Gene Expression Data Mining Research Articles

Single-Nuclei Characterization of Lacrimal Gland in Scopolamine-Induced Dry Eye Disease.

The lacrimal gland (LG) is the main organ responsible for tear secretion and an important pathogenic site for dry eye disease (DED). This study aimed to comprehensively characterize LG cellular heterogeneity under normal and DED conditions using single-nucleus RNA sequencing (snRNA-seq). Single LG nuclei isolated from mice with or without DED induced by scopolamine (SCOP)/desiccating stress (DS) were subjected to snRNA-seq using the 10x Genomics platform. These cells were clustered and annotated using the t-distributed stochastic neighbor embedding (t-SNE) method and unbiased computational informatic analysis. Cluster identification and functional analysis were performed based on marker gene expression and bioinformatic data mining. The snRNA-seq analysis of 30,351 nuclei identified eight major cell types, with acinar cells (∼72.6%) being the most abundant cell type in the LG. Subclustering analysis revealed that the LG mainly contained two acinar cell subtypes, two ductal cell subclusters, three myoepithelial cell (MECs) subtypes, and four immunocyte subclusters. In the SCOP-induced DED model, three major LG parenchymal cell types were significantly altered, characterized by a reduced proportion of acinar cells with a lowered secretion potential and an augmented proportion of ductal cells and MECs. LG immunocytes in DED scenarios showed an intensified inflammatory response and dysregulated intercellular communication with three major LG parenchymal cells. Overall, this study offers a systemic single-nucleus transcriptomic profile of LGs in both normal and DED conditions and an atlas of the complicated interactions of immunocytes with major LG parenchymal cells. The findings also facilitate understanding the pathogenesis of DED.

EryDB: A Transcriptomic Profile Database for Erythropoiesis and Erythroid-related Diseases.

Erythropoiesis is a finely regulated and complex process that involves multiple transformations from hematopoietic stem cells to mature red blood cells at hematopoietic sites from the embryonic to the adult stages. Investigations into its molecular mechanisms have generated a wealth of expression data, including bulk and single-cell RNA sequencing data. A comprehensively integrated and well-curated erythropoiesis-specific database will greatly facilitate the mining of gene expression data and enable large-scale research of erythropoiesis and erythroid-related diseases. Here, we present EryDB, an open-access and comprehensive database dedicated to the collection, integration, analysis, and visualization of transcriptomic data for erythropoiesis and erythroid-related diseases. Currently, the database includes expertly curated quality-assured data of 3803 samples and 1,187,119 single cells derived from 107 public studies of three species (Homo sapiens, Mus musculus, and Danio rerio), nine tissue types, and five diseases. EryDB provides users with the ability to not only browse the molecular features of erythropoiesis between tissues and species, but also perform computational analyses of single-cell and bulk RNA sequencing data, thus serving as a convenient platform for customized queries and analyses. EryDB v1.0 is freely accessible at https://ngdc.cncb.ac.cn/EryDB/home.

Gene expression programming and data mining methods for bushfire susceptibility mapping in New South Wales, Australia

Australia is one of the most bushfire-prone countries. Prediction and management of bushfires in bushfire-susceptible areas can reduce the negative impacts of bushfires. The generation of bushfire susceptibility maps can help improve the prediction of bushfires. The main aim of this study was to use single gene expression programming (GEP) and ensemble of GEP with well-known data mining to generate bushfire susceptibility maps for New South Wales, Australia, as a case study. We used eight methods for bushfire susceptibility mapping: GEP, random forest (RF), support vector machine (SVM), frequency ratio (FR), ensemble techniques of GEP and FR (GEPFR), RF and FR (RFFR), SVM and FR (SVMFR), and logistic regression (LR) and FR (LRFR). Areas under the curve (AUCs) of the receiver operating characteristic were used to evaluate the proposed methods. GEPFR exhibited the best performance for bushfire susceptibility mapping based on the AUC (0.892 for training, 0.890 for testing), while RFFR had the highest accuracy (95.29% for training, 94.70% for testing) among the proposed methods. GEPFR is an ensemble method that uses features from the evolutionary algorithm and the statistical FR method, which results in a better AUC for the bushfire susceptibility maps. Single GEP showed AUC of 0.884 for training and 0.882 for testing. RF also showed AUC of 0.902 and 0.876 for training and testing, respectively. SVM had 0.868 for training and 0.781 for testing for bushfire susceptibility mapping. The ensemble methods had better performances than those of the single methods.

