Differential gene expression pattern in biopsies with renal allograft pyelonephritis and allograft rejection

Oghumu, Steve; Nori, Uday; Bracewell, Anna; Zhang, Jianying; Bott, Cherri; Nadasdy, Gyongyi; Brodsky, Sergey V.; Pelletier, Ronald P.; Satoskar, Abhay R.; Nádasdy, Tibor; Satoskar, Anjali A.

doi:10.1111/ctr.12795

Cited by 12 publications

(9 citation statements)

References 53 publications

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“…Several other studies using URA-based analysis of networks to interpret transcriptomic data, while not quantifying the validation rate, have reported good reproducibility [43][44][45][46]. However, in contrast to the present report, the goal of these studies was to validate the upstream regulators, not to identify and prioritize DEGs for validation as we have done.…”

Section: Discussioncontrasting

confidence: 74%

Separating the Wheat from the Chaff: The Use of Upstream Regulator Analysis to Identify True Differential Expression of Single Genes within Transcriptomic Datasets

Hadwen

Schock

Farooq

et al. 2021

IJMS

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The development of DNA microarray and RNA-sequencing technology has led to an explosion in the generation of transcriptomic differential expression data under a wide range of biologic systems including those recapitulating the monogenic muscular dystrophies. Data generation has increased exponentially due in large part to new platforms, improved cost-effectiveness, and processing speed. However, reproducibility and thus reliability of data remain a central issue, particularly when resource constraints limit experiments to single replicates. This was observed firsthand in a recent rare disease drug repurposing project involving RNA-seq-based transcriptomic profiling of primary cerebrocortical cultures incubated with clinic-ready blood–brain penetrant drugs. Given the low validation rates obtained for single differential expression genes, alternative approaches to identify with greater confidence genes that were truly differentially expressed in our dataset were explored. Here we outline a method for differential expression data analysis in the context of drug repurposing for rare diseases that incorporates the statistical rigour of the multigene analysis to bring greater predictive power in assessing individual gene modulation. Ingenuity Pathway Analysis upstream regulator analysis was applied to the differentially expressed genes from the Care4Rare Neuron Drug Screen transcriptomic database to identify three distinct signaling networks each perturbed by a different drug and involving a central upstream modulating protein: levothyroxine (DIO3), hydroxyurea (FOXM1), dexamethasone (PPARD). Differential expression of upstream regulator network related genes was next assessed in in vitro and in vivo systems by qPCR, revealing 5× and 10× increases in validation rates, respectively, when compared with our previous experience with individual genes in the dataset not associated with a network. The Ingenuity Pathway Analysis based gene prioritization may increase the predictive value of drug–gene interactions, especially in the context of assessing single-gene modulation in single-replicate experiments.

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Section: Discussioncontrasting

confidence: 74%

Separating the Wheat from the Chaff: The Use of Upstream Regulator Analysis to Identify True Differential Expression of Single Genes within Transcriptomic Datasets

Hadwen

Schock

Farooq

et al. 2021

IJMS

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“…Obviously, the specificity of this potential biomarker, as well as other chemokines attracting neutrophils, may be affected by the fact that these cells are the first-line defence in antibacterial immunity. In this respect, increased transcripts of CXCL1 gene have been documented also in renal biopsies of patients with acute pyelonephritis [17]. Our in vitro experiments demonstrated that coculture of renal proximal tubular epithelial cells with monocyte/macrophage cell line augmented the release of CXCL5 and CXCL6 induced by TNF alpha.…”

Section: Discussionsupporting

confidence: 69%

Chemokine Profiles Are Affected in Serum of Patients with Acute Rejection of Kidney Allograft

