Microarray meta-analysis identifies acute lung injury biomarkers in donor lungs that predict development of primary graft failure in recipients

OGDH is involved in sepsis induced acute lung injury through the MAPK pathway

Background

Acute lung injury (ALI) induced by sepsis is a common cause of death in clinical practice, and there remains a lack of clinical effective treatment. Cecal ligation and puncture (CLP) is a classic animal model of sepsis, which can induce ALI. Studies have shown that in the lung injury cell model, OGDH (oxoglutarate dehydrogenase) transcription is up-regulated, which is a potential therapeutic target for acute pneumonia. The purpose of this study was to confirm the effects of OGDH on lung injury and inflammation in animal and cell models, and to explore its mechanism.

Methods

By analyzing the GSE16650 gene set, the upregulated OGDH gene was detected in the lung injury cell model. In a sepsis animal model established by CLP and a lung injury cell model, RT-PCR, immunohistochemistry, WB, and other techniques were used to verify the upregulation of OGDH expression, which was then was down-regulated with shRNA to confirm its relationship with ALI. Further, ELISA, RT-PCR, and WB were used to detect the effect of OGDH on the expression of pro-inflammatory factors including IL-1β, IL-6, IL-18, and TNF-α. The downstream pathway of OGDH was predicted using KEGG and GSEA tools and verified by WB and immunofluorescence.

Results

The results showed OGDH was highly expressed in a lung injury cell model and the lung tissue of ALI mice induced by CLP, and downregulation of OGDH alleviated sepsis induced ALI. In animal models and cell models, the expression of OGDH was positively correlated with the expression of pro-inflammatory factors. OGDH may act through the MAPK pathway.

Conclusions

Under the pathological condition of sepsis, OGDH amplifies the inflammatory response through the MAPK pathway, releases pro-inflammatory factors, and induces ALI.

Integrative Analysis of Axolotl Gene Expression Data from Regenerative and Wound Healing Limb Tissues

Axolotl (Ambystoma mexicanum) is a urodele amphibian endowed with remarkable regenerative capacities manifested in scarless wound healing and restoration of amputated limbs, which makes it a powerful experimental model for regenerative biology and medicine. Previous studies have utilized microarrays and RNA-Seq technologies for detecting differentially expressed (DE) genes in different phases of the axolotl limb regeneration. However, sufficient consistency may be lacking due to statistical limitations arising from intra-laboratory analyses. This study aims to bridge such gaps by performing an integrative analysis of publicly available microarray and RNA-Seq data from axolotl limb samples having comparable study designs using the “merging” method. A total of 351 genes were found DE in regenerative samples compared to the control in data of both technologies, showing an adjusted p-value < 0.01 and log fold change magnitudes >1. Downstream analyses illustrated consistent correlations of the directionality of DE genes within and between data of both technologies, as well as concordance with the literature on regeneration related biological processes. qRT-PCR analysis validated the observed expression level differences of five of the top DE genes. Future studies may benefit from the utilized concept and approach for enhanced statistical power and robust discovery of biomarkers of regeneration.

Ex vivo perfusion induces a time- and perfusate-dependent molecular repair response in explanted porcine lungs

Ex vivo lung perfusion (EVLP) shows promise in ameliorating pretransplant acute lung injury (ALI) and expanding the donor organ pool, but the mechanisms of ex vivo repair remain poorly understood. We aimed to assess the utility of gene expression for characterizing ALI during EVLP. One hundred sixty-nine porcine lung samples were collected in vivo (n = 25), after 0 (n = 11) and 12 (n = 11) hours of cold static preservation (CSP), and after 0 (n = 57), 6 (n = 8), and 12 (n = 57) hours of EVLP, utilizing various ventilation and perfusate strategies. The expression of 53 previously described ALI-related genes was measured and correlated with function and histology. Twenty-eight genes were significantly upregulated and 6 genes downregulated after 12 hours of EVLP. Aggregate gene sets demonstrated differential expression with EVLP (P < .001) but not CSP. Upregulated 28-gene set expression peaked after 6 hours of EVLP, whereas downregulated 6-gene set expression continued to decline after 12 hours. Cellular perfusates demonstrated a greater reduction in downregulated 6-gene set expression vs acellular perfusate (P < .038). Gene set expression correlated with relevant functional and histologic parameters, including P/F ratio (P < .001) and interstitial inflammation (P < .005). Further studies with posttransplant results are warranted to evaluate the clinical significance of this novel molecular approach for assessing organ quality during EVLP.

Identification of potential mechanism and hub genes for neuropathic pain by expression-based genome-wide association study

Neuropathic pain (NP) is a common pathological pain state with limited effective treatments. This study was designed to identify potential mechanisms and candidate genes using gene expression-based genome-wide association study (eGWAS). All NP-related microarray experiments were obtained from Gene Expression Omnibus and ArrayExpress. Significantly dysregulated genes were identified between experimental and untreated groups, and the number of microarray experiments in which each gene was dysregulated was calculated. Significantly dysregulated genes were ranked according to P values of the chi-square test. Using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes database, we performed functional and pathway enrichment analysis. Protein-protein interaction (PPI) network and module analysis was performed using Cytoscape software. A total of 115 candidate genes were identified from 19 independent microarray experiments by eGWAS based on the Bonferroni threshold ( P < 2.97 × 10 ^-6 ). Immune and inflammatory responses, and complement and coagulation cascades, were respectively the most enriched biological process and pathways for candidate genes. The hub genes with highest connectivity in PPI network and two modules Ccl2 and Jun, and Ctss application of the eGWAS methodology can identify mechanisms and candidate genes associated with NP. Our results support the validity and prevalence of inflammatory and immune mechanisms across different NP models, and Ccl2, Jun, and Ctss may be the hub genes for NP.

Gene expression profiles for predicting antibody‑mediated kidney allograft rejection: Analysis of GEO datasets

Antibody‑mediated rejections (AMRs) are one of the most challenging complications that result in the deterioration of renal allograft function and graft loss in a large majority of cases. The purpose of the present study was to characterize a meta‑signature of differentially expressed RNAs associated with AMR in cases of kidney transplantation. Gene Expression Omnibus (GEO) dataset searches up to September 11, 2017, using Medical Subject Heading terms and keywords associated with kidney transplantation, AMR and mRNA arrays were downloaded from the GEO dataset. Using a computational analysis, a meta‑signature was determined that characterized the significant intersection of differentially expressed genes (DEGs). Gene‑set and network analyses were also performed to identify gene sets and sub‑networks associated with the AMR‑related traits. A statistically significant mRNA meta‑signature of upregulated and downregulated gene expression levels that were significantly associated with AMR was identified. C‑X‑C motif chemokine ligand 10 (CXCL10), CXCL9 and guanylate binding protein 1 were the most significantly associated with AMR. DEGs were efficiently identified and were found to be able to predict the occurrence of AMR according to a meta‑analysis approach from publicly available datasets. These methods and results can be applied for a more accurate diagnosis of AMR in transplant cases.

A differential network analysis approach for lineage specifier prediction in stem cell subpopulations

Background:

Stem cell differentiation is a complex biological process. Cellular heterogeneity, such as the co-existence of different cell subpopulations within a population, partly hampers our understanding of this process. The modern single-cell gene expression technologies, such as single-cell RT-PCR and RNA-seq, have enabled us to elucidate such heterogeneous cell subpopulations. However, the identification of a transcriptional regulatory network (TRN) for each cell subpopulation within a population and genes determining specific cell fates (lineage specifiers) remains a challenge due to the slower development of appropriate computational and experimental workflows. Here, we propose a computational differential network analysis approach for predicting lineage specifiers in binary-fate differentiation events.

Methods:

The proposed method is based on a model that considers each stem cell subpopulation being in a stable state maintained by its specific TRN stability core, and cell differentiation involves changes in these stability cores between parental and daughter cell subpopulations. The method first reconstructs topologically different cell-subpopulation specific TRNs from single-cell gene expression data, literature knowledge and transcription factor (TF)–DNA binding-site prediction. Then, it systematically predicts lineage specifiers by identifying genes in the TRN stability cores in both parental and daughter cell subpopulations.

