Project description:Purpose: the goal of this study is to investigate the consequences of USP3 deletion on gene expression in mouse LSK hematopoietic progenitors and in splenic B cells Methods: mRNA profiles of 8 weeks-old wild-type (WT) and ubiquitin specific protease 3 knockout (Usp3−/−) mice were generated by deep sequencing, in duplicate, using Illumina Hiseq2000. The sequence reads that passed quality filters were mapped with TopHat and the gene expressions were calculated using HTSeq-count. qRT–PCR validation was performed using SYBR Green assays Results: We assigned about 8-16 million reads per sample uniquely to a gene of the mouse reference genome (mm9). We identified 23,429 genes in the LSKs, naive B cells and activate B cells of WT and USP3−/− mice using TopHat in combination with HTSeq-count. Comparison of the RNAseq data from LSK with naive or activated B cells show that both the wt and the Usp3-/- LSKs largely exibited a gene expression profile that is specific for wt LSK and distinct from B cells (as supported by statistical significant difference between the transciptional profile of LSK versus naive or activated B cells, p value<0.0001 by Student t test). Comparison of normalized gene expression data for Wt LSKs versus naive B cells of one representative experiment shows Pearson coefficient of r=0.874, and R2=0.763. Distinct LSK-specific expressed genes (such as the MlI receptor and the Kit receptor) and B cells specific genes (such as the MS4A1/CD20 and Spi-B transcription factors) are identified. Expression of a set of 19 genes was assessed by RT-qPCR in three independent LSK mRNA per each genotype. qRT-PCR and the RNA-seq normalized expression data for these genes had a good linear relationship, validating the RNAseq analysis. Comparison of normalized gene expression data for Usp3-/- versus Wt LSK show Pearson coefficient r=0.986; R2=0.9738), naive B cells (Pearson coefficient r=0.987, R2=0.974) and LPS activated B cells (Pearson coefficient r=0.991, R2=0.983). RT-qPCR of a subset of hematopoietic stem cell genes, including Mlp2, ENg, Tek and Fdzl3, show no significant difference beteewn wt and Usp3-/- LSK cells. Less than 100 genes showed differential expression (up or down regulated) between the Wt and Usp3-/- LSK, with a fold change ≥1.5 and p value <0.05. Conclusions: Our results represent the first detailed analyis of the consequences of USP3 deletion on gene expression in hematopoietic populations such as LSKs progenitors and B cells by genome wide expression profiling in wt and Usp3-/- mice. RNAseq of two freshly isolated biological replicas of sorted LSKs from 8 weeks old Usp3-/- animals showed a very limited number of genes either slighly up or down regulated (<100 out of about 25.000) in Usp3-/- LSKs, none of which are reported to be directly involved in hematopoietic stem cell maintenance or to be linked to premature differentiation. We confirmed that Usp3-/- and wt LSKs express hematopoietic stem cell-specific genes to a similar extent. We conclude that young adult hematopoietic stem and progenitor cells (LSKs) perpetuated a stable gene expression program regardless of the homozygous deletion of USP3. mRNA profiles of 8 weeks-old wild type (WT) and Usp3-/- mice were generated by deep sequencing, in duplicate, using Illumina Hiseq2000. For each experiment wt n=4, Usp3-/- n=4 mice were analized. FACS sorted cells from from individual animals were pooled and subjected to deep sequencing. Cells were: LSK (Lin- Sca1+ cKit+) from bone marrow, sorted naive B cells from spleens (CD19+) and activated B cells harvested and FACS sorted after 4 days stimulation with lipopolysaccharide (LPS) in culture.

