A meta-analysis of kidney microarray datasets: investigation of cytokine gene detection and correlation with rt-PCR and detection thresholds

Applications of Transcriptomics in the Research of Antibody-Mediated Rejection in Kidney Transplantation: Progress and Perspectives

Antibody-mediated rejection (ABMR) is the major cause of chronic allograft dysfunction and loss in kidney transplantation. The immunological mechanisms of ABMR that have been featured in the latest studies indicate a highly complex interplay between various immune and nonimmune cell types. Clinical diagnostic standards have long been criticized for being arbitrary and the lack of accuracy. Transcriptomic approaches, including microarray and RNA sequencing of allograft biopsies, enable the identification of differential gene expression and the continuous improvement of diagnostics. Given that conventional bulk transcriptomic approaches only reflect the average gene expression but not the status at the single-cell level, thereby ignoring the heterogeneity of the transcriptome across individual cells, single-cell RNA sequencing is rising as a powerful tool to provide a high-resolution transcriptome map of immune cells, which allows the elucidation of the pathogenesis and may facilitate the development of novel strategies for clinical treatment of ABMR.

The Expression of TRIM6 Activates the mTORC1 Pathway by Regulating the Ubiquitination of TSC1-TSC2 to Promote Renal Fibrosis

Renal fibrosis is considered as the final pathway of all types of kidney diseases, which can lead to the progressive loss of kidney functions and eventually renal failure. The mechanisms behind are diversified, in which the mammalian target of rapamycin (mTOR) pathway is one of the most important regulatory pathways that accounts for the disease. Several processes that are regulated by the mTOR pathway, such as autophagy, epithelial-mesenchymal transition (EMT), and endoplasmic reticulum (ER) stress, are tightly associated with renal fibrosis. In this study, we have reported that the expression of tripartite motif-containing (TRIM) protein 6, a member of TRIM family protein, was highly expressed in renal fibrosis patients and positively correlated with the severity of renal fibrosis. In our established in vitro and in vivo renal fibrosis models, its expression was upregulated by the Angiotensin II-induced nuclear translocation of nuclear factor-κB (NF-κB) p50 and p65. In HK2 cells, the expression of TRIM6 promoted the ubiquitination of tuberous sclerosis proteins (TSC) 1 and 2, two negative regulators of the mTORC1 pathway. Moreover, the knockdown of TRIM6 was found efficient for alleviating renal fibrosis and inhibiting the downstream processes of EMT and ER in both HK2 cells and 5/6-nephrectomized rats. Clinically, the level of TRIM6, TSC1/2, and NF-κB p50 was found closely related to renal fibrosis. As a result, we have presented the first study on the role of TRIM6 in the mTORC1 pathway in renal fibrosis models and our findings suggested that TRIM6 may be a potential target for the treatment of renal fibrosis.

Profiling of mRNA of interstitial fibrosis and tubular atrophy with subclinical inflammation in recipients after kidney transplantation

Interstitial fibrosis and tubular atrophy (IFTA) with inflammation (IFTA-I) is strongly correlated with kidney allograft failure. Diagnosis of IFTA-I accurately and early is critical to prevent graft failure and improve graft survival. In the current study, through analyzing the renal allograft biopsy in patients with stable function after kidney transplantation (STA), IFTA and IFTA-I group with semi-supervised principal components methods, we found that CD2, IL7R, CCL5 based signature could not only distinguish STA and IFTA-I well, but predict IFTA-I with a high degree of accuracy with an area under the curve (AUC) of 0.91 (P = 0.00023). Additionally, IRF8 demonstrated significant differences among STA, IFTA and IFTA-I groups, suggesting that IRF8 had the capacity to discriminate the different classifications of graft biopsies well. Also, with Kaplan-Meier and log-rank methods, we found that IRF8 could serve as the prognostic marker for renal graft failure in those biopsies without rejection (AUC = 0.75) and the recipients expressing high had a higher risk for renal graft loss (P < 0.0001). This research may provide new targets for therapeutic prevention and intervention for post-transplantation IFTA with or with inflammation.