Results:

Application of this method to different stem cell differentiation systems was able to predict known and putative novel lineage specifiers. These examples include the differentiation of inner cell mass into either primitive endoderm or epiblast, different progenitor cells in the hematopoietic system, and the lung alveolar bipotential progenitor into either alveolar type 1 or alveolar type 2.

Conclusions:

The method is generally applicable to any binary-fate differentiation system, for which single-cell gene expression data are available. Therefore, it should aid in understanding stem cell lineage specification, and in the development of experimental strategies for regenerative medicine.

A computational strategy for predicting lineage specifiers in stem cell subpopulations

Stem cell differentiation is a complex biological event. Our understanding of this process is partly hampered by the co-existence of different cell subpopulations within a given population, which are characterized by different gene expression states driven by different underlying transcriptional regulatory networks (TRNs). Such cellular heterogeneity has been recently explored with the modern single-cell gene expression profiling technologies, such as single-cell RT-PCR and RNA-seq. However, the identification of cell subpopulation-specific TRNs and genes determining specific lineage commitment (i.e., lineage specifiers) remains a challenge due to the slower development of appropriate computational and experimental workflows. Here, we propose a computational method for predicting lineage specifiers for different cell subpopulations in binary-fate differentiation events. Our method first reconstructs subpopulation-specific TRNs, which is more realistic than reconstructing a single TRN representing multiple cell subpopulations. Then, it predicts lineage specifiers based on a model that assumes that each parental stem cell subpopulation is in a stable state maintained by its specific TRN stability core. In addition, this stable state is maintained in the parental cell subpopulation by the balanced gene expression pattern of pairs of opposing lineage specifiers for mutually exclusive different daughter cell subpopulations. To this end, we devised a statistical metric for identifying opposing lineage specifier pairs that show a significant ratio change upon differentiation. Application of this computational method to three different stem cell systems predicted known and putative novel lineage specifiers, which could be experimentally tested. Our method does not require pre-selection of putative candidate genes, and can be applied to any binary-fate differentiation system for which single-cell gene expression data are available. Furthermore, this method is compatible with both single-cell RT-PCR and single-cell RNA-seq data. Given the increasing importance of single-cell gene expression data in stem cell biology and regenerative medicine, approaches like ours would be useful for the identification of lineage specifiers and their associated TRN stability cores.

Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery

The diagnostic and prognostic potential of the vast quantity of publicly-available microarray data has driven the development of methods for integrating the data from different microarray platforms. Cross-platform integration, when appropriately implemented, has been shown to improve reproducibility and robustness of gene signature biomarkers. Microarray platform integration can be conceptually divided into approaches that perform early stage integration (cross-platform normalization) versus late stage data integration (meta-analysis). A growing number of statistical methods and associated software for platform integration are available to the user, however an understanding of their comparative performance and potential pitfalls is critical for best implementation. In this review we provide evidence-based, practical guidance to researchers performing cross-platform integration, particularly with an objective to discover biomarkers.

Inhaled nitric oxide in cardiac surgery: Evidence or tradition?

Inhaled nitric oxide (iNO) therapy as a selective pulmonary vasodilator in cardiac surgery has been one of the most significant pharmacological advances in managing pulmonary hemodynamics and life threatening right ventricular dysfunction and failure. However, this remarkable story has experienced a roller-coaster ride with high hopes and nearly universal demonstration of physiological benefits but disappointing translation of these benefits to harder clinical outcomes. Most of our understanding on the iNO field in cardiac surgery stems from small observational or single centre randomised trials and even the very few multicentre trials fail to ascertain strong evidence base. As a consequence, there are only weak clinical practice guidelines on the field and only European expert opinion for the use of iNO in routine and more specialised cardiac surgery such as heart and lung transplantation and left ventricular assist device (LVAD) insertion. In this review the authors from a specialised cardiac centre in the UK with a very high volume of iNO usage provide detailed information on the early observations leading to the European expert recommendations and reflect on the nature and background of these recommendations. We also provide a summary of the progress in each of the cardiac subspecialties for the last decade and initial survey data on the views of senior anaesthetic and intensive care colleagues on these recommendations. We conclude that the combination of high price tag associated with iNO therapy and lack of substantial clinical evidence is not sustainable on the current field and we are risking loosing this promising therapy from our daily practice. Overcoming the status quo will not be easy as there is not much room for controlled trials in heart transplantation or in the current atmosphere of LVAD implantation. However, we call for international cooperation to conduct definite studies to determine the place of iNO therapy in lung transplantation and high risk mitral surgery. This will require new collaboration between the pharmaceutical companies, national grant agencies and the clinical community. Until these trials are realized we should gather multi-institutional experience from large retrospective studies and prospective data from a new international registry. We must step up international efforts if we wish to maintain the iNO modality in the armamentarium of hemodynamic tools for the perioperative management of our high risk cardiac surgical patients.

