RESUMEN
Intestinal mesenchymal cells play essential roles in epithelial homeostasis, matrix remodeling, immunity, and inflammation. But the extent of heterogeneity within the colonic mesenchyme in these processes remains unknown. Using unbiased single-cell profiling of over 16,500 colonic mesenchymal cells, we reveal four subsets of fibroblasts expressing divergent transcriptional regulators and functional pathways, in addition to pericytes and myofibroblasts. We identified a niche population located in proximity to epithelial crypts expressing SOX6, F3 (CD142), and WNT genes essential for colonic epithelial stem cell function. In colitis, we observed dysregulation of this niche and emergence of an activated mesenchymal population. This subset expressed TNF superfamily member 14 (TNFSF14), fibroblastic reticular cell-associated genes, IL-33, and Lysyl oxidases. Further, it induced factors that impaired epithelial proliferation and maturation and contributed to oxidative stress and disease severity in vivo. Our work defines how the colonic mesenchyme remodels to fuel inflammation and barrier dysfunction in IBD.
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Enfermedades Inflamatorias del Intestino/fisiopatología , Mesodermo/fisiología , Animales , Proliferación Celular , Colitis/genética , Colitis/fisiopatología , Colon/fisiología , Células Epiteliales/metabolismo , Fibroblastos/fisiología , Heterogeneidad Genética , Homeostasis , Humanos , Inflamación , Mucosa Intestinal/inmunología , Mucosa Intestinal/fisiología , Intestinos/inmunología , Intestinos/fisiología , Células Madre Mesenquimatosas/fisiología , Mesodermo/metabolismo , Ratones , Ratones Endogámicos C57BL , Miofibroblastos , Pericitos , Células RAW 264.7 , Factores de Transcripción SOXD/fisiología , Análisis de la Célula Individual/métodos , Tromboplastina/fisiología , Miembro 14 de la Superfamilia de Ligandos de Factores de Necrosis Tumoral/genética , Vía de Señalización Wnt/fisiologíaRESUMEN
Motivation: RNA sequencing of single cells enables characterization of transcriptional heterogeneity in seemingly homogeneous cell populations. Single-cell sequencing has been applied in a wide range of researches fields. However, few studies have focus on characterization of isoform-level expression patterns at the single-cell level. In this study, we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of isoform pairs from the same gene in single-cell isoform-level expression data. Results: We define six principal patterns of isoform expression relationships and describe a method for differential-pattern analysis. We demonstrate ISOP through analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in three independent datasets. We assigned the pattern types to each of 16 562 isoform-pairs from 4929 genes. Among those, 26% of the discovered patterns were significant (P<0.05), while remaining patterns are possibly effects of transcriptional bursting, drop-out and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differential-pattern analysis were not detected by differential-expression analysis. Finally, the effects of drop-out events and expression levels of isoforms on ISOP's performances were investigated through simulated datasets. To conclude, ISOP provides a novel approach for characterization of isoform-level preference, commitment and heterogeneity in single-cell RNA-sequencing data. Availability and implementation: The ISOP method has been implemented as a R package and is available at https://github.com/nghiavtr/ISOP under a GPL-3 license. Supplementary information: Supplementary data are available at Bioinformatics online.
Asunto(s)
Perfilación de la Expresión Génica/métodos , Expresión Génica , Isoformas de ARN/genética , Análisis de Secuencia de ARN/métodos , Programas Informáticos , Neoplasias de la Mama/genética , Línea Celular Tumoral , Femenino , HumanosRESUMEN
Motivation: Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalization. Results: We have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalization and visualization of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development. Availability and Implementation: The open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater . Contact: davis@ebi.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.
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Lenguajes de Programación , Análisis de Secuencia de ARN/métodos , Análisis de Secuencia de ARN/normas , Análisis de la Célula Individual/métodos , Programas Informáticos , Línea Celular , Humanos , Análisis de Componente Principal , Control de Calidad , ARN/genética , Estadística como AsuntoRESUMEN
BACKGROUND: Single-cell RNA-Seq can be a valuable and unbiased tool to dissect cellular heterogeneity, despite the transcriptome's limitations in describing higher functional phenotypes and protein events. Perhaps the most important shortfall with transcriptomic 'snapshots' of cell populations is that they risk being descriptive, only cataloging heterogeneity at one point in time, and without microenvironmental context. Studying the genetic ('nature') and environmental ('nurture') modifiers of heterogeneity, and how cell population dynamics unfold over time in response to these modifiers is key when studying highly plastic cells such as macrophages. RESULTS: We introduce the programmable Polaris™ microfluidic lab-on-chip for single-cell sequencing, which performs live-cell imaging while controlling for the culture microenvironment of each cell. Using gene-edited macrophages we demonstrate how previously unappreciated knockout effects of SAMHD1, such as an altered oxidative stress response, have a large paracrine signaling component. Furthermore, we demonstrate single-cell pathway enrichments for cell cycle arrest and APOBEC3G degradation, both associated with the oxidative stress response and altered proteostasis. Interestingly, SAMHD1 and APOBEC3G are both HIV-1 inhibitors ('restriction factors'), with no known co-regulation. CONCLUSION: As single-cell methods continue to mature, so will the ability to move beyond simple 'snapshots' of cell populations towards studying the determinants of population dynamics. By combining single-cell culture, live-cell imaging, and single-cell sequencing, we have demonstrated the ability to study cell phenotypes and microenvironmental influences. It's these microenvironmental components - ignored by standard single-cell workflows - that likely determine how macrophages, for example, react to inflammation and form treatment resistant HIV reservoirs.
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Interacción Gen-Ambiente , Macrófagos/citología , Análisis de Secuencia de ARN , Análisis de la Célula Individual , Técnicas de Inactivación de Genes , Humanos , Macrófagos/metabolismo , Fenotipo , Proteína 1 que Contiene Dominios SAM y HD/deficiencia , Proteína 1 que Contiene Dominios SAM y HD/genéticaRESUMEN
Recent advances in single-cell genomics are opening up unprecedented opportunities to transform cancer genomics. While bulk tissue genomic analysis across large populations of tumour cells has provided key insights into cancer biology, this approach does not provide the resolution that is critical for understanding the interaction between different genetic events within the cellular hierarchy of the tumour during disease initiation, evolution, relapse and metastasis. Single-cell genomic approaches are uniquely placed to definitively unravel complex clonal structures and tissue hierarchies, account for spatiotemporal cell interactions and discover rare cells that drive metastatic disease, drug resistance and disease progression. Here we present five challenges that need to be met for single-cell genomics to fulfil its potential as a routine tool alongside bulk sequencing. These might be thought of as being challenges related to samples (processing and scale for analysis), sensitivity and specificity of mutation detection, sources of heterogeneity (biological and technical), synergies (from data integration) and systems modelling. We discuss these in the context of recent advances in technologies and data modelling, concluding with implications for moving cancer research into the clinic.