An age-independent gene signature for monitoring acute rejection in kidney transplantation

Acute rejection (AR) remains a significant problem that negatively impacts long-term renal allograft survival. Numerous therapies are used to prevent AR that differ by center and recipient age. This variability confounds diagnostic methods. Methods: To develop an age-independent gene signature for AR effective across a broad array of immunosuppressive regimens, we compiled kidney transplant biopsy (n=1091) and peripheral blood (n=392) gene expression profiles from 12 independent public datasets. After removing genes differentially expressed in pediatric and adult patients, we compared gene expression profiles from biopsy and peripheral blood samples of patients with AR to those who were stable (STA), using Mann-Whitney U Tests with validation in independent testing datasets. We confirmed this signature in pediatric and adult patients (42 AR and 47 STA) from our institutional biorepository. Results: We identified a novel age-independent gene network that identified AR from both kidney and blood samples. We developed a 90-probe set signature targeting 76 genes that differentiated AR from STA and found an 8 gene subset (DIP2C, ENOSF1, FBXO21, KCTD6, PDXDC1, REXO2, HLA-E, and RAB31) that was associated with AR. Conclusion: We used publicly available datasets to create a gene signature of AR that identified AR irrespective of immunosuppression regimen or recipient age. This study highlights a novel model to screen and validate biomarkers across multiple treatment regimens.

A qPCR expression assay of IFI44L gene differentiates viral from bacterial infections in febrile children

The diagnosis of bacterial infections in hospital settings is currently performed using bacterial culture from sterile site, but they are lengthy and limited. Transcriptomic biomarkers are becoming promising tools for diagnosis with potential applicability in clinical settings. We evaluated a RT-qPCR assay for a 2-transcript host expression signature (FAM89A and IFI44L genes) inferred from microarray data that allow to differentiate between viral and bacterial infection in febrile children. This assay was able to discriminate viral from bacterial infections (P-value = 1.04 × 10-4; AUC = 92.2%; sensitivity = 90.9%; specificity = 85.7%) and showed very high reproducibility regardless of the reference gene(s) used to normalize the data. Unexpectedly, the monogenic IFI44L expression signature yielded better results than those obtained from the 2-transcript test (P-value = 3.59 × 10-5; AUC = 94.1%; sensitivity = 90.9%; specificity = 92.8%). We validated this IFI44L signature in previously published microarray and whole-transcriptome data from patients affected by different types of viral and bacterial infections, confirming that this gene alone differentiates between both groups, thus saving time, effort, and costs. Herein, we demonstrate that host expression microarray data can be successfully translated into a fast, highly accurate and relatively inexpensive in vitro assay that could be implemented in the clinical routine.

The Use of Genomics and Pathway Analysis in Our Understanding and Prediction of Clinical Renal Transplant Injury

The development and application of high-throughput molecular profiling have transformed the study of human diseases. The problem of handling large, complex data sets has been facilitated by advances in complex computational analysis. In this review, the recent literature regarding the application of transcriptional genomic information to renal transplantation, with specific reference to acute rejection, acute kidney injury in allografts, chronic allograft injury, and tolerance is discussed, as is the current published data regarding other "omics" strategies-proteomics, metabolomics, and the microRNA transcriptome. These data have shed new light on our understanding of the pathogenesis of specific disease conditions after renal transplantation, but their utility as a biomarker of disease has been hampered by study design and sample size. This review aims to highlight the opportunities and obstacles that exist with genomics and other related technologies to better understand and predict renal allograft outcome.

CD26/DPPIV inhibition alters the expression of immune response-related genes in the thymi of NOD mice

The transmembrane glycoprotein CD26 or dipeptidyl peptidase IV (DPPIV) is a multifunctional protein. In immune system, CD26 plays a role in T-cell function and is also involved in thymic maturation and emigration patterns. In preclinical studies, treatment with DPPIV inhibitors reduces insulitis and delays or even reverses the new -onset of type 1 diabetes (T1D) in non-obese diabetic (NOD) mice. However, the specific mechanisms involved in these effects remain unknown. The aim of the present study was to investigate how DPPIV inhibition modifies the expression of genes in the thymus of NOD mice by microarray analysis. Changes in the gene expression of β-cell autoantigens and Aire in thymic epithelial cells (TECs) were also evaluated by using qRT-PCR. A DPPIV inhibitor, MK626, was orally administered in the diet for 4 and 6 weeks starting at 6-8 weeks of age. Thymic glands from treated and control mice were obtained for each study checkpoint. Thymus transcriptome analysis revealed that 58 genes were significantly over-expressed in MK626-treated mice after 6 weeks of treatment. Changes in gene expression in the thymus were confined mainly to the immune system, including innate immunity, chemotaxis, antigen presentation and immunoregulation. Most of the genes are implicated in central tolerance mechanisms through several pathways. No differences were observed in the expression of Aire and β-cell autoantigens in TECs. In the current study, we demonstrate that treatment with the DPPIV inhibitor MK626 in NOD mice alters the expression of the immune response-related genes in the thymus, especially those related to immunological central tolerance, and may contribute to the prevention of T1D.