Influence of glutathione-S-transferase (GST) inhibition on lung epithelial cell injury: role of oxidative stress and metabolism

Oxidant-mediated tissue injury is key to the pathogenesis of acute lung injury. Glutathione- S-transferases (GSTs) are important detoxifying enzymes that catalyze the conjugation of glutathione with toxic oxidant compounds and are associated with acute and chronic inflammatory lung diseases. We hypothesized that attenuation of cellular GST enzymes would augment intracellular oxidative and metabolic stress and induce lung cell injury. Treatment of murine lung epithelial cells with GST inhibitors, ethacrynic acid (EA), and caffeic acid compromised lung epithelial cell viability in a concentration-dependent manner. These inhibitors also potentiated cell injury induced by hydrogen peroxide (H2O2), tert-butyl-hydroperoxide, and hypoxia and reoxygenation (HR). SiRNA-mediated attenuation of GST-π but not GST-μ expression reduced cell viability and significantly enhanced stress (H2O2/HR)-induced injury. GST inhibitors also induced intracellular oxidative stress (measured by dihydrorhodamine 123 and dichlorofluorescein fluorescence), caused alterations in overall intracellular redox status (as evidenced by NAD+/NADH ratios), and increased protein carbonyl formation. Furthermore, the antioxidant N-acetylcysteine completely prevented EA-induced oxidative stress and cytotoxicity. Whereas EA had no effect on mitochondrial energetics, it significantly altered cellular metabolic profile. To explore the physiological impact of these cellular events, we used an ex vivo mouse-isolated perfused lung model. Supplementation of perfusate with EA markedly affected lung mechanics and significantly increased lung permeability. The results of our combined genetic, pharmacological, and metabolic studies on multiple platforms suggest the importance of GST enzymes, specifically GST-π, in the cellular and whole lung response to acute oxidative and metabolic stress. These may have important clinical implications.

Comprehensive gene expression meta-analysis of head and neck squamous cell carcinoma microarray data defines a robust survival predictor

BACKGROUND

Head and neck squamous cell carcinoma refers to a heterogeneous disease frequently aggressive in its biologic behavior. Despite the improvements in the therapeutic modalities, the long-term survival rate remained unchanged over the past decade and patients with this type of cancer are at a high risk of developing recurrence. For this reason, there is a great need to find better ways to foresee outcome, to improve treatment choices, and to enable a more personalized approach.

PATIENTS AND METHODS

Nine microarray gene expression datasets, reporting survival data of a total of 841 samples, were retrieved from publicly repositories. Three datasets, profiled on the same version of microarray chips, were selected and merged following a meta-analysis approach to build a training set. The remaining six studies were used as independent validation sets.

RESULTS

The training set led us to identify a 172-gene signature able to stratify patients in low or high risk of relapse [log-rank, P = 2.44e-05; hazard ratio (HR) = 2.44, 95% confidence interval (CI) 1.58-3.76]. The model based on the 172 genes was validated on the six independent datasets. The performance of the model was challenged against other proposed prognostic signatures (radiosensitivity index, 13-gene oral squamous cell carcinoma signature, hypoxia metagene, 42-gene high-risk signature) and was compared with a human papillomavirus (HPV) signature: our model resulted independent and even better in prediction.