Project description:Purpose: the goal of this study is to investigate the consequences of USP3 deletion on gene expression in mouse LSK hematopoietic progenitors and in splenic B cells Methods: mRNA profiles of 8 weeks-old wild-type (WT) and ubiquitin specific protease 3 knockout (Usp3−/−) mice were generated by deep sequencing, in duplicate, using Illumina Hiseq2000. The sequence reads that passed quality filters were mapped with TopHat and the gene expressions were calculated using HTSeq-count. qRT–PCR validation was performed using SYBR Green assays Results: We assigned about 8-16 million reads per sample uniquely to a gene of the mouse reference genome (mm9). We identified 23,429 genes in the LSKs, naive B cells and activate B cells of WT and USP3−/− mice using TopHat in combination with HTSeq-count. Comparison of the RNAseq data from LSK with naive or activated B cells show that both the wt and the Usp3-/- LSKs largely exibited a gene expression profile that is specific for wt LSK and distinct from B cells (as supported by statistical significant difference between the transciptional profile of LSK versus naive or activated B cells, p value<0.0001 by Student t test). Comparison of normalized gene expression data for Wt LSKs versus naive B cells of one representative experiment shows Pearson coefficient of r=0.874, and R2=0.763. Distinct LSK-specific expressed genes (such as the MlI receptor and the Kit receptor) and B cells specific genes (such as the MS4A1/CD20 and Spi-B transcription factors) are identified. Expression of a set of 19 genes was assessed by RT-qPCR in three independent LSK mRNA per each genotype. qRT-PCR and the RNA-seq normalized expression data for these genes had a good linear relationship, validating the RNAseq analysis. Comparison of normalized gene expression data for Usp3-/- versus Wt LSK show Pearson coefficient r=0.986; R2=0.9738), naive B cells (Pearson coefficient r=0.987, R2=0.974) and LPS activated B cells (Pearson coefficient r=0.991, R2=0.983). RT-qPCR of a subset of hematopoietic stem cell genes, including Mlp2, ENg, Tek and Fdzl3, show no significant difference beteewn wt and Usp3-/- LSK cells. Less than 100 genes showed differential expression (up or down regulated) between the Wt and Usp3-/- LSK, with a fold change ≥1.5 and p value <0.05. Conclusions: Our results represent the first detailed analyis of the consequences of USP3 deletion on gene expression in hematopoietic populations such as LSKs progenitors and B cells by genome wide expression profiling in wt and Usp3-/- mice. RNAseq of two freshly isolated biological replicas of sorted LSKs from 8 weeks old Usp3-/- animals showed a very limited number of genes either slighly up or down regulated (<100 out of about 25.000) in Usp3-/- LSKs, none of which are reported to be directly involved in hematopoietic stem cell maintenance or to be linked to premature differentiation. We confirmed that Usp3-/- and wt LSKs express hematopoietic stem cell-specific genes to a similar extent. We conclude that young adult hematopoietic stem and progenitor cells (LSKs) perpetuated a stable gene expression program regardless of the homozygous deletion of USP3.

Project description:Purpose: the goal of this study is to investigate the consequences of ubiquitin specific protease 15 (USP15) knockdown on gene expression in KBM7 and K562 leukemia cells. Methods: KBM7 or K562 cells were transfected with USP15 ( siGENOME Human USP15 (9958) siRNA-SMART pool M-006066-01) or control (Ctrl) siRNAs (siGENOME Non-Targeting siRNA Control Pool#2 D-001206-14-05, Dharmacon). Cells were transfected using Lipofectamine RNAiMAX Reagent from Life Technologies following the manufacturer’s instructions and assayed at 72 hrs after transfection in Western blotting, RNA-seq and qRT-PCR. Western blotting confirmed knokdown of USP15 protein. mRNA profiles were generated by deep sequencing using Illumina Hiseq2500. Alignments of the sequence reads that passed quality filters were performed using TopHat2.1, Genome build 38 and Ensembl gtf version 77 and genecounts have been generated using Itreecount. https://github.com/NKI-GCF/itreecount. qRT–PCR validation was performed using SYBR Green assays.The amount of target, normalized to an endogenous reference (HPRT), was calculated by 2-DDCT. Results: We assigned about 30 million reads per sample uniquely to a gene of the human reference genome (hg38). We identified 26,227 genes in KBM7 and 28,329 genes in K562 cells using TopHat2.1 in combination with Itreecount (https://github.com/NKI-GCF/itreecount) workflow. Comparison of normalized gene expression data resulted in a total of 657 and 330 differential expressed genes between USP15 KD vs Ctrl in KBM7 and K562 cells (in two separate experiments), respectively, with a treshold of logFC_C1 ≥1. and adj.PVal_C1 value <0.05. Downregulation of USP15 was confirmed by Western blotting and by qRT-PCR. Ingenuity pathway analysis of USP15-dependent genes in the KBM7 and K562 datasets (cut-off logFC > 1; adjusted P value <0.05) uncovered activation of inflammation-related pathways, which involve JAK/STAT and PI3K signal transduction. In K562, we also measured significant down-modulation of TGF-beta signaling. Expression of a set of 17 genes was assessed by RT-qPCR in three independent experiments per cell line and altered expression was validated. Overlap between Top 50 significantly enriched Ingenuity Upstream Regulators in each cell line RNAseq datasets was found. The Upstream Regulators were calculated based on differential gene expression between USP15 and control siRNAs. The nine overlapping terms include: Ifnar, IRF3, STAT1, Interferon alpha, IFNG, CD40LG, IFNB1, TNF and poly rI:rC-RNA. Conclusions: Our results represent the first detailed analyis of the consequences of USP15 depletion on gene expression in malignant hematopoietic populations such as KBM7 and K562 chronic myeloid leukemia cells by genome wide expression profiling in USP15 KD vs Ctrl cells. We confirmed downregulation of USP15 and validated altered expression of a set of 17 genes by qRT-PCR. Ingenuity pathway analysis identified 657 and 330 differentially regulated genes in KBM7 and K562, respectively. The changes in gene expression, though not extensive, indicate that RNAi of USP15 led to activation of inflammation-related pathways, which involve JAK/STAT and PI3K signal transduction in both cell lines. In K562, we also measured significant down-modulation of TGF-beta signaling, which is required for USP15 pro-oncogenic role in human glioma cells (Eichhorn et al., 2012). Regulation of inflammatory signals and TGF-beta are relevant both in normal HSC and in malignant development (Blank and Karlsson, 2015).

