Ann-Sofie Albrekt

A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests

BackgroundAllergic contact dermatitis is an inflammatory skin disease that affects a significant proportion of the population. This disease is caused by an adverse immune response towards chemical haptens, and leads to a substantial economic burden for society. Current test of sensitizing chemicals rely on animal experimentation. New legislations on the registration and use of chemicals within pharmaceutical and cosmetic industries have stimulated significant research efforts to develop alternative, human cell-based assays for the prediction of sensitization. The aim is to replace animal experiments with in vitro tests displaying a higher predictive power.ResultsWe have developed a novel cell-based assay for the prediction of sensitizing chemicals. By analyzing the transcriptome of the human cell line MUTZ-3 after 24 h stimulation, using 20 different sensitizing chemicals, 20 non-sensitizing chemicals and vehicle controls, we have identified a biomarker signature of 200 genes with potent discriminatory ability. Using a Support Vector Machine for supervised classification, the prediction performance of the assay revealed an area under the ROC curve of 0.98. In addition, categorizing the chemicals according to the LLNA assay, this gene signature could also predict sensitizing potency. The identified markers are involved in biological pathways with immunological relevant functions, which can shed light on the process of human sensitization.ConclusionsA gene signature predicting sensitization, using a human cell line in vitro, has been identified. This simple and robust cell-based assay has the potential to completely replace or drastically reduce the utilization of test systems based on experimental animals. Being based on human biology, the assay is proposed to be more accurate for predicting sensitization in humans, than the traditional animal-based tests.

Transcriptional Profiling of Human Dendritic Cell Populations and Models - Unique Profiles of In Vitro Dendritic Cells and Implications on Functionality and Applicability

Background Dendritic cells (DCs) comprise heterogeneous populations of cells, which act as central orchestrators of the immune response. Applicability of primary DCs is restricted due to their scarcity and therefore DC models are commonly employed in DC-based immunotherapy strategies and in vitro tests assessing DC function. However, the interrelationship between the individual in vitro DC models and their relative resemblance to specific primary DC populations remain elusive. Objective To describe and assess functionality and applicability of the available in vitro DC models by using a genome-wide transcriptional approach. Methods Transcriptional profiling was performed with four commonly used in vitro DC models (MUTZ-3-DCs, monocyte-derived DCs, CD34-derived DCs and Langerhans cells (LCs)) and nine primary DC populations (dermal DCs, LCs, blood and tonsillar CD123+, CD1c+ and CD141+ DCs, and blood CD16+ DCs). Results Principal Component Analysis showed that transcriptional profiles of each in vitro DC model most closely resembled CD1c+ and CD141+ tonsillar myeloid DCs (mDCs) among primary DC populations. Thus, additional differentiation factors may be required to generate model DCs that more closely resemble other primary DC populations. Also, no model DC stood out in terms of primary DC resemblance. Nevertheless, hierarchical clustering showed clusters of differentially expressed genes among individual DC models as well as primary DC populations. Furthermore, model DCs were shown to differentially express immunologically relevant transcripts and transcriptional signatures identified for each model DC included several immune-associated transcripts. Conclusion The unique transcriptional profiles of in vitro DC models suggest distinct functionality in immune applications. The presented results will aid in the selection of an appropriate DC model for in vitro assays and assist development of DC-based immunotherapy.

The GARD assay for assessment of chemical skin sensitizers

Allergic contact dermatitis is a skin disease caused by an immunologic reaction to low molecular weight compounds, so called haptens. These substances are commonly present in products used by humans in daily life, such as in cosmetics and fragrances, as well as within chemical industry and in pharmaceuticals. The frequent usage of these compounds in different applications has led to increasing incidences of allergic contact dermatitis, which has become a substantial economic burden for society. As a consequence, chemicals are routinely tested for their ability to induce skin sensitization, using animal models such as the murine Local Lymph Node Assay. However, recent legislations regulate the use of animal models within chemical testing. Thus, there is an urgent need for in vitro alternatives to replace these assays for safety assessment of chemicals. Recently, we identified a signature of predictive genes, which are differentially regulated in the human myeloid cell-line MUTZ-3 when stimulated with sensitizing compounds compared to non-sensitizing compounds. Based on these findings, we have formulated a test strategy for assessment of sensitizing compounds, called Genomic Allergen Rapid Detection, GARD. In this paper, we present a detailed method description of how the assay should be performed.

