scholarly journals Immunophenotyping Using Targeted RNA NGS Recapitulates Traditional AML and CLL FLOW Fingerprints

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 5256-5256
Author(s):  
Cynthie Wong ◽  
Vincent Funari ◽  
Maher Albitar
Keyword(s):  
Gene Expression ◽  
Flow Cytometry ◽  
T Cell ◽  
Differential Expression ◽  
Hierarchical Clustering ◽  
Rna Expression ◽  
Rna Seq ◽  
Protein Marker ◽  
Low Levels ◽  
Marker Expression

Abstract Introduction Flow cytometry is the gold standard for diagnosing hematologic cancers based on morphologic detection and analysis of a few expensive and delicate immunological markers. On the other hand, targeted RNA sequencing panels are not sample or marker limited; in fact, 50ng of RNA stored for up to 6 months could yield results for thousands of markers. We hypothesized that an RNA-Seq-based targeted immuno-oncology gene expression panel could recapitulate the FLOW diagnostic patterns of AML and CLL routinely used in a clinical laboratory. Methods A custom panel of 2207 genes was constructed including 58 typical FLOW markers and well-referenced immune and oncology markers. Housekeeping genes were added to normalize between batches. A total of 52 CLL, 15 AML, and 20 normal clinical samples were tested in parallel with a clinically validated leukemia/lymphoma flow cytometry panel and targeted RNA-Seq. Paired-end 76 x 76 cycles sequencing was performed using Illumina NextSeq. Bowtie analysis suite was performed to determine gene expression. Unsupervised analysis was first performed to identify patterns associated with clinical diagnosis or sequencing artifacts. Two-way hierarchical clustering of genes having a median expression of >1 fpkm and at least 2 fold differential expression than the median in 10% of the samples revealed a strong CLL profile and a less pervasive AML profile without any supervised analysis. To determine which genes in the profiles were significantly associated with AML or CLL, genes with >5 fold differential expression were assessed after Benjamini-Hochberg correction with single tailed T-tests. Further, each FLOW marker was individually tested using a 1-way ANOVA. Pathway analysis was performed on GO terms using the Fischer exact test. All corrected p-values <0.05 were considered significant. Results In general, the FLOW marker gene expression data highly correlated with protein marker expression and was adequate for rendering proper diagnosis. Overall, CLL had a strong immune-oncology pattern with 10+ flow markers including CD19, CD5, CD2, CD200, CD22, CD79, FCER2, IL3RA, IL2RA, PDCD1, and MS4A1 significantly associated with CLL. In addition, 295 other genes including immune targets like CD74, CD33, CD34, CD48, CD40, and gene targets like PAX5, BCL2, PARP3 further help classify CLL. Forty-three of these genes are involved in immune response pathway (p<1.9x10-17). In contrast, two markers used for FLOW (CD34 and CD52) could classify AML with RNA, and 218 other genes including immune (CD3E/G, CD23, CD48, CD6, CD33) and molecular markers (HOXA10, HOXA9, and TGFBR2) could be used to further classify AML. Interestingly, these genes were significantly enriched for T cell co-stimulation (p<2.0x10-18) and other T cell receptor signaling pathways. To determine whether other immune genes may be used to differentiate CLL or AML, hierarchical clustering of the top 200 genes significantly expressed in either CLL or AML was performed. We could clearly identify two clusters of genes which characterize CLL from other disease types: 1) 110 genes which were highly expressed in CLL, but expressed at low levels in both normal and AML samples, 2) 28 genes with low expression in CLL, but highly expressed in both normal and AML samples. Conversely, few genes were able to characterize AML from normal or CLL samples, including BMP1, NPM2, and FLT3, which were highly expressed in AML samples, but were expressed at low levels in both normal and CLL samples. Conclusion Based on our preliminary study, we have shown that protein marker expression determined by flow are reproduced by our RNA expression panel. Importantly, we are able to classify and diagnose CLL and AML samples based on their RNA expression profiles. Disclosures Wong: NeoGenomics: Employment. Funari:NeoGenomics: Employment.

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

scholarly journals RNA-Seq Analysis of Gene Expression, Viral Pathogen, and B-Cell/T-Cell Receptor Signatures in Complex Chronic Disease

2017 ◽  
Vol 64 (4) ◽  
pp. 476-481 ◽  
Author(s):  
Jerome Bouquet ◽  
Jennifer L. Gardy ◽  
Scott Brown ◽  
Jacob Pfeil ◽  
Ruth R. Miller ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chronic Disease ◽  
T Cell ◽  
B Cell ◽  
T Cell Receptor ◽  
Cell Receptor ◽  
Rna Seq ◽  
Viral Pathogen ◽  
Complex Chronic Disease

PD-1/PD-1-Ligand Interaction Contributes to Immunosuppressive Microenvironment of Hodgkin Lymphoma.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 379-379
Author(s):  
Ryo Yamamoto ◽  
Momoko Nishikori ◽  
Toshio Kitawaki ◽  
Tomomi Sakai ◽  
Masakatsu Hishizawa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Flow Cytometry ◽  
T Cells ◽  
T Cell ◽  
Hodgkin Lymphoma ◽  
Cell Lines ◽  
Signaling System ◽  
Peripheral T Cells ◽  
Ifn Γ ◽  
Immunosuppressive Microenvironment

Abstract Programmed death-1 (PD-1), a member of the CD28 costimulatory receptor superfamily, inhibits T cell activity by providing a second signal to T cells in conjunction with signaling through the T-cell receptor. PD-1/PD-1 ligand (PD-L) signaling system is indicated to be involved in the functional impairment of T cells such as in chronic viral infection or tumor immune evasion. We hypothesized that this signaling system is also involved in the pathogenesis of Hodgkin lymphoma (HL). We examined expression of B7-H1 and B7-DC, two known PD-Ls, in lymphoid cell lines using RT-PCR and flow cytometry. They were expressed in HL and several T-cell lines, whereas most B-NHL lines lacked their expression. Immunohistochemical staining of HL tissues demonstrated that PD-Ls were also expressed in primary H/RS cells. As gene expression of B7-H1 and B7-DC was increased in Epstein-Barr virus (EBV)-transformed lymphoblastoid B-cell lines, we examined the effect of EBV latent membrane proteins on their gene regulation. By luciferase reporter assay, both LMP1 and LMP2A were shown to enhance promoter activity of B7-H1 and B7-DC genes. This finding implies that in cases of EBV-positive HL, latent membrane proteins may help H/RS cells escape from host immune surveillance by upregulating PD-L gene expression. We next analyzed PD-1 expression of tumor-infiltrating T cells of HL tissue samples by flow cytometry, and found that PD-1+ cells were elevated markedly in these cells. As HL patients are well recognized as having defective cellular immunity, we compared PD-1 expression level in peripheral blood T cells of HL patients with those of healthy volunteers and B-NHL patients. PD-1 was significantly elevated in peripheral T cells of HL patients compared to the other two groups. PD-1+ T cells were highest in patients with active disease, and tended to decline along with treatment. Although regulatory T cells are reported to play a part in the pathogenesis of HL, FOXP3+ T cells were not significantly elevated in peripheral T cells of HL patients, and PD-1+ T cells did not overlap with these regulatory population. To elucidate whether the PD-1/PD-L signaling pathway is functional in the immunosuppressive microenvironment of HL, we finally examined the effect of blockade of this pathway. After culturing bulk HL tumor cells with anti-PD-L blocking antibodies, IFN-γ production was measured by ELISA. Blockade of PD-Ls augmented IFN-γ production of HL-infiltrating T cells. We concluded that anti-tumor activity of HL-infiltrating T cells was inhibited via the PD-1/PD-L pathway, and this inhibition could be successfully relieved by PD-L blockade. Taken together, our observations indicate that “T-cell exhaustion” is essential to the pathogenesis of HL, and tumor-infiltrating T cells around H/RS cells seem to be kept in balance by this inhibitory signaling. Our findings provide a potentially effective and clinically applicable strategy for the immunotherapy of HL.

Dnmt3a Deletion and FLT3-ITD Cooperate in a Mouse Model of T-Lymphoblastic Leukemia (T-ALL).

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 2428-2428
Author(s):  
Liubin Yang ◽  
Min Luo ◽  
Mira Jeong ◽  
Choladda V. Curry ◽  
Grant Anthony Challen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Bone Marrow ◽  
Flow Cytometry ◽  
T Cell ◽  
Bone Marrow Cells ◽  
Lymphoblastic Leukemia ◽  
Notch Pathway ◽  
Leukemic Transformation ◽  
Aberrant Dna Methylation

