Comparative RNA-Seq and microarray analysis of gene expression changes in B-cell lymphomas of Canis familiaris

Transcriptomic analysis identifies the shared diagnostic biomarkers and immune relationship between Atherosclerosis and abdominal aortic aneurysm based on fatty acid metabolism gene set

Background

Epidemiological research has demonstrated that there is a connection between lipid metabolism disorder and an increased risk of developing arteriosclerosis (AS) and abdominal aortic aneurysm (AAA). However, the precise relationship between lipid metabolism, AS, and AAA is still not fully understood. The objective of this study was to examine the pathways and potential fatty acid metabolism-related genes (FRGs) that are shared between AS and AAA.

Methods

AS- and AAA-associated datasets were retrieved from the Gene Expression Omnibus (GEO) database, and the limma package was utilized to identify differentially expressed FRGs (DFRGs) common to both AS and AAA patients. Functional enrichment analysis was conducted on the (DFRGs), and a protein-protein interaction (PPI) network was established. The selection of signature genes was performed through the utilization of least absolute shrinkage and selection operator (LASSO) regression and random forest (RF). Subsequently, a nomogram was developed using the results of the screening process, and the crucial genes were validated in two separate external datasets (GSE28829 and GSE17901) as well as clinical samples. In the end, single-sample gene set enrichment analysis (ssGSEA) was utilized to assess the immune cell patterns in both AS and AAA. Additionally, the correlation between key crosstalk genes and immune cell was evaluated.

Results

In comparison to control group, both AS and AAA patients exhibited a decrease in fatty acid metabolism score. We found 40 DFRGs overlapping in AS and AAA, with lipid and amino acid metabolism critical in their pathogenesis. PCBD1, ACADL, MGLL, BCKDHB, and IDH3G were identified as signature genes connecting AS and AAA. Their expression levels were confirmed in validation datasets and clinical samples. The analysis of immune infiltration showed that neutrophils, NK CD56dim cells, and Tem cells are important in AS and AAA development. Correlation analysis suggested that these signature genes may be involved in immune cell infiltration.

Conclusion

The fatty acid metabolism pathway appears to be linked to the development of both AS and AAA. Furthermore, PCBD1, ACADL, MGLL, BCKDHB, and IDH3G have the potential to serve as diagnostic markers for patients with AS complicated by AAA.

Integrative analysis of RNA-sequencing and microarray for the identification of adverse effects of UVB exposure on human skin

Background

Ultraviolet B (UVB) from sunlight represents a major environmental factor that causes toxic effects resulting in structural and functional cutaneous abnormalities in most living organisms. Although numerous studies have indicated the biological mechanisms linking UVB exposure and cutaneous manifestations, they have typically originated from a single study performed under limited conditions.

Methods

We accessed all publicly accessible expression data of various skin cell types exposed to UVB, including skin biopsies, keratinocytes, and fibroblasts. We performed biological network analysis to identify the molecular mechanisms and identify genetic biomarkers.

Results

We interpreted the inflammatory response and carcinogenesis as major UVB-induced signaling alternations and identified three candidate biomarkers (IL1B, CCL2, and LIF). Moreover, we confirmed that these three biomarkers contribute to the survival probability of patients with cutaneous melanoma, the most aggressive and lethal form of skin cancer.

Conclusion

Our findings will aid the understanding of UVB-induced cutaneous toxicity and the accompanying molecular mechanisms. In addition, the three candidate biomarkers that change molecular signals due to UVB exposure of skin might be related to the survival rate of patients with cutaneous melanoma.

NOTCH1-mutated chronic lymphocytic leukemia displays high endoplasmic reticulum stress response with druggable potential

Introduction

Constitutive activation of NOTCH1-wild-type (NT1-WT) signaling is associated with poor outcomes in chronic lymphocytic leukemia (CLL), and NOTCH1 mutation (c.7541_7542delCT), which potentiates NOTCH1 signaling, worsens the prognosis. However, the specific mechanisms of NOTCH1 deregulation are still poorly understood. Accumulative evidence mentioned endoplasmic reticulum (ER) stress/unfolded protein response (UPR) as a key targetable pathway in CLL. In this study, we investigated the impact of NOTCH1 deregulation on CLL cell response to ER stress induction, with the aim of identifying new therapeutic opportunities for CLL.

Methods

We performed a bioinformatics analysis of NOTCH1-mutated (NT1-M) and NT1-WT CLL to identify differentially expressed genes (DEGs) using the rank product test. Quantitative real-time polymerase chain reaction (qPCR), Western blotting, cytosolic Ca2+, and annexin V/propidium iodide (PI) assay were used to detect curcumin ER stress induction effects. A median-effect equation was used for drug combination tests. The experimental mouse model Eμ-TCL1 was used to evaluate the impact of ER stress exacerbation by curcumin treatment on the progression of leukemic cells and NOTCH1 signaling.

Results and discussion

Bioinformatics analysis revealed gene enrichment of the components of the ER stress/UPR pathway in NT1-M compared to those in NT1-WT CLL. Ectopic expression of NOTCH1 mutation upregulated the levels of ER stress response markers in the PGA1 CLL cell line. Primary NT1-M CLL was more sensitive to curcumin as documented by a significant perturbation in Ca2+ homeostasis and higher expression of ER stress/UPR markers compared to NT1-WT cells. It was also accompanied by a significantly higher apoptotic response mediated by C/EBP homologous protein (CHOP) expression, caspase 4 cleavage, and downregulation of NOTCH1 signaling in NT1-M CLL cells. Curcumin potentiated the apoptotic effect of venetoclax in NT1-M CLL cells. In Eμ-TCL1 leukemic mice, the administration of curcumin activated ER stress in splenic B cells ex vivo and significantly reduced the percentage of CD19+/CD5+ cells infiltrating the spleen, liver, and bone marrow (BM). These cellular effects were associated with reduced NOTCH1 activity in leukemic cells and resulted in prolonged survival of curcumin-treated mice. Overall, our results indicate that ER stress induction in NT1-M CLL might represent a new therapeutic opportunity for these high-risk CLL patients and improve the therapeutic effect of drugs currently used in CLL.

