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: 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: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

RNAseq in WT and Dnmt3a KO HSCs naive and after 1-month of infection with M. avium

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