Cross-species Gene Expression Analysis Research Articles

Cross-species gene expression analysis of human, canine and murine natural killer cells suggests convergence of activated dog and human transcriptomic profiles

Abstract Key species differences exist between human and murine natural killer (NK) cells. Although outbred dogs are a link for translational NK studies, better characterization of dog NK cells is needed. We used RNAseq to profile resting and activated NK cells from mice, dogs and humans. Cells were isolated based on putative NK cell populations from healthy humans (CD56+CD3−), beagles (CD5dim) and C57BL/6J mice (NK1.1+CD3−) and sequenced from steady-state or following exposure to irradiated human feeder cells (K562 clone 9) with 100 IU/mL rhIL-2 × 14 days (human and dog) or 1000 IU/mL rhIL-2 × 7 days. Purity of expanded human and dog NK cells was ~ 90%, while for mouse it was ~ 70%. We performed differential gene expression (DGE) across species and conditions using ~7000 1:1 orthologous genes. DGE revealed distinct transcriptional profiles for each species (FDR <0.05). Hierarchical clustering demonstrated mouse NK cells as an outgroup compared to dog and human. PCA showed within-species spatial clustering of resting NK cells. After activation, variance between dog and human NK cells decreased, variance between human and mouse NK cells increased (PC1 40%, PC2 28%). Species differed in expression of key genes after activation, including PRF1, GZMA, SPP1, IL2RB, CTSD and FCER1G, which showed greater increases in abundance counts in mice than dogs and humans. In this first transcriptomic sequencing of dog NK cells, we show distinct gene profiles of resting cells across the most commonly used mouse, dog, and human NK populations, with convergence of dog and human NK cells upon stimulation under these standard conditions. By defining the cross-species comparative transcriptional profiles of putative NK cells, these data fill a gap in translational NK studies.

Novel MYC-driven medulloblastoma models from multiple embryonic cerebellar cells

Group3 medulloblastoma (MBG3) that predominantly occur in young children are usually associated with MYC amplification and/or overexpression, frequent metastasis and a dismal prognosis. Physiologically relevant MBG3 models are currently lacking, making inferences related to their cellular origin thus far limited. Using in utero electroporation, we here report that MBG3 mouse models can be developed in situ from different multipotent embryonic cerebellar progenitor cells via conditional expression of Myc and loss of Trp53 function in several Cre driver mouse lines. The Blbp-Cre driver that targets embryonic neural progenitors induced tumors exhibiting a large-cell/anaplastic histopathology adjacent to the fourth ventricle, recapitulating human MBG3. Enforced co-expression of luciferase together with Myc and a dominant-negative form of Trp53 revealed that GABAergic neuronal progenitors as well as cerebellar granule cells give rise to MBG3 with their distinct growth kinetics. Cross-species gene expression analysis revealed that these novel MBG3 models shared molecular characteristics with human MBG3, irrespective of their cellular origin. We here developed MBG3 mouse models in their physiological environment and we show that oncogenic insults drive this MB subgroup in different cerebellar lineages rather than in a specific cell of origin.

Truncating PREX2 mutations activate its GEF activity and alter gene expression regulation in NRAS-mutant melanoma

PREX2 (phosphatidylinositol-3,4,5-triphosphate-dependent Rac-exchange factor 2) is a PTEN (phosphatase and tensin homolog deleted on chromosome 10) binding protein that is significantly mutated in cutaneous melanoma and pancreatic ductal adenocarcinoma. Here, genetic and biochemical analyses were conducted to elucidate the nature and mechanistic basis of PREX2 mutation in melanoma development. By generating an inducible transgenic mouse model we showed an oncogenic role for a truncating PREX2 mutation (PREX2(E824)*) in vivo in the context of mutant NRAS. Using integrative cross-species gene expression analysis, we identified deregulated cell cycle and cytoskeleton organization as significantly perturbed biological pathways in PREX2 mutant tumors. Mechanistically, truncation of PREX2 activated its Rac1 guanine nucleotide exchange factor activity, abolished binding to PTEN and activated the PI3K (phosphatidyl inositol 3 kinase)/Akt signaling pathway. We further showed that PREX2 truncating mutations or PTEN deletion induces down-regulation of the tumor suppressor and cell cycle regulator CDKN1C (also known as p57(KIP2)). This down-regulation occurs, at least partially, through DNA hypomethylation of a differentially methylated region in chromosome 11 that is a known regulatory region for expression of the CDKN1C gene. Together, these findings identify PREX2 as a mediator of NRAS-mutant melanoma development that acts through the PI3K/PTEN/Akt pathway to regulate gene expression of a cell cycle regulator.