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Genómica , Neoplasias/genética , Análisis de la Célula Individual/métodos , Animales , Humanos , Neoplasias/patología , Neoplasias/terapia , Sensibilidad y EspecificidadRESUMEN
MOTIVATION: Single-cell RNA-sequencing technology allows detection of gene expression at the single-cell level. One typical feature of the data is a bimodality in the cellular distribution even for highly expressed genes, primarily caused by a proportion of non-expressing cells. The standard and the over-dispersed gamma-Poisson models that are commonly used in bulk-cell RNA-sequencing are not able to capture this property. RESULTS: We introduce a beta-Poisson mixture model that can capture the bimodality of the single-cell gene expression distribution. We further integrate the model into the generalized linear model framework in order to perform differential expression analyses. The whole analytical procedure is called BPSC. The results from several real single-cell RNA-seq datasets indicate that â¼90% of the transcripts are well characterized by the beta-Poisson model; the model-fit from BPSC is better than the fit of the standard gamma-Poisson model in > 80% of the transcripts. Moreover, in differential expression analyses of simulated and real datasets, BPSC performs well against edgeR, a conventional method widely used in bulk-cell RNA-sequencing data, and against scde and MAST, two recent methods specifically designed for single-cell RNA-seq data. AVAILABILITY AND IMPLEMENTATION: An R package BPSC for model fitting and differential expression analyses of single-cell RNA-seq data is available under GPL-3 license at https://github.com/nghiavtr/BPSC CONTACT: yudi.pawitan@ki.se or mattias.rantalainen@ki.se SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Perfilación de la Expresión Génica , Análisis de Secuencia de ARN , Análisis de la Célula Individual , Biología Computacional/métodos , Modelos Teóricos , ARNRESUMEN
Rapid development of next generation sequencing technology has enabled the identification of genomic alterations from short sequencing reads. There are a number of software pipelines available for calling single nucleotide variants from genomic DNA but, no comprehensive pipelines to identify, annotate and prioritize expressed SNVs (eSNVs) from non-directional paired-end RNA-Seq data. We have developed the eSNV-Detect, a novel computational system, which utilizes data from multiple aligners to call, even at low read depths, and rank variants from RNA-Seq. Multi-platform comparisons with the eSNV-Detect variant candidates were performed. The method was first applied to RNA-Seq from a lymphoblastoid cell-line, achieving 99.7% precision and 91.0% sensitivity in the expressed SNPs for the matching HumanOmni2.5 BeadChip data. Comparison of RNA-Seq eSNV candidates from 25 ER+ breast tumors from The Cancer Genome Atlas (TCGA) project with whole exome coding data showed 90.6-96.8% precision and 91.6-95.7% sensitivity. Contrasting single-cell mRNA-Seq variants with matching traditional multicellular RNA-Seq data for the MD-MB231 breast cancer cell-line delineated variant heterogeneity among the single-cells. Further, Sanger sequencing validation was performed for an ER+ breast tumor with paired normal adjacent tissue validating 29 out of 31 candidate eSNVs. The source code and user manuals of the eSNV-Detect pipeline for Sun Grid Engine and virtual machine are available at http://bioinformaticstools.mayo.edu/research/esnv-detect/.
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Perfilación de la Expresión Génica/métodos , Variación Genética , Análisis de Secuencia de ARN/métodos , Neoplasias de la Mama/genética , Línea Celular , Línea Celular Tumoral , Exoma , Femenino , Humanos , Mutación , Polimorfismo de Nucleótido Simple , Alineación de Secuencia , Análisis de la Célula Individual , Programas InformáticosRESUMEN
AIMS/HYPOTHESIS: Beta cell death is a hallmark of diabetes. It is not known whether specific cellular stresses associated with type 1 or type 2 diabetes require specific factors to protect pancreatic beta cells. No systematic comparison of endogenous soluble factors in the context of multiple pro-apoptotic conditions has been published. METHODS: Primary mouse islet cells were cultured in conditions mimicking five type 1 or type 2 diabetes-related stresses: basal 5 mmol/l glucose, cytokine cocktail (25 ng/ml TNF-α, 10 ng/ml IL-1ß, 10 ng/ml IFN-γ), 1 µmol/l thapsigargin, 1.5 mmol/l palmitate and 20 mmol/l glucose (all in the absence of serum). We surveyed the effects of a library of 206 endogenous factors (selected based on islet expression of their receptors) on islet cell survival through multi-parameter, live-cell imaging. RESULTS: Our survey pointed to survival factors exhibiting generalised protective effects across conditions meant to model different types of diabetes and stages of the diseases. For example, our survey and follow-up experiments suggested that OLFM1 is a novel protective factor for mouse and human beta cells across multiple conditions. Most strikingly, we also found specific protective survival factors for each model stress condition. For example, semaphorin4A (SEMA4A) was toxic to islet cells in the serum-free baseline and serum-free 20 mmol/l glucose conditions, but protective in the context of lipotoxicity. Rank product testing supported the consistency of our observations. CONCLUSIONS/INTERPRETATION: Collectively, our survey reveals previously unidentified islet cell survival factors and suggest their potential utility in individualised medicine.