Abstract Abstract 2428 Aberrant DNA methylation repeatedly has been implicated in cancer development. DNA methyltransferase (DNMT) 3A, which mediates de novo DNA methylation, was found to be mutated in 20% of patients with acute myeloid leukemia and 10% of patients with myelodysplastic syndrome. Recently, mutations associated with myeloid malignancies such as DNMT3A and FLT3 have also been uncovered in patients with early T-cell precursor lymphoblastic leukemia (ETP-ALL) (Neumann et al., 2012; Van Vlierberghe et al., 2011; Zaremba et al., 2012). ETP-ALL is a type of very high-risk ALL associated with myeloid/stem cell gene expression signature and myeloid markers. We have demonstrated that Dnmt3a deletion in mouse causes increased self-renewal of hematopoietic stem cells and an impairment of differentiation (Challen et al., 2011). Dnmt3a loss also produces aberrant methylation associated with oncogenes and tumor suppressor genes. Yet, whether aberrant DNA methylation can drive leukemia remains unknown. As Dnmt3a deletion alone was insufficient for malignancy, secondary mutations are likely necessary for leukemic transformation. Because FLT3 internal tandem duplication (ITD) frequently co-exist with DNMT3A mutations in acute leukemias, we hypothesized that Dnmt3a-loss may cooperate with FLT3-ITD to promote leukemic transformation; and we established a mouse model to test this. Deletion of conditional Dnmt3a with Mx1-cre was induced by injections of pIpC. Subsequently, bone marrow from Dnmt3a-deleted (Dnmt3aKO) donor mice was transduced with MSCV-FLT3-ITD-GFP retrovirus or MSCV-GFP control and transplanted into lethally irradiated recipients. The mice were monitored monthly for development of malignancies by complete blood count and peripheral blood analysis by flow cytometry and followed for disease latency. Moribund mice were sacrificed and analyzed with peripheral blood smears, histology, and immunophenotyping. Dnmt3a deletion with overexpression of FLT3-ITD caused rapid onset T-ALL in 6/8 mice (n=6) with a median latency of 78 days compared to 121 days in WT mice (n=4) overexpressing FLT3-ITD (p&lt;0.0001 Log-rank Mantel-Cox Test) (See figure). Mice from both groups exhibited leukocytosis, splenomegaly, and thymomegaly with high GFP expression detected by FACS. Even after we transduced bone marrow cells enriched for myeloid progenitor and stem cells, Dnmt3a deletion again accelerated T-ALL with median survival of 89 days (n=9) versus 110 days in WT-FLT3-ITD (n=10) mice. T-ALL was observed in 2/4 WT-FLT3-ITD mice and 5/6 Dnmt3aKO-FLT3-ITD mice analyzed (p&lt;0.0001 Log-rank Mantel-Cox Test). By flow cytometry, two distinct types of T-ALL were observed in the bone marrow of Dnmt3a deleted leukemic mice: one was characterized by a double positive population (DP) of CD4+CD8+ lympoblasts (1/6) and another early immature T-cell-like type of CD4-CD8-CD44+CD25-CD11bloCD117+ lymphoblasts (4/6). Gene expression analysis by RT-PCR in the early immature T-ALL showed downregulation of Notch-pathway genes (such as Notch1, Notch 3, Deltex, Hes1) and upregulation of stem cell-associated genes Lyl1 and Scl1, suggesting an ETP-like T-ALL. The ETP-like ALL phenotype has not been seen in WT mice overexpressing FLT3-ITD. The opposite gene expression pattern was seen in the DP population with upregulation of Notch-pathway genes. Furthermore, the DP leukemia was transplantable to secondary recipients within 2 weeks. Whether ETP-like ALL can be transplanted is still under investigation. We are also currently studying the changes in global CpG methylation among the leukemias that have Dnmt3a loss, FLT3-ITD overexpression, and control and also anticipate data from transcriptome analysis by RNA-Seq. These data suggest that stem or progenitor bone marrow cells primed by early loss of Dnmt3a are transformed into DP T-ALL and ETP-like ALL fueled by the overexpression of the oncogene FLT3-ITD. The ETP-like ALL phenotype has not been seen previously in WT mice overexpressing FLT3-ITD, suggesting that Dnmt3a ablation is required. The Dnmt3a-deleted-FLT3-ITD mice with T-ALL is, to our knowledge, the first animal model of human immature T-cell leukemia. This model can enhance our understanding of the pathogenesis of ETP-like ALL with respect to aberrant DNA methylation and will serve as a powerful tool to test novel therapeutic strategies. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Single Cell RNA Sequencing and V(D)J Profiling of T-Cell Large Granular Lymphocytosis