Skin sensitizers differentially regulate signaling pathways in MUTZ-3 cells in relation to their individual potency.

BackgroundDue to the recent European legislations posing a ban of animal tests for safety assessment within the cosmetic industry, development of in vitro alternatives for assessment of skin sensitization is highly prioritized. To date, proposed in vitro assays are mainly based on single biomarkers, which so far have not been able to classify and stratify chemicals into subgroups, related to risk or potency.MethodsRecently, we presented the Genomic Allergen Rapid Detection (GARD) assay for assessment of chemical sensitizers. In this paper, we show how the genome wide readout of GARD can be expanded and used to identify differentially regulated pathways relating to individual chemical sensitizers. In this study, we investigated the mechanisms of action of a range of skin sensitizers through pathway identification, pathway classification and transcription factor analysis and related this to the reactive mechanisms and potency of the sensitizing agents.ResultsBy transcriptional profiling of chemically stimulated MUTZ-3 cells, 33 canonical pathways intimately involved in sensitization to chemical substances were identified. The results showed that metabolic processes, cell cycling and oxidative stress responses are the key events activated during skin sensitization, and that these functions are engaged differently depending on the reactivity mechanisms of the sensitizing agent. Furthermore, the results indicate that the chemical reactivity groups seem to gradually engage more pathways and more molecules in each pathway with increasing sensitizing potency of the chemical used for stimulation. Also, a switch in gene regulation from up to down regulation, with increasing potency, was seen both in genes involved in metabolic functions and cell cycling. These observed pathway patterns were clearly reflected in the regulatory elements identified to drive these processes, where 33 regulatory elements have been proposed for further analysis.ConclusionsThis study demonstrates that functional analysis of biomarkers identified from our genomics study of human MUTZ-3 cells can be used to assess sensitizing potency of chemicals in vitro, by the identification of key cellular events, such as metabolic and cell cycling pathways.

A microarray for large-scale genomic and transcriptional analyses of the zebra finch (Taeniopygia guttata) and other passerines

The microarray technology has revolutionized biological research in the last decade. By monitoring the expression of many genes simultaneously, microarrays can elucidate gene function, as well as scan entire genomes for candidate genes encoding complex traits. However, because of high costs of sequencing and design, microarrays have largely been restricted to a few model species. Cross‐species microarray (CSM) analyses, where microarrays are used for other species than the one they were designed for, have had varied success. We have conducted a CSM analysis by hybridizing genomic DNA from the common whitethroat (Sylvia communis) on a newly developed Affymetrix array designed for the zebra finch (Taeniopygia guttata), the Lund‐zf array. The results indicate a very high potential for the zebra finch array to act as a CSM utility in other passerine birds. When hybridizing zebra finch genomic DNA, 98% of the gene representatives had higher signal intensities than the background cut‐off, and for the common whitethroat, we found the equivalent proportion to be as high as 96%. This was surprising given the fact that finches and warblers diverged 25–50 million years ago, but may be explained by a relatively low sequence divergence between passerines (89–93%). Passerine birds are widely used in studies of ecology and evolution, and a zebra finch array that can be used for many species may have a large impact on future research directions.

TECHNICAL ADVANCES: A microarray for large‐scale genomic and transcriptional analyses of the zebra finch (Taeniopygia guttata) and other passerines

The microarray technology has revolutionized biological research in the last decade. By monitoring the expression of many genes simultaneously, microarrays can elucidate gene function, as well as scan entire genomes for candidate genes encoding complex traits. However, because of high costs of sequencing and design, microarrays have largely been restricted to a few model species. Cross‐species microarray (CSM) analyses, where microarrays are used for other species than the one they were designed for, have had varied success. We have conducted a CSM analysis by hybridizing genomic DNA from the common whitethroat (Sylvia communis) on a newly developed Affymetrix array designed for the zebra finch (Taeniopygia guttata), the Lund‐zf array. The results indicate a very high potential for the zebra finch array to act as a CSM utility in other passerine birds. When hybridizing zebra finch genomic DNA, 98% of the gene representatives had higher signal intensities than the background cut‐off, and for the common whitethroat, we found the equivalent proportion to be as high as 96%. This was surprising given the fact that finches and warblers diverged 25–50 million years ago, but may be explained by a relatively low sequence divergence between passerines (89–93%). Passerine birds are widely used in studies of ecology and evolution, and a zebra finch array that can be used for many species may have a large impact on future research directions.

