scholarly journals Intragraft Molecular Pathways Associated with Tolerance Induction in Renal Transplantation

2017 ◽  
Vol 29 (2) ◽  
pp. 423-433 ◽  
Author(s):  
Lorenzo Gallon ◽  
James M. Mathew ◽  
Sai Vineela Bontha ◽  
Catherine I. Dumur ◽  
Pranav Dalal ◽  
...  

The modern immunosuppression regimen has greatly improved short-term allograft outcomes but not long-term allograft survival. Complications associated with immunosuppression, specifically nephrotoxicity and infection risk, significantly affect graft and patient survival. Inducing and understanding pathways underlying clinical tolerance after transplantation are, therefore, necessary. We previously showed full donor chimerism and immunosuppression withdrawal in highly mismatched allograft recipients using a bioengineered stem cell product (FCRx). Here, we evaluated the gene expression and microRNA expression profiles in renal biopsy samples from tolerance-induced FCRx recipients, paired donor organs before implant, and subjects under standard immunosuppression (SIS) without rejection and with acute rejection. Unlike allograft samples showing acute rejection, samples from FCRx recipients did not show upregulation of T cell– and B cell–mediated rejection pathways. Gene expression pathways differed slightly between FCRx samples and the paired preimplantation donor organ samples, but most of the functional gene networks overlapped. Notably, compared with SIS samples, FCRx samples showed upregulation of genes involved in pathways, like B cell receptor signaling. Additionally, prediction analysis showed inhibition of proinflammatory regulators and activation of anti-inflammatory pathways in FCRx samples. Furthermore, integrative analyses (microRNA and gene expression profiling from the same biopsy sample) identified the induction of regulators with demonstrated roles in the downregulation of inflammatory pathways and maintenance of tissue homeostasis in tolerance-induced FCRx samples compared with SIS samples. This pilot study highlights the utility of molecular intragraft evaluation of pathways related to FCRx-induced tolerance and the use of integrative analyses for identifying upstream regulators of the affected downstream molecular pathways.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 268-268
Author(s):  
Christian Steidl ◽  
Tang Lee ◽  
Pedro Farinha ◽  
Adele Telenius ◽  
Merrill Boyle ◽  
...  

Abstract Abstract 268 INTRODUCTION: Classical Hodgkin lymphoma (cHL) is unique among lymphomas due to the scarcity of the malignant Hodgkin Reed Sternberg (HRS) cells, which are derived from clonal germinal center B cells. Investigations using laser capture microdissection permit more detailed analysis of these cells. However, most recent studies were limited by low case numbers and lack of available clinical data. PATIENTS AND METHODS: We studied 29 cases of cHL and the 5 HL lines KMH2, HDLM2, L428, L540, and L1236 by gene expression profiling. All patients were treated at the BC Cancer Agency between 1984 and 2006 and received at least 4 cycles of polychemotherapy and stage-dependent radiotherapy. The cohort also included 5 biopsies taken at relapse. Treatment failure was defined as disease progression or relapse at any time (n=14) after initiation of treatment; treatment success as absence of progression (n=15). We used laser microdissection (Molecular Machines & Industries Cellcut with Nikon Eclipse TE2000-S microscope) to study the enriched HRS cell compartment separately from the microenvironment. RNA extraction was performed on pools of 1000 microdissected HRS cells in each case. Gene expression profiles were generated using Affymetrix HG UA133 2.0 Plus arrays using two-cycle labeling reactions. HRS cell profiles were compared to microdissected germinal centers (GC), and HL cell line profiles compared to enriched tonsillar CD77+ centroblasts (MACS cell separation, Miltenyi). Furthermore, we compared gene expression profiles of treatment failures to those of treatment successes. RESULTS: We identified 1342 differentially expressed probesets (fold change >5, False Discovery Rate (FDR) adjusted p value <0.001) between HRS and GC cells. Using overrepresentation analysis we found genes involved in NFκB, JAK/Stat, IL-6, IL-9 signaling, cytotoxic T lymphocyte-mediated apoptosis and IL-15 production to be significantly over-expressed in HRS cells and genes involved in B cell, T cell receptor signaling and many transcriptional regulators such as FOXO1, E2F5, IRF8, NFATC1, NFYB, POU2AF1 to be significantly under-expressed in HRS cell. Comparison of these data to differentially expressed genes in the HL cell lines (1004 genes, fold change >5, FDR-adjusted p value <0.001) showed significant overlap of genes involved in proliferation, apoptosis, IL15 signaling and B cell receptor signaling, while overrepresentation of metabolism genes was unique to the cell lines. Hierarchical clustering of all 29 primary HL cases identified 3 separate clusters characterized by 1) a cytotoxic signature, 2) TNF/TGFB receptor signaling or 3) a residual B cell signature. Dichotomizing the profiles into the two treatment outcome groups demonstrated that NFγB signaling, complement system genes and genes involved in the developmental process of hematopoietic progenitor cells, macrophages and blood vessels were overexpressed in treatment failure samples. DISCUSSION: Using microdissection of HRS cells in a large number of cases we were able to further characterize the unique expression program of HL and refine the data inventory about dysregulated cellular functions and pathways in this disease. Overexpression of genes associated with NFκB, complement and hematopoietic progenitor cells proliferation correlate strongly with treatment failure. Further study using immunohistochemistry is currently ongoing to validate these findings and to develop clinically useful biomarkers. Disclosures: Gascoyne: Roche Canada: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.


Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 4388-4388
Author(s):  
Alessandra Trojani ◽  
Barbara Di Camillo ◽  
Alessandra Tedeschi ◽  
Milena Lodola ◽  
Chiara Colombo ◽  
...  

Abstract Abstract 4388 Introduction Chronic lymphocytic leukemia (CLL) is a heterogeneous disease; ZAP-70 protein expression and IgVH mutational status have shown to be strong associated and to offer important prognostic information. Aim Our aim was to determine gene expression profiles of 46 CLL patients divided into three classes: group one (n=26) with mutated IgVH and ZAP-70-, group two (n=12) with unmutated IgVH and ZAP-70+, and group three (n=8) included CLL patients with unmutated IgVH and ZAP-70-, or mutated IgVH and ZAP-70+ respectively. Afterwards, in a second phase of the study, 16 other patients were investigated. Finally, the purpose was to define prognostic biomarkers and the biological pathways related to CLL. Patients and Methods We determined gene expression profiles using Affymetrix HG U133 Plus 2.0 in CD19+ leukemic cells. Differentially expressed genes were detected using ANOVA and t-test adapted for microarray data analysis and corrected for multiple testing using false discovery rate p-values. Subjects were clustered in groups with similar expression signature using cluster analysis (K-means, Euclidean distance). Results Statistical analysis revealed 154 differentially expressed probe-sets in the first (mutated IgVH and ZAP-70-) vs the second (unmutated IgVH and ZAP-70+) group, corresponding to 88 genes annotated in public databases. Interestingly, six genes were associated to the following biological pathways: MAPKsignaling (heat shock 70kD protein 8 HSPA8), B cell receptor signaling (ZAP-70, CKLF-like MARVEL transmembrane domain containing 3 CMTM3, dual adaptor of phosphotyrosine and 3-phosphoinositides DAPP1), Matrix Metalloproteinasis (transcription factor 20, TCF20), Apoptosis (X-linked inhibitor of apoptosis XIAP) and T cell receptor signaling (ZAP-70). In particular, ZAP-70, HSPA8, CMTM3 were significatively underexpressed while XIAP, TCF20 and DAPP1 were overexpressed in the first group of patients in comparison to the second group, respectively. Based on the expression of the 88 genes identified in the comparison between the first and the second group of patients, the 8 patients of the third class were divided in two clusters: 5 subjects were more similar to the first class, while 3 subjects appeared to be more similar to the second one. In particular, cluster analysis revealed that the 46 patients were better partitioned in two rather than in three classes, based on their expression profiles. 16 additional subjects were independently analyzed in a second phase of the project. Based only on the expression of the 88 genes previously identified, all of them were correctly classified in group one and group two. Further analysis was carried on the total of 62 subjects (n=46+16), dividing them in group A (n=17), showing deletion of 17p13 region and group B (n=45) without the deletion. Statistical analysis showed no correlation between groups A and B with respect to the previously defined group one, two and three. Moreover, no genes were identified as significantly differentially expressed in group A vs B. Conclusions In conclusion, our preliminary data revealed different gene expression signatures in B-cell chronic lymphocytic leukemia prognostic subgroups of patients, defined by IgVH mutational status and ZAP-70 expression. The functional pathways related to: MAPK signaling, B cell receptor signaling, apoptosis and T cell receptor signaling may ultimately influence CLL biology. Gene expression profiling studies are in progress on larger series of CLL patients in order to assess the association of the molecular signature, based on the identified genes and their pathways, with respect to prognostic information. No different gene expression signatures appeared considering CLL patients divided in two groups differing from the presence or the absence of deletion of 17p13 region respectively Disclosures: No relevant conflicts of interest to declare.