Insight into the potential candidate genes and signaling pathways involved in lymphoma disease in dogs using a comprehensive whole blood transcriptome analysis

Lymphoma is one of the most prevalent hematological cancers, accounting for 15-20 % of new cancer diagnoses in dogs. Therefore, this study aims to explore the important genes and pathways involved in canine lymphoma progression and understand the underlying molecular mechanisms using RNA sequencing. In this study, RNAs acquired from seven pairs of lymphoma and non-lymphoma blood samples were sequenced from different breeds of dogs. Sequencing reads were preprocessed, aligned with the reference genome, assembled and expressions were estimated through bioinformatics approaches. At a false discovery rate (FDR) < 0.05 and fold change (FC) ≥ 1.5, a total of 625 differentially expressed genes (DEGs) were identified between lymphoma and non-lymphoma samples, including 347 up-regulated DEGs such as SLC38A11, SCN3A, ZIC5 etc. and 278 down-regulated DEGs such as LOC475937, CSMD1, KRT14 etc. GO enrichment analysis showed that these DEGs were highly enriched for molecular function of ATP binding and calcium ion binding, cellular process of focal adhesion, and biological process of immune response, and defense response to virus. Similarly, KEGG pathways analysis revealed 11 significantly enriched pathways such as ECM-receptor interaction, cell cycle, PI3K-Akt signaling pathway, ABC transporters etc. In the protein-protein interaction (PPI) network, CDK1 was found to be a top hub gene with highest degree of connectivity. Three modules selected from the PPI network showed that canine lymphoma was highly associated with cell cycle, ECM-receptor interaction, hypertrophic cardiomyopathy, dilated cardiomyopathy and RIG-I-like receptor signaling pathway. Overall, our findings highlighted new candidate therapeutic targets for further testing in canine lymphoma and facilitate the understanding of molecular mechanism of lymphoma's progression in dogs.

Integrative transcriptome-wide analysis of atopic dermatitis for drug repositioning

Atopic dermatitis (AD) is one of the most common inflammatory skin diseases, which significantly impact the quality of life. Transcriptome-wide association study (TWAS) was conducted to estimate both transcriptomic and genomic features of AD and detected significant associations between 31 expression quantitative loci and 25 genes. Our results replicated well-known genetic markers for AD, as well as 4 novel associated genes. Next, transcriptome meta-analysis was conducted with 5 studies retrieved from public databases and identified 5 additional novel susceptibility genes for AD. Applying the connectivity map to the results from TWAS and meta-analysis, robustly enriched perturbations were identified and their chemical or functional properties were analyzed. Here, we report the first research on integrative approaches for an AD, combining TWAS and transcriptome meta-analysis. Together, our findings could provide a comprehensive understanding of the pathophysiologic mechanisms of AD and suggest potential drug candidates as alternative treatment options.

Integrative genomic and transcriptomic analyses on publicly available data-sets together with in silico drug repositioning identifies alternative therapeutic options to treat atopic dermatitis.

A Robust Immuno-Prognostic Model of Non-Muscle-Invasive Bladder Cancer Indicates Dynamic Interaction in Tumor Immune Microenvironment Contributes to Cancer Progression

Non-muscle-invasive bladder cancer (NMIBC) accounts for more than 70% of urothelial cancer. More than half of NMIBC patients experience recurrence, progression, or metastasis, which essentially reduces life quality and survival time. Identifying the high-risk patients prone to progression remains the primary concern of risk management of NMIBC. In this study, we included 1370 NMIBC transcripts data from nine public datasets, identified nine tumor-infiltrating marker cells highly related to the survival of NMIBC, quantified the cells’ proportion by self-defined differentially expressed signature genes, and established a robust immuno-prognostic model dividing NMIBC patients into low-risk versus high-risk progression groups. Our model implies that the loss of crosstalk between tumor cells and adjacent normal epithelium, along with enriched cell proliferation signals, may facilitate tumor progression. Thus, evaluating tumor progression should consider various components in the tumor immune microenvironment instead of the single marker in a single dimension. Moreover, we also appeal to the necessity of using appropriate meta-analysis methods to integrate the evidence from multiple sources in the feature selection step from large-scale heterogeneous omics data such as our study.

Developing a novel DNA methylation risk score for survival and identification of prognostic gene mutations in endometrial cancer: a study based on TCGA data

Background

Few studies have focused on DNA methylation in endometrial cancer. The aim of our study is identify its role in endometrial cancer prognosis.

Methods

A publicly available dataset was retrieved from The Cancer Genome Atlas. For validation of expression alteration due to methylation, RNA sequencing data were obtained from other independent cohorts. MethSurv was used to search for candidate CpG probes, which were then filtered by least absolute shrinkage and selection operator Cox regression and multivariate Cox regression analyses to identify final set of CpG probes for overall survival. A methylation-based risk model was developed and receiver operating characteristic analysis with area under curve was used for evaluation. Patients were divided into high- and low-risk groups using an optimal cut-off point. Comprehensive bioinformatic analyses were conducted to identify hub genes, key transcription factors, and enriched cancer-related pathways. Kaplan–Meier curve was used for survival analysis.

Results

A 5-CpG signature score was established. Its predictive value for 5-year overall survival was high, with area under curve of 0.828, 0.835 and 0.816 for the training, testing and entire cohorts. cg27487839 and cg12885678 had strong correlation with their gene expression, XKR6 and PTPRN2, and lower PTPRN2 expression was associated with poorer survival in both The Cancer Genome Atlas and the validation datasets. Low-risk group was associated with significantly better survival. Low-risk group harboured more mutations in hub genes and key transcription factors, and mutations in SP1 and MECP2 represented favourable outcome.

Conclusion

We developed a methylation-based prognostic stratification system for endometrial cancer. Low-risk group was associated with better survival and harboured more mutations in the key regulatory genes.

Gene network profiling in muscle-invasive bladder cancer: A systematic review and meta-analysis

BACKGROUND

Determining meta-analysis of transcriptional profiling of muscle-invasive bladder cancer (MIBC) through Gene Expression Omnibus (GEO) datasets has not been investigated. This study aims to define gene expression profiles in MIBC and to identify potential candidate genes and pathways.

OBJECTIVES

To review and evaluate gene expression studies in MIBC through publicly available RNA sequencing (RNA-Seq) and microarray data in order to identify potential prognostic and therapeutic targets for MIBC.

METHODS

A systematic literature search of the Ovid MEDLINE, PubMed, and Wiley Cochrane Central Register of Controlled Trials databases was performed using the terms "gene," "gene expression," and "bladder cancer" January 1, 1990 through March 2021 focused on populations with MIBC.

RESULTS

In the final analysis, GEO datasets were included. Fixed effect model was employed in the meta-analysis. Gene networking connections and gene-set functional analyses of the identified genes as differentially expressed in MIBC were performed using ImaGEO and GeneMANIA software. A heatmap for the upregulated and downregulated genes was generated along with the correlated pathways.

CONCLUSION

A total of 9 genes were reported in this analysis. Six genes were reported as upregulated (ProTα, SPINT1, UBE2E1, RAB25, KPNB1, HDAC1) and 3 genes as downregulated (NUP188, IPO13, NUP124). Genes were found to be involved in "ubiquitin mediated proteolysis," "protein processing in endoplasmic reticulum," "transcriptional misregulation in cancer," and "RNA transport" pathways.

Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery

Objectives: Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM molecular network is largely unknown, the aim was to identify specific disease-related molecular targets combining an original machine learning (ML) approach with protein-protein interaction network. Methods: The transcriptomic profiles of human myocardial tissues were investigated integrating an original computational approach, based on the Custom Decision Tree algorithm, in a differential expression bioinformatic framework. Validation was performed by quantitative real-time PCR. Results: Our preliminary study, using samples from transplanted tissues, allowed the discovery of specific DCM-related genes, including MYH6, NPPA, MT-RNR1 and NEAT1, already known to be involved in cardiomyopathies Interestingly, a combination of these expression profiles with clinical characteristics showed a significant association between NEAT1 and left ventricular end-diastolic diameter (LVEDD) (Rho = 0.73, p = 0.05), according to severity classification (NYHA-class III). Conclusions: The use of the ML approach was useful to discover preliminary specific genes that could lead to a rapid selection of molecular targets correlated with DCM clinical parameters. For the first time, NEAT1 under-expression was significantly associated with LVEDD in the human heart.

Compendiums of cancer transcriptomes for machine learning applications

There are massive transcriptome profiles in the form of microarray. The challenge is that they are processed using diverse platforms and preprocessing tools, requiring considerable time and informatics expertise for cross-dataset analyses. If there exists a single, integrated data source, data-reuse can be facilitated for discovery, analysis, and validation of biomarker-based clinical strategy. Here, we present merged microarray-acquired datasets (MMDs) across 11 major cancer types, curating 8,386 patient-derived tumor and tumor-free samples from 95 GEO datasets. Using machine learning algorithms, we show that diagnostic models trained from MMDs can be directly applied to RNA-seq-acquired TCGA data with high classification accuracy. Machine learning optimized MMD further aids to reveal immune landscape across various carcinomas critically needed in disease management and clinical interventions. This unified data source may serve as an excellent training or test set to apply, develop, and refine machine learning algorithms that can be tapped to better define genomic landscape of human cancers.

Suitability of ultrasound-guided fine-needle aspiration biopsy for transcriptome sequencing of the canine prostate

Ultrasound-guided fine-needle aspiration (US-FNA) biopsy is a widely used minimally invasive sampling procedure for cytological diagnosis. This study investigates the feasibility of using US-FNA samples for both cytological diagnosis and whole transcriptome RNA-sequencing analysis (RNA-Seq), with the ultimate aim of improving canine prostate cancer management. The feasibility of the US-FNA procedure was evaluated intra vitam on 43 dogs. Additionally, aspirates from 31 euthanised dogs were collected for standardising the procedure. Each aspirate was separated into two subsamples: for cytology and RNA extraction. Additional prostate tissue samples served as control for RNA quantity and quality evaluation, and differential expression analysis. The US-FNA sampling procedure was feasible in 95% of dogs. RNA isolation of US-FNA samples was successfully performed using phenol-chloroform extraction. The extracted RNA of 56% of a subset of US-FNA samples met the quality requirements for RNA-Seq. Expression analysis revealed that only 153 genes were exclusively differentially expressed between non-malignant US-FNAs and tissues. Moreover, only 36 differentially expressed genes were associated with the US-FNA sampling technique and unrelated to the diagnosis. Furthermore, the gene expression profiles clearly distinguished between non-malignant and malignant samples. This proves US-FNA to be useful for molecular profiling.

Machine learning approaches to predict lupus disease activity from gene expression data

The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different microarray platforms. Here we deployed machine learning approaches to integrate gene expression data from three SLE data sets and used it to classify patients as having active or inactive disease as characterized by standard clinical composite outcome measures. Both raw whole blood gene expression data and informative gene modules generated by Weighted Gene Co-expression Network Analysis from purified leukocyte populations were employed with various classification algorithms. Classifiers were evaluated by 10-fold cross-validation across three combined data sets or by training and testing in independent data sets, the latter of which amplified the effects of technical variation. A random forest classifier achieved a peak classification accuracy of 83 percent under 10-fold cross-validation, but its performance could be severely affected by technical variation among data sets. The use of gene modules rather than raw gene expression was more robust, achieving classification accuracies of approximately 70 percent regardless of how the training and testing sets were formed. Fine-tuning the algorithms and parameter sets may generate sufficient accuracy to be informative as a standalone estimate of disease activity.

Identification of two molecular subtypes in canine mast cell tumours through gene expression profiling

Mast cell tumours (MCTs) are common neoplasms in dogs and are usually regarded as potentially malignant. Several studies have attempted to identify biomarkers to better predict biological behaviours for this tumour. The aim of this study was to identify pathways connected to clinical and histopathological malignancies, shorter survival times, and poor prognoses associated with MCTs. We performed genome-wide gene expression analyses on tissues obtained from 15 dogs with single MCTs, and identified two distinct tumour subtypes—high-risk and low-risk—associated with differences in histological grades, survival times, Ki67 indices, and occurrence of death due the disease. Comparative analyses of RNA sequence profiles revealed 71 genes that were differentially expressed between high- and low-risk MCTs. In addition to these analyses, we also examined gene co-expression networks to explore the biological functions of the identified genes. The network construction revealed 63 gene modules, of which 4 were significantly associated with the more aggressive tumour group. Two of the gene modules positively correlated with high-risk MCTs were also associated with cell proliferation and extracellular matrix-related terms. At the top of the extracellular matrix module category, genes with functions directly related to those of cancer-associated fibroblasts (CAFs) were identified. Immunohistochemical analyses also revealed a greater number of CAFs in high-risk MCTs. This study provides a method for the molecular characterisation of canine MCTs into two distinct subtypes. Our data indicate that proliferation pathways are significantly involved in malignant tumour behaviours, which are known to be relevant for the induction and maintenance of MCTs. Finally, animals presenting high-risk MCTs overexpress genes associated with the extracellular matrix that can be robustly linked to CAF functions. We suggest that CAFs in the MCT stroma contribute to cancer progression.

BarkBase: Epigenomic Annotation of Canine Genomes

Dogs are an unparalleled natural model for investigating the genetics of health and disease, particularly for complex diseases like cancer. Comprehensive genomic annotation of regulatory elements active in healthy canine tissues is crucial both for identifying candidate causal variants and for designing functional studies needed to translate genetic associations into disease insight. Currently, canine geneticists rely primarily on annotations of the human or mouse genome that have been remapped to dog, an approach that misses dog-specific features. Here, we describe BarkBase, a canine epigenomic resource available at barkbase.org. BarkBase hosts data for 27 adult tissue types, with biological replicates, and for one sample of up to five tissues sampled at each of four carefully staged embryonic time points. RNA sequencing is complemented with whole genome sequencing and with assay for transposase-accessible chromatin using sequencing (ATAC-seq), which identifies open chromatin regions. By including replicates, we can more confidently discern tissue-specific transcripts and assess differential gene expression between tissues and timepoints. By offering data in easy-to-use file formats, through a visual browser modeled on similar genomic resources for human, BarkBase introduces a powerful new resource to support comparative studies in dogs and humans.

Novel Treatments for Lymphoma

Lymphoma is a common disease in companion animals. Although conventional chemotherapy has the potential to induce remission and prolong life, relapse is common, and novel treatments are needed to improve outcome. This review discusses recent modifications/adjustments to conventional standard of care therapy for canine and feline lymphoma, as well as cutting-edge immunotherapy and small-molecule-based approaches that are in varying stages of regulatory approval.