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2404-2404
Author(s):  
Shouguo Gao ◽  
Zhijie Wu ◽  
Carrie Diamond ◽  
Bradley Arnold ◽  
Valentina Giudice ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Single Cell ◽  
Research Funding ◽  
Clonal Expansion ◽  
Rna Seq ◽  
Healthy Donors ◽  
Tcr Repertoire ◽  
Before And After ◽  
Repertoire Diversity

Abstract Introduction . T-cell large granular lymphocytosis (T-LGL) is a low grade lymphoproliferative disorder, often clinically manifest as bone marrow failure. Treatment with immunosuppressive therapies is effective, but the dominant clone may persist even in responding patients. The pathogenesis of T-LGL has not been fully elucidated. In this study, we performed single cell RNA sequencing (sc-RNA seq) and V(D)J profiling to discern clonotypes and gene expression patterns of T lymphocytes from T-LGL patients who were sampled before and after treatment. Methods. Blood was obtained from patients participating in a phase 2 protocol of alemtuzumab as second line therapy (NCT00345345; Dumitriu B et al, Lancet Haematol 2016). Leukapheresis was performed in 13 patients (M/F 7/6; median age 51 years, range 26-85) before and after 3-6 months alemtuzumab administration and in 7 age-matched healthy donors. Cryopreserved blood was enriched for T cells with the EasySep Human T cell Isolation Kit (Stem cell). sc-RNA seq was performed on the 10XGenomics Chromium Single Cell V(D)J + 5' Gene Expression platform, and sequencing obtained on the HiSeq3000 Platform. Barcode assignment, alignment, unique molecular index counting and T cell receptor sequence assembly were performed using Cell Ranger 2.1.1. Results. Four hundred fifty thousand cells from 13 patients and 107,000 cells from 7 healthy donors were profiled. We measured productive TCR chains (which fully span the V and J regions, with a recognizable start codon in the V region and lacking a stop codon in the V-J region, thus potentially generating a protein). We detected at least one productive TCR α-chain in 50%, one productive TCR β-chain in 69% and paired productive αβ-chains in 47% of all cells. There was loss of TCR repertoire diversity in patients which was quantified by Simpson's diversity index; most patients showed oligoclonal or, less frequently, monoclonal expansion of the TCR repertoire (Fig. A). Regardless of clinical response, alemtuzumab treatment did not correct the low TCR repertoire diversity. TCR repertoires can be classified as "public", when they express identical TCR sequences across multiple individuals, or "private", when each individual displays distinct TCR clonotypes. No TCRA or TCRB CDR3 homology among patients was observed: most TCR clonotypes appeared to be private. Our data suggests that T-LGL is etiologically heterogenous disease, consistent with T cell expansion in response to a variety antigens, in diverse HLA contexts, or randomly. Despite differences of TCR among patients and healthy donors, and the presence of large clones in patients, distribution of TCR diversity followed the power law distribution in healthy donors and patients (Fig. B, showing the negative linear relationship between logarithmic expression of clone frequency and clone size). The observed distribution is consistent with a somatic evolution model, in which cell fitness depends on cellular receptor response to specific antigens and stimulation of cells by cytokine and other signals from the environment; fitted clones have higher birth-death ratios and thus expand (Desponds J et al, PNAS 2016). CD4 and CD8 T cells can be virtually separated by imputation from their transcriptomes (Fig. C). Comparison of gene expression between patients and healthy donors showed dysregulation of genes involved in pathways related to the immune response and cell apoptosis, consistent with a pathophysiology of T cell clonal expansion. We used diffusion mapping, which localizes datapoints to their eigen components in low-dimesional space, to characterize sources contributing to the gene expression phenotype: the first component was mainly from T cell activation and the second was associated with TCR expression. In LGL the T cell transcriptome appeared to be shaped by both lineage development and TCR rearrangement. Conclusion. We describe at the single cell level T clonal expansion profiles in T-LGL, pre- and post-treatment. Single cell analysis allows accurate recovery of paired α and β chains in the same cell and demonstrates a continuum of cell lineage differentiation. We found a range of differences in transcriptome and TCR repertoires across patients. Transcriptome data, coupled with detailed TCR-based lineage information, provides a rich resource for understanding of the pathology of T-LGL and has implications for prognosis, treatment, and monitoring in the clinic. Figure. Figure. Disclosures Young: GlaxoSmithKline: Research Funding; CRADA with Novartis: Research Funding; National Institute of Health: Research Funding.

scholarly journals Transcriptomic Evaluation of CD34+ Marrow Cells from Myelodysplastic Syndrome (MDS) Patients