Differential Ly6C Expression after Renal Ischemia-Reperfusion Identifies Unique Macrophage Populations

Macrophages are a heterogeneous cell type implicated in injury, repair, and fibrosis after AKI, but the macrophage population associated with each phase is unclear. In this study, we used a renal bilateral ischemia-reperfusion injury mouse model to identify unique monocyte/macrophage populations by differential expression of Ly6C in CD11b(+) cells and to define the function of these cells in the pathophysiology of disease on the basis of microarray gene signatures and reduction strategies. Macrophage populations were isolated from kidney homogenates by fluorescence-activated cell sorting for whole genome microarray analysis. The CD11b(+)/Ly6C(high) population associated with the onset of renal injury and increase in proinflammatory cytokines, whereas the CD11b(+)/Ly6C(intermediate) population peaked during kidney repair. The CD11b(+)/Ly6C(low) population emerged with developing renal fibrosis. Principal component and hierarchical cluster analyses identified gene signatures unique to each population. The CD11b(+)/Ly6C(intermediate) population had a distinct phenotype of wound healing, confirmed by results of studies inhibiting the macrophage colony-stimulating factor 1 receptor,whereas the CD11b(+)/Ly6C(low) population had a profibrotic phenotype. All populations, including the CD11b(+)/Ly6C(high) population, carried differential inflammatory signatures. The expression of M2-specific markers was detected in both the CD11b(+)/Ly6C(intermediate) and CD11b(+)/Ly6C(low) populations, suggesting these in vivo populations do not fit into the traditional classifications defined by in vitro systems. Results of this study in a renal ischemia-reperfusion injury model allow phenotype and function to be assigned to CD11b(+)/Ly6C(+) monocyte/macrophage populations in the pathophysiology of disease after AKI.

Validation of reference genes for RT-qPCR analysis of CYP4T expression in crucian carp

Reference genes are commonly used for normalization of target gene expression during RT-qPCR analysis. However, no housekeeping genes or reference genes have been identified to be stable across different tissue types or under different experimental conditions. To identify the most suitable reference genes for RT-qPCR analysis of target gene expression in the hepatopancreas of crucian carp (Carassius auratus) under various conditions (sex, age, water temperature, and drug treatments), seven reference genes, including beta actin (ACTB), beta-2 microglobulin (B2M), embryonic elongation factor-1 alpha (EEF1A), glyceraldehyde phosphate dehydrogenase (GAPDH), alpha tubulin (TUBA), ribosomal protein l8 (RPL8) and glucose-6-phosphate dehydrogenase (G6PDH), were evaluated in this study. The stability and ranking of gene expression were analyzed using three different statistical programs: GeNorm, Normfinder and Bestkeeper. The expression errors associated with selection of the genes were assessed by the relative quantity of CYP4T. The results indicated that all the seven genes exhibited variability under the experimental conditions of this research, and the combination of ACTB/TUBA/EEF1A or of ACTB/EEF1A was the best candidate that raised the accuracy of quantitative analysis of gene expression. The findings highlighted the importance of validation of housekeeping genes for research on gene expression under different conditions of experiment and species.