CONCLUSIONS

We have identified and validated a prognostic model based on the expression of 172 genes, independent from HPV status and able to improve assessment of patient's risk of relapse compared with other molecular signatures. In order to transpose our model into a useful clinical grade assay, additional work is needed following the framework established by the Institute of Medicine and REMARK guidelines.

Integrative clinical transcriptomics analyses for new therapeutic intervention strategies: a psoriasis case study

Psoriasis is a chronic inflammatory skin disease with complex pathological features and unmet pharmacotherapy needs. Here, we present a framework for developing new therapeutic intervention strategies for psoriasis by utilizing publicly available clinical transcriptomics data sets. By exploring the underlying molecular mechanisms of psoriasis, the effects of subsequent perturbation of these mechanisms by drugs and an integrative analysis, we propose a psoriasis disease signature, identify potential drug repurposing opportunities and present novel target selection methodologies. We anticipate that the outlined methodology or similar approaches will further support biomarker discovery and the development of new drugs for psoriasis.

Assessing the quality of studies supporting genetic susceptibility and outcomes of ARDS

The acute respiratory distress syndrome (ARDS) is a severe inflammatory disease manifested as a result of pulmonary and systemic responses to several insults. It is now well accepted that genetic variation influences these responses. However, little is known about the genes that are responsible for patient susceptibility and outcome of ARDS. Methodological flaws are still abundant among genetic association studies with ARDS and here, we aimed to highlight the quality criteria where the standards have not been reached, to expose the associated genes to facilitate replication attempts, and to provide quick-reference guidance for future studies. We conducted a PubMed search from January 2008 to September 2012 for original articles. Studies were considered if a statistically significant association was declared with either susceptibility or outcomes of all-cause ARDS. Fourteen criteria were used for evaluation and results were compared to those from a previous quality assessment report. Significant improvements affecting study design and statistical analysis were detected. However, major issues such as adjustments for the underlying population stratification and replication studies remain poorly addressed.

Evaluation of the inflammatory response in a two-hit acute lung injury model using [18F]FDG microPET

The aim of this study was to investigate whether a two-hit acute lung injury (ALI) model is better than a one-hit model in simulating ALI, and to evaluate the inflammatory response in the lungs in these two models using micro-positron emission tomography (microPET) with [¹⁸F]fluorodeoxyglucose (FDG). Sprague Dawley rats were divided into four groups; rats in the lipopolysaccharide (LPS; n=10) and LPS-HCl (n=10) groups were challenged by the intraperitoneal administration of 5 mg/kg LPS, while rats in the normal saline (NS; n=3) and HCl (n=10) groups received the same volume of normal saline solution. Sixteen hours following the administration, the rats in the HCl and LPS-HCl groups received an acid instillation (IT) of 0.5 ml/kg HCl (pH=1.2), while the rats in the remaining two groups received the same volume of normal saline solution. The mean arterial blood pressure (MAP) and blood gas concentrations were measured in all four groups. MicroPET was performed 4 h following HCl IT and the lungs were excised for histopathological examination. The rats in the LPS-HCl group exhibited a higher arterial PaO₂ and a lower arterial PaCO₂ compared with the rats in the remaining groups. The MAP decreased markedly in the LPS-HCl group, but remained stable in the LPS, HCl and NS groups. MicroPET results identified that the region of interest ratio in the LPS-HCl group (9.00±1.41) was significantly higher compared with those in the LPS (4.01±0.60) and HCl (3.33±0.55) groups (P<0.01). In addition, histological examination showed that the mean lung injury score in the LPS-HCl group (12.70±0.95) was significantly higher compared with those in the HCl (8.40±1.26) and LPS (7.00±0.82) groups (P<0.01). The present study demonstrates that LPS pretreatment significantly magnifies and prolongs the inflammatory response to subsequent acid IT in the lungs. Moreover, it is simpler to induce ALI using the two-hit model than with the one-hit model, and [¹⁸F]FDG microPET is a useful tool for evaluating the inflammatory reaction during ALI.