Identification and Characterization of SOG1 (Suppressor of Gamma Response 1) Homologues in Plants Using Data Mining Resources and Gene Expression Profiling.

SOG1 (Suppressor of the Gamma response 1) is the master-regulator of plant DNA damage response (DDR), a highly coordinated network of DNA damage sensors, transducers, mediators, and effectors, with highly coordinated activities. SOG1 transcription factor belongs to the NAC/NAM protein family, containing the well-conserved NAC domain and five serine-glutamine (SQ) motifs, preferential targets for phosphorylation by ATM and ATR. So far, the information gathered for the SOG1 function comes from studies on the model plant Arabidopsis thaliana. To expand the knowledge on plant-specific DDR, it is opportune to gather information on other SOG1 orthologues. The current study identified plants where multiple SOG1 homologues are present and evaluated their functions by leveraging the information contained in publicly available transcriptomics databases. This analysis revealed the presence of multiple SOG1 sequences in thirteen plant species, and four (Medicago truncatula, Glycine max, Kalankoe fedtschenkoi, Populus trichocarpa) were selected for gene expression data mining based on database availability. Additionally, M. truncatula seeds and seedlings exposed to treatments known to activate DDR pathways were used to evaluate the expression profiles of MtSOG1a and MtSOG1b. The experimental workflow confirmed the data retrieved from transcriptomics datasets, suggesting that the SOG1 homologues have redundant functions in different plant species.

Evidence against involvement of kynurenate branch of kynurenine pathway in pathophysiology of Fuchs’ dystrophy and keratoconus

Kynurenine aminotransferases (KAT) are enzymes catalyzing formation of kynurenic acid (KYNA) from kynurenine. KYNA is a Janus-faced molecule of high biological activity. On the one hand KYNA was identified as a UV filter and neuroprotectant with free radical scavenging properties, but on the other hand it may contribute to photodamage of lens proteins resulting in cataract formation. Fuchs endothelial corneal dystrophy (FECD) and keratoconus (KC) are common, vision threatening corneal dystrophies whose etiology is not fully understood. In our previous works, we confirmed the presence of KATs in the human cornea together with GPR35, a receptor for KYNA. This prompted us to investigate the potential changes in the expression of three isoforms: KAT I, KAT II, and KAT III in normal and FECD- and KC-affected corneas. Immunohistochemistry accompanied by gene expression data mining revealed that the levels of neither KAT I, KAT II, nor KAT III are affected in FECD and KC. This constitutes evidence against the involvement of KATs in the pathophysiology of FECD and KC.

BET Proteins Regulate Expression of Osr1 in Early Kidney Development.

In utero renal development is subject to maternal metabolic and environmental influences affecting long-term renal function and the risk of developing chronic kidney failure and cardiovascular disease. Epigenetic processes have been implicated in the orchestration of renal development and prenatal programming of nephron number. However, the role of many epigenetic modifiers for kidney development is still unclear. Bromodomain and extra-terminal domain (BET) proteins act as histone acetylation reader molecules and promote gene transcription. BET family members Brd2, Brd3 and Brd4 are expressed in the nephrogenic zone during kidney development. Here, the effect of the BET inhibitor JQ1 on renal development is evaluated. Inhibition of BET proteins via JQ1 leads to reduced growth of metanephric kidney cultures, loss of the nephron progenitor cell population, and premature and disturbed nephron differentiation. Gene expression of key nephron progenitor transcription factor Osr1 is downregulated after 24 h BET inhibition, while Lhx1 and Pax8 expression is increased. Mining of BRD4 ChIP-seq and gene expression data identify Osr1 as a key factor regulated by BRD4-controlled gene activation. Inhibition of BRD4 by BET inhibitor JQ1 leads to downregulation of Osr1, thereby causing a disturbance in the balance of nephron progenitor cell self-renewal and premature differentiation of the nephron, which ultimately leads to kidney hypoplasia and disturbed nephron development. This raises questions about the potential teratogenic effects of BET inhibitors for embryonic development. In summary, our work highlights the role of BET proteins for prenatal programming of nephrogenesis and identifies Osr1 as a potential target of BET proteins.

SARS-CoV-2 infects human adult donor eyes and hESC-derived ocular epithelium.

The SARS-CoV-2 pandemic has caused unparalleled disruption of global behavior and significant loss of life. To minimize SARS-CoV-2 spread, understanding the mechanisms of infection from all possible routes of entry is essential. While aerosol transmission is thought to be the primary route of spread, viral particles have been detected in ocular fluid, suggesting that the eye may be a vulnerable point of viral entry. To this end, we confirmed SARS-CoV-2 entry factor and antigen expression in post-mortem COVID-19 patient ocular surface tissue and observed productive viral replication in cadaver samples and eye organoid cultures, most notably in limbal regions. Transcriptional analysis of ex vivo infected ocular surface cells and hESC-derived eye cultures revealed robust induction of NF-κB in infected cells as well as diminished type I/III interferon signaling. Together these data suggest that the eye can be directly infected by SARS-CoV-2 and implicate limbus as a portal for viral entry.

Plant TDP1 (Tyrosyl-DNA Phosphodiesterase 1): A Phylogenetic Perspective and Gene Expression Data Mining.

The TDP1 (tyrosyl-DNA phosphodiesterase 1) enzyme removes the non-specific covalent intermediates between topoisomerase I and DNA, thus playing a crucial role in preventing DNA damage. While mammals possess only one TDP1 gene, in plants two genes (TDP1α and TDP1β) are present constituting a small gene subfamily. These display a different domain structure and appear to perform non-overlapping functions in the maintenance of genome integrity. Namely, the HIRAN domain identified in TDP1β is involved in the interaction with DNA during the recognition of stalled replication forks. The availability of transcriptomic databases in a growing variety of experimental systems provides new opportunities to fill the current gaps of knowledge concerning the evolutionary origin and the specialized roles of TDP1 genes in plants. Whereas a phylogenetic approach has been used to track the evolution of plant TDP1 protein, transcriptomic data from a selection of representative lycophyte, eudicots, and monocots have been implemented to explore the transcriptomic dynamics in different tissues and a variety of biotic and abiotic stress conditions. While the phylogenetic analysis indicates that TDP1α is of non-plant origin and TDP1β is plant-specific originating in ancient vascular plants, the gene expression data mining comparative analysis pinpoints for tissue- and stress-specific responses.

Mining of combined human placental gene expression data across pregnancy, applied to PPAR signaling pathway

IntroductionTo date, we have only an incomplete understanding of how gene expression in the human placenta changes at the genome-wide scale from very early in gestation to term. Our aim was to investigate the dynamic changes in gene expression throughout placentation. MethodsIn our study, gene expression profiles were collected of human placentas from 4 to 40 gestational weeks of age. Simple linear regression and weighted correlation network analysis were applied to identify genes of interest. Analyses of gene enrichment, including gene ontology and pathways from the Kyoto Encyclopedia of Genes and Genomes, were performed using clusterProfiler. Finally, dynamic changes in the expression of individual genes were represented using line graphs of scaled and adjusted gene expression. ResultsOur results highlighted a total of 5173 genes that are involved in different periods of placentation. Downstream annotation of these genes revealed the biological processes and pathways involved, from which we chose to further investigate the PPAR signaling pathway. We were able to detect changes over time in many genes involved in lipid storage/metabolism, including members of the FABP family and LPL. These patterns were corroborated by lipid staining of placental sections, which revealed a significant decrease in lipid droplet content in placentas from early in the first trimester to term. ConclusionOur study provides detailed information on the dynamics of biological processes and pathways across human placentation. These findings give us new clues for deciphering the normal functions of placentation and the ways in which the mis-regulation of these pathways may be linked to pregnancy-related diseases. As an example, our results show that the PPAR signaling pathway mediates a constant decrease in placental lipid content over the course of pregnancy.

Using the TCGA Database to Predict and Analyze Tumor Microenvironment Genes Related to Poor Prognosis of Colon Cancer.

BackgroundColon cancer (COAD) is a highly malignant gastrointestinal cancer. The existence of the TCGA database allows us to more easily perform gene expression profiling and data mining on colon cancer patients worldwide, and to more easily discover the correlation between genes and survival prognosis of colon cancer. Related reports show that the degree of infiltration of tumor immune cells and stromal cells in tumor microenvironment cells has a significant impact on the prognosis of cancer patients.Material/MethodsThe immune and stromal components in colon cancer can be quantitatively analyzed using relevant scores obtained by use of the ESTIMATE calculation method. To better explain the effect of relevant genes of cells associated with immunity and stroma on the survival prognosis of colon cancer, we divided the data from 191 downloaded case into high and low groups according to their scores of immunity and stroma, and identified differentially expressed genes.ResultsThe results showed that immune and stromal scores were significantly associated with survival prognosis. After performing biological function enrichment analysis and protein interaction network on the target genes, the results showed that these genes are mainly involved in inflammatory response, immune response, and chemotaxis. We then performed relevant survival prognosis analysis of these genes.ConclusionsWe found a number of genes that possess the properties of tumor immune microenvironment and can predict poor prognosis of colon cancer.

The grapevine NaE sodium exclusion locus encodes sodium transporters with diverse transport properties and localisation

Grapevine (Vitis vinifera L.) is a valuable crop for human consumption and wine production, and is prone to suffering from salinity stress in arid regions or when exposed to low quality irrigation water. A previous study identified a quantitative trait locus (QTL) NaE, containing six High-affinity Potassium Transporter 1 genes, that was associated with shoot Na+ exclusion in grapevine. While HKT1;1 was predicted to be the most likely gene within this QTL to encode for this important salinity tolerance sub-trait, four other HKTs within the QTL remained uncharacterised; VviHKT1;2 encodes a truncated transcript unlikely to form a functional transporter. In this study, two allelic variants for each of VviHKT1;6, VviHKT1;7 and VviHKT1;8 from the heterozygous grapevine variety Cabernet Sauvignon were functionally characterised. Using the Xenopus laevis oocyte heterologous expression system, as well as transient expression in tobacco leaves, we found that the VviHKT1;6 and VviHKT1;7 alleles encoded plasma membrane localised proteins that facilitated significant non-rectifying Na+ transport. Conversely, proteins encoded by the VviHKT1;8 alleles were inwardly-rectifying, weak Na+ transporters that localised to intracellular organelles. Mining of previous RNA-seq gene expression data suggested that VviHKT1;6–8 are weakly expressed in grapevine roots, flower buds, and seeds under normal conditions and different nutrient regimes. We propose that VviHKT1;6 and VviHKT1;7 are likely to have a less significant role in grapevine leaf Na+ exclusion than VviHKT1;1, and that VviHKT1;8 is involved in endomembrane Na+ transport.

Decision tree underfitting in mining of gene expression data. An evolutionary multi-test tree approach

The problem of underfitting and overfitting in machine learning is often associated with a bias-variance trade-off. The underfitting most clearly manifests in the tree-based inducers when used to classify the gene expression data. To improve the generalization ability of decision trees, we are introducing an evolutionary, multi-test tree approach tailored to this specific application domain. The general idea is to apply gene clusters of varying size, which consist of functionally related genes in each splitting rule. It is achieved by using a few simple tests that mimic each other’s predictions and built-in information about the discriminatory power of genes. The tendencies to underfit and overfit are limited by the multi-objective fitness function that minimizes tree error, split divergence and attribute costs. Evolutionary search for multi-tests in internal nodes, as well as the overall tree structure, is performed simultaneously.This novel approach called Evolutionary Multi-Test Tree (EMTTree) may bring far-reaching benefits to the domain of molecular biology including biomarker discovery, finding new gene-gene interactions and high-quality prediction. Extensive experiments carried out on 35 publicly available gene expression datasets show that we managed to significantly improve the accuracy and stability of decision tree. Importantly, EMTTree does not substantially increase the overall complexity of the tree, so that the patterns in the predictive structures are kept comprehensible.