Comparison of gene expression profiles between pansensitive and multidrug-resistant strains of Mycobacterium tuberculosis

Mycobacterium tuberculosis has developed resistance to anti-tuberculosis first-line drugs. Multidrug-resistant strains complicate the control of tuberculosis and have converted it into a worldwide public health problem. Mutational studies of target genes have tried to envisage the resistance in clinical isolates; however, detection of these mutations in some cases is not sufficient to identify drug resistance, suggesting that other mechanisms are involved. Therefore, the identification of new markers of susceptibility or resistance to first-line drugs could contribute (1) to specifically diagnose the type of M. tuberculosis strain and prescribe an appropriate therapy, and (2) to elucidate the mechanisms of resistance in multidrug-resistant strains. In order to identify specific genes related to resistance in M. tuberculosis, we compared the gene expression profiles between the pansensitive H37Rv strain and a clinical CIBIN:UMF:15:99 multidrug-resistant isolate using microarray analysis. Quantitative real-time PCR confirmed that in the clinical multidrug-resistant isolate, the esxG, esxH, rpsA, esxI, and rpmI genes were upregulated, while the lipF, groES, and narG genes were downregulated. The modified genes could be involved in the mechanisms of resistance to first-line drugs in M. tuberculosis and could contribute to increased efficiency in molecular diagnosis approaches of infections with drug-resistant strains.

Genomic meta-analysis of growth factor and integrin pathways in chronic kidney transplant injury

BACKGROUND

Chronic Allograft Nephropathy (CAN) is a clinical entity of progressive kidney transplant injury. The defining histology is tubular atrophy with interstitial fibrosis (IFTA). Using a meta-analysis of microarrays from 84 kidney transplant biopsies, we revealed growth factor and integrin adhesion molecule pathways differentially expressed and correlated with histological progression. A bioinformatics approach mining independent datasets leverages new and existing data to identify correlative changes in integrin and growth factor signaling pathways.

RESULTS

Analysis of CAN/IFTA Banff grades showed that hepatocyte growth factor (HGF), and epidermal growth factor (EGF) pathways are significantly differentially expressed in all classes of CAN/IFTA. MAPK-dependent pathways were also significant. However, the TGFβ pathways, albeit present, failed to differentiate CAN/IFTA progression. The integrin subunits β8, αv, αμ and β5 are differentially expressed, but β1, β6 and α6 specifically correlate with progression of chronic injury. Results were validated using our published proteomic profiling of CAN/IFTA.

CONCLUSIONS

CAN/IFTA with chronic kidney injury is characterized by expression of distinct growth factors and specific integrin adhesion molecules as well as their canonical signaling pathways. Drug target mapping suggests several novel candidates for the next generation of therapeutics to prevent or treat progressive transplant dysfunction with interstitial fibrosis.

A new 12-gene diagnostic biomarker signature of melanoma revealed by integrated microarray analysis

Genome-wide microarray technology has facilitated the systematic discovery of diagnostic biomarkers of cancers and other pathologies. However, meta-analyses of published arrays often uncover significant inconsistencies that hinder advances in clinical practice. Here we present an integrated microarray analysis framework, based on a genome-wide relative significance (GWRS) and genome-wide global significance (GWGS) model. When applied to five microarray datasets on melanoma published between 2000 and 2011, this method revealed a new signature of 200 genes. When these were linked to so-called ‘melanoma driver’ genes involved in MAPK, Ca²⁺, and WNT signaling pathways we were able to produce a new 12-gene diagnostic biomarker signature for melanoma (i.e., EGFR, FGFR2, FGFR3, IL8, PTPRF, TNC, CXCL13, COL11A1, CHP2, SHC4, PPP2R2C, and WNT4). We have begun to experimentally validate a subset of these genes involved in MAPK signaling at the protein level, including CXCL13, COL11A1, PTPRF and SHC4 and found these to be over-expressed in metastatic and primary melanoma cells in vitro and in situ compared to melanocytes cultured from healthy skin epidermis and normal healthy human skin. While SHC4 has been reported previously to be associated to melanoma, this is the first time CXCL13, COL11A1, and PTPRF have been associated with melanoma on experimental validation. Our computational evaluation indicates that this 12-gene biomarker signature achieves excellent diagnostic power in distinguishing metastatic melanoma from normal skin and benign nevus. Further experimental validation of the role of these 12 genes in a new signaling network may provide new insights into the underlying biological mechanisms driving the progression of melanoma.

