Dataset Information

Bulk RNA-seq libraries of WT and Batf2KO HSCs with 1-mon M.avium infection

ABSTRACT: Purpose: To define the mechanism underlying BAFT2-driven HSC depletion during chronic infection, we performed RNA sequencing (RNA-seq) analysis of WT and Batf2 KO HSCs in the presence and absence of M. avium infection Methods: 10,000-50,000 HSCs (CD45.1/CD45.2 KL CD150+ CD48- CD34-) into HBSS from naive or 1-month infected WT abd Batf2KO mice (n=10-12 per group). DNA and RNA were isolated with the RNeasy Kit (Qiagen, Cat#74004). RNA-seq libraries were prepared using SMARTer® Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian (Takara Bio Usa). Illumina NovaSeq SP was used for sequencing with a paired-end sequencing length of 10bp. Samples were sequenced at Admera Health using an Illumina HiSeq 2x150 at sequencing depth of ~40 million reads. FASTQ files were preprocessed using HTS stream (https://github.com/ibest/HTStream) and the clean FASTQ file were aligned using STAR. Differential expression (DE) analysis of gene expression was performed using Limma-Voom. False discovery rate (FDR)<0.05 was considered statistically significant. Further analysis was completed using Illumina Basespace packages and programs (deSeq2) We performed gene ontology analysis for differentially expressed genes with q value of <0.05. Gene set enrichment analysis (GSEA, Broad Institute and UC San Diego) was completed using normalized gene counts as previously described ((Subramanian et al., 2005). Results were visualized using Tidyverse packages in R. Comparison of differentially expressed gene lists and generation of Venn diagrams were generated using the GeneVenn webtool. Biological process and molecular function gene ontology analysis of differentially expressed genes found in bulk RNA-seq datasets were completed using GENEONTOLOGY of the Mus musculus database (Ashburner et al., 2000; Gene Ontology, 2021). Results: Infection significantly stimulated the expression of inflammatory response pathways (Figure 6A-C) as well as similar induction of common IFNγ-regulated genes such as Stat1 and Stat2 (Figure 6D). Interestingly, we found an increased number of IFN response genes were upregulated during M. avium infection in WT but not Batf2 KO HSC, including Stat3, Mx2, Bst2, Ifi35, and Ifngr2 (Figure 6D and S5C), and the overall degree of induction of all IFN response genes was higher in the WT compared to Batf2 KO. Conclusion: BATF2 amplifies pro-inflammatory signaling pathways in HSCs during chronic infection

ORGANISM(S): Mus musculus

PROVIDER: GSE206275 | GEO | 2023/01/17

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Purpose: to identify the binding sites for BATF2 in HSCs during M. avium infection, we conducted CUT&RUN sequencing of the HSCs from infected WT mice using an anti-BATF2 antibody. We used an anti-IgG antibody as a control to exclude noise and nonspecific binding and an anti-H3K27ac to assess open chromatin Methods: 10,000-50,000 HSCs (CD45.1/CD45.2 KL CD150+ CD48-) into HBSS from1-month infected WT mice (n=10-12 per group). CUT&RUN sequencing was performed with modified methods as previously described (Henikoff et al., 2020; Skene et al., 2018). 50,000 HSCs (LK CD150+, CD48-) were sorted pools of naïve or 1-month (M. avium) infected WT mice for CUT&RUN sequencing (N=7-8). Cells were washed with Wash Buffer (50mL total with 20 mM HEPES pH 7.5, 150mM NaCl, 0.5 mM Spermidine, and one Roche Complete protein inhibitor tablet) and bound to Concanavalin A-coated magnetic beads (Bang Laboratories L200731C) for 15 minutes room temperature. Sample slurries were magnetically separated, washed, and incubated with primary antibody diluted in wash buffer containing 0.05% digitonin (Dig Wash) overnight at 4 °C. Slurries were then magnetically separated and washed with Dig Wash three times and then incubated with protein A-MNase (pA-MN, Epicypher 15-1016) for 1 hour at 4 °C. Slurries were magnetically separated and washed, resuspended in 200uL Dig Wash, and placed on an iced heating block. 2uL of 2mM CaCl2 was added to catalyze the pA-MN reaction and digestion. After 45 minutes, one equal volume of Stop Buffer (340mM NaCl, 20 mM EDTA, 4 mM EGTA, 0.05% Digitonin, 0.05 mg/mL glycogen, 5 ug/mL RNase A, 2 pg/mL heterologous spike-in DNA) was added to end the reaction, and sample slurries were then incubated for 30 minutes at 37 °C to release fragments and then magnetically separated. The supernatant was collected, and DNA was purified via phenol-chloroform extraction and ethanol precipitation. Samples were resuspended in molecular-grade water after ethanol precipitation. DNA was quantified with Qubit 2.0 DNA HS Assay (ThermoFisher, Massachusetts, USA) and quality was assessed by Tapestation High sensitivity D1000 DNA Assay (Agilent Technologies, California, USA). Library preparation was performed using the KAPA Hyper Prep kit (Roche, Basel, Switzerland) according to the manufacturer’s recommendations. Library quality and quantity were assessed with Qubit 2.0 DNA HS Assay (ThermoFisher, Massachusetts, USA), Tapestation High Sensitivity D1000 Assay (Agilent Technologies, California, USA), and QuantStudio ® 5 System (Applied Biosystems, California, USA). Illumina® 8-nt dual-indices were used. Equimolar pooling of libraries was performed based on QC values and sequenced on an Illumina® NovaSeq (Illumina, California, USA) with a read length configuration of 150 paired-end (PE). Antibodies used were anti-BATF2, Rabbit-anti mouse IgG (Jackson ImmunoResearch 315-005-003), and Rabbit anti-H3K27ac (Cell Signaling Technologies 8173T). PE reads were aligned to mm10 using Bowtie2 version 2.4.5. MACS2 version 2.2.7.1 was used to call peaks and CUT&RUN IgG was used as a negative control. Results: We found binding of BATF2 at cis-elements of ccr5, ifngr2 (interferon gamma receptor 2), ly6c1, and ccl28 genes (Figure 7C). The locations were highly aligned with accessible chromatin positions marked by H3K27ac, suggesting that BATF2 enhanced gene expression in response to infection. The observations were aligned with our RNA-seq analysis of gene expression in WT or Batf2 KO HSCs in the presence or absence of M. avium infection, as ccr5, ifngr2, ly6c1, and ccl28 were more effectively induced in WT compared to Batf2 HSCs during M. avium infection (Figure S5B). Conclusion: BATF2 interacts with regulatory elements in IFN response genes and facilitates increased expression in response to infection