Comprehensive gene expression analysis of canine invasive urothelial bladder carcinoma by RNA-Seq

BACKGROUND

Invasive urothelial carcinoma (iUC) is a major cause of death in humans, and approximately 165,000 individuals succumb to this cancer annually worldwide. Comparative oncology using relevant animal models is necessary to improve our understanding of progression, diagnosis, and treatment of iUC. Companion canines are a preferred animal model of iUC due to spontaneous tumor development and similarity to human disease in terms of histopathology, metastatic behavior, and treatment response. However, the comprehensive molecular characterization of canine iUC is not well documented. In this study, we performed transcriptome analysis of tissue samples from canine iUC and normal bladders using an RNA sequencing (RNA-Seq) approach to identify key molecular pathways in canine iUC.

METHODS

Total RNA was extracted from bladder tissues of 11 dogs with iUC and five healthy dogs, and RNA-Seq was conducted. Ingenuity Pathway Analysis (IPA) was used to assign differentially expressed genes to known upstream regulators and functional networks.

RESULTS

Differential gene expression analysis of the RNA-Seq data revealed 2531 differentially expressed genes, comprising 1007 upregulated and 1524 downregulated genes, in canine iUC. IPA revealed that the most activated upstream regulator was PTGER2 (encoding the prostaglandin E₂ receptor EP2), which is consistent with the therapeutic efficiency of cyclooxygenase inhibitors in canine iUC. Similar to human iUC, canine iUC exhibited upregulated ERBB2 and downregulated TP53 pathways. Biological functions associated with cancer, cell proliferation, and leukocyte migration were predicted to be activated, while muscle functions were predicted to be inhibited, indicating muscle-invasive tumor property.

CONCLUSIONS

Our data confirmed similarities in gene expression patterns between canine and human iUC and identified potential therapeutic targets (PTGER2, ERBB2, CCND1, Vegf, and EGFR), suggesting the value of naturally occurring canine iUC as a relevant animal model for human iUC.

Transcriptome Analysis of Canine Cutaneous Melanoma and Melanocytoma Reveals a Modulation of Genes Regulating Extracellular Matrix Metabolism and Cell Cycle

Interactions between tumor cells and tumor microenvironment are considered critical in carcinogenesis, tumor invasion and metastasis. To examine transcriptome changes and to explore the relationship with tumor microenvironment in canine cutaneous melanocytoma and melanoma, we extracted RNA from formalin-fixed, paraffin-embedded (FFPE) specimens and analyzed them by means of RNA-seq for transcriptional analysis. Melanocytoma and melanoma samples were compared to detect differential gene expressions and significant enriched pathways were explored to reveal functional relations between differentially expressed genes. The study demonstrated a differential expression of 60 genes in melanomas compared to melanocytomas. The differentially expressed genes cluster in the extracellular matrix-receptor interaction, protein digestion and absorption, focal adhesion and PI3K-Akt (phosphoinositide 3-kinase/protein kinase B) signaling pathways. Genes encoding for several collagen proteins were more commonly differentially expressed. Results of the RNA-seq were validated by qRT-PCR and protein expression of some target molecules was investigated by means of immunohistochemistry. We hypothesize that the developing melanoma actively promotes collagen metabolism and extracellular matrix remodeling as well as enhancing cell proliferation and survival contributing to disease progression and metastasis. In this study, we also detected unidentified genes in human melanoma expression studies and uncover new candidate drug targets for further testing in canine melanoma.

Application of post-genomic techniques in dog cancer research

Omics techniques have been widely applied to veterinary science, although mostly on farm animal productions and infectious diseases. In canine oncology, on the contrary, the use of omics methodologies is still far behind. This review presents the most recent achievement in the application of postgenomic techniques, such as transcriptomics, proteomics, and metabolomics, to canine cancer research. The protocols to recover material suitable for omics analyses from formalin-fixed, paraffin-embedded tissues are presented, and omics applications for biomarker discovery and their potential for cancer diagnostics in veterinary medicine are highlighted.

Distant eQTLs and Non-coding Sequences Play Critical Roles in Regulating Gene Expression and Quantitative Trait Variation in Maize

A detailed understanding of genetic architecture of mRNA expression by millions of genetic variants is important for studying quantitative trait variation. In this study, we identified 1.25M SNPs with a minor allele frequency greater than 0.05 by combining reduced genome sequencing (GBS), high-density array technologies (600K), and previous deep RNA-sequencing data from 368 diverse inbred lines of maize. The balanced allelic frequencies and distributions in a relatively large and diverse natural panel helped to identify expression quantitative trait loci (eQTLs) associated with more than 18 000 genes (63.4% of tested genes). We found that distant eQTLs were more frequent (∼75% of all eQTLs) across the whole genome. Thirteen novel associated loci affecting maize kernel oil concentration were identified using the new dataset, among which one intergenic locus affected the kernel oil variation by controlling expression of three other known oil-related genes. Altogether, this study provides resources for expanding our understanding of cellular regulatory mechanisms of transcriptome variation and the landscape of functional variants within the maize genome, thereby enhancing the understanding of quantitative variations.

A comparative analytical assay of gene regulatory networks inferred using microarray and RNA-seq datasets

A Gene Regulatory Network (GRN) is a collection of interactions between molecular regulators and their targets in cells governing gene expression level. Omics data explosion generated from high-throughput genomic assays such as microarray and RNA-Seq technologies and the emergence of a number of pre-processing methods demands suitable guidelines to determine the impact of transcript data platforms and normalization procedures on describing associations in GRNs. In this study exploiting publically available microarray and RNA-Seq datasets and a gold standard of transcriptional interactions in Arabidopsis, we performed a comparison between six GRNs derived by RNA-Seq and microarray data and different normalization procedures. As a result we observed that compared algorithms were highly data-specific and Networks reconstructed by RNA-Seq data revealed a considerable accuracy against corresponding networks captured by microarrays. Topological analysis showed that GRNs inferred from two platforms were similar in several of topological features although we observed more connectivity in RNA-Seq derived genes network. Taken together transcriptional regulatory networks obtained by Robust Multiarray Averaging (RMA) and Variance-Stabilizing Transformed (VST) normalized data demonstrated predicting higher rate of true edges over the rest of methods used in this comparison.

Transcriptomic analysis identified up-regulation of a solute carrier transporter and UDP glucuronosyltransferases in dogs with aggressive cutaneous mast cell tumours

Gene expression analyses have been recently used in cancer research to identify genes associated with tumorigenesis and potential prognostic markers or therapeutic targets. In the present study, the transcriptome of dogs that had died because of mast cell tumours (MCTs) was characterised to identify a fingerprint having significant influence on prognosis determination and treatment selection. A dataset (GSE50433) obtained using a commercial canine DNA microarray platform was used. The transcriptome of seven biopsies obtained from dogs with histologically confirmed, surgically removed MCTs, treated with chemotherapy, and dead for MCT-related causes, was compared with the transcriptional portrait of 40 samples obtained from dogs with histologically confirmed, surgically removed MCTs and that were still alive at the end of the follow-up period. Among the differentially expressed genes (DEGs), eight transcripts were validated by quantitative real time PCR and their mRNA levels were measured in a cohort of 22 additional MCTs. Statistical analysis identified 375 DEGs (fold change 2, false discovery rate 5%). The functional annotation analysis indicated that the DEGs were associated with drug metabolism and cell cycle pathways. Particularly, members of solute carrier transporter (SLC) and UDP glucuronosyltransferase (UGT) gene families were identified as dysregulated. Principal component analysis (PCA) of the 22 additional MCTs identified the separate cluster dogs dead for MCT-related causes. SLCs and UGTs have been recently recognised in human cancer as important key factors in tumour progression and chemo-resistance. An in-depth analysis of their roles in aggressive canine MCT is warranted in future studies.

Cross-platform normalization of microarray and RNA-seq data for machine learning applications

Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training datasets can be created and validation can be performed on newly generated data. We developed Training Distribution Matching (TDM), which transforms RNA-seq data for use with models constructed from legacy platforms. We evaluated TDM, as well as quantile normalization, nonparanormal transformation, and a simple log 2 transformation, on both simulated and biological datasets of gene expression. Our evaluation included both supervised and unsupervised machine learning approaches. We found that TDM exhibited consistently strong performance across settings and that quantile normalization also performed well in many circumstances. We also provide a TDM package for the R programming language.

Gene Expression Analysis in the Age of Mass Sequencing: An Introduction

During the last years the technology used for gene expression analysis has changed dramatically. The old mainstay, DNA microarray, has served its due course and will soon be replaced by next-generation sequencing (NGS), the Swiss army knife of modern high-throughput nucleic acid-based analysis. Therefore preparation technologies have to adapt to suit the emerging NGS technology platform. Moreover, interpretation of the results is still time consuming and employs the use of high-end computers usually not found in molecular biology laboratories. Alternatively, cloud computing might solve this problem. Nevertheless, these new challenges have to be embraced for gene expression analysis in general.

Transcriptome sequencing reveals thousands of novel long non-coding RNAs in B cell lymphoma

Background

Gene profiling of diffuse large B cell lymphoma (DLBCL) has revealed broad gene expression deregulation compared to normal B cells. While many studies have interrogated well known and annotated genes in DLBCL, none have yet performed a systematic analysis to uncover novel unannotated long non-coding RNAs (lncRNA) in DLBCL. In this study we sought to uncover these lncRNAs by examining RNA-seq data from primary DLBCL tumors and performed supporting analysis to identify potential role of these lncRNAs in DLBCL.

Methods

We performed a systematic analysis of novel lncRNAs from the poly-adenylated transcriptome of 116 primary DLBCL samples. RNA-seq data were processed using de novo transcript assembly pipeline to discover novel lncRNAs in DLBCL. Systematic functional, mutational, cross-species, and co-expression analyses using numerous bioinformatics tools and statistical analysis were performed to characterize these novel lncRNAs.

Results

We identified 2,632 novel, multi-exonic lncRNAs expressed in more than one tumor, two-thirds of which are not expressed in normal B cells. Long read single molecule sequencing supports the splicing structure of many of these lncRNAs. More than one-third of novel lncRNAs are differentially expressed between the two major DLBCL subtypes, ABC and GCB. Novel lncRNAs are enriched at DLBCL super-enhancers, with a fraction of them conserved between human and dog lymphomas. We see transposable elements (TE) overlap in the exonic regions; particularly significant in the last exon of the novel lncRNAs suggest potential usage of cryptic TE polyadenylation signals. We identified highly co-expressed protein coding genes for at least 88 % of the novel lncRNAs. Functional enrichment analysis of co-expressed genes predicts a potential function for about half of novel lncRNAs. Finally, systematic structural analysis of candidate point mutations (SNVs) suggests that such mutations frequently stabilize lncRNA structures instead of destabilizing them.

Conclusions

Discovery of these 2,632 novel lncRNAs in DLBCL significantly expands the lymphoma transcriptome and our analysis identifies potential roles of these lncRNAs in lymphomagenesis and/or tumor maintenance. For further studies, these novel lncRNAs also provide an abundant source of new targets for antisense oligonucleotide pharmacology, including shared targets between human and dog lymphomas.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-015-0230-7) contains supplementary material, which is available to authorized users.

Generation of a de novo transcriptome from equine lamellar tissue

BACKGROUND

Laminitis, the structural failure of interdigitated tissue that suspends the distal skeleton within the hoof capsule, is a devastating disease that is the second leading cause of both lameness and euthanasia in the horse. Current transcriptomic research focuses on the expression of known genes. However, as this tissue is quite unique and equine gene annotation is largely derived from computational predictions, there are likely yet uncharacterized transcripts that may be involved in the etiology of laminitis. In order to create a novel annotation resource, we performed whole transcriptome sequencing of sagittal lamellar sections from one control and two laminitis affected horses.

RESULTS

Whole transcriptome sequencing of the three samples resulted in 113 million reads. Overall, 88 % of the reads mapped to the equCab2 reference genome, allowing for the identification of 119,430 SNPs. The de novo assembly generated around 75,000 transcripts, of which 36,000 corresponded to known annotations. Annotated transcript models are hosted in a public data repository and thus can be easily accessed or loaded into genome browsers. RT-PCR of 12 selected assemblies confirmed structure and expression in lamellar tissue.

CONCLUSIONS

Transcriptome sequencing represents a powerful tool to expand on equine annotation and identify novel targets for further laminitis research.

Transcriptomic Analysis of Drought Stress Responses in Ammopiptanthus mongolicus Leaves Using the RNA-Seq Technique

Ammopiptanthus mongolicus (Maxim. Ex Kom.) Cheng f., a relic tree of the Tertiary period, plays a critical role in maintaining desert ecosystems in the Mid-Asia region. Genome-scale gene expression profiling studies will provide deep insight into the molecular mechanism underlying the drought tolerance of A. mongolicus. In the present study, we investigated the transcriptional changes induced by drought treatment in A. mongolicus leaves by establishing a comprehensive transcriptome database and then performing a Digital Gene Expression (DGE) analysis using Solexa sequencing technology. A comprehensive transcriptome database was obtained by assembling the Illumina unigenes with expressed sequence tags (EST) available publicly, and other high throughput sequencing data. To analyze the dynamic and complicated gene regulation network during PEG6000-induced drought treatment in leaves of A. mongolicus, a time-course gene expression analysis was performed using tag-based DGE technology, which identified 437, 1,247 and 802 differentially expressed transcripts in 1, 24 and 72 h drought stress libraries, respectively. GO and KEGG analyses revealed hormone signal transduction and phenylpropanoid biosynthesis were enriched during drought treatment. A batch of drought-regulated transcription factor transcripts were identified, including the subsets of HD-ZIP, bZIP, WRKY, AP2/ERF and bHLH family members, which may play roles in drought response in A. mongolicus. The sequence collection assembled in the present study represents one of the most comprehensive transcriptome databases for A. mongolicus currently. The differentially expressed transcripts identified in our study provide a good start for identifying the key genes in stress response and performing functional analysis to reveal their roles in stress adaptation in planta.