Intragraft gene expression in positive crossmatch kidney allografts: ongoing inflammation mediates chronic antibody-mediated injury

We studied intragraft gene expression profiles of positive crossmatch (+XM) kidney transplant recipients who develop transplant glomerulopathy (TG) and those who do not. Whole genome microarray analysis and quantitative rt-PCR were performed on RNA from protocol renal allograft biopsies in three groups: (1) +XM/TG+ biopsies before and after TG; (2) +XM/NoTG; and (3) negative crossmatch kidney transplants (control). Microarray comparisons showed few differentially expressed genes between paired biopsies from +XM/TG+ recipients before and after the diagnosis of TG. Comparing +XM/TG+ and control groups, significantly altered expression was seen for 2447 genes (18%) and 3200 genes (24%) at early and late time points, respectively. Canonical pathway analyses of differentially expressed genes showed inflammatory genes associated with innate and adaptive immune responses. Comparing +XM/TG+ and +XM/NoTG groups, 3718 probe sets were differentially expressed but these were over-represented in only four pathways. A classic accommodation phenotype was not identified. Using rt-PCR, the expression of inflammatory genes was significantly increased in +XM/TG+ recipients compared to the +XM/NoTG and control groups. In conclusion, pretransplant donor-specific anti-HLA antibodies results in a gene expression profile characterized by inflammation and cellular infiltration and the majority of +XM grafts are exposed to chronic injury.

Investigating meta-approaches for reconstructing gene networks in a mammalian cellular context

The output of state-of-the-art reverse-engineering methods for biological networks is often based on the fitting of a mathematical model to the data. Typically, different datasets do not give single consistent network predictions but rather an ensemble of inconsistent networks inferred under the same reverse-engineering method that are only consistent with the specific experimentally measured data. Here, we focus on an alternative approach for combining the information contained within such an ensemble of inconsistent gene networks called meta-analysis, to make more accurate predictions and to estimate the reliability of these predictions. We review two existing meta-analysis approaches; the Fisher transformation combined coefficient test (FTCCT) and Fisher's inverse combined probability test (FICPT); and compare their performance with five well-known methods, ARACNe, Context Likelihood or Relatedness network (CLR), Maximum Relevance Minimum Redundancy (MRNET), Relevance Network (RN) and Bayesian Network (BN). We conducted in-depth numerical ensemble simulations and demonstrated for biological expression data that the meta-analysis approaches consistently outperformed the best gene regulatory network inference (GRNI) methods in the literature. Furthermore, the meta-analysis approaches have a low computational complexity. We conclude that the meta-analysis approaches are a powerful tool for integrating different datasets to give more accurate and reliable predictions for biological networks.

Biomarkers in solid organ transplantation: establishing personalized transplantation medicine

Technological advances in molecular and in silico research have enabled significant progress towards personalized transplantation medicine. It is now possible to conduct comprehensive biomarker development studies of transplant organ pathologies, correlating genomic, transcriptomic and proteomic information from donor and recipient with clinical and histological phenotypes. Translation of these advances to the clinical setting will allow assessment of an individual patient's risk of allograft damage or accommodation. Transplantation biomarkers are needed for active monitoring of immunosuppression, to reduce patient morbidity, and to improve long-term allograft function and life expectancy. Here, we highlight recent pre- and post-transplantation biomarkers of acute and chronic allograft damage or adaptation, focusing on peripheral blood-based methodologies for non-invasive application. We then critically discuss current findings with respect to their future application in routine clinical transplantation medicine. Complement-system-associated SNPs present potential biomarkers that may be used to indicate the baseline risk for allograft damage prior to transplantation. The detection of antibodies against novel, non-HLA, MICA antigens, and the expression of cytokine genes and proteins and cytotoxicity-related genes have been correlated with allograft damage and are potential post-transplantation biomarkers indicating allograft damage at the molecular level, although these do not have clinical relevance yet. Several multi-gene expression-based biomarker panels have been identified that accurately predicted graft accommodation in liver transplant recipients and may be developed into a predictive biomarker assay.

Statistical Test of Expression Pattern (STEPath): a new strategy to integrate gene expression data with genomic information in individual and meta-analysis studies

Background

In the last decades, microarray technology has spread, leading to a dramatic increase of publicly available datasets. The first statistical tools developed were focused on the identification of significant differentially expressed genes. Later, researchers moved toward the systematic integration of gene expression profiles with additional biological information, such as chromosomal location, ontological annotations or sequence features. The analysis of gene expression linked to physical location of genes on chromosomes allows the identification of transcriptionally imbalanced regions, while, Gene Set Analysis focuses on the detection of coordinated changes in transcriptional levels among sets of biologically related genes.