Abstract A189: Identification of antibody-drug conjugate targets using curated public data, in-house glycoproteomics, and a surrogate in vitro ADC assay

Abstract Antibody-drug conjugates (ADCs) are a promising approach for cancer therapy, combining the specificity of an antibody with the potency of small-molecule toxins. To identify cellular targets for the development of new ADCs, we have set out to identify proteins that (1) are expressed on the cell surface; (2) have high specificity for tumors, with relatively low expression on normal tissues; and (3) can internalize into the tumor cell by a mechanism that enables the delivery and activation of sufficient amounts of toxin to kill cancer cells. Here at the NRC, we have built a pipeline to identify new ADC targets, incorporating public gene expression data mining and glycoproteomic profiling, followed by in vitro screening through a surrogate ADC assay. Public data enable the analysis of large numbers of human tumors and normal tissues, providing a population-based estimate of gene expression. Through curation of the Gene Expression Omnibus, we have built a microarray database that contains >26,000 tumor samples and >8,800 normal samples, all on the Affymetrix HGU133 Plus 2.0 platform. We have also collected RNA-seq data for >5,000 normal samples from the GTEx database, and 6,900 tumor samples from The Cancer Genome Atlas. These samples cover a broad range of tissues: blood, bone marrow, brain, breast, colon, heart, kidney, liver, lung, muscle, ovary, pancreas, prostate, skin, stomach, and uterus. To identify candidates for ADC development, we first classify tumors into subtypes through consensus clustering followed by a Monte Carlo implementation of our iterative ensemble classification methods. Next, we perform differential gene expression analysis between normal tissues and known or novel cancer subtypes. In one example, we have identified 50 breast cancer targets, 7 of which have already been developed as ADCs to the clinical trial stage by others, demonstrating the validity and promise of this approach (Fauteux et al., 2016). Glycoproteomics data are typically derived from small numbers of samples, making a population-based analysis less informative. Therefore, we have integrated glycoproteomic data into our target selection pipeline in two ways. First, glycoproteomics has been used to profile the cell surface of 11 tumor cell lines. Using an approach with high specificity for cell-surface glycoproteins, over 200 cell-surface proteins have been identified for each cell line. This data enables the selection of targets that are amenable to our in vitro functional assay for ADC activity, based on expression in our screen-adapted cell lines. Glycoproteomics has also been used to identify and prioritize targets upregulated during hypoxia or epithelial-mesenchymal transition, two important aspects of tumor biology. For example, cellular glycoproteins from four pancreatic cell lines were profiled under normoxic and hypoxic conditions, identifying >70 proteins upregulated under hypoxic conditions. These glycoproteomic datasets, in conjunction with the public data analysis, are being used to identify promising ADC targets. Based on these target selection methods, we are currently producing and screening thousands of NRC monoclonal and single-domain antibodies generated against a variety of cancer-associated cell surface targets and screening them for ADC activity, in vitro and in vivo. Citation Format: Jennifer J. Hill, François Fauteux, Tammy-Lynn Tremblay, Maria Jaramillo. Identification of antibody-drug conjugate targets using curated public data, in-house glycoproteomics, and a surrogate in vitro ADC assay [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A189.

ScanGEO: parallel mining of high-throughput gene expression data

Current options to mine publicly available gene expression data deposited in NCBI's gene expression omnibus (GEO), such as the GEO web portal and related applications, are optimized to reanalyze a single study, or search for a single gene, and therefore require manual intervention to reanalyze multiple studies for user-specified gene sets. ScanGEO is a simple, user-friendly Shiny web application designed to identify differentially expressed genes across all GEO studies matching user-specified criteria, for a flexible set of genes, visualize results and provide summary statistics and other reports using a single command. The ScanGEO source code is written in R and implemented as a Shiny app that can be freely accessed at http://scangeo.dartmouth.edu/ScanGEO/. For users who would like to run a local instantiation of the app, the R source code is available under a GNU GPLv3 license at https://github.com/StantonLabDartmouth/AppScanGEO. katja.koeppen@dartmouth.edu. Supplementary data are available at Bioinformatics online.

New heuristics for the Bicluster Editing Problem

The NP-hard Bicluster Editing Problem (BEP) consists of editing a minimum number of edges of an input bipartite graph G in order to transform it into a vertex-disjoint union of complete bipartite subgraphs. Editing an edge consists of either adding it to the graph or deleting it from the graph. Applications of the BEP include data mining and analysis of gene expression data. In this work, we generate and analyze random bipartite instances for the BEP to perform empirical tests. A new reduction rule for the problem is proposed, based on the concept of critical independent sets, providing an effective reduction in the size of the instances. We also propose a set of heuristics using concepts of the metaheuristics ILS, VNS, and GRASP, including a constructive heuristic based on analyzing vertex neighborhoods, three local search procedures, and an auxiliary data structure to speed up the local search. Computational experiments show that our heuristics outperform other methods from the literature with respect to both solution quality and computational time.

Exploration of Stereoselectivity in Embryo-Larvae (Danio rerio) Induced by Chiral PCB149 at the Bioconcentration and Gene Expression Levels.

This paper was designed to study stereoselective enrichment and changes in gene expression when zebrafish (Danio rerio) embryo-larvae were exposed to racemic, (-)- or (+)- PCB149 (2,2’,3,4’,5’,6- hexachlorobiphenyl). Based on bioconcentration analysis, non-racemic enrichment was significantly observed after racemic exposure. No isomerization between the two isomers was found after (-)/(+)-PCB149 exposure. Furthermore, based on gene expression-data mining, CYPs genes (cyp2k6, cyp19a1b, and cyp2aa4) were differential genes after (+)-PCB149 exposure. No obvious differences of dysregulation of gene expression caused by racemic and (-)-PCB149, were observed in embryo-larvae. The above results suggested that (-)-PCB149 could be considered as the main factor causing the dysregulation of gene expression in embryo-larvae after racemic exposure; and (+)-PCB149 should be pursued apart from the racemate, when considering the toxicity of chiral PCB149. Thus, the information in our study could provide new insights to assess the environmental risk of chiral PCBs in aquatic systems.

Cartography of Pathway Signal Perturbations Identifies Distinct Molecular Pathomechanisms in Malignant and Chronic Lung Diseases.

Lung diseases are described by a wide variety of developmental mechanisms and clinical manifestations. Accurate classification and diagnosis of lung diseases are the bases for development of effective treatments. While extensive studies are conducted toward characterization of various lung diseases at molecular level, no systematic approach has been developed so far. Here we have applied a methodology for pathway-centered mining of high throughput gene expression data to describe a wide range of lung diseases in the light of shared and specific pathway activity profiles. We have applied an algorithm combining a Pathway Signal Flow (PSF) algorithm for estimation of pathway activity deregulation states in lung diseases and malignancies, and a Self Organizing Maps algorithm for classification and clustering of the pathway activity profiles. The analysis results allowed clearly distinguish between cancer and non-cancer lung diseases. Lung cancers were characterized by pathways implicated in cell proliferation, metabolism, while non-malignant lung diseases were characterized by deregulations in pathways involved in immune/inflammatory response and fibrotic tissue remodeling. In contrast to lung malignancies, chronic lung diseases had relatively heterogeneous pathway deregulation profiles. We identified three groups of interstitial lung diseases and showed that the development of characteristic pathological processes, such as fibrosis, can be initiated by deregulations in different signaling pathways. In conclusion, this paper describes the pathobiology of lung diseases from systems viewpoint using pathway centered high-dimensional data mining approach. Our results contribute largely to current understanding of pathological events in lung cancers and non-malignant lung diseases. Moreover, this paper provides new insight into molecular mechanisms of a number of interstitial lung diseases that have been studied to a lesser extent.

Integrated pathway-based transcription regulation network mining and visualization based on gene expression profiles

Conventionally, workflows examining transcription regulation networks from gene expression data involve distinct analytical steps. There is a need for pipelines that unify data mining and inference deduction into a singular framework to enhance interpretation and hypotheses generation. We propose a workflow that merges network construction with gene expression data mining focusing on regulation processes in the context of transcription factor driven gene regulation. The pipeline implements pathway-based modularization of expression profiles into functional units to improve biological interpretation. The integrated workflow was implemented as a web application software (TransReguloNet) with functions that enable pathway visualization and comparison of transcription factor activity between sample conditions defined in the experimental design. The pipeline merges differential expression, network construction, pathway-based abstraction, clustering and visualization. The framework was applied in analysis of actual expression datasets related to lung, breast and prostrate cancer.

RNA Sequencing of Formalin-Fixed, Paraffin-Embedded Specimens for Gene Expression Quantification and Data Mining.

Background. Proper rRNA depletion is crucial for the successful utilization of FFPE specimens when studying gene expression. We performed a study to evaluate two major rRNA depletion methods: Ribo-Zero and RNase H. RNAs extracted from 4 samples were treated with the two rRNA depletion methods in duplicate and sequenced (N = 16). We evaluated their reducibility, ability to detect RNA, and ability to molecularly subtype these triple negative breast cancer specimens. Results. Both rRNA depletion methods produced consistent data between the technical replicates. We found that the RNase H method produced higher quality RNAseq data as compared to the Ribo-Zero method. In addition, we evaluated the RNAseq data generated from the FFPE tissue samples for noncoding RNA, including lncRNA, enhancer/super enhancer RNA, and single nucleotide variation (SNV). We found that the RNase H is more suitable for detecting high-quality, noncoding RNAs as compared to the Ribo-Zero and provided more consistent molecular subtype identification between replicates. Unfortunately, neither method produced reliable SNV data. Conclusions. In conclusion, for FFPE specimens, the RNase H rRNA depletion method performed better than the Ribo-Zero. Neither method generates data sufficient for SNV detection.

A Computational Gene Expression Score for Predicting Immune Injury in Renal Allografts.

BackgroundWhole genome microarray meta-analyses of 1030 kidney, heart, lung and liver allograft biopsies identified a common immune response module (CRM) of 11 genes that define acute rejection (AR) across different engrafted tissues. We evaluated if the CRM genes can provide a molecular microscope to quantify graft injury in acute rejection (AR) and predict risk of progressive interstitial fibrosis and tubular atrophy (IFTA) in histologically normal kidney biopsies.MethodsComputational modeling was done on tissue qPCR based gene expression measurements for the 11 CRM genes in 146 independent renal allografts from 122 unique patients with AR (n = 54) and no-AR (n = 92). 24 demographically matched patients with no-AR had 6 and 24 month paired protocol biopsies; all had histologically normal 6 month biopsies, and 12 had evidence of progressive IFTA (pIFTA) on their 24 month biopsies. Results were correlated with demographic, clinical and pathology variables.ResultsThe 11 gene qPCR based tissue CRM score (tCRM) was significantly increased in AR (5.68 ± 0.91) when compared to STA (1.29 ± 0.28; p < 0.001) and pIFTA (7.94 ± 2.278 versus 2.28 ± 0.66; p = 0.04), with greatest significance for CXCL9 and CXCL10 in AR (p <0.001) and CD6 (p<0.01), CXCL9 (p<0.05), and LCK (p<0.01) in pIFTA. tCRM was a significant independent correlate of biopsy confirmed AR (p < 0.001; AUC of 0.900; 95% CI = 0.705–903). Gene expression modeling of 6 month biopsies across 7/11 genes (CD6, INPP5D, ISG20, NKG7, PSMB9, RUNX3, and TAP1) significantly (p = 0.037) predicted the development of pIFTA at 24 months.ConclusionsGenome-wide tissue gene expression data mining has supported the development of a tCRM-qPCR based assay for evaluating graft immune inflammation. The tCRM score quantifies injury in AR and stratifies patients at increased risk of future pIFTA prior to any perturbation of graft function or histology.