Blood ◽  
2011 ◽  
Vol 117 (13) ◽  
pp. 3596-3608 ◽  
Author(s):  
Pier Paolo Piccaluga ◽  
Giulia De Falco ◽  
Manjunath Kustagi ◽  
Anna Gazzola ◽  
Claudio Agostinelli ◽  
...  

Abstract Burkitt lymphoma (BL) is classified into 3 clinical subsets: endemic, sporadic, and immunodeficiency-associated BL. So far, possible differences in their gene expression profiles (GEPs) have not been investigated. We studied GEPs of BL subtypes, other B-cell lymphomas, and B lymphocytes; first, we found that BL is a unique molecular entity, distinct from other B-cell malignancies. Indeed, by unsupervised analysis all BLs clearly clustered apart of other lymphomas. Second, we found that BL subtypes presented slight differences in GEPs. Particularly, they differed for genes involved in cell cycle control, B-cell receptor signaling, and tumor necrosis factor/nuclear factor κB pathways. Notably, by reverse engineering, we found that endemic and sporadic BLs diverged for genes dependent on RBL2 activity. Furthermore, we found that all BLs were intimately related to germinal center cells, differing from them for molecules involved in cell proliferation, immune response, and signal transduction. Finally, to validate GEP, we applied immunohistochemistry to a large panel of cases and showed that RBL2 can cooperate with MYC in inducing a neoplastic phenotype in vitro and in vivo. In conclusion, our study provided substantial insights on the pathobiology of BLs, by offering novel evidences that may be relevant for its classification and possibly future treatment.


Neurology ◽  
2017 ◽  
Vol 89 (16) ◽  
pp. 1676-1683 ◽  
Author(s):  
Ron Shamir ◽  
Christine Klein ◽  
David Amar ◽  
Eva-Juliane Vollstedt ◽  
Michael Bonin ◽  
...  

Objective:To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples).Methods:Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks.Results:A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, p = 7.13E–6) and a subsequent independent test cohort (AUC = 0.74, p = 4.2E–4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of COX4I1, ATP5A1, and VDAC3.Conclusions:We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 30-31
Author(s):  
Hanyin Wang ◽  
Shulan Tian ◽  
Qing Zhao ◽  
Wendy Blumenschein ◽  
Jennifer H. Yearley ◽  
...  