In this field, meta-analysis offers the possibility to compare different studies, addressing the same biological question to fully exploit public gene expression datasets.

Results

We describe STEPath, a method that starts from gene expression profiles and integrates the analysis of imbalanced region as an a priori step before performing gene set analysis. The application of STEPath in individual studies produced gene set scores weighted by chromosomal activation. As a final step, we propose a way to compare these scores across different studies (meta-analysis) on related biological issues. One complication with meta-analysis is batch effects, which occur because molecular measurements are affected by laboratory conditions, reagent lots and personnel differences. Major problems occur when batch effects are correlated with an outcome of interest and lead to incorrect conclusions. We evaluated the power of combining chromosome mapping and gene set enrichment analysis, performing the analysis on a dataset of leukaemia (example of individual study) and on a dataset of skeletal muscle diseases (meta-analysis approach).

In leukaemia, we identified the Hox gene set, a gene set closely related to the pathology that other algorithms of gene set analysis do not identify, while the meta-analysis approach on muscular disease discriminates between related pathologies and correlates similar ones from different studies.

Conclusions

STEPath is a new method that integrates gene expression profiles, genomic co-expressed regions and the information about the biological function of genes. The usage of the STEPath-computed gene set scores overcomes batch effects in the meta-analysis approaches allowing the direct comparison of different pathologies and different studies on a gene set activation level.

Proteomic profiling reveals a catalogue of new candidate proteins for human skin aging

Studies of skin aging are usually performed at the genomic level by investigating differentially regulated genes identified through subtractive hybridization or microarray analyses. In contrast, relatively few studies have investigated changes in protein expression of aged skin using proteomic profiling by two-dimensional (2-D) gel electrophoresis and mass spectrometry, although this approach at the protein level is suggested to reflect more accurately the aging phenotype. We undertook such a proteomic analysis of intrinsic human skin aging by quantifying proteins extracted and fluorescently labeled from sun-protected human foreskin samples pooled from 'young' and 'old' men. In addition, we analyzed these candidate gene products by 1-D and 2-D western blotting to obtain corroborative protein expression data, and by both real-time PCR (RT-PCR) and microarray analyses to confirm expression at the mRNA level. We discovered 30 putative proteins for skin aging, including previously unrecognized, post-translationally regulated candidates such as phosphatidyl-ethanolamine binding protein (PEBP) and carbonic anhydrase 1 (CA1).

Fibrosis with inflammation at one year predicts transplant functional decline

Lack of knowledge regarding specific causes for late loss of kidney transplants hampers improvements in long-term allograft survival. Kidney transplants with both interstitial fibrosis and subclinical inflammation but not fibrosis alone after 1 year have reduced survival. This study tested whether fibrosis with inflammation at 1 year associates with decline of renal function in a low-risk cohort and characterized the nature of the inflammation. We studied 151 living-donor, tacrolimus/mycophenolate-treated recipients without overt risk factors for reduced graft survival. Transplants with normal histology (n = 86) or fibrosis alone (n = 45) on 1-year protocol biopsy had stable renal function between 1 and 5 years, whereas those with both fibrosis and inflammation (n = 20) exhibited a decline in GFR and reduced graft survival. Immunohistochemistry confirmed increased interstitial T cells and macrophages/dendritic cells in the group with both fibrosis and inflammation, and there was increased expression of transcripts related to innate and cognate immunity. Pathway- and pathologic process-specific analyses of microarray profiles revealed that potentially damaging immunologic activities were enriched among the overexpressed transcripts (e.g., Toll-like receptor signaling, antigen presentation/dendritic cell maturation, IFN-γ-inducible response, cytotoxic T lymphocyte-associated and acute rejection-associated genes). Therefore, the combination of fibrosis and inflammation in 1-year protocol biopsies associates with reduced graft function and survival as well as a rejection-like gene expression signature, even among recipients with no clinical risk factors for poor outcomes. Early interventions aimed at altering rejection-like inflammation may improve long-term survival of kidney allografts.

Global gene expression changes in rat retinal ganglion cells in experimental glaucoma

PURPOSE

Intraocular pressure (IOP) is an important risk factor in glaucoma. Gene expression changes were studied in glaucomatous rat retinal ganglion cells (RGCs) to elucidate altered transcriptional pathways.