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 1894-1894
Author(s):  
Hogune Im ◽  
Varsha Rao ◽  
Kunju Joshi Sridhar ◽  
Rui Chen ◽  
George Mias ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Differential Expression ◽  
High Throughput ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Reference Sequence ◽  
Rna Seq ◽  
Cd34 Cells ◽  
Marrow Cells

Abstract Background: Prior studies using microarray platforms have shown alterations of gene expression profiles (GEPs) in MDS CD34+ marrow cells related to clinical outcomes (Sridhar et al, Blood 2009, Pellagatti et al, JCO 2013). Given the increased sensitivity and accuracy of high-throughput RNA sequencing (RNA-Seq) (Mortazavi et al, Nat Meth 2008, Soon et al, Mol Syst Bio 2012) for detecting and quantifying mRNA transcripts, we applied this methodology for evaluating differential gene expression between MDS and normal CD34+ marrow cells. Methods:RNA was isolated from magnetic bead affinity-enriched CD34+ (>90%) marrow aspirate cells (Miltenyi Biotec, Auburn, CA) and amplified using the Smarter Kit (Clontech, Mt View, CA). The amplified product (ds DNA) was fragmented to a size distribution of ~200-300bp using the E220 Focused Ultrasonicator (Covaris Inc, Woburn, MA). End repair, adapter ligation and PCR amplification were performed using the NEBNext Ultra RNA library prep kit for Illumina (New England Biolabs, Ipswich, MA). The indexed cDNA libraries were sequenced (paired end, 100bp) on an Illumina HiSeq2000 platform with median read counts of 69 million. The sequences were aligned to Human Reference sequence hg19 using DNAnexus mapper with gene detection focused on known annotated genes. The differential expression was analyzed using edgeR. DAVID and Ingenuity IPA programs were used for pathway analyses. Gene Set Enrichment Analysis (GSEA) was used to identify biologic processes in our dataset present across phenotypes. Results: Correlations of RNA-Seq data from unamplified to amplified transcripts demonstrated high fidelity of transcripts obtained (Pearson and Spearman R2 = 0.80). After filtering samples for adequate read counts, 12,323 genes were evaluated. Differential expression analysis yielded 719 differentially expressed genes (DEGs) in MDS (n=30) vs normal (n=21) with FDR <.05. Among the DEGs, 548 and 171 were over- and under-expressed ≥2 fold in MDS vs Normal, respectively: 20% of the overexpressed genes were present in >50% of the patients. Hierarchical cluster analysis using these DEGs confirmed clear separation of MDS patients from normals, with 2 differential expression clusters—one region overexpressed and one underexpressed. A distinctive trend toward clustering of the patients was seen which related to their IPSS categories and marrow blast %. In functional pathway analysis of the 2 distinctive gene clusters which distinguished MDS from normal, the underexpressed MDS DEGs demonstrated enrichment of inflammatory cytokines, oxidative stress and interleukin signaling pathways, plus mitochondrial calcium transport; whereas the MDS overexpressed DEG cluster showed enrichment of adherens junction/cytokeletal remodeling, cell cycle control of chromosome replication and DNA damage response pathways. Using GSEA analysis, significantly increased numbers of genes in MDS vs normal, common to those in gene sets present within curated public databases, were involved with TP53 targets and mTOR signaling pathways. Conclusions: Our study demonstrated that RNA-Seq methodology, a high-throughput and more comprehensive technique than most gene expression microarrays, was capable of showing significant and distinctive differences in gene expression between MDS and normal marrow CD34+ cells. Specific clustering of the DEGs was demonstrated to distinguish patient subsets associated with their major clinical features. Further, the stringently identified DEGs shown to be engaged in functional pathways and biologic processes highly relevant for MDS were extant within the patients’ CD34+ cells. These transcriptomic data provide information complementary to exomic mutational findings contributing to improved understanding of biologic mechanisms underlying MDS. Disclosures No relevant conflicts of interest to declare.

scholarly journals Comprehensive Cell Specific Transcriptome Profiling of a Pediatric Hodgkin Lymphoma Cohort

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2773-2773
Author(s):  
Jennifer Agrusa ◽  
Elmoataz A Abdel Fattah ◽  
Howard Lin ◽  
Rikhia Chakraborty ◽  
Brooks Scull ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Hodgkin Lymphoma ◽  
Hierarchical Clustering ◽  
Response To Therapy ◽  
Transcriptomic Analysis ◽  
Telomere Maintenance ◽  
Rt Pcr ◽  
Cd8 Cells

Introduction: Pathogenic Hodgkin Reed-Sternberg (HRS) cells constitute approximately 1% of Hodgkin lymphoma (HL) tumor cells. Studies characterizing genomic lesions and gene expression of HRS gene cells have been limited due to technical challenges of studying these rare cells, and the majority of existing data has focused on adult HL. We therefore developed a multi-parameter flow sorting strategy to isolate viable cells from pediatric HL tumors and to define the transcriptomes of HRS cells and infiltrating lymphocytes in order to inform underlying mechanisms of HL pathogenesis and also create an opportunity to identify cell-specific biomarkers to predict disease risk and response to therapy. Methods : Flow cytometry was used to sort HRS cells, CD4+ T cells, CD8+ T cells, and CD20+/30+B cells from pediatric subjects' HL lesions and control tonsils. Purity was confirmed by quantitative reverse transcriptase polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC). Affymetrix GeneChip HTA 2.0 was used to assess the gene expression profiles (GEPs) for 16 HRS primary tumor cell samples, 14 HL CD4+ and CD8+ T cell samples, 6 control tonsillar CD20+, CD30+, CD4+, and CD8+ cell samples, and 6 HL cell lines. Unsupervised hierarchical clustering and principal component analysis (PCA) were used to determine relatedness, and Cibersort was performed to confirm the phenotype of the sorted cell types. GEPs of HRS, HL CD4+, and HL CD8+ cells were compared to respective controls using a univariate t-test. Significance was determined using a multivariate permutation test with the confidence level of FDR assessment at 80 percent and the maximum allowed proportion of false-positive proteins at 0.1. Gene set enrichment analysis (GSEA) and ingenuity pathway analysis (IPA) were performed to analyze DEGs. Results: Effectiveness of the sorting strategy of HRS cells was confirmed by quantitative RT-PCR and IHC that demonstrated significant enrichment of CD30expression and CD30+ cells in the sorted HRS cell fraction. GEP comparisons were performed for 13 HL samples with matched HRS/CD4+/CD8+ cells: HRS vs. control tonsil CD20+/CD30+ (1934 and 3846 DEGs, respectively), HL CD4+ vs. control CD4+ (635 DEGs), HL CD8+ vs. control CD8+ (2 DEGs). We carried out a transcriptomic analysis of HRS cells, and a set of multifunctional genes were more than 2-fold downregulated (P < .001), involved in telomere maintenance and packaging (TERF2, RFC3, DNA2 and a group of HIST1) when compared to healthy lymph node CD30+ cells. A set of genes related to cytokine/chemokine dysregulation was also upregulated in HRS cells, including IL6, CCL18, and CXCL9. IPA and GSEA of specific HRS genes were also performed and demonstrated pathways associated with HL pathogenesis, including NFĸB activation and T cell exhaustion. Over-expression of genes associated with T cell pathways was demonstrated in HRS cells. While this may be a result of T cell rosetting and contamination, it may also reflect innate T cell signature within HRS cells, as HRS cells clustered separately from T cells in both unsupervised hierarchical clustering and PCA. Cibersort analysis of HRS cells revealed a heterogeneous phenotype that may reflect aberrant differentiation. In comparing clinical characteristics within HRS cells, TCEAL1 was elevated in slow vs. rapid early responders and 3 DEGs were identified when comparing EBV+/- samples. Within HL CD8 cells, KLF2 was elevated in EBV- samples. Conclusions: This study was the first to successfully isolate highly purified HRS cell populations from whole HL lesions in a pediatric HL cohort. Transcriptomic analysis of pediatric HRS cells identified mechanisms previously associated with HL pathogenesis, and also identified potential novel mechanisms, including telomere maintenance. Additional analyses demonstrated significant heterogeneity of HRS trasncriptomes across specimens that may reflect distinct differentiation pathways and differences in HRS-immune cell interactions. Finally, this study identified increased expression of some genes associated with EBV status and response to therapy. Future studies in an expanded cohort will validate these findings, compare pediatric and adult GEPs, and test these cell-specific biomarkers into the current risk stratification strategies of prospective clinical trials. Disclosures No relevant conflicts of interest to declare.

scholarly journals The SF3b Splicing Complex Regulates DNA Damage Response in Acute Lymphoblastic Leukemia