Introduction: Richter's syndrome (RS) represents transformation of chronic lymphocytic leukemia (CLL) into a highly aggressive lymphoma with dismal prognosis. Transcriptomic alterations have been described in CLL but most studies focused on peripheral blood samples with minimal data on RS-involved tissue. Moreover, transcriptomic features of RS have not been well defined in the era of CLL novel therapies. In this study we investigated transcriptomic profiles of CLL/RS-involved nodal tissue using samples from a clinical trial cohort of refractory CLL and RS patients treated with Pembrolizumab (NCT02332980). Methods: Nodal samples from 9 RS and 4 CLL patients in MC1485 trial cohort were reviewed and classified as previously published (Ding et al, Blood 2017). All samples were collected prior to Pembrolizumab treatment. Targeted gene expression profiling of 789 immune-related genes were performed on FFPE nodal samples using Nanostring nCounter® Analysis System (NanoString Technologies, Seattle, WA). Differential expression analysis was performed using NanoStringDiff. Genes with 2 fold-change in expression with a false-discovery rate less than 5% were considered differentially expressed. Results: The details for the therapy history of this cohort were illustrated in Figure 1a. All patients exposed to prior ibrutinib before the tissue biopsy had developed clinical progression while receiving ibrutinib. Unsupervised hierarchical clustering using the 300 most variable genes in expression revealed two clusters: C1 and C2 (Figure 1b). C1 included 4 RS and 3 CLL treated with prior chemotherapy without prior ibrutinib, and 1 RS treated with prior ibrutinib. C2 included 1 CLL and 3 RS received prior ibrutinib, and 1 RS treated with chemotherapy. The segregation of gene expression profiles in samples was largely driven by recent exposure to ibrutinib. In C1 cluster (majority had no prior ibrutinb), RS and CLL samples were clearly separated into two subgroups (Figure 1b). In C2 cluster, CLL 8 treated with ibrutinib showed more similarity in gene expression to RS, than to other CLL samples treated with chemotherapy. In comparison of C2 to C1, we identified 71 differentially expressed genes, of which 34 genes were downregulated and 37 were upregulated in C2. Among the upregulated genes in C2 (majority had prior ibrutinib) are known immune modulating genes including LILRA6, FCGR3A, IL-10, CD163, CD14, IL-2RB (figure 1c). Downregulated genes in C2 are involved in B cell activation including CD40LG, CD22, CD79A, MS4A1 (CD20), and LTB, reflecting the expected biological effect of ibrutinib in reducing B cell activation. Among the 9 RS samples, we compared gene profiles between the two groups of RS with or without prior ibrutinib therapy. 38 downregulated genes and 10 upregulated genes were found in the 4 RS treated with ibrutinib in comparison with 5 RS treated with chemotherapy. The top upregulated genes in the ibrutinib-exposed group included PTHLH, S100A8, IGSF3, TERT, and PRKCB, while the downregulated genes in these samples included MS4A1, LTB and CD38 (figure 1d). In order to delineate the differences of RS vs CLL, we compared gene expression profiles between 5 RS samples and 3 CLL samples that were treated with only chemotherapy. RS samples showed significant upregulation of 129 genes and downregulation of 7 genes. Among the most significantly upregulated genes are multiple genes involved in monocyte and myeloid lineage regulation including TNFSF13, S100A9, FCN1, LGALS2, CD14, FCGR2A, SERPINA1, and LILRB3. Conclusion: Our study indicates that ibrutinib-resistant, RS-involved tissues are characterized by downregulation of genes in B cell activation, but with PRKCB and TERT upregulation. Furthermore, RS-involved nodal tissues display the increased expression of genes involved in myeloid/monocytic regulation in comparison with CLL-involved nodal tissues. These findings implicate that differential therapies for RS and CLL patients need to be adopted based on their prior therapy and gene expression signatures. Studies using large sample size will be needed to verify this hypothesis. Figure Disclosures Zhao: Merck: Current Employment. Blumenschein:Merck: Current Employment. Yearley:Merck: Current Employment. Wang:Novartis: Research Funding; Incyte: Research Funding; Innocare: Research Funding. Parikh:Verastem Oncology: Honoraria; GlaxoSmithKline: Honoraria; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria; AbbVie: Honoraria, Research Funding; Merck: Research Funding; TG Therapeutics: Research Funding; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Kenderian:Sunesis: Research Funding; MorphoSys: Research Funding; Humanigen: Consultancy, Patents & Royalties, Research Funding; Gilead: Research Funding; BMS: Research Funding; Tolero: Research Funding; Lentigen: Research Funding; Juno: Research Funding; Mettaforge: Patents & Royalties; Torque: Consultancy; Kite: Research Funding; Novartis: Patents & Royalties, Research Funding. Kay:Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Acerta Pharma: Research Funding; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; MEI Pharma: Research Funding; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Ding:DTRM: Research Funding; Astra Zeneca: Research Funding; Abbvie: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees.


Blood ◽  
2002 ◽  
Vol 99 (7) ◽  
pp. 2285-2290 ◽  
Author(s):  
James Z. Huang ◽  
Warren G. Sanger ◽  
Timothy C. Greiner ◽  
Louis M. Staudt ◽  
Dennis D. Weisenburger ◽  
...  

