Spred1 deficit promotes treatment resistance and transformation of chronic phase CML

Total RNA was extracted from Lin-Sca-1+c-Kit+ (LSK) cells sorted from the BM of Spred1HSC?/?SCLtTA/BCR-ABL (HSC KO) and Spred1HSCwt/wtSCLtTA/BCR-ABL (HSC wt) (n=5 mice per group, both group given 7 doses of 250µg poly(I:C), ip, every two days, to activate Mx1-cre), Spred1EC?/?SCLtTA/BCR-ABL (EC KO) and Spred1ECwt/wtSCLtTA/BCR-ABL (EC wt) (n=5 mice per group) mice using the miRNeasy micro Kit (Qiagen, Valencia, CA). SMART-Seq® Ultra Low Input RNA Kit for Sequencing – v4 (TaKaRa, Cat 634888) was used for generating amplified double stranded (ds) cDNA from each sample with 2ng of input total RNA according to the manufacturer's protocol. The resulting ds cDNA was sheared with Covaris LE220 with the setting of DNA fragment size of 200bp peak. The sheared DNA was used for sequencing library preparation by using KAPA HyperPrep Kits (Roche, Cat KK8500). The final libraries were quantified with qubit and bioanalyzer. The sequencing was performed with the single read mode of 51 cycles of read1 and 7 cycles of index read with V4 reagents on Illumina Hiseq2500. Real-time analysis (RTA) 2.2.38 software was used to process the image analysis and base calling. The nf-core RNAseq pipeline v1.3 was used to process the raw sequencing reads (https://nf-co.re). RNA-Seq reads were trimmed to remove sequencing adapters using Trimmomatic3 and polyA tails using FASTP4. The processed reads were mapped back to the mouse genome (mm10) using STAR software (v. 020201)5. The HTSeq software (v.0.6.0)6 was applied to generate the count matrix, with default parameters. Differential expression analysis was conducted by adjusting read counts to normalized expression values using TMM normalization method in R7. Briefly, for each comparison between HSC KO LSKs and HSC wt LSKs and between EC KO LSKs and EC wt LSKs, general linear models were applied to identify DEGs using TMM normalization expression level as depending variable, and genotype as independent variable. Genes with a p-value less than 0.05 and with a fold change (FC) greater than 1.5 or less than 0.7 were considered as significant up- and down-regulated genes, respectively. Pathway analysis was conducted using GSEAPreranked algorithm using GSEA Desktop program in Java8, 9. The former requires a list of up- and down-regulated genes, while the latter a ranked list of whole genes according to their log2 fold change and p-values. Overall design: To examine gene expresison profiles and upregulated pathways in LSKs (leukemic stem cells) selected from HSC-Spred1 KO CML versus LSKs from HSC-Spred1 wt CML mice, and in LSKs from EC-Spred1 KO CML versus LSKs from EC-Spred1 wt CML mice, and in LSKs from global Spred1 KO CML versus LSKs from Spred1 wt CML mice.