DOI: 10.5061/dryad.ghx3ffc05
title: Data from: TIPE drives a cancer stem-like phenotype by promoting glycolysis via PKM2/HIF-1α axis in melanoma

description: TIPE (TNFAIP8) has been identified as an oncogene and participates in
 tumor biology. However, how its role in the metabolism of tumor cells
 during melanoma development remains unclear. Here, we demonstrated that
 TIPE promoted glycolysis by interacting with pyruvate kinase M2 (PKM2) in
 melanoma. We found that TIPE induced PKM2 dimerization, thereby
 facilitating its translocation from the cytoplasm to the nucleus.
 TIPE-mediated PKM2 dimerization consequently promoted HIF-1α activation
 and glycolysis, which contributed to melanoma progression and increased
 its stemness features. Notably, TIPE specifically phosphorylated PKM2 at
 Ser 37 in an ERK-dependent manner. Consistently, the expression of TIPE
 was positively correlated with the levels of PKM2 Ser37 phosphorylation
 and cancer stem cell markers in melanoma tissues from clinical samples and
 tumor bearing mice. In summary, our findings indicate that the
 TIPE/PKM2/HIF-1α signaling pathway plays a pivotal role in promoting
 cancer stem cell properties by facilitating the glycolysis, which would
 provide a promising therapeutic target for melanoma intervention.
description: Transcriptomics  Sample collection and
 preparation RNA quantification and
 qualification ①     RNA degradation and contamination
 was monitored on 1% agarose gels. ②     RNA purity was
 checked using the NanoPhotometer® spectrophotometer (IMPLEN, CA, USA)
 . ③     RNA integrity was assessed using the RNA Nano
 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA,
 USA). <strong>Library preparation for
 Transcriptome sequencing</strong> A total amount
 of 1 μg RNA per sample was used as input material for the RNA sample
 preparations. Sequencing libraries were generated using NEBNext® UltraTM
 RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s
 recommendations and index codes were added to attribute sequences to each
 sample. Briefly, mRNA was purified from total RNA using poly-T
 oligo-attached magnetic beads. Fragmentation was carried out using
 divalent cations under elevated temperature in NEBNext First Strand
 Synthesis Reaction Buffer(5X). First strand cDNA was synthesized using
 random hexamer primer and M-MuLV Reverse Transcriptase(RNase H-). Second
 strand cDNA synthesis was subsequently performed using DNA Polymerase I
 and RNase H. Remaining overhangs were converted into blunt ends via
 exonuclease/polymerase activities. After adenylation of 3’ ends of DNA
 fragments, NEBNext Adaptor with hairpin loop structure were ligated to
 prepare for hybridization. In order to select cDNA fragments of
 preferentially 250~300 bp in length, the library fragments were purified
 with AMPure XP system (Beckman Coulter, Beverly, USA). Then 3 μl USER
 Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at
 37°C for 15 min followed by 5 min at 95 °C before PCR. Then PCR was
 performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers
 and Index (X) Primer. At last, PCR products were purified (AMPure XP
 system) and library quality was assessed on the Agilent Bioanalyzer 2100
 system. <strong>Clustering and sequencing
 (Novogene Experimental Department)</strong> The
 clustering of the index-coded samples was performed on a cBot Cluster
 Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia)
 according to the manufacturer’s instructions. After cluster generation,
 the library preparations were sequenced on an Illumina Novaseq platform
 and 150 bp paired-end reads were generated.
 <strong>Data Analysis</strong>
 <strong>Quality control</strong>
 Raw data (raw reads) of fastq format were firstly processed
 through in-house perl scripts. In this step, clean data (clean reads) were
 obtained by removing reads containing adapter, reads containing ploy-N and
 low quality reads from raw data. At the same time, Q20, Q30 and GC content
 the clean data were calculated. All the downstream analyses were based on
 the clean data with high quality. <strong>Reads
 mapping to the reference genome</strong>
 Reference genome and gene model annotation files were downloaded
 from genome website directly. Index of the reference genome was built
 using Hisat2 v2.0.5 and paired-end clean reads were aligned to the
 reference genome using Hisat2 v2.0.5. We selected Hisat2 as the mapping
 tool for that Hisat2 can generate a database of splice junctions based on
 the gene model annotation file and thus a better mapping result than other
 non-splice mapping tools. <strong>Novel
 transcripts prediction</strong> The mapped reads
 of each sample were assembled by StringTie (v1.3.3b) (Mihaela Pertea.et
 al. 2015) in a reference-based approach. StringTie uses a novel network
 flow algorithm as well as an optional de novo assembly step to assemble
 and quantitate fulllength transcripts representing multiple splice
 variants for each gene locus.
 <strong>Quantification of gene expression
 level</strong> featureCounts v1.5.0-p3 was used
 to count the reads numbers mapped to each gene. And then FPKM of each gene
 was calculated based on the length of the gene and reads count mapped to
 this gene. FPKM, expected number of Fragments Per Kilobase of transcript
 sequence per Millions base pairs sequenced, considers the effect of
 sequencing depth and gene length for the reads count at the same time, and
 is currently the most commonly used method for estimating gene expression
 levels. <strong>Differential expression
 analysis</strong> <em>(For DESeq2 with
 biological replicates) </em>Differential expression analysis of two
 conditions/groups (two biological replicates per condition) was performed
 using the DESeq2 R package (1.16.1). DESeq2 provide statistical routines
 for determining differential expression in digital gene expression data
 using a model based on the negative binomial distribution. The resulting
 P-values were adjusted using the Benjamini and Hochberg’s approach for
 controlling the false discovery rate . Genes with an adjusted P-value
 &lt;0.05 found by DESeq2 were assigned as differentially expressed.
 <em>(For edgeR without biological replicates) </em>Prior to
 differential gene expression analysis, for each sequenced library, the
 read counts were adjusted by edgeR program package through one scaling
 normalized factor. Differential expression analysis of two conditions was
 performed using the edgeR R package (3.18.1). The P values were adjusted
 using the Benjamini & Hochberg method. Corrected P-value of 0.05
 and absolute foldchange of 2 were set as the threshold for significantly
 differential expression. <strong>GO and KEGG
 enrichment analysis of differentially expressed
 genes</strong> Gene Ontology (GO) enrichment
 analysis of differentially expressed genes was implemented by the
 clusterProfiler R package, in which gene length bias wascorrected. GO
 terms with corrected Pvalue less than 0.05 were considered significantly
 enriched by differential expressed genes. KEGG is a database resource for
 understanding high-level functions and utilities of the biological system,
 such as the cell, the organism and the ecosystem, from molecular-level
 information, especially large-scale molecular datasets generated by genome
 sequencing and other high-through put experimental technologies
 (http://www.genome.jp/kegg/). We used clusterProfiler R package to test
 the statistical enrichment of differential expression genes in KEGG
 pathways.
 <strong>Metabolomics </strong>
 <strong>Sample Extraction Method</strong>
 After slowly thawing the sample at 4°C, an appropriate amount of
 the sample is taken and added to a pre-cooled methanol/acetonitrile/water
 solution (2:2:1, v/v). The mixture is vortexed for mixing, followed by
 low-temperature ultrasonic treatment for 30 minutes. The sample is then
 allowed to stand at -20°C for 10 minutes and centrifuged at 14,000 g at
 4°C for 20 minutes. The supernatant is collected and vacuum-dried. For
 mass spectrometry analysis, 100 μL of acetonitrile/water solution
 (acetonitrile:water = 1:1, v/v) is added to redissolve the dried sample,
 which is then vortexed. The sample is centrifuged again at 14,000 g at 4°C
 for 15 minutes, and the supernatant is taken for injection and
 analysis. <strong>Chromatographic
 Conditions</strong> The samples were separated
 using an Agilent 1290 Infinity LC Ultra-High-Performance Liquid
 Chromatography (UHPLC) system with a HILIC column. The column temperature
 was maintained at 25°C, and the flow rate was set to 0.5 mL/min. The
 injection volume was 2 μL. The mobile phase composition was as follows: A
 = water + 25 mM ammonium acetate + 25 mM ammonia solution, and B =
 acetonitrile. The gradient elution program was as follows: 0 to 0.5 min,
 95% B; 0.5 to 7 min, a linear decrease from 95% B to 65% B; 7 to 8 min, a
 linear decrease from 65% B to 40% B; 8 to 9 min, B maintained at 40%; 9 to
 9.1 min, a linear increase from 40% B to 95% B; and 9.1 to 12 min, B
 maintained at 95%. During the entire analysis, the samples were kept in a
 4°C autosampler. To avoid the impact of fluctuations in instrument
 detection signals, the samples were analyzed consecutively in a random
 order. Quality control (QC) samples were inserted into the sample queue to
 monitor and evaluate the stability of the system and the reliability of
 the experimental data. <strong>Q-TOF Mass
 Spectrometry Conditions</strong> The AB Triple
 TOF 6600 mass spectrometer was used to collect the first-order and
 second-order spectra of the samples. The ESI source
 conditions after HILIC chromatographic separation were as follows: Ion
 Source Gas1 (Gas1): 60, Ion Source Gas2 (Gas2): 60, Curtain gas (CUR): 30,
 source temperature: 600°C, IonSpray Voltage Floating (ISVF): ±5500 V (both
 positive and negative modes); TOF MS scan m/z range: 60-1000 Da, product
 ion scan m/z range: 25-1000 Da, TOF MS scan accumulation time: 0.20
 s/spectrum, product ion scan accumulation time: 0.05 s/spectrum. The
 second-order mass spectrometry was acquired using information-dependent
 acquisition (IDA) in high sensitivity mode. The Declustering Potential
 (DP) was set to ±60 V (both positive and negative modes), and the
 Collision Energy was 35 ± 15 eV. The IDA settings were as follows:
 isotopes within 4 Da were excluded, and 10 candidate ions were monitored
 per cycle. <strong>Data Analysis
 Workflow</strong> The raw data in Wiff format is
 converted to .mzXML format using ProteoWizard. Subsequently, XCMS software
 is utilized for peak alignment, retention time correction, and peak area
 extraction. The data obtained through XCMS extraction undergoes initial
 metabolite structure identification and data preprocessing. Following
 this, the quality of the experimental data is evaluated, and finally, the
 data analysis is conducted. The data analysis encompasses univariate
 statistical analysis, multivariate statistical analysis, differential
 metabolite screening, correlation analysis of differential metabolites,
 and KEGG pathway analysis.
description: # Data from: TIPE drives a cancer stem-like phenotype by promoting
 glycolysis via PKM2/HIF-1α axis in melanoma
 [https://doi.org/10.5061/dryad.ghx3ffc05](https://doi.org/10.5061/dryad.ghx3ffc05) ## Description of the data and file structure Transcriptomics Data We conducted RNA sequencing analysis by overexpressing TIPE in G361 cells, which comprised three experimental groups and three control groups. Metabolomics Data We conducted a metabolomic analysis after interfering with TIPE in A375 cells, which consisted of six experimental groups and six control groups. ### Files and variables #### **File: Metabolomics\_Data.xlsx** **Description:**  ID: The identification number of the metabolite in mass spectrometry detection m/z: Mass-to-charge ratio rt (s): Retention time of the metabolite on the chromatography, also known as the peak emergence time, with the unit being seconds Name: The name of the metabolite Adduct: Information on the adduct ions of the compound Score: A scoring value ranging from 0 to 1. The higher the value, the higher the degree of matching and the more reliable the qualitative result HMDB: The HMDB number can be used to obtain detailed annotation information about the metabolite on the HMDB official website KEGG: The corresponding C number in KEGG. The C number can be used to query detailed information about the metabolite and the metabolic pathways it participates in on the KEGG official website Superclass: Parent class, representing the classification of the metabolite Class: Subclass, further classifying the metabolite Subclass: Sub-subclass, providing an even more detailed classification of the metabolite Sample: The relative content of each metabolite in the corresponding sample. In this test, control group including NC1, NC2, NC3, NC4, NC5, NC6, and the treatment group including KD1, KD2, KD3, KD4, KD5, KD6 The missing values are displayed as "blank cells" #### **File: Transcriptomics\_Data.xlsx** **Description:**  gene_id: A unique identifier for each gene. samples: The standardized readcount values for each sample. The control group including N1, N2, and N3. The treatment group(overexpression of TIPE) including V1, V2, and V3. log2FoldChange: The ratio of gene expression levels between the treatment group and the control group, followed by taking the logarithm to the base 2. pvalue: P-value of significance test padj: Adjusted p-value for multiple hypothesis testing gene_name: The name assigned to the gene. gene_chr: Chromosome name where the gene is located. gene_start: Start position of the gene on the chromosome. gene_end: End position of the gene on the chromosome. gene_strand: Positive or negative strand information of the chromosome where the gene is located. gene_length: The sum of all non-overlapping exon regions from the start to the end of the gene. gene_biotype: Gene type, such as protein-coding gene, long non-coding RNA, etc. gene_description: Functional description of the gene. gene_tf_family: Transcription factor family annotation of the gene. Used NA" to indicate missing values.