Prediction of Chemical Respiratory Sensitizers Using GARD, a Novel In Vitro Assay Based on a Genomic Biomarker Signature.

Background Repeated exposure to certain low molecular weight (LMW) chemical compounds may result in development of allergic reactions in the skin or in the respiratory tract. In most cases, a certain LMW compound selectively sensitize the skin, giving rise to allergic contact dermatitis (ACD), or the respiratory tract, giving rise to occupational asthma (OA). To limit occurrence of allergic diseases, efforts are currently being made to develop predictive assays that accurately identify chemicals capable of inducing such reactions. However, while a few promising methods for prediction of skin sensitization have been described, to date no validated method, in vitro or in vivo, exists that is able to accurately classify chemicals as respiratory sensitizers. Results Recently, we presented the in vitro based Genomic Allergen Rapid Detection (GARD) assay as a novel testing strategy for classification of skin sensitizing chemicals based on measurement of a genomic biomarker signature. We have expanded the applicability domain of the GARD assay to classify also respiratory sensitizers by identifying a separate biomarker signature containing 389 differentially regulated genes for respiratory sensitizers in comparison to non-respiratory sensitizers. By using an independent data set in combination with supervised machine learning, we validated the assay, showing that the identified genomic biomarker is able to accurately classify respiratory sensitizers. Conclusions We have identified a genomic biomarker signature for classification of respiratory sensitizers. Combining this newly identified biomarker signature with our previously identified biomarker signature for classification of skin sensitizers, we have developed a novel in vitro testing strategy with a potent ability to predict both skin and respiratory sensitization in the same sample.

Evaluation of high throughput gene expression platforms using a genomic biomarker signature for prediction of skin sensitization

BackgroundAllergic contact dermatitis (ACD) develops upon exposure to certain chemical compounds termed skin sensitizers. To reduce the occurrence of skin sensitizers, chemicals are regularly screened for their capacity to induce sensitization. The recently developed Genomic Allergen Rapid Detection (GARD) assay is an in vitro alternative to animal testing for identification of skin sensitizers, classifying chemicals by evaluating transcriptional levels of a genomic biomarker signature. During assay development and biomarker identification, genome-wide expression analysis was applied using microarrays covering approximately 30,000 transcripts. However, the microarray platform suffers from drawbacks in terms of low sample throughput, high cost per sample and time consuming protocols and is a limiting factor for adaption of GARD into a routine assay for screening of potential sensitizers. With the purpose to simplify assay procedures, improve technical parameters and increase sample throughput, we assessed the performance of three high throughput gene expression platforms - nCounter®, BioMark HD™ and OpenArray® - and correlated their performance metrics against our previously generated microarray data. We measured the levels of 30 transcripts from the GARD biomarker signature across 48 samples. Detection sensitivity, reproducibility, correlations and overall structure of gene expression measurements were compared across platforms.ResultsGene expression data from all of the evaluated platforms could be used to classify most of the sensitizers from non-sensitizers in the GARD assay. Results also showed high data quality and acceptable reproducibility for all platforms but only medium to poor correlations of expression measurements across platforms. In addition, evaluated platforms were superior to the microarray platform in terms of cost efficiency, simplicity of protocols and sample throughput.ConclusionsWe evaluated the performance of three non-array based platforms using a limited set of transcripts from the GARD biomarker signature. We demonstrated that it was possible to achieve acceptable discriminatory power in terms of separation between sensitizers and non-sensitizers in the GARD assay while reducing assay costs, simplify assay procedures and increase sample throughput by using an alternative platform, providing a first step towards the goal to prepare GARD for formal validation and adaption of the assay for industrial screening of potential sensitizers.

Multimodel Pathway Enrichment Methods for Functional Evaluation of Expression Regulation.

Functional analysis of quantitative expression data is becoming common practice within the proteomics and transcriptomics fields; however, a gold standard for this type of analysis has yet not emerged. To grasp the systemic changes in biological systems, efficient and robust methods are needed for data analysis following expression regulation experiments. We discuss several conceptual and practical challenges potentially hindering the emergence of such methods and present a novel method, called FEvER, that utilizes two enrichment models in parallel. We also present analysis of three disparate differential expression data sets using our method and compare our results to other established methods. With many useful features such as pathway hierarchy overview, we believe the FEvER method and its software implementation will provide a useful tool for peers in the field of proteomics. Furthermore, we show that the method is also applicable to other types of expression data.