Laura Badi

Expression profiling of human immune cell subsets identifies miRNA-mRNA regulatory relationships correlated with cell type specific expression

Blood consists of different cell populations with distinct functions and correspondingly, distinct gene expression profiles. In this study, global miRNA expression profiling was performed across a panel of nine human immune cell subsets (neutrophils, eosinophils, monocytes, B cells, NK cells, CD4 T cells, CD8 T cells, mDCs and pDCs) to identify cell-type specific miRNAs. mRNA expression profiling was performed on the same samples to determine if miRNAs specific to certain cell types down-regulated expression levels of their target genes. Six cell-type specific miRNAs (miR-143; neutrophil specific, miR-125; T cells and neutrophil specific, miR-500; monocyte and pDC specific, miR-150; lymphoid cell specific, miR-652 and miR-223; both myeloid cell specific) were negatively correlated with expression of their predicted target genes. These results were further validated using an independent cohort where similar immune cell subsets were isolated and profiled for both miRNA and mRNA expression. miRNAs which negatively correlated with target gene expression in both cohorts were identified as candidates for miRNA/mRNA regulatory pairs and were used to construct a cell-type specific regulatory network. miRNA/mRNA pairs formed two distinct clusters in the network corresponding to myeloid (nine miRNAs) and lymphoid lineages (two miRNAs). Several myeloid specific miRNAs targeted common genes including ABL2, EIF4A2, EPC1 and INO80D; these common targets were enriched for genes involved in the regulation of gene expression (p<9.0E-7). Those miRNA might therefore have significant further effect on gene expression by repressing the expression of genes involved in transcriptional regulation. The miRNA and mRNA expression profiles reported in this study form a comprehensive transcriptome database of various human blood cells and serve as a valuable resource for elucidating the role of miRNA mediated regulation in the establishment of immune cell identity.

Disease Modeling and Phenotypic Drug Screening for Diabetic Cardiomyopathy using Human Induced Pluripotent Stem Cells

Diabetic cardiomyopathy is a complication of type 2 diabetes, with known contributions of lifestyle and genetics. We develop environmentally and genetically driven in vitro models of the condition using human-induced-pluripotent-stem-cell-derived cardiomyocytes. First, we mimic diabetic clinical chemistry to induce a phenotypic surrogate of diabetic cardiomyopathy, observing structural and functional disarray. Next, we consider genetic effects by deriving cardiomyocytes from two diabetic patients with variable disease progression. The cardiomyopathic phenotype is recapitulated in the patient-specific cells basally, with a severity dependent on their original clinical status. These models are incorporated into successive levels of a screening platform, identifying drugs that preserve cardiomyocyte phenotype in vitro during diabetic stress. In this work, we present a patient-specific induced pluripotent stem cell (iPSC) model of a complex metabolic condition, showing the power of this technique for discovery and testing of therapeutic strategies for a disease with ever-increasing clinical significance.

Characterization of a novel CRAC inhibitor that potently blocks human T cell activation and effector functions

Store operated calcium entry (SOCE) downstream of T cell receptor (TCR) activation in T lymphocytes has been shown to be mediated mainly through the Calcium Release Activated Calcium (CRAC) channel. Here, we compared the effects of a novel, potent and selective CRAC current inhibitor, 2,6-Difluoro-N-{5-[4-methyl-1-(5-methyl-thiazol-2-yl)-1,2,5,6-tetrahydro-pyridin-3-yl]-pyrazin-2-yl}-benzamide (RO2959), on T cell effector functions with that of a previously reported CRAC channel inhibitor, YM-58483, and a calcineurin inhibitor Cyclosporin A (CsA). Using both electrophysiological and calcium-based fluorescence measurements, we showed that RO2959 is a potent SOCE inhibitor that blocked an IP3-dependent current in CRAC-expressing RBL-2H3 cells and CHO cells stably expressing human Orai1 and Stim1, as well as SOCE in human primary CD4(+) T cells triggered by either TCR stimulation or thapsigargin treatment. Furthermore, we demonstrated that RO2959 completely inhibited cytokine production as well as T cell proliferation mediated by TCR stimulation or MLR (mixed lymphocyte reaction). Lastly, we showed by gene expression array analysis that RO2959 potently blocked TCR triggered gene expression and T cell functional pathways similar to CsA and another calcineurin inhibitor FK506. Thus, both from a functional and transcriptional level, our data provide evidence that RO2959 is a novel and selective CRAC current inhibitor that potently inhibits human T cell functions.

Cardiac spheroids as promising in vitro models to study the human heart microenvironment

Three-dimensional in vitro cell systems are a promising alternative to animals to study cardiac biology and disease. We have generated three-dimensional in vitro models of the human heart (“cardiac spheroids”, CSs) by co-culturing human primary or iPSC-derived cardiomyocytes, endothelial cells and fibroblasts at ratios approximating those present in vivo. The cellular organisation, extracellular matrix and microvascular network mimic human heart tissue. These spheroids have been employed to investigate the dose-limiting cardiotoxicity of the common anti-cancer drug doxorubicin. Viability/cytotoxicity assays indicate dose-dependent cytotoxic effects, which are inhibited by the nitric oxide synthase (NOS) inhibitor L-NIO, and genetic inhibition of endothelial NOS, implicating peroxynitrous acid as a key damaging agent. These data indicate that CSs mimic important features of human heart morphology, biochemistry and pharmacology in vitro, offering a promising alternative to animals and standard cell cultures with regard to mechanistic insights and prediction of toxic effects in human heart tissue.

Functional analysis and transcriptional output of the Göttingen minipig genome.

BackgroundIn the past decade the Göttingen minipig has gained increasing recognition as animal model in pharmaceutical and safety research because it recapitulates many aspects of human physiology and metabolism. Genome-based comparison of drug targets together with quantitative tissue expression analysis allows rational prediction of pharmacology and cross-reactivity of human drugs in animal models thereby improving drug attrition which is an important challenge in the process of drug development.ResultsHere we present a new chromosome level based version of the Göttingen minipig genome together with a comparative transcriptional analysis of tissues with pharmaceutical relevance as basis for translational research. We relied on mapping and assembly of WGS (whole-genome-shotgun sequencing) derived reads to the reference genome of the Duroc pig and predict 19,228 human orthologous protein-coding genes. Genome-based prediction of the sequence of human drug targets enables the prediction of drug cross-reactivity based on conservation of binding sites. We further support the finding that the genome of Sus scrofa contains about ten-times less pseudogenized genes compared to other vertebrates. Among the functional human orthologs of these minipig pseudogenes we found HEPN1, a putative tumor suppressor gene. The genomes of Sus scrofa, the Tibetan boar, the African Bushpig, and the Warthog show sequence conservation of all inactivating HEPN1 mutations suggesting disruption before the evolutionary split of these pig species. We identify 133 Sus scrofa specific, conserved long non-coding RNAs (lncRNAs) in the minipig genome and show that these transcripts are highly conserved in the African pigs and the Tibetan boar suggesting functional significance. Using a new minipig specific microarray we show high conservation of gene expression signatures in 13 tissues with biomedical relevance between humans and adult minipigs. We underline this relationship for minipig and human liver where we could demonstrate similar expression levels for most phase I drug-metabolizing enzymes. Higher expression levels and metabolic activities were found for FMO1, AKR/CRs and for phase II drug metabolizing enzymes in minipig as compared to human. The variability of gene expression in equivalent human and minipig tissues is considerably higher in minipig organs, which is important for study design in case a human target belongs to this variable category in the minipig. The first analysis of gene expression in multiple tissues during development from young to adult shows that the majority of transcriptional programs are concluded four weeks after birth. This finding is in line with the advanced state of human postnatal organ development at comparative age categories and further supports the minipig as model for pediatric drug safety studies.ConclusionsGenome based assessment of sequence conservation combined with gene expression data in several tissues improves the translational value of the minipig for human drug development. The genome and gene expression data presented here are important resources for researchers using the minipig as model for biomedical research or commercial breeding. Potential impact of our data for comparative genomics, translational research, and experimental medicine are discussed.

Detect tissue heterogeneity in gene expression data with BioQC

BackgroundGene expression data can be compromised by cells originating from other tissues than the target tissue of profiling. Failures in detecting such tissue heterogeneity have profound implications on data interpretation and reproducibility. A computational tool explicitly addressing the issue is warranted.ResultsWe introduce BioQC, a R/Bioconductor software package to detect tissue heterogeneity in gene expression data. To this end BioQC implements a computationally efficient Wilcoxon-Mann-Whitney test and provides more than 150 signatures of tissue-enriched genes derived from large-scale transcriptomics studies.Simulation experiments show that BioQC is both fast and sensitive in detecting tissue heterogeneity. In a case study with whole-organ profiling data, BioQC predicted contamination events that are confirmed by quantitative RT-PCR. Applied to transcriptomics data of the Genotype-Tissue Expression (GTEx) project, BioQC reveals clustering of samples and suggests that some samples likely suffer from tissue heterogeneity.ConclusionsOur experience with gene expression data indicates a prevalence of tissue heterogeneity that often goes unnoticed. BioQC addresses the issue by integrating prior knowledge with a scalable algorithm. We propose BioQC as a first-line tool to ensure quality and reproducibility of gene expression data.