Asunto(s)
Apoptosis , Diabetes Mellitus Experimental/fisiopatología , Células Secretoras de Insulina/citología , Animales , Células Cultivadas , Biología Computacional , Glucosa/metabolismo , Proteínas Fluorescentes Verdes/metabolismo , Humanos , Insulina/metabolismo , Interferón gamma/metabolismo , Interleucina-1beta/metabolismo , Ratones , Persona de Mediana Edad , Palmitatos/metabolismo , Transducción de Señal , Tapsigargina/metabolismo , Factor de Necrosis Tumoral alfa/metabolismoRESUMEN
Metabolic Syndrome (MetS) is highly prevalent and has considerable public health impact, but its underlying genetic factors remain elusive. To identify gene networks involved in MetS, we conducted whole-genome expression and genotype profiling on abdominal (ABD) and gluteal (GLU) adipose tissue, and whole blood (WB), from 29 MetS cases and 44 controls. Co-expression network analysis for each tissue independently identified nine, six, and zero MetS-associated modules of coexpressed genes in ABD, GLU, and WB, respectively. Of 8,992 probesets expressed in ABD or GLU, 685 (7.6%) were expressed in ABD and 51 (0.6%) in GLU only. Differential eigengene network analysis of 8,256 shared probesets detected 22 shared modules with high preservation across adipose depots (D(ABD-GLU)â=â0.89), seven of which were associated with MetS (FDR P<0.01). The strongest associated module, significantly enriched for immune response-related processes, contained 94/620 (15%) genes with inter-depot differences. In an independent cohort of 145/141 twins with ABD and WB longitudinal expression data, median variability in ABD due to familiality was greater for MetS-associated versus un-associated modules (ABD: 0.48 versus 0.18, Pâ=â0.08; GLU: 0.54 versus 0.20, Pâ=â7.8×10(-4)). Cis-eQTL analysis of probesets associated with MetS (FDR P<0.01) and/or inter-depot differences (FDR P<0.01) provided evidence for 32 eQTLs. Corresponding eSNPs were tested for association with MetS-related phenotypes in two GWAS of >100,000 individuals; rs10282458, affecting expression of RARRES2 (encoding chemerin), was associated with body mass index (BMI) (Pâ=â6.0×10(-4)); and rs2395185, affecting inter-depot differences of HLA-DRB1 expression, was associated with high-density lipoprotein (Pâ=â8.7×10(-4)) and BMI-adjusted waist-to-hip ratio (Pâ=â2.4×10(-4)). Since many genes and their interactions influence complex traits such as MetS, integrated analysis of genotypes and coexpression networks across multiple tissues relevant to clinical traits is an efficient strategy to identify novel associations.
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Tejido Adiposo/metabolismo , Perfilación de la Expresión Génica , Redes Reguladoras de Genes , Síndrome Metabólico/genética , Índice de Masa Corporal , Quimiocinas/genética , Femenino , Sitios Genéticos , Estudio de Asociación del Genoma Completo , Cadenas HLA-DRB1/genética , Humanos , Péptidos y Proteínas de Señalización Intercelular , Síndrome Metabólico/patología , Especificidad de Órganos , Fenotipo , Sitios de Carácter CuantitativoRESUMEN
The stochastic nature of generating eukaryotic transcripts challenges conventional methods for obtaining and analyzing single-cell gene expression data. In order to address the inherent noise, detailed methods are described on how to collect data on multiple genes in a large number of single cells using microfluidic arrays. As part of a study exploring the effect of genotype on Wnt pathway activation, data were collected for 96 qPCR assays on 1440 lymphoblastoid cells. The description of methods includes preliminary data processing steps. The methods used in the collection and analysis of single-cell qPCR data are contrasted with those used in conventional qPCR.
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Perfilación de la Expresión Génica/métodos , Células Progenitoras Linfoides/metabolismo , Reacción en Cadena en Tiempo Real de la Polimerasa , Análisis de la Célula Individual , Línea Celular , Interpretación Estadística de Datos , Humanos , Límite de Detección , Reacción en Cadena de la Polimerasa de Transcriptasa Inversa , Vía de Señalización WntRESUMEN
As a branch of pharmacogenomics aimed at predicting drug safety concerns, toxicogenomics drew much excitement with the emergence of technologies such as gene expression microarrays. A few years down the line, the evidence is scant that current approaches to toxicogenomics are really making an impact in areas such as preclinical toxicology. It has been argued that there needs to be a re-focus of application toward high-throughput approaches which combine the best of tissue and genomic modelling. This commentary gives a brief introduction to in vitro toxicogenomics, drawn from the perspectives of the specialist toxicogenomics company, SimuGen.
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Descubrimiento de Drogas , Industria Farmacéutica/tendencias , Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos , Regulación de la Expresión Génica/efectos de los fármacos , Toxicogenética/tendencias , Células Cultivadas , Seguridad de Productos para el Consumidor , Relación Dosis-Respuesta a Droga , Industria Farmacéutica/métodos , Ensayos Analíticos de Alto Rendimiento , Humanos , Modelos Biológicos , Análisis de Secuencia por Matrices de Oligonucleótidos , Toxicogenética/métodosRESUMEN
We demonstrate that model-based unsupervised learning can uniquely discriminate single-cell subpopulations by their gene expression distributions, which in turn allow us to identify specific genes for focused functional studies. This method was applied to MDA-MB-231 breast cancer cells treated with the antidiabetic drug metformin, which is being repurposed for treatment of triple-negative breast cancer. Unsupervised learning identified a cluster of metformin-treated cells characterized by a significant suppression of 230 genes (p-value < 2E-16). This analysis corroborates known studies of metformin action: a) pathway analysis indicated known mechanisms related to metformin action, including the citric acid (TCA) cycle, oxidative phosphorylation, and mitochondrial dysfunction (p-value < 1E-9); b) 70% of these 230 genes were functionally implicated in metformin response; c) among remaining lesser functionally-studied genes for metformin-response was CDC42, down-regulated in breast cancer treated with metformin. However, CDC42's mechanisms in metformin response remained unclear. Our functional studies showed that CDC42 was involved in metformin-induced inhibition of cell proliferation and cell migration mediated through an AMPK-independent mechanism. Our results points to 230 genes that might serve as metformin response signatures, which needs to be tested in patients treated with metformin and, further investigation of CDC42 and AMPK-independence's role in metformin's anticancer mechanisms.
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Proteínas Quinasas Activadas por AMP/metabolismo , Neoplasias de la Mama/metabolismo , Movimiento Celular/efectos de los fármacos , Metformina/farmacología , Transducción de Señal/efectos de los fármacos , Aprendizaje Automático no Supervisado , Neoplasias de la Mama/tratamiento farmacológico , Neoplasias de la Mama/genética , Neoplasias de la Mama/patología , Línea Celular Tumoral , Movimiento Celular/genética , Análisis por Conglomerados , Biología Computacional/métodos , Femenino , Perfilación de la Expresión Génica , Regulación Neoplásica de la Expresión Génica/efectos de los fármacos , Técnicas de Silenciamiento del Gen , Humanos , Proteína de Unión al GTP cdc42/genéticaRESUMEN
Worldwide efforts are underway to replace or repair lost or dysfunctional pancreatic ß-cells to cure diabetes. However, it is unclear what the final product of these efforts should be, as ß-cells are thought to be heterogeneous. To enable the analysis of ß-cell heterogeneity in an unbiased and quantitative way, we developed model-free and model-based statistical clustering approaches, and created new software called TraceCluster. Using an example data set, we illustrate the utility of these approaches by clustering dynamic intracellular Ca(2+) responses to high glucose in â¼300 simultaneously imaged single islet cells. Using feature extraction from the Ca(2+) traces on this reference data set, we identified 2 distinct populations of cells with ß-like responses to glucose. To the best of our knowledge, this report represents the first unbiased cluster-based analysis of human ß-cell functional heterogeneity of simultaneous recordings. We hope that the approaches and tools described here will be helpful for those studying heterogeneity in primary islet cells, as well as excitable cells derived from embryonic stem cells or induced pluripotent cells.