The evolutionary panorama of organ-specifically expressed or repressed orthologous genes in nine vertebrate species

RNA sequencing (RNA-Seq) technology provides the detailed transcriptomic information for a biological sample. Using the RNA-Seq data of six organs from nine vertebrate species, we identified a number of organ-specifically expressed or repressed orthologous genes whose expression patterns are mostly conserved across nine species. Our analyses show the following results: (i) About 80% of these genes have a chordate or more ancient origin and more than half of them are the legacy of one or multiple rounds of large-scale gene duplication events. (ii) Their evolutionary rates are shaped by the organ in which they are expressed or repressed, e.g. the genes specially expressed in testis and liver generally evolve more than twice as fast as the ones specially expressed in brain and cerebellum. The organ-specific transcription factors were discriminated from these genes. The ChIP-seq data from the ENCODE project also revealed the transcription-related factors that might be involved in regulating human organ-specifically expressed or repressed genes. Some of them are shared by all six human organs. The comparison of ENCODE data with mouse/chicken ChIP-seq data proposes that organ-specifically expressed or repressed orthologous genes are regulated in various combinatorial fashions in different species, although their expression features are conserved among these species. We found that the duplication events in some gene families might help explain the quick organ/tissue divergence in vertebrate lineage. The phylogenetic analysis of testis-specifically expressed genes suggests that some of them are prone to develop new functions for other organs/tissues.

Gene profiling of bone around orthodontic mini-implants by RNA-sequencing analysis

This study aimed to evaluate the genes that were expressed in the healing bones around SLA-treated titanium orthodontic mini-implants in a beagle at early (1-week) and late (4-week) stages with RNA-sequencing (RNA-Seq). Samples from sites of surgical defects were used as controls. Total RNA was extracted from the tissue around the implants, and an RNA-Seq analysis was performed with Illumina TruSeq. In the 1-week group, genes in the gene ontology (GO) categories of cell growth and the extracellular matrix (ECM) were upregulated, while genes in the categories of the oxidation-reduction process, intermediate filaments, and structural molecule activity were downregulated. In the 4-week group, the genes upregulated included ECM binding, stem cell fate specification, and intramembranous ossification, while genes in the oxidation-reduction process category were downregulated. GO analysis revealed an upregulation of genes that were related to significant mechanisms, including those with roles in cell proliferation, the ECM, growth factors, and osteogenic-related pathways, which are associated with bone formation. From these results, implant-induced bone formation progressed considerably during the times examined in this study. The upregulation or downregulation of selected genes was confirmed with real-time reverse transcription polymerase chain reaction. The RNA-Seq strategy was useful for defining the biological responses to orthodontic mini-implants and identifying the specific genetic networks for targeted evaluations of successful peri-implant bone remodeling.

The importance of comparative oncology in translational medicine

Human cancer is so complex that in vivo preclinical models are needed if effective therapies are to be developed. Naturally occurring cancers in companion animals are therefore a great resource, as shown by the remarkable growth that comparative oncology has seen over the last 30 years. Cancer has become a leading cause of death in companion animals now that more pets are living long enough to develop the disease. Furthermore, more owners are seeking advanced and novel therapies for their pets as they are very much considered family members. Living in the same environments, pets and humans are often afflicted by the same types of cancer which show similar behavior and, in some species, express the same antigen molecules. The treatment of pet tumors using novel therapies is of compelling translational significance.

Transcriptome profiling of expression changes during neuronal death by RNA-Seq

The molecular mechanisms underlying neuronal death are poorly understood. One of the most widely used models to study neuronal death are cultured cerebellar granule neurons (CGNs) which undergo apoptosis when switched from a medium containing depolarizing levels of potassium (HK) to a medium with low non-depolarizing levels of potassium (LK). Previously, other labs have used DNA microarray analysis to characterize gene expression changes in LK-treated CGNs. However, microarray analysis is only capable of measuring the status of known transcripts, and expression of low-abundance mRNAs is often not detected by the hybridization-based approach. We have used RNA-sequencing to conduct a more detailed and comprehensive analysis of gene expression changes in CGNs induced to die by LK treatment. RNA-seq investigates the status of both known transcripts as well as exploring new ones and is substantially more sensitive than the microarray approach. We have found that the expression of 4334 genes is significantly altered in LK-treated CGNs with 2199 being up-regulated while 2135 are down-regulated. Genes functioning in cell death and survival regulation, cell growth and proliferation and molecular transport were most affected by LK treatment. Further, a large number of genes involved in nervous system development and function were also deregulated. Analysis of signaling pathways that were affected in LK-induced death included but were not limited to mitochondrial dysfunction and oxidative phosphorylation, consistent with a number of studies showing perturbations of these pathways in neurodegenerative disorders. Thus, our study identifies a large number of new genes that are affected during the process of neuronal death. While a majority of these changes may reflect consequences of the induction of neuronal death, many of the genes that we have identified are likely to be critical and potentially novel mediators of neuronal death, including death associated with neurodegenerative disease.

Detecting and correcting systematic variation in large-scale RNA sequencing data

High-throughput RNA sequencing (RNA-seq) enables comprehensive scans of entire transcriptomes, but best practices for analyzing RNA-seq data have not been fully defined, particularly for data collected with multiple sequencing platforms or at multiple sites. Here we used standardized RNA samples with built-in controls to examine sources of error in large-scale RNA-seq studies and their impact on the detection of differentially expressed genes (DEGs). Analysis of variations in guanine-cytosine content, gene coverage, sequencing error rate and insert size allowed identification of decreased reproducibility across sites. Moreover, commonly used methods for normalization (cqn, EDASeq, RUV2, sva, PEER) varied in their ability to remove these systematic biases, depending on sample complexity and initial data quality. Normalization methods that combine data from genes across sites are strongly recommended to identify and remove site-specific effects and can substantially improve RNA-seq studies.

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R(2)0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.

Systems-based analyses of brain regions functionally impacted in Parkinson's disease reveals underlying causal mechanisms

Detailed analysis of disease-affected tissue provides insight into molecular mechanisms contributing to pathogenesis. Substantia nigra, striatum, and cortex are functionally connected with increasing degrees of alpha-synuclein pathology in Parkinson's disease. We undertook functional and causal pathway analysis of gene expression and proteomic alterations in these three regions, and the data revealed pathways that correlated with disease progression. In addition, microarray and RNAseq experiments revealed previously unidentified causal changes related to oligodendrocyte function and synaptic vesicle release, and these and other changes were reflected across all brain regions. Importantly, subsets of these changes were replicated in Parkinson's disease blood; suggesting peripheral tissue may provide important avenues for understanding and measuring disease status and progression. Proteomic assessment revealed alterations in mitochondria and vesicular transport proteins that preceded gene expression changes indicating defects in translation and/or protein turnover. Our combined approach of proteomics, RNAseq and microarray analyses provides a comprehensive view of the molecular changes that accompany functional loss and alpha-synuclein pathology in Parkinson's disease, and may be instrumental to understand, diagnose and follow Parkinson's disease progression.