METHODS

RGCs were back-labeled with Fluorogold. Unilateral IOP elevation was produced by injection of hypertonic saline into the episcleral veins. Laser capture microdissection (LCM) was used to capture an equal number of RGCs from normal and glaucomatous retinal sections. RNA was extracted and amplified, labeled, and hybridized to rat genome microarrays, and data analysis was performed. After selected microarray data were confirmed by RT-qPCR and immunohistochemistry, biological pathway analyses were performed.

RESULTS

Significant changes were found in the expression of 905 genes, with 330 genes increasing and 575 genes decreasing in glaucomatous RGCs. Multiple cellular pathways were involved. Ingenuity pathway analysis demonstrated significant changes in cardiac beta-adrenergic signaling, interferon signaling, glutamate receptor signaling, cAMP-mediated signaling, chemokine signaling, 14-3-3-mediated signaling, and G-protein-coupled receptor signaling. Gene set enrichment analysis showed that the genes involved in apoptotic pathways were enriched in glaucomatous RGCs. The prosurvival gene Stat3 was upregulated in response to elevated IOP, and immunohistochemistry confirmed that Stat3 and phosphorylated-Stat3 levels were increased in RGCs in experimental glaucoma. In addition, the expression of several prosurvival genes normally expressed in RGCs was decreased.

CONCLUSIONS

There are extensive changes in gene expression in glaucomatous RGCs involving multiple molecular pathways, including prosurvival and prodeath genes. The alteration in the balance between prosurvival and prodeath may contribute to RGC death in glaucoma.

Gene expression profiles for the human pancreas and purified islets in type 1 diabetes: new findings at clinical onset and in long-standing diabetes

Type 1 diabetes (T1D) is caused by the selective destruction of the insulin-producing beta cells of the pancreas by an autoimmune response. Due to ethical and practical difficulties, the features of the destructive process are known from a small number of observations, and transcriptomic data are remarkably missing. Here we report whole genome transcript analysis validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and correlated with immunohistological observations for four T1D pancreases (collected 5 days, 9 months, 8 and 10 years after diagnosis) and for purified islets from two of them. Collectively, the expression profile of immune response and inflammatory genes confirmed the current views on the immunopathogenesis of diabetes and showed similarities with other autoimmune diseases; for example, an interferon signature was detected. The data also supported the concept that the autoimmune process is maintained and balanced partially by regeneration and regulatory pathway activation, e.g. non-classical class I human leucocyte antigen and leucocyte immunoglobulin-like receptor, subfamily B1 (LILRB1). Changes in gene expression in islets were confined mainly to endocrine and neural genes, some of which are T1D autoantigens. By contrast, these islets showed only a few overexpressed immune system genes, among which bioinformatic analysis pointed to chemokine (C-C motif) receptor 5 (CCR5) and chemokine (CXC motif) receptor 4) (CXCR4) chemokine pathway activation. Remarkably, the expression of genes of innate immunity, complement, chemokines, immunoglobulin and regeneration genes was maintained or even increased in the long-standing cases. Transcriptomic data favour the view that T1D is caused by a chronic inflammatory process with a strong participation of innate immunity that progresses in spite of the regulatory and regenerative mechanisms.

In praise of arrays

Microarray technologies have both fascinated and frustrated the transplant community since their introduction roughly a decade ago. Fascination arose from the possibility offered by the technology to gain a profound insight into the cellular response to immunogenic injury and the potential that this genomic signature would be indicative of the biological mechanism by which that stress was induced. Frustrations have arisen primarily from technical factors such as data variance, the requirement for the application of advanced statistical and mathematical analyses, and difficulties associated with actually recognizing signature gene-expression patterns and discerning mechanisms. To aid the understanding of this powerful tool, its versatility, and how it is dramatically changing the molecular approach to biomedical and clinical research, this teaching review describes the technology and its applications, as well as the limitations and evolution of microarrays, in the field of organ transplantation. Finally, it calls upon the attention of the transplant community to integrate into multidisciplinary teams, to take advantage of this technology and its expanding applications in unraveling the complex injury circuits that currently limit transplant survival.

