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Cut&Run sequencing of WT HSCs with 1-mon M.avium infection

ABSTRACT: 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

ORGANISM(S): Mus musculus

PROVIDER: GSE206276 | GEO | 2023/01/19

REPOSITORIES: GEO

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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:The following cell lines were used for this study: Epstein-Barr virus (EBV) transformed lymphoblastoid cell lines (LCLs) derived from 5 human (Coriell YRI, NIGMS Human Genetic Cell Repository, GM18505, GM18507, GM18516, GM19193, GM19204), and 5 chimpanzee (Pan troglodytes) individuals (New Iberia Research Center: Min 18358, Min 18359; Coriell/IPBIR: NS03659, NS04973, Arizona State University, Pt91), and rhesus Herpesvirus papio transformed LCLs from 5 rhesus macaque (Macaca mulatta) individuals (Harvard Medical School, NEPRC: 150-99, R181-96, R249-97, 265-95, R290-96). Cells were maintained at identical conditions of 37° with 5% CO2 in RPMI media with 15% FBS, supplemented with 2 mM L-glutamate, 100 IU/ml penicillin, and 100 ug/ml streptomycin. Internal standard LCL (Coriell YRI, NIGMS Human Genetic Cell Repository, GM19238) was grown in RPMI minus L-Lysine and L-Arginine, 15% dialyzed FBS, and L-13C615N4-arginine (Arg-10) and L-13C615N2-lysine (Lys-8) (Cambridge Isotopes, Andover, MA, USA) supplemented with 2 mM L-glutamate, 100 IU/ml penicillin, and 100 ug/ml streptomycin under identical conditions as the unlabeled LCLs. The internal standard LCL was grown for 6 doublings to assure complete SILAC label incorporation. Complete label incorporation was verified by analyzing the protein lysate from the labeled LCL alone by high-resolution LC-MS/MS. LCLs were washed in PBS three times and then lysed using the UPX Universal Protein Extraction Kit (Expedeon Inc., San Diego, CA, USA). Protein quantitation was performed using the Qubit fluorometry assay (Invitrogen, Carlsbad California, USA) and the reducing agent-compatible (RAC) version of the BCA Protein Assay (Thermo Scientific, Waltham, Massachusetts, USA). 12ug of each sample was combined with 12ug of the SILAC labeled lysate from human LCL GM19238. Note that the SILAC lysate was prepared once and used as an internal standard through the quantification of the 15 cell lines. 24ug of each combined sample was then processed by SDS-PAGE using a 4-12% Bis Tris NuPage mini-gel (Invitrogen, Carlsbad California, USA). Calibration was with Thermo PageRuler broad range markers. Each of 40 gel segments were processed by in-gel digestion using a ProGest robot (DigiLab, Marlborough, MA, USA) with the following protocol: wash with 25mM ammonium bicarbonate followed by acetonitrile, reduce with 10mM dithiothreitol at 60°C followed by alkylation with 50mM iodoacetamide at room temperature, digest with trypsin (Promega, Madison, WI, USA) at 37°C for 4h, and quench with formic acid. The supernatant was analyzed directly without further processing. Each of gel digest was analyzed by nano-LC/MS/MS with a Waters NanoAcquity HPLC system interfaced to a ThermoFisher LTQ-Orbitrap Velos Pro. Peptides were loaded on a trapping column and eluted over a 75um analytical column at 350nL/min using a 1-hour LC gradient. Both columns were packed with Jupiter Proteo resin (Phenomenex, Torrance, California, USA). The mass spectrometer was operated in data-dependent mode, with MS performed in the Orbitrap at 60,000 FWHM resolution and MS/MS performed in the LTQ. The fifteen most abundant ions were selected for MS/MS. Low-level data analysis was performed using the open-source proteomics software tool PVIEW (Release December 23, 2012; http://compbio.cs.princeton.edu/pview). As input to PVIEW, we generated in silico translations of coding genes from the UCSC Genome Browser database based on gene models from build hg19 of the human genome. Each protein sequence entry retained the corresponding Ensembl gene identifier and gene symbol. Database searches were performed using +-4 p.p.m. MS1 tolerance and an MS2 window tolerance of +-0.5 Da. Up to 2 missed tryptic cleavages were allowed during search. Carboxyamidomethylation of cysteine was used as fixed modification. Up to two methionine oxidations were allowed as variable modifications of a tryptic peptide. Peptide spectrum matches were obtained at a stringent false discovery rate (FDR) of 1%. We used the median log2(sample/standard) ratio across all independent quantifications of a protein (distinct peptides including duplicate peptide measurements across fractions and for differing charge states). Note that use of the hg19 database was sufficient as SILAC pairs as the expected isotope shift used for quantification were only present for peptides that that had the same underlying sequence in the human internal standard line. Associated RNA-seq data have been deposited to GEO with accession number GSE49682.

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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Cut&Run sequencing of WT HSCs with 1-mon M.avium infection

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