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1237-1237 ◽  
Author(s):  
Shalini Sankar ◽  
Miriam Guillen Navarro ◽  
Frida Ponthan ◽  
Simon Bomken ◽  
Sirintra Nakjang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Damage ◽  
Differential Expression ◽  
Differential Gene Expression ◽  
Research Funding ◽  
Gene Set Enrichment Analysis ◽  
Rna Seq ◽  
Self Renewal ◽  
Strand Breaks ◽  
Differential Gene

To identify potential regulators of propagation and self-renewal of Acute Lymphoblastic Leukaemia (ALL), we performed an explorative genome-wide RNAi screen followed by CRISPR ex vivo and in vivo validation screens in the t(4;11)-positive ALL cell line SEM. These screens identified the splicing factor PHF5A as a crucial component of the leukemic program. PHF5A is a subunit of the SF3b protein complex, which directs alternative splicing by binding to the branchpoint of pre-mRNA. Mutations in members of this complex including SF3B1 have been implicated in several haematological malignancies. Functional perturbation experiments demonstrated that PHF5A depletion impairs proliferation, viability and clonogenicity in a range of ALL and AML cell lines strongly suggesting that PHF5A is required for leukemic propagation and self-renewal. To identify genetic programs affected by PHF5A inhibition, we performed RNA-seq followed by analysis of differential gene expression and splicing events. We identified 473 genes with differential expression upon PHF5A knockdown. In addition, we performed in-depth analysis of splicing patterns by examining both differential exon/intron usage and exon junction formation. These analyses demonstrated that loss of PHF5A affects splicing of more than 2500 genes with exon skipping and intron retention being the most frequent splicing events. In order to identify processes and pathways affected by PHF5A, we performed gene set enrichment analysis (GSEA) on both differential expression and splicing. While gene sets associated with RNA processing including splicing, turnover and translation were enriched in both data sets, the differential gene expression signature was also linked to DNA repair processes including base excision, mismatch and homologous recombination repair. In line with these findings, knockdown of either PHF5A or its partner protein SF3B1 induced DNA strand breaks as indicated by comet assay and increased y-H2AX levels. Furthermore, both PHF5A and SF3B1 depletion sensitized ALL cells towards the DNA crosslinking agent mitomycin C. Closer inspection of RNA-seq datasets revealed reduced FANCD2 expression and skipping of exon 22 associated with impaired mono-ubiquitination of the FANCD2 protein as a consequence of PHF5A and SF3B1 knockdown. Furthermore, expression of RAD51, a key component of double strand break repair, also decreased upon PHF5A and SF3B1 knockdown. Notably, in vitro pharmacological inhibition of SF3b complex activity using H3B-8800 (or Pladienolide B) showed a very similar effect on FANCD2 expression, and ubiquitination as well as decrease of RAD51 and an increase in y-H2AX levels on a dose and time-dependent manner. This strongly suggests a mechanistic link between impaired RNA splicing and the repair of DNA double-strand breaks. These combined data show that leukemic cells are highly dependent on a functional SF3b splicing complex. Interference with its function results in DNA damage and also sensitizes towards DNA damaging agents pointing towards a possible benefit of the combined application of inhibitors targeting the SF3b complex with more conventional chemotherapy. Disclosures Ponthan: Epistem Ltd: Employment. Zwaan:Sanofi: Consultancy; Incyte: Consultancy; BMS: Research Funding; Roche: Consultancy; Janssen: Consultancy; Daiichi Sankyo: Consultancy; Servier: Consultancy; Jazz Pharmaceuticals: Other: Travel support; Pfizer: Research Funding; Celgene: Consultancy, Research Funding. Vormoor:Abbvie (uncompensated): Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Roche/Genentech: Consultancy, Honoraria, Research Funding; AstraZeneca: Research Funding.

scholarly journals Integrative Genomics of the Mammalian Alveolar Macrophage Response to Intracellular Mycobacteria

2020 ◽  
Author(s):  
Thomas J. Hall ◽  
Michael P. Mullen ◽  
Gillian P. McHugo ◽  
Kate E. Killick ◽  
Siobhán C. Ring ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alveolar Macrophage ◽  
Differential Expression ◽  
Gene Expression Data ◽  
Host Response ◽  
Gwas Data ◽  
Data Sets ◽  
Expression Data ◽  
Rna Seq ◽  
Time Point