Cross-species gene expression analysis identifies a novel set of genes implicated in human insulin sensitivity

Objective:Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavored to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans.Methods:We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single gene, gene set, and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in three independent human data sets (n=115).Results:This GEM of 93 genes substantially improved diagnosis of IR compared with routine clinical measures across multiple independent data sets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between β-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action.Conclusions:This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the β-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation.

A Computational Pipeline for Cross-Species Analysis of RNA-seq Data Using R and Bioconductor.

RNA sequencing (RNA-seq) has revolutionized transcriptome analysis through profiling the expression of thousands of genes at the same time. Systematic analysis of orthologous transcripts across species is critical for understanding the evolution of gene expression and uncovering important information in animal models of human diseases. Several computational methods have been published for analyzing gene expression between species, but they often lack crucial details and therefore cannot serve as a practical guide. Here, we present the first step-by-step protocol for cross-species RNA-seq analysis with a concise workflow that is largely based on the free open-source R language and Bioconductor packages. This protocol covers the entire process from short-read mapping, gene expression quantification, differential expression analysis to pathway enrichment. Many useful utilities for data visualization are included. This complete and easy-to-follow protocol provides hands-on guidance for users who are new to cross-species gene expression analysis.

Cross-species gene expression analysis of species specific differences in the preclinical assessment of pharmaceutical compounds.

Animals are frequently used as model systems for determination of safety and efficacy in pharmaceutical research and development. However, significant quantitative and qualitative differences exist between humans and the animal models used in research. This is as a result of genetic variation between human and the laboratory animal. Therefore the development of a system that would allow the assessment of all molecular differences between species after drug exposure would have a significant impact on drug evaluation for toxicity and efficacy. Here we describe a cross-species microarray methodology that identifies and selects orthologous probes after cross-species sequence comparison to develop an orthologous cross-species gene expression analysis tool. The assumptions made by the use of this orthologous gene expression strategy for cross-species extrapolation is that; conserved changes in gene expression equate to conserved pharmacodynamic endpoints. This assumption is supported by the fact that evolution and selection have maintained the structure and function of many biochemical pathways over time, resulting in the conservation of many important processes. We demonstrate this cross-species methodology by investigating species specific differences of the peroxisome proliferator-activator receptor (PPAR) α response in rat and human.

A novel method for cross-species gene expression analysis

BackgroundAnalysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no one-to-one correspondence between genes from different species which makes comparison of their expression profiles complex.ResultsIn this paper we describe a new method for cross-species meta-analysis of gene expression. The method takes the homology structure between compared species into account and can therefore compare expression data from genes with any number of orthologs and paralogs. A simulation study shows that the proposed method results in a substantial increase in statistical power compared to previously suggested procedures. As a proof of concept, we analyzed microarray data from heat stress experiments performed in eight species and identified several well-known evolutionarily conserved transcriptional responses. The method was also applied to gene expression profiles from five studies of estrogen exposed fish and both known and potentially novel responses were identified.ConclusionsThe method described in this paper will further increase the potential and reliability of meta-analysis of gene expression profiles from evolutionarily distant species. The method has been implemented in R and is freely available at http://bioinformatics.math.chalmers.se/Xspecies/.

An expression signature of phenotypic resistance to hepatocellular carcinoma identified by cross-species gene expression analysis

Hepatocarcinogenesis is under polygenic control. We analyzed gene expression patterns of dysplastic liver nodules (DNs) and hepatocellular carcinomas (HCCs) chemically-induced in F344 and BN rats, respectively susceptible and resistant to hepatocarcinogenesis. Expression profiles were performed by microarray and validated by quantitative RT-PCR and Western blot. Cluster analysis revealed two distinctive gene expression patterns, the first of which included normal liver of both strains and BN nodules, and the second one F344 nodules and HCC of both strains. We identified a signature predicting DN and HCC progression, characterized by highest expression of oncosuppressors Csmd1, Dmbt1, Dusp1, and Gnmt, in DNs, and Bhmt, Dmbt1, Dusp1, Gadd45g, Gnmt, Napsa, Pp2ca, and Ptpn13 in HCCs of resistant rats. Integrated gene expression data revealed highest expression of proliferation-related CTGF, c-MYC, and PCNA, and lowest expression of BHMT, DMBT1, DUSP1, GADD45g, and GNMT, in more aggressive rat and human HCC. BHMT, DUSP1, and GADD45g expression predicted patients' survival. Our results disclose, for the first time, a major role of oncosuppressor genes as effectors of genetic resistance to hepatocarcinogenesis. Comparative functional genomic analysis allowed discovering an evolutionarily conserved gene expression signature discriminating HCC with different propensity to progression in rat and human.

Abstract SY01-03: Comparative genomics as a tool for the identification of novel Kras synthetic lethal interactions

Abstract Gene expression profiling has been widely used as a tool to characterize genome-wide changes in expression level in various cancers. Expression profiling is generally used to identify specific genes with altered expression or to identify patterns that distinguish cancer subtypes. More recently, several approaches to use expression data to identify interactions between genes or build cancer-specific networks have been described. We have previously demonstrated that cross-species gene expression analysis can be used as a tool to identify oncogene-specific alterations in gene expression. Cross-species analysis is useful for this because it allows for filtering of noise inherent in human gene expression analysis. The identification of an oncogenic Kras-specific gene expression signature was accomplished using this approach. A central focus of our laboratory has been to use this and other oncogene-specific signatures to identify new critical downstream pathways necessary for oncogene function. Using several computational approaches, we identified several upstream transcriptional regulators of our previously identified oncogenic Kras signature. We hypothesized that some of the transcriptional regulators represent specific dependencies that are required for Kras to function as an oncogene. Thus, inhibition of the transcription factors could uncover novel synthetic lethal interactions specific for cells that express oncogenic Kras. Using an shRNA pooled library, we have confirmed one such synthetic lethal interaction betweeen oncogenic Kras and the Wilms-tumor 1 (Wt1), a gene known to function as a tumor suppressor in some contexts. Loss of Wt1 induces senescence in primary cells expressing oncogenic Kras. Human non-small cell lung cancer cells expressing oncogenic Kras also become senescent when Wt1 is knocked-down using shRNA. Thus, synthetic senescence is induced on Wt1 loss in oncogenic Kras expressing cells. A role for Wt1 in Kras-driven oncogenesis was further confirmed using a mouse model for lung cancer in which a mouse carrying a conditional Kras allele was crossed a mouse carrying an allelel that allows for conditional deletion of Wt1. The biological signficance of this intereation was further demonstrated by using a “Wt1 “high” or “Wt1 low” signature to classify human NSCLC gene expression profiles. This allowed us to determine that human tumor samples that express oncogenic Kras and express high levels of Wt1-regulated genes have a worse prognosis compared to samples expressing oncogenic Kras but having low low level of Wt1-regulated genes. In human NSCLC cell lines, loss of Wt1 leads to significant upregulation of the cell cycle regulator p21. Wt1 appears to regulate p21 levels posttranscriptionally, as p21 mRNA levels did not change. Further studies to elucidate the mechanisms leading to p21 upregulation upon Wt1 loss are underway and preliminary results will be discussed. To take these studies further, we began by generating a more refined Kras-specific signature using FACs-sorted tumor cells from the LSL-KrasG12D mouse model crossed to a fluorescent reporter. We have used this mouse-generated Kras signature for cross-species analysis using a much larger NSCLC dataset than was possible in our previously published studies. We then applied the ARACNE algorithm to identify transcription factors that regulate this Kras-signature. Using this approach, we have identified other transcription factors that are potential regulators of oncogenic Kras. The fact that Wt1 once again was identified as a regulator of oncogenic Kras using this novel method suggests the validity of this approach. Preliminary results testing the functional significance of these transcription factors for Kras-driven oncogenesis will be discussed. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr SY01-03. doi:10.1158/1538-7445.AM2011-SY01-03

TEMPORAL GRAPHICAL MODELS FOR CROSS-SPECIES GENE REGULATORY NETWORK DISCOVERY

Many genes and biological processes function in similar ways across different species. Cross-species gene expression analysis, as a powerful tool to characterize the dynamical properties of the cell, has found a number of applications, such as identifying a conserved core set of cell cycle genes. However, to the best of our knowledge, there is limited effort on developing appropriate techniques to capture the causality relations between genes from time-series microarray data across species. In this paper, we present hidden Markov random field regression with L(1) penalty to uncover the regulatory network structure for different species. The algorithm provides a framework for sharing information across species via hidden component graphs and is able to incorporate domain knowledge across species easily. We demonstrate our method on two synthetic datasets and apply it to discover causal graphs from innate immune response data.

INTRODUCTION: ADVANCES IN COMPUTATIONAL SYSTEMS BIOINFORMATICS

This special issue of the Journal of Bioinformatics and Computational Biology is devoted to the Computational Systems Bioinformatics Conference (CSB) held in August 2010 at Stanford University. Out of 19 peer-reviewed manuscripts that have been presented at the conference and subsequently been published in the Proceedings of the Nineth Computational Systems Bioinformatics Conference (CSB’10), we selected eight papers to be published here. The selected papers have reached the highest scores in the initial peer-reviewing process. As we offered the authors to expand the conference manuscript by up to 30%, a second review process has been conducted to strengthen their scientific quality. The final result is this exciting collection of eight high-quality papers: Parker et al. present in “Optimization of therapeutic proteins to delete T-Cell epitopes while maintaining beneficial residue interactions” an integer programming approach that attacks the NP-hard problem of selecting sets of mutations predicted to delete immunogenic T-cell epitopes, while simultaneously maintaining important residues and residue interactions. In “Temporal graphical models for cross-species gene regulatory network discovery”, Liu et al. concentrated on cross-species gene expression analysis. They developed a hidden Markov random field regression to jointly uncover the regulatory networks for multiple species, thus capturing the causal relations between genes from time-series microarray data across species. In their paper “Classification of large microarray datasets using fast random forest construction”, Manilich et al. customized the widely used random forest classifier to address specific properties of microarray data. By reducing overlapping computations and eliminating dependency on the size of the main memory, their implementation shows an increased performance for this application. Ozer et al. studied ways to compare multiple ChIP-seq experiments in their manuscript “Comparing multiple ChIP-sequencing experiments” in order to attack the challenge of comparing multiple cell lines under different experimental conditions despite the massive amount of data produced by high-throughput sequencing

An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis

Using advanced gene targeting methods, generating mouse models of cancer that accurately reproduce the genetic alterations present in human tumors is now relatively straightforward. The challenge is to determine to what extent such models faithfully mimic human disease with respect to the underlying molecular mechanisms that accompany tumor progression. Here we describe a method for comparing mouse models of cancer with human tumors using gene-expression profiling. We applied this method to the analysis of a model of Kras2-mediated lung cancer and found a good relationship to human lung adenocarcinoma, thereby validating the model. Furthermore, we found that whereas a gene-expression signature of KRAS2 activation was not identifiable when analyzing human tumors with known KRAS2 mutation status alone, integrating mouse and human data uncovered a gene-expression signature of KRAS2 mutation in human lung cancer. We confirmed the importance of this signature by gene-expression analysis of short hairpin RNA-mediated inhibition of oncogenic Kras2. These experiments identified both a pattern of gene expression indicative of KRAS2 mutation and potential effectors of oncogenic KRAS2 activity in human cancer. This approach provides a strategy for using genomic analysis of animal models to probe human disease.