Project description:Purpose: To evaluate whether methylation changes occuring upon M. avium infection correlate with transcriptional alterations in trained HSCs, we reisolated CD45.2 untrained vs. trained HSCs (LK CD150+CD48–) from transplanted mice one-month after M. avium challenge to generate bulk RNA-Seq libraries. We compared both libraries to a library from HSCs of WT donor mice following primary M. avium infection. This comparison revealed changes in global transcription in trained HSCs after M. avium challenge compared to primary infection, with the untrained set serving as the irradiation and transplant control. Methods: 10,000-50,000 HSCs (CD45.1/CD45.2 KL CD150+ CD48-) into HBSS from naive or 1-month infected WT/transplanted mice (n=10-12 per group). DNA and RNA were isolated with the NucleoSpin ® RNA Plus XS kit (Macherey Nagel). RNA-seq libraries were prepared using SMARTer® Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian (Takara Bio Usa). Illumina NovaSeq SP was used for sequencing with a paired-end sequencing length of 10bp. Samples were sequenced at Admera Health using an Illumina HiSeq 2x150 at sequencing depth of ~40 million reads. FASTQ files were preprocessed using HTS stream (https://github.com/ibest/HTStream) and the clean FASTQ file were aligned using STAR. Differential expression (DE) analysis of gene expression was performed using Limma-Voom. False discovery rate (FDR)<0.05 was considered statistically significant. Further analysis was completed using Illumina Basespace packages and programs (deSeq2) We performed gene ontology analysis for differentially expressed genes with q value of <0.05. Gene set enrichment analysis (GSEA, Broad Institute and UC San Diego) was completed using normalized gene counts as previously described ((Subramanian et al., 2005). Results were visualized using Tidyverse packages in R. Comparison of differentially expressed gene lists and generation of Venn diagrams were generated using the GeneVenn webtool. Biological process and molecular function gene ontology analysis of differentially expressed genes found in bulk RNA-seq datasets were completed using GENEONTOLOGY of the Mus musculus database (Ashburner et al., 2000; Gene Ontology, 2021). Results:The gene expression profile of M. avium-challenged untrained HSCs was compared to that of control infected animals . Next, the gene expression profile of trained HSCs was compared to the same untransplanted controls to detect transcriptional changes potentiated by training. This analysis revealed many more genes (670) upregulated upon secondary infection in the trained population compared to untrained (255). GO analysis of the trained HSPC gene signature showed that there was an enrichment in pathways related to G protein-coupled receptor signaling, cellular adhesion, cell differentiation, immunity, and autophagy . Strikingly, gene set enrichment analysis (GSEA) of the genes uniquely upregulated upon infectious challenge in the trained HSPCs aligned with enhanced metabolism — glycolysis, oxidative phosphorylation, and fatty acid metabolism — genes that are reportedly rewired in macrophages in other trained immunity models (Figure 4C). Pathways related to transcription and translation were also upregulated in trained HSPCs (Supplemental Figure 4C). On the other hand, untrained HSPCs primarily upregulated chemokine responses and immune response pathways, as we previously reported. Together, these analyses reveal trained versus untrained HSPCs have differing responses to infection at 1 month post-challenge compared to untransplanted controls. When directly comparing untrained versus trained gene expression, those with a primary infectious exposure (untrained HSPCs) upregulated immune response pathways while trained cells upregulated metabolic pathway genes. We speculate that relatively lower immune response pathways in trained HSPCs may reflect greater control of infection in the host, enabling the cells to prioritize other cellular pathways. Conclusions: Upregulation of metabolism and gene regulation pathways in trained HSPCs that have been rechallenged with M. avium may reflect greater control of infection and improved immunity within the host compared to untrained controls.

Project description:Since DNMT3A is a de novo DNA methyltransferase, transcriptional differences in WT versus Dnmt3a-/- HSCs upon infection suggest a possible role for epigenetic regulation in the HSC transcriptional response to inflammation. Therefore, we wanted to investigate if differences in stress and presence of Dnmt3a affect the methylome overall, and specific IFNg responses genes. 100000 LT-HSCs (LK CD150+ CD48- were sorted into lysis buffer from the pools of naive or 1-month (M. avium) infected WT/ Dnmt3-/-_x0001_ mice (n = 7-8 per group). Around 300 ng of DNA per group was extracted with AllPrep DNA/RNA Mini Kit. NEBNext Ultra II DNA library prep kit was used for library preparation. Bisulfite conversion step was added after ‘‘methylated’’ adaptor ligation and USER excision. Then, methylated adaptor ligated DNA fragment was treated with bisulfite conversion using EZ DNA methylationlightning kit. These modified DNA fragments were then amplified with index primers with Kapa HiFi urasil+ specialized polymerase for bisulfite converted DNA. For WGBS library sequencing, 150-high output kit from illumine was used. Samples (400 million reads/ sample (15-20X coverage)) were run in NovaSeq S1 FC (2, lanes 1,600 Million reads). Global methylation of Dnmt3a-/- cells was more divergent upon infection compared to WT. There were vastly more hyper- and hypomethylated regions in infected versus naive Dnmt3a-/- HSCs compared to WT. Among the altered regions, both WT and Dnmt3a-/- HSCs showed slightly more hypomethylated than hypermethylated regions, although changes in both directions were present. In the WT background, regions of hyper- and hypomethylation were mostly restricted to CpG islands. By contrast, highly significant differences in hypomethylation were found in the Dnmt3a-/- HSCs in CpG islands, shores, enhancers, and promoters DNMT3A is highly active in HSCs during infection and that the loss of DNMT3A function results in significant global changes in methylation, including both hyper- and hypomethylation, in genome regions that likely affect gene transcription. we examined the methylation of Batf2, Jun, and Fos. All 3 of these prodifferentiation genes showed increased methylation in the promoter region in the Dnmt3a-/- background as compared to the WT background. Hypermethylation of these promoter regions is consistent with their transcriptional repression.

Project description:purpose: Chronic inflammation induced by Mycobacterium avium infection (timepoint= 1 month) results in transcriptomic changes within the HSC population subset upon bulk RNA-seq, particularly in pathways involved in the immune response, interferon gamma signaling (Th1 immune response) pathway, stat signaling, and antigen processing and presentation. We have also shown that chronic inflammation results in a myeloid differentiation bias of HSPCs. We hypothesize that these transcriptomic changes induced by chronic inflammation may be specific to a certain subset of HSPCs and could be passed on to specific terminally differentiated subsets. To investigate this hypothesis, we performed scRNA-seq using the 10x genomics platform to test the transcriptomic differences induced upon infection in HSC, progenitor, myeloid, and lymphoid cell populations along with heterogeneity observed within the HSC population. methods: CD45.2 mice were infected with 2 x 10^6 CFU M. avium for one month prior to bone marrow harvesting. One month post infection, experimental mice were euthanized and infection was confirmed by measuring splenic weight and size. WBM from the hips, tibias, and femurs of each mouse was extracted through crushing with a mortar and pestle. The suspension was then RBC lysed and CD117 (c-kit)+ and cKit- cells were separated using a manual magnetic separation system (Miltenyi). Experimental samples were stained with desired antibodies (listed in key resources table) and sorted using a BD FACS Aria cell sorter. The following cell types were isolated according to the following phenotypic definitions (please see file phenotypic_definitions.txt). The cellular purity and count of each sample was confirmed prior to pooling for submission to the BCM Single Cell Core. Depending on the rarity of the isolated population, 20,000-50,000 sorted cells were combined to a concentration of ~7200 cells/uL. Samples were loaded into the Chromium controller system and generated 3’ 10x scRNAseq barcoded libraries for each sample. Prior to sequencing, quality control of the libraries was completed using a Bioanalyzer (Agilent, Santa Clara, CA) and Nanodrop system. Samples were sequenced on a NovaSeq at a sequencing depth of 300M reads at the BCM Genomic and RNA Profiling (GARP) core. After sequencing, fastq files for each sample were generated (R1, R2, and I1 files) to demultiplex the data using the CellRanger pipeline results: scRNA-seq studies revealed that training led to a bimodal distribution of gene expression, with a subset showing activation of immunity-related genes. This gene signature was propagated to innate immune cells but was not conserved in B cells, indicating specific patterns of gene signature inheritance. M. avium training also led to the emergence of an “activated” HSC subpopulation, indicating subsets of HSCs are more responsive to training conclusions: Transcriptomic signatures induced by M. avium exposure, particularly in IFNg response genes, are heterogeneous and are propagated in a myeloid biased manner

Dataset Information

Bulk RNA-seq libraries of WT and Batf2KO HSCs with 1-mon M.avium infection

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets