s protocols. 10% of the
 beads, which were eluted by 1×SDS loading buffer and boiled for 10 mins,
 were used for to silver stain assay and Immunoblotting. The rest 90% of
 the beads were sent to the Wuhan University instrument and equipment
 sharing platform for MS analysis. <strong>RNA seq
 and analysis</strong> Total RNA was extracted
 from cells with the indicated treatment and sent to BGI to construct a
 single-stranded circular DNA library. The constructed library is
 qualitatively inspected, and sequencing is carried out after it is
 qualified.
description: # Data from: Gal-1 promotes lung cancer cell survival by enhancing
 PARP1/H1.2 interaction to promote DNA repair upon DNA damage response
 Dataset DOI: [10.5061/dryad.wstqjq2wg](10.5061/dryad.wstqjq2wg) ##
 Description of the data and file structure This dataset encompasses
 transcriptomic data and proximity labeling-based mass spectrometry (MS)
 data derived from experimental analyses. For the transcriptomic profiling,
 we performed RNA sequencing on four experimental groups: the parental A549
 cell line, the genetically engineered A549-GAL-1 cell line, and both cell
 lines treated with 100 μM Etoposide for 2 hours. Comparative analysis
 revealed a panel of differentially transcribed genes (adjusted p-value
 &lt; 0.05, fold-change &gt; 2) for subsequent functional investigations.
 Regarding the proximity labeling proteomics, 293T-GAL-1-TurboID stable
 cells underwent biotinylation treatment followed by streptavidin-mediated
 magnetic bead enrichment. The processed samples were subjected to LC-MS/MS
 analysis at the Core Facility of Life Sciences College, Wuhan University,
 identifying 1,718 putative GAL-1-interacting proteins through stringent
 filtering criteria. Integrative multi-omics analysis demonstrated
 significant enrichment of DNA damage repair pathway components in both
 differentially expressed gene clusters and GAL-1-associated protein
 networks, suggesting GAL-1's pivotal role in genomic stability
 maintenance. ### Files and variables #### File:
 Mass\_spectrometry\_data.xlsx **Description:** The provided table enables
 systematic extraction of key proteomic parameters, including: **Protein
 Accession** (unique identifier), **Description** (functional annotation),
 **q-value** (false discovery rate-adjusted significance metric), **Sum PEP
 Score** (posterior error probability aggregation reflecting identification
 confidence), **Sequence Coverage** (percentage of amino acid residues
 detected by peptide-spectrum matches), **Total Peptides** (number of
 distinct peptide sequences mapped), **PSMs** (spectral counts supporting
 peptide identification), **Unique Peptides** (proteotypic sequences
 unambiguously assigned to the protein), **Amino Acid Length (AAs)**,
 **Molecular Weight (MW)**, **Isoelectric Point (pI)**,
 **Post-Translational Modifications** (PTMs identified with localization
 probabilities), and **Peptide Sequences** (exact matched proteolytic
 fragments). NA indicate missing values. #### File: Transcriptome\_data.gz
 **Description:** Total RNA was extracted from four experimental groups:
 parental A549 cells, genetically modified A549-GAL-1 cells, and both cell
 lines post-treatment with the therapeutic agent. The RNA samples were
 subjected to high-throughput sequencing at BGI Genomics (Shenzhen, China).
 Raw sequencing data underwent rigorous quality control through SOAPnuke
 (v2.2.1) with the following parameters: 1) Removal of adapter-contaminated
 reads (≥5 bp overlap), 2) Exclusion of low-quality sequences (&gt;40%
 bases with Phred score &lt; 20), and 3) Elimination of reads with &gt;5% N
 content. Processed reads were subsequently aligned to the human reference
 genome (GRCh38) using HISAT2 (v2.2.1) with default splicing-aware
 parameters, achieving an average alignment rate of 92.4% ± 1.3% across
 samples. Transcript quantification was performed through RSEM (v1.3.1)
 employing the expectation-maximization algorithm, generating normalized
 expression values (TPM and FPKM) with batch effect correction using
 Combat-seq. This analytical pipeline ensured the reliability of downstream
 differential expression analysis through DESeq2 . ## Code/software
 Microsoft Excel can be used to view  Mass spectrometry data. The raw date
 data were filtered using **SOAPnuke (v2.2.1)** software for mRNA
 alignment, and **HISTA2 (v2.2.1)** software was used to align the filtered
 data to the human genome ## Access information Other publicly accessible
 locations of the data: * The data that support the findings of this study
 are openly available in Dryad.