Validating nutrient-related gene expression changes from microarrays using RT(2) PCR-arrays

Microarray technology allows us to perform high-throughput screening of changes in gene expression. The outcome of microarray experiments largely depends on the applied analysis methods and cut-off values chosen. Results are often required to be verified using a more sensitive detection technique, such as quantitative real-time PCR (qPCR or RT-PCR). Throughout the years, this technique has become a de facto golden standard. Individual qPCRs are time-consuming, but the technology to perform high-throughput qPCR reactions has become available through PCR-arrays that allow up to 384 PCR reactions simultaneously. Our current aim was to investigate the usability of a RT² Profiler™ PCR-array as validation in a nutritional intervention study, where the measured changes in gene expression were low. For some differentially expressed genes, the PCR-array confirmed the microarray prediction, though not for all. Furthermore, the PCR-array allowed picking up the expression of genes that were not measurable on the microarray platform but also vice versa. We conclude that both techniques have their own (dis)advantages and specificities, and for less pronounced changes using both technologies may be useful as complementation rather than validation.

Comparing microarray versus RT-PCR assessment of renal allograft biopsies: similar performance despite different dynamic ranges

In renal allografts, assessing gene expression can add relevant diagnostic information to histopathology. Results can be expressed as single genes or gene sets, representing pathogenesis-based transcript sets (PBTs): cytotoxic T-cell-associated, interferon gamma- induced or decreased kidney parenchymal transcripts. Two technology platforms are available: RT-PCR and microarrays. We compared RT-PCR, U133plus2.0 microarrays and histopathology in 86 biopsies. We compared 13 potentially diagnostic genes as RT-PCR probes to microarray-derived PBTs, 'mini'-PBTs (small sets of 3-5 transcripts) and a histology classifier. Most RT-PCR probes (10/13) correlated well with the corresponding microarray probe sets (r > 0.8). Exceptions included FASLG and CD8B1 microarray probe sets, which were not performing on microarrays but were detectable by RT-PCR most likely due to differences in sensitivity. In general, RT-PCR showed greater dynamic range, detecting small changes in normal kidneys, but RT-PCR and microarrays gave similar results in abnormal kidneys. Individual transcripts or mini-PBTs assessed by either platform correlated well with one another, with microarray PBTs and the histology classifier. Thus, microarrays and RT-PCR assessments agree strongly with one another and histopathology in assessing transplant inflammation, particularly, when results are expressed as PBTs or mini-PBTs. The dynamic range of both platforms was sufficient to detect the relevant changes in rejection.

A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments

MOTIVATION

The proliferation of public data repositories creates a need for meta-analysis methods to efficiently evaluate, integrate and validate related datasets produced by independent groups. A t-based approach has been proposed to integrate effect size from multiple studies by modeling both intra- and between-study variation. Recently, a non-parametric 'rank product' method, which is derived based on biological reasoning of fold-change criteria, has been applied to directly combine multiple datasets into one meta study. Fisher's Inverse chi(2) method, which only depends on P-values from individual analyses of each dataset, has been used in a couple of medical studies. While these methods address the question from different angles, it is not clear how they compare with each other.

RESULTS

We comparatively evaluate the three methods; t-based hierarchical modeling, rank products and Fisher's Inverse chi(2) test with P-values from either the t-based or the rank product method. A simulation study shows that the rank product method, in general, has higher sensitivity and selectivity than the t-based method in both individual and meta-analysis, especially in the setting of small sample size and/or large between-study variation. Not surprisingly, Fisher's chi(2) method highly depends on the method used in the individual analysis. Application to real datasets demonstrates that meta-analysis achieves more reliable identification than an individual analysis, and rank products are more robust in gene ranking, which leads to a much higher reproducibility among independent studies. Though t-based meta-analysis greatly improves over the individual analysis, it suffers from a potentially large amount of false positives when P-values serve as threshold. We conclude that careful meta-analysis is a powerful tool for integrating multiple array studies.

What can be learned using microarrays?

Whole-genome microarrays identify large numbers of gene expression changes that appear specifically related to disease states such as antineutrophil cytoplasmic autoantibody (ANCA) and systemic lupus erythematosus. Although understanding this enormous volume of esoteric data is difficult, a few basic concepts regarding microarray studies can significantly improve the general reader's comprehension.