Recently we have identified subgroups of de novo primary diffuse large B-cell lymphoma (DLBCL) based on complementary DNA microarray-generated gene expression profiles. To correlate the gene expression profiles with cytogenetic abnormalities in these DLBCLs, we examined the occurrence of the t(14;18)(q32;q21) in the 2 distinctive subgroups of DLBCL: one with the germinal center B-cell gene expression signature and the other with the activated B cell–like gene expression signature. The t(14;18) was detected in 7 of 35 cases (20%). All 7 t(14;18)-positive cases had a germinal center B-cell gene expression profile, representing 35% of the cases in this subgroup, and 6 of these 7 cases had very similar gene expression profiles. The expression of bcl-2 and bcl-6 proteins was not significantly different between the t(14;18)-positive and -negative cases, whereas CD10 was detected only in the group with the germinal center B-cell expression profile, and CD10 was most frequently expressed in the t(14;18)-positive cases. This study supports the validity of subdividing DLBCL into 2 major subgroups by gene expression profiling, with the t(14;18) being an important event in the pathogenesis of a subset of DLBCL arising from germinal center B cells. CD10 protein expression is useful in identifying cases of DLBCL with a germinal center B-cell gene expression profile and is often expressed in cases with the t(14;18).


2021 ◽  
Vol 288 (1945) ◽  
pp. 20202793
Author(s):  
Alexander Yermanos ◽  
Daniel Neumeier ◽  
Ioana Sandu ◽  
Mariana Borsa ◽  
Ann Cathrin Waindok ◽  
...  

Neuroinflammation plays a crucial role during ageing and various neurological conditions, including Alzheimer's disease, multiple sclerosis and infection. Technical limitations, however, have prevented an integrative analysis of how lymphocyte immune receptor repertoires and their accompanying transcriptional states change with age in the central nervous system. Here, we leveraged single-cell sequencing to simultaneously profile B cell receptor and T cell receptor repertoires and accompanying gene expression profiles in young and old mouse brains. We observed the presence of clonally expanded B and T cells in the central nervous system of aged male mice. Furthermore, many of these B cells were of the IgM and IgD isotypes, and had low levels of somatic hypermutation. Integrating gene expression information additionally revealed distinct transcriptional profiles of these clonally expanded lymphocytes. Our findings implicate that clonally related T and B cells in the CNS of elderly mice may contribute to neuroinflammation accompanying homeostatic ageing.


2017 ◽  
Vol 64 (4) ◽  
pp. 476-481 ◽  
Author(s):  
Jerome Bouquet ◽  
Jennifer L. Gardy ◽  
Scott Brown ◽  
Jacob Pfeil ◽  
Ruth R. Miller ◽  
...  

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e19521-e19521
Author(s):  
Bartlomiej Przychodzen

e19521 Background: Histone deacetylase inhibitors (HDACi) are small molecules that increase acetylation of lysine residues by blocking histone deactylases. These anticancer agents affect epigenetic and non-epigenetic gene expression resulting in cell cycle arrest of cancer cells. Furthermore HDACi can enhance its anti-tumor effects via the pharmacologic modulation of macrophage. Some HDACi’s such as Trichostatin A (TSA) can also affected the tumor immune microenvironment by suppressing the activity of infiltrating macrophages and inhibiting myeloid-derived suppressor cell recruiement (Li et al.,). Methods: We conducted a high throughput screen comparing gene expression profiles in known hematological cell lines to identify transcriptional signatures associated with TSA sensitivity obtained from GDSC. Results: We selected genes that showed at least 2fold expression difference and were statistically significant (p < 0.05). We identified 49 genes that were upregulated and 85 that were downregulated. The most significant results include multiple genes known to be correlated with the B-cell maturation process. We found that CD24 a small GPI linked glycoprotein expressed at the surface of most B lymphocyte precursors, neutrophils, epithelial cells and frequently found to be highly expressed in various hematological and solid neoplasms, was up/downregulatred by XX. Interestingly, CD24 plays a role in the activation and differentiation of the cells as bone marrow samples lacking CD24 resulted in decreased numbers of both pre-B and immature B-cell populations. We also found that IKZF2, a transcription factor regulating lymphocyte development and queiesence and which is frequently deleted in hypodiploid B-ALLs. This result could revelent as other reports suggest a role of IKZF2 as a tumor suppressor with a central role regulating the balance of self-renewal and differentiation in leukemic stem cells. Conclusions: Our study identified transcriptional profiles which suggest that TSA sensitivity could be related to B cell maturation. Further experiments warrant replication of these findings which could prove useful in creating optimal, TSA-based treatments acting either as potent single agents or in combination enhancing anti-tumor effects of immunotherapies.


Author(s):  
Crescenzio Gallo

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.


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