Abstract BackgroundBovine TB (BTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production, particularly in many developing countries. The key innate immune that first encounters the pathogen is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host response to infection with M. bovis at the transcriptome level to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection.ResultsThe host gene expression data consisted of bovine RNA-seq data from alveolar macrophages infected with M. bovis at 24 and 48 hours post-infection. These RNA-seq data were analysed using three distinct analysis pipelines and novel response pathways and modules were further refined using cross-comparison and integration of the results. First, a differential expression analysis was carried out to determine the most significantly differentially expressed (DE) genes between conditions at each time point. Second, two networks were constructed at each time point using gene correlation patterns to determine changes in expression across conditions. Functional sub-modules within each correlation network were selected by statistical criteria for modularity. Third, a base gene interaction network of the mammalian host response to mycobacterial infection was generated using the GeneCards database and InnateDB. Differential gene expression data were superimposed on this base network to extract functional modules of interconnected DE genes.ConclusionsBovine GWAS data was obtained from a published BTB susceptibility/resistance study. The results from the three parallel analyses were integrated with this data to determine which of the three approaches identified genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets.

MO919THE INTERPLAY BETWEEN IL-6 AND IL-17 MIGHT PLAY A SIGNIFICANT ROLE IN CHRONIC ANTIBODY MEDIATED REJECTION IN RENAL ALLOGRAFT RECIPIENTS

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Mantabya Singh ◽  
Narayan Prasad ◽  
Mohit Rai ◽  
Akhilesh Kumar Jaiswal ◽  
Manas Ranjan Behera ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Chronic Inflammation ◽  
Allograft Rejection ◽  
Culture Supernatant ◽  
Critical Role ◽  
Graft Loss ◽  
Rna Expression ◽  
Antibody Mediated Rejection ◽  
Synergistic Activation

Abstract Background and Aims Chronic antibody-mediated rejection (CABMR) plays a critical role in kidney allograft loss and consider among one of the most important barriers that is responsible for late term graft loss. Previously we believed that alloreactive T-cell and de-novo DSA responsible for late term graft loss but recent study suggested that not only immune cell, non-immune cells like fibroblast also plays important role in chronic inflammation and allograft rejection via IL-6 amplifier loop (IL-6+IL-17). The interaction between non-immune tissues/cells and the immune system plays a critical role in chronic inflammation and late graft rejection. In chronic inflammation IL-6 enhance the production of acute phase proteins, T cell Subset differentiation, Maturation of Plasma cells, Generation of cellular and humoral immune responses and Control the transition from acute to chronic inflammation by changing the nature of leucocyte infiltration (from neutrophils to monocyte). We sought to see whether IL-6 and IL-17A mediated synergistic activation of inflammation amplifier is operational in CABMR. Method Recruitment of patients according to Banff 2017criteria and biopsy was taken from consented patients and establishment of fibroblast culture from renal biopsy of patients with CABMR. Fibroblast culture from CABMR patients were cultured to purity and pre stimulated with IL-6 (20ng/ µl), IL-17(50ng/ µl), IL-6 plus IL-17 for 24 hours and culture supernatant were collected for IL-6 ELISA to see synergistic activation. Serum IL-6, MCP1 and CCL20 levels of Healthy control (HC), CABMR and Non-CABMR patients and MCP1, CCL20 level in culture supernatant were measured by ELISA. m-RNA expression of IL-6, MCP1, CCL20 and SOCS3 gene were measured by real time PCR (Syber-green method) One-way ANOVA and Non-parametric Student t tests (two-tailed) were used for the statistical analysis of differences between groups. Results In comparison to IL-6 and IL-17 alone these cytokines synergistically induced more IL-6 production from renal fibroblasts. (Fig 1) also, we found that concentrations of effectors of inflammation amplifiers like IL-6, CCL-20 & MCP-1 in sera were significantly higher in CABMR patients compared to Non rejection patients, while their concentration in culture supernatant was higher when fibroblast cell stimulated with IL-6 and IL-17 together as compared either IL-6 or IL-17 alone. (Fig 2) Gene expression analysis of IL-6, MCP1 and CCL20 was significant higher (p&lt;0.001) with synergistic activation of IL-6 and IL-17 as compared to either IL-6 or IL-17 alone, while SOCS3 gene expression was downregulated. (Fig 3) There was significant reduction in IL-6 concentration in culture supernatant with IL-6 and IL-17 inhibitor together (Fig 4) and m-RNA expression of IL-6 and MCP-1 was significantly reduced. (Fig 5) Conclusion CABMR is perpetuated by inflammation amplifier loop or synergistic induction of IL-6 and IL-17. Inhibition of IL-6 with Anti-IL-6 (Tocilizumab) and IL-17 with Anti-IL-17 reduces the tissue injury marker (IL-6, MCP1, CCL20) and allograft rejection.

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