Project description: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

Project description:Hemophagocytic lymphohistiocytosis is a cytokine storm syndrome characterized by the excessive activation of myeloid and T cells. In primary HLH (pHLH), disease pathophysiology is driven by CD8 T cells proliferation and IFNg production. Therefore, targeting IFNg has been a viable option for the treatment of HLH. Since many cytokines that signaling throught the JAK/STAT pathway, including IFNg, largely drive cytokine storm in HLH, we and others have demonstrated that targeting JAK1/2 with ruxolitinib is effective in ameliorating disease manifestations in pre-clinical models of HLH. Both IFNg and ruxolitinib treatments have also shown great efficacy in dampening inflammation in patients with HLH, albeit not completely. Since ruxolitinib treatment does not fully inhibit IFNg production, we sought to determine the effect of combining ruxolitinib treatment with IFNg neutralization as a treatment option in a LCMV-infected Prf1 mice. We aimed to elucidate the effects of these treatments on the main hematologic and cellular parameters of disease. Moreover, we determined the main changes in the transcriptional landscape on CD8 T cells isolated from mice infected with LCMV either untreated or treated with aIFNg, ruxolitinib, or combination treatments. We demonstrate that combination treatment was very effective in reversing anemia and thrombocytopenia, but failed to reduce splenomegaly, hypercytokinemia, and myeloid and T cell numbers compared to ruxolitinib treatment. Transcriptional analysis revealed distinct pathways targeted by aIFNg and ruxolitinib; while aIFNg was more effective in downregulating the expression of interferon gamma response genes, allograft rejection, and complement genes, ruxolitinib treated CD8 T cells displayed a reduction in expression of genes involved in reactive oxygen species, glycolysis, E2F targets, and protein secretion pathways. Therefore, this study provide novel insights on the effects of combining ruxolitinib treatment with IFNg neutralization in the treatment of primary HLH.

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.

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WT vs IFN-gamma treated bulk RNA-seq libraries of HSCs

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