Project description:Total RNA was extracted from Lin-Sca-1+c-Kit+ (LSK) cells sorted from the BM of Spred1HSCΔ/ΔSCLtTA/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), Spred1ECΔ/ΔSCLtTA/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.

Project description:Purpose: The histone H3 lysine 36 methyltransferase SETD2 is frequently mutated in various cancers, including leukemia. However, there has not been any functional model to show the contribution of SETD2 in hematopoiesis or the causal role of SETD2 mutation in tumorigenesis. In this study, using a conditional Setd2 knock-out (KO) mouse model, we show that Setd2 deficiency skews hematopoietic differentiation and impaired HSC (hematopoietic stem cell) self-renewal. Intriguingly, Setd2-deleted HSCs, through a latency period, can acquire abilities to overcome the growth disadvantage and eventually develop into hematopoietic malignancy characteristic of myelodysplastic syndrome (MDS). This study aimed at finding the mechanism controlled by Setd2, which was required for MDS formation. Methods: Young and old Setd2 WT and KO LSKs (Lin-c-Kit+Sca1+) were generated by deep RNA-seq, using Illumina Hiseq2500. Using Stringtie (version:1.3.0) software to analyze the sequence reads that passed quality filter to acquire the expression level of all genes. qRT–PCR validation was performed using SYBR Green assays. Young Setd2 WT and KO LSKs were also generated by whole genome bisulfite sequencing (WGBS), using Illumina Hiseq2500. Results: Using an optimized data analysis workflow, 246 genes down-regulated and 226 up-regulated, showed significant differential expression (fold change, FC ≤ 0.5 or FC ≥ 2; p < 0.05) between young WT and KO LSKs. Old LSKs displayed about 1112 down-regulated and 777 up-regulated genes between WT and KO. The results of WGBS showed that the Setd2 KO genome carries a much larger number of hypomethylated DMRs (different methylation regions) relative to hypermethylated ones (5,839 versus 1,294). Conclusions: Through RNA-seq, gene expression profile of young Setd2-deleted LSKs partially resembles the Dnmt3a/Tet2 double knock-out. WGBS also indicated that Setd2 deficiency can regulate the distribution of whole genome DNA methylation. Furthermore, young Setd2 deficiency also induces DNA replication stress in HSCs, as reflected by activated E2F gene regulatory network and repressed ribonucleotide reductase subunit Rrm2b. Old Setd2-deleted LSKs displayed a MDS related transcriptional signature.

Dataset Information

Quantitative Analysis by Next Generation Sequencing of LSK (Lin- Sca1+ cKit+) hematopoietic progenitors transcriptomes from wild type and Usp15-/- mice.

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