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Señalización del Calcio/fisiología , Calcio/metabolismo , Glucosa/metabolismo , Islotes Pancreáticos/citología , Animales , Humanos , Islotes Pancreáticos/metabolismo , Programas InformáticosRESUMEN
Gene expression in multiple individual cells from a tissue or culture sample varies according to cell-cycle, genetic, epigenetic and stochastic differences between the cells. However, single-cell differences have been largely neglected in the analysis of the functional consequences of genetic variation. Here we measure the expression of 92 genes affected by Wnt signaling in 1,440 single cells from 15 individuals to associate single-nucleotide polymorphisms (SNPs) with gene-expression phenotypes, while accounting for stochastic and cell-cycle differences between cells. We provide evidence that many heritable variations in gene function--such as burst size, burst frequency, cell cycle-specific expression and expression correlation/noise between cells--are masked when expression is averaged over many cells. Our results demonstrate how single-cell analyses provide insights into the mechanistic and network effects of genetic variability, with improved statistical power to model these effects on gene expression.
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Expresión Génica , Estudios de Asociación Genética , Sitios de Carácter Cuantitativo/genética , Vía de Señalización Wnt/genética , Mapeo Cromosómico , Perfilación de la Expresión Génica , Humanos , Análisis de Secuencia por Matrices de Oligonucleótidos , Polimorfismo de Nucleótido Simple , Análisis de la Célula IndividualRESUMEN
To understand how miRNAs contribute to the molecular phenotype of adipose tissues and related traits, we performed global miRNA expression profiling in subcutaneous abdominal and gluteal adipose tissue of 70 human subjects and characterised which miRNAs were differentially expressed between these tissues. We found that 12% of the miRNAs were significantly differentially expressed between abdominal and gluteal adipose tissue (FDR adjusted p<0.05) in the primary study, of which 59 replicated in a follow-up study of 40 additional subjects. Further, 14 miRNAs were found to be associated with metabolic syndrome case-control status in abdominal tissue and three of these replicated (primary study: FDR adjusted p<0.05, replication: p<0.05 and directionally consistent effect). Genome-wide genotyping was performed in the 70 subjects to enable miRNA expression quantitative trait loci (eQTL) analysis. Candidate miRNA eQTLs were followed-up in the additional 40 subjects and six significant, independent cis-located miRNA eQTLs (primary study: p<0.001; replication: p<0.05 and directionally consistent effect) were identified. Finally, global mRNA expression profiling was performed in both tissues to enable association analysis between miRNA and target mRNA expression levels. We find 22% miRNAs in abdominal and 9% miRNAs in gluteal adipose tissue with expression levels significantly associated with the expression of corresponding target mRNAs (FDR adjusted p<0.05). Taken together, our results indicate a clear difference in the miRNA molecular phenotypic profile of abdominal and gluteal adipose tissue, that the expressions of some miRNAs are influenced by cis-located genetic variants and that miRNAs are associated with expression levels of their predicted mRNA targets.
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Grasa Abdominal/fisiología , Biomarcadores/metabolismo , Nalgas/fisiología , MicroARNs/genética , Sitios de Carácter Cuantitativo , ARN Mensajero/genética , Adulto , Estudios de Casos y Controles , Femenino , Estudios de Seguimiento , Perfilación de la Expresión Génica , Estudio de Asociación del Genoma Completo , Genotipo , Humanos , Masculino , Síndrome Metabólico/genética , Análisis de Secuencia por Matrices de Oligonucleótidos , Polimorfismo de Nucleótido Simple/genéticaRESUMEN
BACKGROUND: MicroRNAs (miRNAs) are non-coding RNA molecules involved in post-transcriptional control of gene expression of a wide number of genes, including those involved in glucose homeostasis. Type 2 diabetes (T2D) is characterized by hyperglycaemia and defects in insulin secretion and action at target tissues. We sought to establish differences in global miRNA expression in two insulin-target tissues from inbred rats of spontaneously diabetic and normoglycaemic strains. METHODS: We used a miRNA microarray platform to measure global miRNA expression in two insulin-target tissues: liver and adipose tissue from inbred rats of spontaneously diabetic (Goto-Kakizaki [GK]) and normoglycaemic (Brown-Norway [BN]) strains which are extensively used in genetic studies of T2D. MiRNA data were integrated with gene expression data from the same rats to investigate how differentially expressed miRNAs affect the expression of predicted target gene transcripts. RESULTS: The expression of 170 miRNAs was measured in liver and adipose tissue of GK and BN rats. Based on a p-value for differential expression between GK and BN, the most significant change in expression was observed for miR-125a in liver (FC = 5.61, P = 0.001, Padjusted = 0.10); this overexpression was validated using quantitative RT-PCR (FC = 13.15, P = 0.0005). MiR-125a also showed over-expression in the GK vs. BN analysis within adipose tissue (FC = 1.97, P = 0.078, Padjusted = 0.99), as did the previously reported miR-29a (FC = 1.51, P = 0.05, Padjusted = 0.99). In-silico tools assessing the biological role of predicted miR-125a target genes suggest an over-representation of genes involved in the MAPK signaling pathway. Gene expression analysis identified 1308 genes with significantly different expression between GK and BN rats (Padjusted < 0.05): 233 in liver and 1075 in adipose tissue. Pathways related to glucose and lipid metabolism were significantly over-represented among these genes. Enrichment analysis suggested that differentially expressed genes in GK compared to BN included more predicted miR-125a target genes than would be expected by chance in adipose tissue (FDR = 0.006 for up-regulated genes; FDR = 0.036 for down-regulated genes) but not in liver (FDR = 0.074 for up-regulated genes; FDR = 0.248 for down-regulated genes). CONCLUSION: MiR-125a is over-expressed in liver in hyperglycaemic GK rats relative to normoglycaemic BN rats, and our array data also suggest miR-125a is over-expressed in adipose tissue. We demonstrate the use of in-silico tools to provide the basis for further investigation of the potential role of miR-125a in T2D. In particular, the enrichment of predicted miR-125a target genes among differentially expressed genes has identified likely target genes and indicates that integrating global miRNA and mRNA expression data may give further insights into miRNA-mediated regulation of gene expression.
RESUMEN
Toxicogenomics is, arguably, the most exciting endeavor to better understand and/or predict the toxicity of drugs during their development, using technologies such as gene-expression microarrays. Through much of its (sometimes overzealous) build-up, toxicogenomics has found a natural niche as a bioinformatics and/or pattern-recognition tool, aiming to improve the mechanistic understanding of toxicity and animal-to-human extrapolation. The problem is that current approaches still need maturing and are expensive, slow, highly variable, often qualitative and do not easily yield information useful to decide whether to progress a drug candidate or not. Most crucially, they fail to address the main problem that industry faces--to be able to predict toxicity earlier in the discovery/development process. Rather than providing a conventional animal toxicogenomics service, SimuGen Ltd is launching a product that models gene expression in human cell culture, using an entirely novel approach to predict a remarkable spectrum of human in vivo (clinical) toxic end points, and to predict dose-toxicity relationships and molecular structure-toxicity relationships.