Dissecting the regulatory microenvironment of a large animal model of non-Hodgkin lymphoma: evidence of a negative prognostic impact of FOXP3+ T cells in canine B cell lymphoma

The cancer microenvironment plays a pivotal role in oncogenesis, containing a number of regulatory cells that attenuate the anti-neoplastic immune response. While the negative prognostic impact of regulatory T cells (Tregs) in the context of most solid tissue tumors is well established, their role in lymphoid malignancies remains unclear. T cells expressing FOXP3 and Helios were documented in the fine needle aspirates of affected lymph nodes of dogs with spontaneous multicentric B cell lymphoma (BCL), proposed to be a model for human non-Hodgkin lymphoma. Multivariable analysis revealed that the frequency of lymph node FOXP3⁺ T cells was an independent negative prognostic factor, impacting both progression-free survival (hazard ratio 1.10; p = 0.01) and overall survival (hazard ratio 1.61; p = 0.01) when comparing dogs showing higher than the median FOXP3 expression with those showing the median value of FOXP3 expression or less. Taken together, these data suggest the existence of a population of Tregs operational in canine multicentric BCL that resembles thymic Tregs, which we speculate are co-opted by the tumor from the periphery. We suggest that canine multicentric BCL represents a robust large animal model of human diffuse large BCL, showing clinical, cytological and immunophenotypic similarities with the disease in man, allowing comparative studies of immunoregulatory mechanisms.

Genomics of pancreatic ductal adenocarcinoma

Pancreatic cancer is one of the worst prognostic cancers because of the late diagnosis and the absence of effective treatment. Within all subtypes of this disease, ductal adenocarcinoma has the shortest survival time. In recent years, global genomics profiling allowed the identification of hundreds of genes that are perturbed in pancreatic cancer. The integration of different omics sources in the study of pancreatic cancer has revealed several molecular mechanisms, indicating the complex history of its development. However, validation of these genes as biomarkers for early diagnosis, prognosis or treatment efficacy is still incomplete but should lead to new approaches for the treatment of the disease in the future.

Pollen-specific activation of Arabidopsis retrogenes is associated with global transcriptional reprogramming

Duplications allow for gene functional diversification and accelerate genome evolution. Occasionally, the transposon amplification machinery reverse transcribes the mRNA of a gene, integrates it into the genome, and forms an RNA-duplicated copy: the retrogene. Although retrogenes have been found in plants, their biology and evolution are poorly understood. Here, we identified 251 (216 novel) retrogenes in Arabidopsis thaliana, corresponding to 1% of protein-coding genes. Arabidopsis retrogenes are derived from ubiquitously transcribed parents and reside in gene-rich chromosomal regions. Approximately 25% of retrogenes are cotranscribed with their parents and 3% with head-to-head oriented neighbors. This suggests transcription by novel promoters for 72% of Arabidopsis retrogenes. Many retrogenes reach their transcription maximum in pollen, the tissue analogous to animal spermatocytes, where upregulation of retrogenes has been found previously. This implies an evolutionarily conserved mechanism leading to this transcription pattern of RNA-duplicated genes. During transcriptional repression, retrogenes are depleted of permissive chromatin marks without an obvious enrichment for repressive modifications. However, this pattern is common to many other pollen-transcribed genes independent of their evolutionary origin. Hence, retroposition plays a role in plant genome evolution, and the developmental transcription pattern of retrogenes suggests an analogous regulation of RNA-duplicated genes in plants and animals.

RNA-sequencing analysis reveals new alterations in cardiomyocyte cytoskeletal genes in patients with heart failure

Changes in cardiomyocyte cytoskeletal components, a crucial scaffold of cellular structure, have been found in heart failure (HF); however, the altered cytoskeletal network remains to be elucidated. This study investigated a new map of cytoskeleton-linked alterations that further explain the cardiomyocyte morphology and contraction disruption in HF. RNA-Sequencing (RNA-Seq) analysis was performed in 29 human LV tissue samples from ischemic cardiomyopathy (ICM; n=13) and dilated cardiomyopathy (DCM, n=10) patients undergoing cardiac transplantation and six healthy donors (control, CNT) and up to 16 ICM, 13 DCM and 7 CNT tissue samples for qRT-PCR. Gene Ontology analysis of RNA-Seq data demonstrated that cytoskeletal processes are altered in HF. We identified 60 differentially expressed cytoskeleton-related genes in ICM and 58 genes in DCM comparing with CNT, hierarchical clustering determined that shared cytoskeletal genes have a similar behavior in both pathologies. We further investigated MYLK4, RHOU, and ANKRD1 cytoskeletal components. qRT-PCR analysis revealed that MYLK4 was downregulated (-2.2-fold; P<0.05) and ANKRD1 was upregulated (2.3-fold; P<0.01) in ICM patients vs CNT. RHOU mRNA levels showed a statistical trend to decrease (-2.9-fold). In DCM vs CNT, MYLK4 (-4.0-fold; P<0.05) and RHOU (-3.9-fold; P<0.05) were downregulated and ANKRD1 (2.5-fold; P<0.05) was upregulated. Accordingly, MYLK4 and ANKRD1 protein levels were decreased and increased, respectively, in both diseases. Furthermore, ANKRD1 and RHOU mRNA levels were related with LV function (P<0.05). In summary, we have found a new map of changes in the ICM and DCM cardiomyocyte cytoskeleton. ANKRD1 and RHOU mRNA levels were related with LV function which emphasizes their relevance in HF. These new cytoskeletal changes may be responsible for altered contraction and cell architecture disruption in HF patients. Moreover, these results improve our knowledge on the role of cytoskeleton in functional and structural alterations in HF.

A multistep screening method to identify genes using evolutionary transcriptome of plants

We introduced a multistep screening method to identify the genes in plants using microarrays and ribonucleic acid (RNA)-seq transcriptome data. Our method describes the process for identifying genes using the salt-tolerance response pathways of the potato (Solanum tuberosum) plant. Gene expression was analyzed using microarrays and RNA-seq experiments that examined three potato lines (high, intermediate, and low salt tolerance) under conditions of salt stress. We screened the orthologous genes and pathway genes involved in salinity-related biosynthetic pathways, and identified nine potato genes that were candidates for salinity-tolerance pathways. The nine genes were selected to characterize their phylogenetic reconstruction with homologous genes of Arabidopsis thaliana, and a Circos diagram was generated to understand the relationships among the selected genes. The involvement of the selected genes in salt-tolerance pathways was verified by reverse transcription polymerase chain reaction analysis. One candidate potato gene was selected for physiological validation by generating dehydration-responsive element-binding 1 (DREB1)-overexpressing transgenic potato plants. The DREB1 overexpression lines exhibited increased salt tolerance and plant growth when compared to that of the control. Although the nine genes identified by our multistep screening method require further characterization and validation, this study demonstrates the power of our screening strategy after the initial identification of genes using microarrays and RNA-seq experiments.

RNA sequencing analysis and atrial natriuretic peptide production in patients with dilated and ischemic cardiomyopathy

BACKGROUND

The atrium is the major site of ANP synthesis, which has been said to increase in heart failure as a result of increased production in the left ventricular (LV) myocardium. This is a key issue related to its diagnostic and prognostic capabilities. We aimed to evaluate protein levels of proANP and ANP and the enzymes that cleave the natriuretic peptides, corin and furin, in the LV tissue of heart transplant patients with dilated (DCM) and ischemic (ICM) cardiomyopathy compared with control donors (CNT). We also evaluate mRNA levels of ANP gene (NPPA) by RNA sequencing in the same tissue.

METHODS AND RESULTS

Seventy-three human LV tissue samples from ICM (n=30) and DCM (n=33) patients and CNT (n=10) were analyzed by Western blot and RNA sequencing. Comparing protein levels according to etiology, neither DCM nor ICM showed levels of proANP or ANP different from those of CNT. However, NPPA was increased in both groups compared to CNT (DCM 32 fold, p<0.0001; ICM 10 fold, p<0.0001). Corin (but not furin) was elevated in the ICM group compared to CNT (112 ± 24 vs. 100 ± 7, p<0.05), and its level was inversely related with LV ejection fraction (LVEF) (r=-0.399, p<0.05).

CONCLUSIONS

Patients present with elevated levels of NPPA but not of proANP or ANP proteins in LV tissue, which may be due to posttranscripcional regulation of NPPA or different pathways for ANP secretion between the atrium and ventricle. Moreover, there are differences between DCM and ICM in corin levels, indicating that a different molecular mechanism may exist that converge in this syndrome. Further, LV concentration of corin is inversely related to LVEF in ICM.

A comparison of RNA-seq and exon arrays for whole genome transcription profiling of the L5 spinal nerve transection model of neuropathic pain in the rat

BACKGROUND

The past decade has seen an abundance of transcriptional profiling studies of preclinical models of persistent pain, predominantly employing microarray technology. In this study we directly compare exon microarrays to RNA-seq and investigate the ability of both platforms to detect differentially expressed genes following nerve injury using the L5 spinal nerve transection model of neuropathic pain. We also investigate the effects of increasing RNA-seq sequencing depth. Finally we take advantage of the "agnostic" approach of RNA-seq to discover areas of expression outside of annotated exons that show marked changes in expression following nerve injury.

RESULTS

RNA-seq and microarrays largely agree in terms of the genes called as differentially expressed. However, RNA-seq is able to interrogate a much larger proportion of the genome. It can also detect a greater number of differentially expressed genes than microarrays, across a wider range of fold changes and is able to assign a larger range of expression values to the genes it measures. The number of differentially expressed genes detected increases with sequencing depth. RNA-seq also allows the discovery of a number of genes displaying unusual and interesting patterns of non-exonic expression following nerve injury, an effect that cannot be detected using microarrays.

CONCLUSION

We recommend the use of RNA-seq for future high-throughput transcriptomic experiments in pain studies. RNA-seq allowed the identification of a larger number of putative candidate pain genes than microarrays and can also detect a wider range of expression values in a neuropathic pain model. In addition, RNA-seq can interrogate the whole genome regardless of prior annotations, being able to detect transcription from areas of the genome not currently annotated as exons. Some of these areas are differentially expressed following nerve injury, and may represent novel genes or isoforms. We also recommend the use of a high sequencing depth in order to detect differential expression for genes with low levels of expression.

Transforming RNA-Seq data to improve the performance of prognostic gene signatures

Gene expression measurements have successfully been used for building prognostic signatures, i.e for identifying a short list of important genes that can predict patient outcome. Mostly microarray measurements have been considered, and there is little advice available for building multivariable risk prediction models from RNA-Seq data. We specifically consider penalized regression techniques, such as the lasso and componentwise boosting, which can simultaneously consider all measurements and provide both, multivariable regression models for prediction and automated variable selection. However, they might be affected by the typical skewness, mean-variance-dependency or extreme values of RNA-Seq covariates and therefore could benefit from transformations of the latter. In an analytical part, we highlight preferential selection of covariates with large variances, which is problematic due to the mean-variance dependency of RNA-Seq data. In a simulation study, we compare different transformations of RNA-Seq data for potentially improving detection of important genes. Specifically, we consider standardization, the log transformation, a variance-stabilizing transformation, the Box-Cox transformation, and rank-based transformations. In addition, the prediction performance for real data from patients with kidney cancer and acute myeloid leukemia is considered. We show that signature size, identification performance, and prediction performance critically depend on the choice of a suitable transformation. Rank-based transformations perform well in all scenarios and can even outperform complex variance-stabilizing approaches. Generally, the results illustrate that the distribution and potential transformations of RNA-Seq data need to be considered as a critical step when building risk prediction models by penalized regression techniques.

The transcriptional landscape of Campylobacter jejuni under iron replete and iron limited growth conditions

The genome-wide Campylobacter jejuni transcriptional response under iron replete and iron limited conditions was characterized using RNA-seq. We have identified 111 novel C. jejuni 5’UTRs and mapped 377 co-transcribed genes into 230 transcriptional operons. In contrast to previous microarray results, the C. jejuni iron stimulon is less extensive than previously believed and consists of 77 iron activated genes and 50 iron repressed genes. As anticipated, the iron repressed genes are primarily those involved in iron acquisition or oxidative stress defense. Interestingly, these experiments have revealed that iron is an important modulator of flagellar biogenesis with almost all the components of the flagella found to be iron activated. Given that motility is a well-known C. jejuni colonization factor, this suggests that there is an important regulatory coupling of flagellar biogenesis and iron level in C. jejuni. In addition we have identified several consensus mutations in the C. jejuni NCTC11168 strain that are widespread in the Campylobacter research community and which may explain conflicting phenotypic reports for this strain. Comparative analysis of iron responsive genes with the known Fur regulon indicates that many iron responsive genes are not Fur responsive; suggesting that additional iron regulatory factors remain to be characterized in C. jejuni. Further analysis of the RNA-seq data identified multiple novel transcripts including 19 potential ncRNAs. The expression of selected ncRNAs was confirmed and quantified with qRT-PCR. The qRT-PCR results indicate that several of these novel transcripts are either Fur and/or iron responsive. The fact that several of these ncRNAs are iron responsive or Fur regulated suggests that they may perform regulatory roles in iron homeostasis.