Krupickova

Fialová

Novotny

et al. 2021

Mediators of Inflammation

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Kidney allograft transplantation improved the prognosis and quality of life of patients with end-stage renal diseases but the occurrence of acute rejection represents a limitation of the final outcome. Noninvasive biomarkers are needed as well as further advancements in the understanding of immune mechanisms of reaction to the allograft. Our study of 138 patients focused on one-year monitoring of serum concentrations of 12 chemokines regulating the recruitment of different immune cells into transplanted allograft and on in vitro regulation of the same chemokines release by interactions of renal proximal epithelial cells with monocyte/macrophage cell line stimulated with TNF alpha. In a group of 44 patients with acute rejection, higher serum pretransplant levels of CXCL1, CXCL5, CXCL6, CCL2, CCL21, and particularly CXCL10 and CX3CL1(both p < 0.001 ) were found suggesting their higher proinflammatory status as compared to subjects with the uncomplicated outcome. In samples collected at the day of biopsy positive for acute rejection, chemokines CXCL9 and CXCL11 attracting preferentially Th1 lymphocytes were found to be upregulated. In our in vitro model with TNF alpha induction, renal proximal epithelial cells seemed to be a more potent source of chemokines attracting neutrophils as compared to monocyte/macrophage cell line but the coculture of these cells potentiated release of neutrophilic chemokines CXCL5 and CXCL6. Similar augmentation of chemokine production was found also in the case of CCL2. On the other hand, adding of monocytes/macrophages to a culture of renal epithelial cells suppressed the release of CXCL10 and CXCL11 attracting T lymphocytes. We assume from our data that in kidney allograft transplantation, chemokines attracting neutrophils, T lymphocytes, and monocytes are induced simultaneously and measurement some of them in combination might be used as biomarkers of acute rejection. Mutual cell-cell interactions of immune cells with renal parenchyma seem to be important for fine regulation of chemokine release.

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“…There is reluctance from the renal community to either sacrifice any of these usual samples, or to take more tissue for RNA analysis, because this is associated with additional cost and increased potential risk and inconvenience for patients 12 . Therefore there has been considerable interest in a novel high throughput gene expression platform that works on formalin-fixed paraffin-embedded (FFPE) tissue, the NanoString nCounter Analysis System (NanoString Technologies, Seattle, WA, USA) 7,[16][17][18][19] . Comparisons between results of gene expression analysis using Nanostring and qRT-PCR have been previously published, using optimally handled RNA from cell cultures and other organisms (sea urchins), or large tissue samples from humans (oral and lung tumours) mostly 7,16,17,[20][21][22][23] .…”

Section: Amrmentioning

confidence: 99%

Technical considerations when designing a gene expression panel for renal transplant diagnosis

Toulza

Dominy

Cook

et al. 2020

Sci Rep

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Gene expression analysis is emerging as a new diagnostic tool in transplant pathology, in particular for the diagnosis of antibody-mediated rejection. Diagnostic gene expression panels are defined on the basis of their pathophysiological relevance, but also need to be tested for their robustness across different preservatives and analysis platforms. The aim of this study is the investigate the effect of tissue sampling and preservation on candidate genes included in a renal transplant diagnostic panel. Using the NanoString platform, we compared the expression of 219 genes in 51 samples, split for formalin-fixation and paraffin-embedding (FFPE) and RNAlater preservation (RNAlater). We found that overall, gene expression significantly correlated between FFPE and RNAlater samples. However, at the individual gene level, 46 of the 219 genes did not correlate across the 51 matched FFPE and RNAlater samples. Comparing gene expression results using NanoString and qRT-PCR for 18 genes in the same pool of RNA (RNAlater), we found a significant correlation in 17/18 genes. Our study indicates that, in samples from the same routine diagnostic renal transplant biopsy procedure split for FFPE and RNAlater, 21% of 219 genes of potential biological significance do not correlate in expression. Whether this is due to fixatives or tissue sampling, selection of gene panels for routine diagnosis should take this information into consideration.

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Differential gene expression pattern in biopsies with renal allograft pyelonephritis and allograft rejection

Cited by 12 publications

References 53 publications

Separating the Wheat from the Chaff: The Use of Upstream Regulator Analysis to Identify True Differential Expression of Single Genes within Transcriptomic Datasets

Separating the Wheat from the Chaff: The Use of Upstream Regulator Analysis to Identify True Differential Expression of Single Genes within Transcriptomic Datasets

Chemokine Profiles Are Affected in Serum of Patients with Acute Rejection of Kidney Allograft

Technical considerations when designing a gene expression panel for renal transplant diagnosis

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