HtrA1 Mediated Intracellular Effects on Tubulin Using a Polarized RPE Disease Model

Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss. The protein HtrA1 is enriched in retinal pigment epithelial (RPE) cells isolated from AMD patients and in drusen deposits. However, it is poorly understood how increased levels of HtrA1 affect the physiological function of the RPE at the intracellular level. Here, we developed hfRPE (human fetal retinal pigment epithelial) cell culture model where cells fully differentiated into a polarized functional monolayer. In this model, we fine-tuned the cellular levels of HtrA1 by targeted overexpression. Our data show that HtrA1 enzymatic activity leads to intracellular degradation of tubulin with a corresponding reduction in the number of microtubules, and consequently to an altered mechanical cell phenotype. HtrA1 overexpression further leads to impaired apical processes and decreased phagocytosis, an essential function for photoreceptor survival. These cellular alterations correlate with the AMD phenotype and thus highlight HtrA1 as an intracellular target for therapeutic interventions towards AMD treatment.

Selective pharmacological inhibition of DDR1 prevents experimentally-induced glomerulonephritis in prevention and therapeutic regime

BackgroundDiscoidin domain receptor 1 (DDR1) is a collagen-activated receptor tyrosine kinase extensively implicated in diseases such as cancer, atherosclerosis and fibrosis. Multiple preclinical studies, performed using either a gene deletion or a gene silencing approaches, have shown this receptor being a major driver target of fibrosis and glomerulosclerosis.MethodsThe present study investigated the role and relevance of DDR1 in human crescentic glomerulonephritis (GN). Detailed DDR1 expression was first characterized in detail in human GN biopsies using a novel selective anti-DDR1 antibody using immunohistochemistry. Subsequently the protective role of DDR1 was investigated using a highly selective, novel, small molecule inhibitor in a nephrotoxic serum (NTS) GN model in a prophylactic regime and in the NEP25 GN mouse model using a therapeutic intervention regime.ResultsDDR1 expression was shown to be mainly limited to renal epithelium. In humans, DDR1 is highly induced in injured podocytes, in bridging cells expressing both parietal epithelial cell (PEC) and podocyte markers and in a subset of PECs forming the cellular crescents in human GN. Pharmacological inhibition of DDR1 in NTS improved both renal function and histological parameters. These results, obtained using a prophylactic regime, were confirmed in the NEP25 GN mouse model using a therapeutic intervention regime. Gene expression analysis of NTS showed that pharmacological blockade of DDR1 specifically reverted fibrotic and inflammatory gene networks and modulated expression of the glomerular cell gene signature, further validating DDR1 as a major mediator of cell fate in podocytes and PECs.ConclusionsTogether, these results suggest that DDR1 inhibition might be an attractive and promising pharmacological intervention for the treatment of GN, predominantly by targeting the renal epithelium.

Using OWL reasoning to support the generation of novel gene sets for enrichment analysis

BackgroundThe Gene Ontology (GO) consists of over 40,000 terms for biological processes, cell components and gene product activities linked into a graph structure by over 90,000 relationships. It has been used to annotate the functions and cellular locations of several million gene products. The graph structure is used by a variety of tools to group annotated genes into sets whose products share function or location. These gene sets are widely used to interpret the results of genomics experiments by assessing which sets are significantly over- or under-represented in results lists. F Hoffmann-La Roche Ltd. has developed a bespoke, manually maintained controlled vocabulary (RCV) for use in over-representation analysis. Many terms in this vocabulary group GO terms in novel ways that cannot easily be derived using the graph structure of the GO. For example, some RCV terms group GO terms by the cell, chemical or tissue type they refer to. Recent improvements in the content and formal structure of the GO make it possible to use logical queries in Web Ontology Language (OWL) to automatically map these cross-cutting classifications to sets of GO terms. We used this approach to automate mapping between RCV and GO, largely replacing the increasingly unsustainable manual mapping process. We then tested the utility of the resulting groupings for over-representation analysis.ResultsWe successfully mapped 85% of RCV terms to logical OWL definitions and showed that these could be used to recapitulate and extend manual mappings between RCV terms and the sets of GO terms subsumed by them. We also show that gene sets derived from the resulting GO terms sets can be used to detect the signatures of cell and tissue types in whole genome expression data.ConclusionsThe rich formal structure of the GO makes it possible to use reasoning to dynamically generate novel, biologically relevant groupings of GO terms. GO term groupings generated with this approach can be used in.over-representation analysis to detect cell and tissue type signatures in whole genome expression data.

Correction to: Detect tissue heterogeneity in gene expression data with BioQC

After the publication of this work [1], a mistake was noticed in the Eq. 1. Given an m  ×  n expression matrix with m genes and samples of n tissues, the correct definition of the Gini index for gene i is: