Patent Application
20220178913
The present invention provides a non-naturally occurring dendritic-like cell line and methods and kits utilising such cells.
Dendritic cells (DCs) play key roles in the immune response by bridging the essential connections between innate and adaptive immunity. They can, upon triggering, rapidly produce large amounts of mediators, which influence migration and activation of other cells at the site of inflammation, and selectively respond to various pathogens and environmental factors, by fine-tuning the cellular response through antigen-presentation. Thus, exploring and utilizing the immunological decision-making by DCs during stimulation with sensitizers, can serve as a potent test strategy for prediction of sensitization.
However, multifaceted phenotypes and specialized functions of different DC subpopulations, as well as their wide and scarce distribution, are complicating factors, which impede the employment of primary DCs as a test platform. Hence, there is a real need to establish accurate and reliable in vitro assays that also circumvent the problems associated with variability of and difficulty in obtaining primary DCs, such as by utilising dendritic-like model cell lines.
The Genomic Allergen Rapid Detection (GARD) is an in vitro assay developed for the assessment of chemical and protein sensitizers and allergens. It uses transcriptional profiles from a dendritic cell-like cell line, to predict the sensitizing or allergenic potential of test agents (9). The transcriptional profiles consist of genes that were identified as having the greatest predicting performance in distinguishing sensitizers from non-sensitizers. Specifically, GARD has been developed to identify: skin sensitizers (WO 2012/056236; Johansson et al. (2017) Evaluation of the GARD assay in a blind Cosmetics Europe study. ALTEX Online first Feb. 17, 2017; Forreryd et al. (2016) From genome-wide arrays to tailor-made biomarker readout—Progress towards routine analysis of skin sensitizing chemicals with GARD. Toxicolgy In Vitro; Johansson et al. (2014) GARD in-house validation—A proof of concept. Tox Sci; Johansson et al., (2011) A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests. BMC Genomics, 2011); respiratory sensitizers (WO 2013/160882; WO 2016/083604; Forreryd et al. (2015) Prediction of chemical Respiratory sensitizers using GARD, a novel in vitro assay based on a genomic biomarker signature. PLoS One 10(3)); the potency of skin sensitizers (PCT/EP2017/056878; Zeller et al. (2017) The GARD platform for potency assessment of skin sensitizing chemicals. ALTEX Online first published Apr. 12, 2017, version 2 https://doi.org/10.14573/altex.1701101); and the allergenicity of proteins.
The inventors now provide a non-naturally occurring dendritic-like myeloid leukemia cell line termed “SenzaCell” (ATCC Patent Deposit Designation PTA-123875) as a preferred dendritic cell-like cell line suitable for use in GARD assays. SenzaCell has been compared to the MUTZ-3 cell line which may also be used in GARD assays; MUTZ-3 cells are dendritic-like cells available from Deutsche Sammlung für Mikroorganismen and Zellkulturen GmbH (DSMZ), Braunschweig, Germany (www.dsmz.de; DSMZ No. ACC 295). MUTZ-3 cells are however protocolled to require culture with a feeder cell line which complicates their use. Therefore, there remains a need for alternatives to MUTZ-3 for use in methods such as the GARD assays.
The comparison between SenzaCell and MUTZ-3 included a phenotypic analysis where the expression of a panel of biomarkers were compared, a transcriptional analysis where the transcription levels of the cell lines were compared, a comparison of their DNA sequences and a functional analysis, which revealed many quantifiable differences in the phenotype and transcriptional profiles and yet showed shared functionality in the GARD assays.
Therefore, in a first aspect of the invention there is provided a non-naturally occurring dendritic-like myeloid leukaemia cell according to ATCC Patent Deposit Designation PTA-123875.
The cell is also referred to herein as “SenzaCell”. SenzaCell (ATCC Patent Deposit Designation PTA-123875) was deposited at the American Type Culture Collection (ATCC), 10801 University Blvd, Manassas, Va. 20110, USA, by SenzaGen AB on 9 Mar. 2017.
By “non-naturally occurring”, we mean that the cell is different to, modified from, and/or a variant of, those which would be found in nature; in other words, they are not cells which would normally occur in nature. For example, different to, modified from, and/or a variant of, a naturally occurring human myeloid leukaemia cell or a naturally occurring dendritic cell.
In a second aspect of the invention there is provided a cell culture comprising a population of cells according to the first aspect.
In one embodiment the cell or population of cells comprises or consists of immortal or immortalised cells. By “immortal” we mean cells that are not limited by a point at which they can no longer continue to divide, which might otherwise be due to DNA damage or shortened telomeres. In an additional or alternative embodiment the cell or population of cells comprises or consists of undifferentiated cells.
In a third aspect of the invention there is provided an in vitro method for identifying agents capable of inducing sensitization and/or allergenicity in a mammal comprising or consisting of the steps of:
a) exposing a population of the dendritic-like cells according to the first or second aspect to a test agent; and
b) measuring in the cells the expression of one or more biomarker(s) selected from the group defined in one or more of Tables A, B, C, D, and E;
wherein the expression in the cells of the one or more biomarkers measured in step (b) is indicative of the sensitizing and/or allergenic effect of the test agent.
In a fourth aspect of the invention there is provided an in vitro method for identifying agents capable of inducing sensitization of mammalian skin comprising or consisting of the steps of:
a) exposing a population of the dendritic-like cells according to the first or second aspect to a test agent; and
b) measuring in the cells the expression of one or more biomarker(s) selected from the group defined in Table A;
wherein the expression in the cells of the one or more biomarkers measured in step (b) is indicative of the skin sensitizing effect of the test agent.
Methods for identifying agents capable of inducing sensitization of mammalian skin which may use the dendritic-like cells according to the invention are described in the functional analysis section of Example 1. The methods described in WO 2012/056236; Johansson et al. (2017) Evaluation of the GARD assay in a blind Cosmetics Europe study. ALTEX Online first Feb. 17, 2017; Forreryd et al. (2016) From genome-wide arrays to tailor-made biomarker readout—Progress towards routine analysis of skin sensitizing chemicals with GARD. Toxicolgy In Vitro; Johansson et al. (2014) GARD in-house validation—A proof of concept. Tox Sci; Johansson et al., (2011) A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests. BMC Genomics, 2011, which are all incorporated herein by reference, may also be used.
In one embodiment, the method is for identifying agents capable of inducing a hypersensitivity response in human skin. Preferably, the hypersensitivity response is a cell-mediated hypersensitivity response, for example, a Type IV hypersensitivity response. Preferably, the hypersensitivity response is a Type IV delayed-type hypersensitivity reaction in a mammal. Preferably, the Type IV delayed-type hypersensitivity reaction is DC-mediated. Preferably, the method is for identifying agents capable of inducing allergic contact dermatitis (ACD) (i.e. the hypersensitivity response is ACD).
In one embodiment, the “agents capable of inducing sensitization of mammalian skin” is an agent capable of inducing and triggering a Type IV delayed-type hypersensitivity reaction at a site of epidermal contact in a mammal.
In a fifth aspect of the invention there is provided a method for identifying agents capable of inducing respiratory sensitization in a mammal comprising or consisting of the steps of:
a) exposing a population of the dendritic-like cells according to the first or second aspect to a test agent; and
b) measuring in the cells the expression of one or more biomarker(s) selected from the group defined in Table B;
wherein the expression in the cells of the one or more biomarkers measured in step (b) is indicative of the respiratory sensitizing effect of the test agent.
In a sixth aspect of the invention there is provided a method for identifying agents capable of inducing respiratory sensitization in a mammal comprising or consisting of the steps of:
a) exposing a population of the dendritic-like cells according to the first or second aspect to a test agent; and
b) measuring in the cells the expression of one or more biomarker(s) selected from the group defined in Table C;
wherein the expression in the cells of the one or more biomarkers measured in step (b) is indicative of the respiratory sensitizing effect of the test agent.
The methods of the fifth and sixth aspects may be combined such that step (b) comprises measuring in the cells the expression of one or more biomarker(s) selected from the group defined in Tables B and/or C.
Methods for identifying agents capable of inducing respiratory sensitization in a mammal which may use the dendritic-like cells according to the invention are discussed in Example 2. The methods described in WO 2013/160882; WO 2016/083604; and Forreryd et al. (2015) Prediction of chemical Respiratory sensitizers using GARD, a novel in vitro assay based on a genomic biomarker signature. PLoS One 10(3), which are all incorporated herein by reference, may also be used.
By “agents capable of inducing respiratory sensitization” we mean any agent capable of inducing and triggering a Type I immediate hypersensitivity reaction in the respiratory tract of a mammal. Preferably, the Type I immediate hypersensitivity reaction is DC-mediated and/or involves the differentiation of T cells into Th2 cells. Preferably the Type I immediate hypersensitivity reaction results in humoral immunity and/or respiratory allergy.
In one embodiment, the “agents capable of inducing sensitization of mammalian skin” is an agent capable of inducing and triggering a Type I immediate hypersensitivity reaction at a site of lung epithelium in a mammal. Preferably, the site of lung epithelium is in the respiratory zone of the lung, but may alternatively or additionally be in the conductive zone of the lung.
In a seventh aspect of the invention there is provided a method for determining the skin sensitizing potency of an agent comprising or consisting of the steps of:
(a) providing a population of the dendritic-like cells according to the first or second aspect;
(b) exposing the cells provided in step (a) to a test agent; and
(c) measuring in the cells of step (b) the expression of one or more biomarkers selected from the group defined in Table D;
wherein the expression of the one or more biomarkers measured in step (c) is indicative of the skin sensitizing potency of the test agent of step (b).
Methods for determining the skin sensitizing potency of an agent which may use the dendritic-like cells according to the invention are discussed in Example 3. The methods described in Zeller et al. (2017), The GARD platform for potency assessment of skin sensitizing chemicals. ALTEX Online first published Apr. 12, 2017, version 2 https://doi.org/10.14573/altex.1701101 and PCT/EP2017/056878, which is incorporated herein by reference, may also be used.
In an eighth aspect of the invention there is provided a method for identifying proteins which are allergenic in a mammal comprising or consisting of the steps of:
(a) providing a population of the dendritic-like cells according to the first or second aspect;
(b) exposing the cells provided in step (a) to a test protein; and
(c) measuring in the cells of step (b) the expression of one or more biomarkers selected from the group defined in Table E;
wherein the expression of the one or more biomarkers measured in step (c) is indicative of the allergenicity of the test protein of step (b).
Methods for identifying proteins which are allergenic in a mammal which may use the dendritic-like cells according to the invention are discussed in detail in Example 4.
In one embodiment the method according to any aspect of the invention further comprises:
d) exposing a separate population of the dendritic-like cells according to the first or second aspect to one or more negative control agent that does not induce sensitization and/or allergy in a mammal; and
e) measuring in the cells of step (d) the expression of the one or more biomarkers measured in step (b) or (c) wherein the test agent is identified as a sensitizer and/or as allergenic in the event that the expression of the one or more biomarkers measured in step (e) differs from the expression of the one or more biomarkers measured in step (b) or (c).
A vehicle control may be used as the negative control agent. The vehicle control may comprise DMSO and/or distilled water.
In an additional or alternative embodiment unstimulated cells may be used as the negative control. By “unstimulated cells” we include or mean cells which have not been exposed to a specific test agent.
In an additional or alternative embodiment the one or more negative control agent may comprise or consist of one or more agent selected from the group consisting of 1-Butanol; 2-Aminophenol; 2-Hydroxyethyl acrylate; 2-nitro-1,4-Phenylenediamine; 4-Aminobenzoic acid; Chlorobenzene; Dimethyl formamide; Ethyl vanillin; Formaldehyde; Geraniol; Hexylcinnamic aldehyde; Isopropanol; Kathon CG*; Methyl salicylate; Penicillin G; Propylene glycol; Potassium Dichromate; Potassium permanganate; Tween 80; and Zinc sulphate.
In an additional or alternative embodiment the expression of the one or more biomarkers measured in step (b) or (c) is measured in the cells provided in step (a) prior to and following exposure to the test agent, and wherein the difference in expression between the one or more biomarkers prior to and following exposure to the test agent is indicative of the allergenicity and/or sensitizing effect of the test agent. Hence, the cells provided in step (a) may provide both the negative control and the test result.
By “differs from the expression of the one or more biomarkers measured in step (b) or (c)” and “difference in expression” we include that the presence and or amount in a first sample (e.g., a test agent sample) differs from that of a second sample (e.g., a control agent sample).
For example, the presence and/or amount in the test sample may differ from that of the one or more negative control sample in a statistically significant manner. Preferably the expression of the one or more biomarkers in the cell population exposed to the test agent is:
less than or equal to 80% of that of the cell population exposed to the negative control agent, for example, no more than 79%, 78%, 77%, 76%, 75%, 74%, 73%, 72%, 71%, 70%, 69%, 68%, 67%, 66%, 65%, 64%, 63%, 62%, 61%, 60%, 59%, 58%, 57%, 56%, 55%, 54%, 53%, 52%, 51%, 50%, 49%, 48%, 47%, 46%, 45%, 44%, 43%, 42%, 41%, 40%, 39%, 38%, 37%, 36%, 35%, 34%, 33%, 32%, 31%, 30%, 29%, 28%, 27%, 26%, 25%, 24%, 23%, 22%, 21%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1% or 0% of that of the cell population exposed to the negative control or negative control agent; or
at least 120% of that of the cell population exposed to the negative control agent, for example, at least 121%, 122%, 123%, 124%, 125%, 126%, 127%, 128%, 129%, 130%, 131%, 132%, 133%, 134%, 135%, 136%, 137%, 138%, 139%, 140%, 141%, 142%, 143%, 144%, 145%, 146%, 147%, 148%, 149%, 150%, 151%, 152%, 153%, 154%, 155%, 156%, 157%, 158%, 159%, 160%, 161%, 162%, 163%, 164%, 165%, 166%, 167%, 168%, 169%, 170%, 171%, 172%, 173%, 174%, 175%, 176%, 177%, 178%, 179%, 180%, 181%, 182%, 183%, 184%, 185%, 186%, 187%, 188%, 189%, 190%, 191%, 192%, 193%, 194%, 195%, 196%, 197%, 198%, 199%, 200%, 225%, 250%, 275%, 300%, 325%, 350%, 375%, 400%, 425%, 450%, 475% or at least 500% of that of the cell population exposed to the negative control or negative control agent
By “differs from the expression of the one or more biomarkers measured in step (b) or (c)” we alternatively or additionally include that the test sample is classified as belonging to a different group as the one or more negative control sample. For example, where an SVM is used, the test sample is on the other side of the decision value threshold as the one or more negative control sample (e.g., if the test agent is classified as a protein allergen if one or more test (or replicate thereof) has an SVM decision value of ≤0, then the one or more positive control samples (or the majority thereof) should also have an SVM decision value of ≤0).
In an additional or alternative embodiment the method according to any aspect of the invention further comprises:
f) exposing a separate population of the dendritic-like cells according to the first or second aspect to one or more positive control agent that induces sensitization and/or is allergenic in a mammal; and
g) measuring in the cells of step (f) the expression of the one or more biomarkers measured in step (b) or (c) wherein the test agent is identified as a sensitizer and/or as allergenic in the event that the expression of the one or more biomarkers measured in step (f) corresponds to the expression of the one or more biomarkers measured in step (b) or (c).
In an additional or alternative embodiment, the one or more positive control agent provided in step (f) comprises or consists of one or more agent selected from the group consisting of: Der p 1; and Der p 7.
In an additional or alternative embodiment, the one or more positive control agent may comprise or consist of one or more agent selected from the group consisting of Ammonium hexachloroplatinate; Ammonium persulfate; Ethylenediamine; Glutaraldehyde; Hexamethylen diisocyanate; Maleic Anhydride; Methylene diphenol diisocyanate; Phtalic Anhydride; Toluendiisocyanate; and Trimellitic anhydride.
By “corresponds to the expression of the one or more biomarkers measured in step (b) or (c)” we mean the expression of the one or more biomarkers in the cell population exposed to the test agent is identical to, or does not differ significantly from, that of the cell population exposed to the one more positive control agent. Preferably the expression of the one or more biomarkers in the cell population exposed to the test agent is between 81% and 119% of that of the cell population exposed to the one more positive control agent, for example, greater than or equal to 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of that of the cell population exposed to the one more positive control agent, and less than or equal to 101%, 102%, 103%, 104%, 105%, 106%, 107%, 108%, 109%, 110%, 111%, 112%, 113%, 114%, 115%, 116%, 117%, 118% or 119% of that of the cell population exposed to the one more positive control agent.
By “corresponds to the expression of the one or more biomarkers measured in step (b) or (c)” we alternatively or additionally include that the test sample is classified as belonging to the same group as the one or more positive control sample. For example, where an SVM is used, the test sample is on the same side of the decision value threshold as the one or more positive control sample (e.g., if the test agent is classified as allergenic if one or more test (or replicate thereof) has an SVM decision value of >0, then the one or more positive control samples (or the majority thereof) should also have an SVM decision value of >0).
In an additional or alternative embodiment, the method is indicative of the allergenic and/or sensitizing potency of the agent to be tested. For example, the method may be used to predict the relative allergenic and/or sensitizing potency of a test agent compared to a positive control and/or compared to one or more additional test agents.
In an additional or alternative embodiment the methods comprises the further step of:
(h) identifying the allergenic and/or sensitizing effect of the test agent.
For example, step (h) may identify the test agent as being an allergen or a non-allergen and/or as a sensitizer or a non-sensitizer. Alternatively or additionally, step (h) may identify the relative allergenicity or allergenic potency and/or sensitizing effect or sensitizing potency of the test agent compared to a positive control and/or one or more additional test agents.
The identification may be performed using any suitable statistical method or machine learning algorithm known in the art, such as Random Forest (RF), Support Vector Machine (SVM), Principal Component Analysis (PCA), ordinary least squares (OLS), partial least squares regression (PLS), orthogonal partial least squares regression (O-PLS) and other multivariate statistical analyses (e.g., backward stepwise logistic regression model). For a review of multivariate statistical analysis see, for example, Schervish, Mark J. (November 1987). “A Review of Multivariate Analysis”. Statistical Science 2 (4): 396-413 which is incorporated herein by reference. Preferably, Support Vector Machine (SVM) is used.
Typically, allergenic or sensitizing agents are identified using a support vector machine (SVM), such as those available from http://cran.r-project.org/web/packages/e1071/index.html (e.g. e1071 1.5-24). However, any other suitable means may also be used. SVMs may also be used to determine the ROC AUCs of biomarker signatures comprising or consisting of one or more biomarkers as defined herein.
Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. Intuitively, an SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.
More formally, a support vector machine constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training datapoints of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. For more information on SVMs, see for example, Burges, 1998, Data Mining and Knowledge Discovery, 2:121-167.
In one embodiment of the invention, the SVM is ‘trained’ prior to performing the methods of the invention using biomarker profiles of known agents (namely, known allergenic/sensitizer or non-allergenic/non-sensitizer agents). By running such training samples, the SVM is able to learn what biomarker profiles are associated with agents capable of inducing allergy and/or sensitization. Once the training process is complete, the SVM is then able to predict whether or not the biomarker sample tested is from an allergenic or non-allergenic/sensitizing or non-sensitizing agent.
Decision values for individual SVMs can be determined by the skilled person on a case-by-case basis. In one embodiment, the test agent is classified as allergenic and/or a sensitizer if one or more test (or replicate thereof) have an SVM decision value of >0. In one embodiment, the test agent is classified as non-allergenic and/or a non-sensitizer if one or more test (or replicate thereof) have an SVM decision value of ≤0. This allows test agents to be classified as allergenic or non-allergenic/sensitizing or non-sensitizing.
However, this training procedure can be by-passed by pre-programming the SVM with the necessary training parameters. For example, allergenic and/or sensitizing agents can be identified according to the known SVM parameters using the SVM algorithm described in WO 2012/056236, WO 2013/160882, or WO 2016/083604, based on the measurement of all the biomarkers listed in one or more of Tables A-E.
It will be appreciated by skilled persons that suitable SVM parameters can be determined for any combination of the biomarkers listed in Tables A-E by training an SVM machine with the appropriate selection of data (i.e. biomarker measurements from cells exposed to known allergenic and/or non-allergenic agents or sensitizing and/or non-sensitizing agents). Alternatively, the Table A-E biomarkers may be used to identify allergenic proteins and/or sensitizing agents according to any other suitable statistical method known in the art.
Alternatively, the Table A-E data may be used to identify agents capable of inducing allergy and/or sensitization according to any other suitable statistical method known in the art (e.g., ANOVA, ANCOVA, MANOVA, MANCOVA, Multivariate regression analysis, Principal components analysis (PCA), Factor analysis, Canonical correlation analysis, Canonical correlation analysis, Redundancy analysis Correspondence analysis (CA; reciprocal averaging), Multidimensional scaling, Discriminant analysis, Linear discriminant analysis (LDA), Clustering systems, Recursive partitioning and Artificial neural networks).
Preferably the methods of the invention are performed in vitro.
In an additional or alternative embodiment steps (b), (c), (e) and/or (g) of the methods of the invention comprise or consist of measuring the expression of two or more biomarkers listed in Tables A, B, C, D or E, for example, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, or 391 of the biomarkers listed in Tables A, B, C, D or E. For example, steps (b), (c), (e) and/or (g) may comprise or consist of measuring the expression of all of the biomarkers listed in Tables A, B, C, D or E.
By “expression” we mean the presence, level and/or amount of the biomarker.
By “biomarker” we include any biological molecule, or component or fragment thereof, the measurement of which can provide information useful in determining the sensitizing effect and/or the allergenicity of a test agent. Thus, in the context of Tables A, B, C, D, and E, the biomarker may be a nucleic acid molecule, such as a mRNA or cDNA. Alternatively, the biomarker may be a protein encoded by the nucleic acid molecule or carbohydrate moiety, or an antigenic component or fragment thereof.
In an additional or alternative embodiment of the method of the seventh aspect the test agent is already known to be, or suspected of being, capable of inducing sensitization of the skin. For example, the test agent may already be known to be capable of inducing sensitization of the skin by using a method already known in the art, for instance a method described WO 2012/056236 and/or Johansson H et al. The GARD assay for assessment of chemical skin sensitizers. Toxicology in vitro 2013 which are incorporated herein by reference. In an alternative or additional embodiment, the method is for identifying skin sensitizer potency and skin sensitizer/non-sensitizer status of the test agent (i.e., identifying whether the test agent is a sensitizer or not and identifying its potency as a skin sensitizer). In an alternative or additional embodiment, the method comprises identifying whether the test agent is a sensitizer using the method described in WO 2012/056236 and/or Johansson H et al.
By “skin sensitizing potency” we include or mean the strength of the skin sensitizing ability of an agent. For example, the relative potency or strength of sensitizing ability of an agent might lead to the ordering of a group of test agents from most potent to least potent or vice versa, and/or it might lead to their categorization according to one or more known regulation or system. By “sensitization status” we include or mean whether or not a chemical entity (or mixture of chemical entities) is a sensitizer or not (e.g., a skin sensitizer and/or a respiratory sensitizer).
By “skin sensitizing” we mean any agent capable of inducing and triggering a Type IV delayed-type hypersensitivity reaction in a mammal. Preferably, the Type IV delayed-type hypersensitivity reaction is DC-mediated.
In an additional or alternative embodiment the skin sensitization potency determined by the method is categorised according to the European Classification, Labelling and Packaging (CLP) Regulation (EC) 1272/2008 (http://echa.europa.eu/clp-2015). This system is based on the United Nations' Globally Harmonised System (GHS) and from June 2015, the only legislation to apply to the classification and labelling of both substances and mixtures. It requires companies to classify, label and package their products appropriately before placing them on the market. It provides the categories: 1A (strong), 1B (weak), or no cat (no sensitizer).
For example, the method may provide:
(i) one or more agent of potency category 1A;
(ii) one or more agent of potency category 1B; and/or
(iii) one or more agent of potency category no category
In an additional or alternative embodiment the skin sensitization potency determined by the method is categorised according to the system described in Basketter et al., 2014, ‘Categorization of chemicals according to their relative human skin sensitizing potency,’ Dermatitis, 25(1):11-21, i.e. categories 1 (strongest sensitizer), 2, 3, 4, 5, or 6 (true non-sensitizer) (e.g. Table 4,
For example, the method may provide:
(i) one or more agent of potency category 1;
(ii) one or more agent of potency category 2;
(iii) one or more agent of potency category 3;
(iv) one or more agent of potency category 4;
(v) one or more agent of potency category 5; and/or
(vi) one or more agent of potency category 6 (e.g., see present Table 8 and/or Basketter et al., 2014 supra.).
In an additional or alternative embodiment skin sensitization potency is categorised according to the local lymph node assay (LLNA) classification, Guinea pig maximisation test (GPMT) or no observed-effect level (NOEL).
For a detailed description of LLNA see Basketter, D. A., et al., Local lymph node assay—validation, conduct and use in practice. Food Chem Toxicol, 2002. 40(5): p. 593-8 which is incorporated herein by reference. For a detailed description of the guinea pig maximization test see Magnusson, B. and A. M. Kligman, The identification of contact allergens by animal assay. The guinea pig maximization test. J Invest Dermatol, 1969. 52(3): p. 268-76, which is incorporated herein by reference. For a detailed description of the no observed-effects level (NOEL) test in relation to skin sensitizer potency see Basketter et al., 2005, ‘Evaluation of the skin sensitizing potency of chemicals by using the existing methods and considerations of relevance for elicitation’ Contact Dermatitis, 52(1):39-43; and Griem, P., et al., 2003, ‘Proposal for a risk assessment methodology for skin sensitization based on sensitization potency data.’ Regul. Toxicol. Pharmacol., 38:269-290 which are incorporated herein by reference. For a correlation between NOEL and potency levels, see also WHO Library Cataloguing-in-Publication Data. Skin sensitization in chemical risk assessment. (IPCS harmonization project document: no. 5), ISBN 978 92 4 156360 4 (in particular, Table 1 on pages 26-28) which is incorporated herein by reference. For a detailed description of CLP, see (http://echa.europa.eu/clp-2015), which is incorporated herein by reference.
In an additional or alternative embodiment the expression of one or more biomarkers measured in step (c) is measured in the cells provided in step (a) prior to and following exposure to the skin sensitizing agent of predetermined potency, and wherein the difference in expression between the one or more biomarkers prior to and following exposure to the test agent is indicative of the potency of the skin sensitizing agent of step (b).
In an additional or alternative embodiment the expression of one or more biomarkers measured in step (c) is measured in the cells provided in step (a) prior to and following exposure to the skin sensitizing agent of predetermined potency, and wherein the difference in expression between the one or more biomarkers prior to and following step (c) is indicative of the potency of the skin sensitizing agent of step (b).
By ‘difference in expression’ we include that the presence and or amount in a first sample (e.g., a test agent sample) differs from that of a second sample (e.g., a control agent sample). Preferably the presence and/or amount is no more than 40% of that of the comparison sample, for example, no more than 39%, 38%, 37%, 36%, 35%, 34%, 33%, 32%, 31%, 30%, 29%, 28%, 27%, 26%, 25%, 24%, 23%, 22%, 21%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1% or 0%.
In an additional or alternative embodiment the one or more biomarker is measured in the cells provided in step (a) prior to and following exposure to the test agent, and wherein the difference in expression between the one or more biomarkers prior to and following exposure to the test agent is indicative of the skin sensitizing potency of the test agent of step (b). Hence, the cells provided in step (a) may provide both the negative control and the test result.
In an additional or alternative embodiment the one or more biomarker is measured in the cells provided in step (a) prior to and following exposure to the test agent, and wherein the difference in expression between the one or more biomarkers prior to and following step (c) is indicative of the skin sensitizing potency of the test agent of step (b). Hence, the cells provided in step (a) may provide both the negative control and the test result.
In an additional or alternative embodiment the method comprises the further steps of:
(i) providing a further population of dendritic-like cells according to the first or second aspect;
(j) exposing the cells provided in step (i) to a skin sensitizing agent of predetermined potency;
(k) measuring in the cells of step (j) the expression of the one or more biomarkers measured in step (c);
wherein the correspondence in expression between the one or more biomarkers measured in step (c) and the one or more biomarkers measured in step (k) is indicative of the skin sensitizing potency of the test agent.
By ‘correspondence in expression’ we include that that the presence and or amount in a first sample (e.g., a test agent sample) is similar or identical to the presence and/or amount in a second sample (e.g., a control sample). Preferably the presence and/or amount is at least 60% of that of the control sample, for example, at least 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100%.
In an additional or alternative embodiment the method comprises the further step of:
(I) identifying the skin sensitizing potency of the test agent.
In an additional or alternative embodiment the skin sensitizing agent of predetermined potency comprises or consist of agents selected from the group consisting of 1-Butanol, 4-Aminobenzoic acid, Benzaldehyde, Chlorobenzene, Diethyl phthalate, Dimethyl formamide, Ethyl vanillin, Glycerol, Isopropanol, Lactic acid, Methyl salicylate, Octanoic acid, Propylene glycol, Phenol, p-hydroxybenzoic acid, Potassium permanganate, Salicylic acid, Sodium dodecyl sulphate, Tween 80, Zinc sulphate, 2,4-Dinitrochlorobenzene, Oxazolone, Potassium dichromate, Kathon CG (MC/MCI), Formaldehyde, 2-Aminophenol, 2-nitro-1,4-Phenylendiamine, p-Phenylendiamine, Hexylcinnamic aldehyde, 2-Hydroxyethyl acrylate, 2-Mercaptobenzothiazole, Glyoxal, Cinnamaldehyde, Isoeugenol, Ethylendiamine, Resorcinol, Cinnamic alcohol, Eugenol, Penicillin G, Geraniol and DMSO.
In a preferred embodiment of the method of any aspect of the invention, step (b), (c), (e), (g) and/or (k) comprises or consists of measuring the expression of a nucleic acid molecule of one or more of the biomarkers. The nucleic acid molecule may be a DNA molecule or a cDNA molecule or an mRNA molecule. Preferably, the nucleic acid molecule is an mRNA molecule. However, the nucleic acid molecule may be a cDNA molecule.
In one embodiment the measurement of the expression of one or more of the biomarkers in step (b), (c), (e), (g) and/or (k) is performed using a method selected from the group consisting of Southern hybridisation, Northern hybridisation, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), quantitative real-time PCR (qRT-PCR), nanoarray, microarray, macroarray, autoradiography and in situ hybridisation. Preferably, the expression of one or more biomarker(s) is measured using a DNA microarray.
In an additional or alternative embodiment the one or more biomarkers measured in step (b), (c), (e), (g) and/or (k) is measured using an array (e.g., a DNA array). In an additional or alternative embodiment the one or more biomarkers measured in step (b), (c), (e), (g) and/or (k) is measured using a whole genome array (e.g., the Affymetrix Human Gene 1.0 ST array or Affymetrix Human Gene 2.0 ST array). In an alternative or additional embodiment, the Nanostring nCounter system is used (e.g., custom Nanostring nCounter code sets based on selection from a whole genome array (e.g., Affymetrix Human Gene 1.0 ST array or Affymetrix Human Gene 2.0 ST array).
The method may comprise measuring the expression of one or more biomarkers in step (b), (c), (e), (g) and/or (k) using one or more binding moieties, each capable of binding selectively to a nucleic acid molecule encoding one of the biomarkers identified in one or more of Tables A, B, C, D, E. Preferably, the method comprises measuring the expression of two or more biomarkers in step (b), (c), (e), (g) and/or (k) using two or more binding moieties, each capable of binding selectively to a nucleic acid molecule encoding one of the biomarkers identified in one or more of Tables A, B, C, D, E. For example, the expression of any particular combination of biomarkers described above may be measured using an equivalent combination of binding moieties capable of binding selectively to each of those biomarkers.
In one embodiment the one or more binding moieties each comprise or consist of a nucleic acid molecule. In a further embodiment the one or more binding moieties each comprise or consist of DNA, RNA, PNA, LNA, GNA, TNA or PMO. Preferably, the one or more binding moieties each comprise or consist of DNA. In one embodiment, the one or more binding moieties are 5 to 100 nucleotides in length. However, in an alternative embodiment, they are 15 to 35 nucleotides in length.
The one or more binding moieties may comprise or consist of one or more probe from the Human Gene 1.0 ST Array (Affymetrix, Santa Clara, Calif., USA). Probe identification numbers are provided in Tables A-E herein.
Suitable binding agents (also referred to as binding molecules or binding moieties) may be selected or screened from a library based on their ability to bind a given nucleic acid, protein or amino acid motif, as discussed below.
In a preferred embodiment, the binding moiety comprises a detectable moiety.
By a “detectable moiety” we include a moiety which permits its presence and/or relative amount and/or location (for example, the location on an array) to be determined, either directly or indirectly.
Suitable detectable moieties are well known in the art.
For example, the detectable moiety may be a fluorescent and/or luminescent and/or chemiluminescent moiety which, when exposed to specific conditions, may be detected. Such a fluorescent moiety may need to be exposed to radiation (i.e. light) at a specific wavelength and intensity to cause excitation of the fluorescent moiety, thereby enabling it to emit detectable fluorescence at a specific wavelength that may be detected.
Alternatively, the detectable moiety may be an enzyme which is capable of converting a (preferably undetectable) substrate into a detectable product that can be visualised and/or detected. Examples of suitable enzymes are discussed in more detail below in relation to, for example, ELISA assays.
The detectable moiety may be a radioactive moiety and comprise or consists of a radioactive atom. The radioactive atom may be selected from the group consisting of technetium-99m, iodine-123, iodine-125, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, phosphorus-32, sulphur-35, deuterium, tritium, rhenium-186, rhenium-188 and yttrium-90.
Hence, the detectable moiety may be selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety (for example, a radioactive atom); or an enzymatic moiety.
Clearly, the agent to be detected (such as, for example, the one or more biomarkers in the test sample and/or control sample described herein and/or an antibody molecule for use in detecting a selected protein) must have sufficient of the appropriate atomic isotopes in order for the detectable moiety to be readily detectable.
In an alternative preferred embodiment, the detectable moiety of the binding moiety is a fluorescent moiety.
The radio- or other labels may be incorporated into the biomarkers present in the samples of the methods of the invention and/or the binding moieties of the invention in known ways. For example, if the binding agent is a polypeptide it may be biosynthesised or may be synthesised by chemical amino acid synthesis using suitable amino acid precursors involving, for example, fluorine-19 in place of hydrogen. Labels such as 99mTc, 123I, 186Rh, 188Rh and 111In can, for example, be attached via cysteine residues in the binding moiety. Yttrium-90 can be attached via a lysine residue. The IODOGEN method (Fraker et al (1978) Biochem. Biophys. Res. Comm. 80, 49-57) can be used to incorporate 123I. Reference (“Monoclonal Antibodies in Immunoscintigraphy”, J-F Chatal, CRC Press, 1989) describes other methods in detail. Methods for conjugating other detectable moieties (such as enzymatic, fluorescent, luminescent, chemiluminescent or radioactive moieties) to proteins are well known in the art.
It will be appreciated by persons skilled in the art that biomarkers in the sample(s) to be tested may be labelled with a moiety which indirectly assists with determining the presence, amount and/or location of said proteins. Thus, the moiety may constitute one component of a multicomponent detectable moiety. For example, the biomarkers in the sample(s) to be tested may be labelled with biotin, which allows their subsequent detection using streptavidin fused or otherwise joined to a detectable label.
The method provided in the first aspect of the present invention may comprise or consist of, in step (b), (c), (e), (g) and/or (k), determining the expression of the protein of one or more biomarker defined in one or more of Tables A-E. The method may comprise measuring the expression of one or more biomarkers in step (b), (c), (e), (g) and/or (k) using one or more binding moieties each capable of binding selectively to one of the biomarkers identified in one or more of Tables A-E. The one or more binding moieties may comprise or consist of an antibody or an antigen-binding fragment thereof such as a monoclonal antibody or fragment thereof.
The term “antibody” includes any synthetic antibodies, recombinant antibodies or antibody hybrids, such as but not limited to, a single-chain antibody molecule produced by phage-display of immunoglobulin light and/or heavy chain variable and/or constant regions, or other immunointeractive molecules capable of binding to an antigen in an immunoassay format that is known to those skilled in the art. We also include the use of antibody-like binding agents, such as affibodies and aptamers.
The one or more protein-binding moieties may comprise a detectable moiety. The detectable moiety may be selected from the group consisting of a fluorescent moiety, a luminescent moiety, a chemiluminescent moiety, a radioactive moiety and an enzymatic moiety.
In a further embodiment of the methods of the invention, step (b), (c), (e), (g) and/or (k) may be performed using an assay comprising a second binding agent capable of binding to the one or more proteins, the second binding agent also comprising a detectable moiety. Suitable second binding agents are described in detail above in relation to the first binding agents.
Thus, the proteins of interest in the sample to be tested may first be isolated and/or immobilised using the first binding agent, after which the presence and/or relative amount of said biomarkers may be determined using a second binding agent.
In one embodiment, the second binding agent is an antibody or antigen-binding fragment thereof; typically a recombinant antibody or fragment thereof. Conveniently, the antibody or fragment thereof is selected from the group consisting of: scFv; Fab; a binding domain of an immunoglobulin molecule. Alternatively, the second binding agent may be an antibody-like binding agent, such as an affibody or aptamer.
Alternatively, where the detectable moiety on the protein in the sample to be tested comprises or consists of a member of a specific binding pair (e.g. biotin), the second binding agent may comprise or consist of the complimentary member of the specific binding pair (e.g. streptavidin).
Where a detection assay is used, it is preferred that the detectable moiety is selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety; an enzymatic moiety. Examples of suitable detectable moieties for use in the methods of the invention are described above.
Preferred assays for detecting serum or plasma proteins include enzyme linked immunosorbent assays (ELISA), radioimmunoassay (RIA), immunoradiometric assays (IRMA) and immunoenzymatic assays (IEMA), including sandwich assays using monoclonal and/or polyclonal antibodies. Exemplary sandwich assays are described by David et al in U.S. Pat. Nos. 4,376,110 and 4,486,530, hereby incorporated by reference. Antibody staining of cells on slides may be used in methods well known in cytology laboratory diagnostic tests, as well known to those skilled in the art.
Thus, in one embodiment the assay is an ELISA (Enzyme Linked Immunosorbent Assay) which typically involves the use of enzymes which give a coloured reaction product, usually in solid phase assays. Enzymes such as horseradish peroxidase and phosphatase have been widely employed. A way of amplifying the phosphatase reaction is to use NADP as a substrate to generate NAD which now acts as a coenzyme for a second enzyme system. Pyrophosphatase from Escherichia coli provides a good conjugate because the enzyme is not present in tissues, is stable and gives a good reaction colour. Chemiluminescent systems based on enzymes such as luciferase can also be used.
Conjugation with the vitamin biotin is frequently used since this can readily be detected by its reaction with enzyme-linked avidin or streptavidin to which it binds with great specificity and affinity.
In an alternative embodiment, the assay used for protein detection is conveniently a fluorometric assay. Thus, the detectable moiety of the second binding agent may be a fluorescent moiety, such as an Alexa fluorophore (for example Alexa-647).
Preferably, steps (b) (c), (e), (g) and/or (k) of the methods are performed using an array. The array may be a bead-based array or a surface-based array. The array may be selected from the group consisting of: macroarray; microarray; nanoarray.
Arrays per se are well known in the art. Typically they are formed of a linear or two-dimensional structure having spaced apart (i.e. discrete) regions (“spots”), each having a finite area, formed on the surface of a solid support. An array can also be a bead structure where each bead can be identified by a molecular code or colour code or identified in a continuous flow. Analysis can also be performed sequentially where the sample is passed over a series of spots each adsorbing the class of molecules from the solution. The solid support is typically glass or a polymer, the most commonly used polymers being cellulose, polyacrylamide, nylon, polystyrene, polyvinyl chloride or polypropylene. The solid supports may be in the form of tubes, beads, discs, silicon chips, microplates, polyvinylidene difluoride (PVDF) membrane, nitrocellulose membrane, nylon membrane, other porous membrane, non-porous membrane (e.g. plastic, polymer, perspex, silicon, amongst others), a plurality of polymeric pins, or a plurality of microtitre wells, or any other surface suitable for immobilising proteins, polynucleotides and other suitable molecules and/or conducting an immunoassay. The binding processes are well known in the art and generally consist of cross-linking covalently binding or physically adsorbing a protein molecule, polynucleotide or the like to the solid support. Alternatively, affinity coupling of the probes via affinity-tags or similar constructs may be employed. By using well-known techniques, such as contact or non-contact printing, masking or photolithography, the location of each spot can be defined. For reviews see Jenkins, R. E., Pennington, S. R. (2001, Proteomics, 2, 13-29) and Lal et al (2002, Drug Discov Today 15; 7(18 Suppl):S143-9).
Typically the array is a microarray. By “microarray” we include the meaning of an array of regions having a density of discrete regions of at least about 100/cm2, and preferably at least about 1000/cm2. The regions in a microarray have typical dimensions, e.g. diameter, in the range of between about 10-250 μm, and are separated from other regions in the array by about the same distance. The array may alternatively be a macroarray or a nanoarray.
Once suitable binding molecules (discussed above) have been identified and isolated, the skilled person can manufacture an array using methods well known in the art of molecular biology.
In an additional or alternative embodiment one or more biomarker measured in step (b), (c), (e), (g) and/or (k) is a nucleic acid (e.g., DNA, mRNA or cDNA etc). In an additional or alternative embodiment one or more biomarkers measured in step (b), (c), (e), (g) and/or (k) is a protein or polypeptide.
In an additional or alternative embodiment the method is performed in vitro, in vivo, ex vivo or in silico. Preferably, the method is performed in vitro.
In an additional or alternative embodiment, the method comprises one or more of the following steps:
(i) cultivating dendritic-like cells according to the first or second aspect;
(ii) seeding cells of (i) in one or more wells, preferably at steady state growth phase, e.g. wells of one or more multi-well assay plate;
(iii) adding to one or more well(s) of (ii) the agent(s) to be tested;
(iv) adding to one or more separate well(s) of (ii) positive control(s);
(v) adding to one or more separate well(s) of (ii) negative control(s); and/or leaving one or more separate well(s) of (ii) unstimulated to obtain a medium control;
(vi) incubating cells in wells of (iii)-(v), preferably for about 24 hours; and, optionally, harvesting cells from wells of (iii)-(v); and, further optionally, removing supernatant and storing in TRIzol reagent;
(vii) isolating purified total RNA from the cells of (vi) and, optionally, converting mRNA into cDNA;
(viii) quantifying expression levels of individual mRNA transcripts from (vii), e.g. using an array, such as an Affymetrix Human Gene 1.0 ST array;
(ix) exporting and normalizing data from (viii), e.g. using appropriate algorithms;
(x) isolating data from (ix) originating from biomarkers of one or more of Tables A-E);
(xi) applying a prediction model to the data of (x), e.g. a frozen SVM model previously established and trained on historical data to predict the allergenicity or sensitizer status (e.g. classify as allergen/non-allergen and/or sensitizer/non-sensitizer), of tested agents(s) and negative/positive control(s).
By “test protein” we include any protein or proteinaceous entity (or mixture of proteins or proteinaceous entities) for which allergenic or sensitization status is to be determined.
By “allergenic” we include or mean a protein (or mixture of proteins) which is an allergen, and/or which is capable of inducing an allergic response, in a mammal.
In an additional or alternative embodiment the allergenicity comprises a hypersensitivity response (e.g., a cell-mediated hypersensitivity response). In an additional or alternative embodiment the hypersensitivity response is a type I hypersensitivity response. In an additional or alternative embodiment the hypersensitivity response is respiratory allergy.
In an additional or alternative embodiment, the method of the eighth aspect is for identifying the sensitization status of a protein in a mammal. For example, the expression of the one or more biomarkers measured in step (b) or (c) may be indicative of the sensitization status of the test protein. By “sensitization status” we include or mean whether or not a test protein (or mixture of test proteins) is a sensitizer or not (e.g., a skin sensitizer and/or a respiratory sensitizer). In an additional or alternative embodiment, the method is for identifying proteins which are capable of inducing respiratory sensitization in a mammal. For example, the expression of the two or more biomarkers measured in step (c) may be indicative of the respiratory sensitizing effect of the test protein. In one embodiment, the method is for identifying proteins capable of inducing a respiratory hypersensitivity response. Preferably, the hypersensitivity response is a humoral hypersensitivity response, for example, a type I hypersensitivity response. In one embodiment, the method is for identifying agents capable of inducing respiratory allergy.
By “indicative of the respiratory sensitizing effect of the test protein” we include determining whether or not the test protein is a respiratory sensitizer and/or determining the potency of the test protein as a respiratory sensitizer. By proteins “capable of inducing respiratory sensitization” we mean any protein capable of inducing and triggering a Type I immediate hypersensitivity reaction in the respiratory tract of a mammal. Preferably the mammal is a human. Preferably, the Type I immediate hypersensitivity reaction is DC-mediated and/or involves the differentiation of T cells into Th2 cells. Preferably the Type I immediate hypersensitivity reaction results in humoral immunity and/or respiratory allergy.
The conducting zone of the mammalian lung contains the trachea, the bronchi, the bronchioles, and the terminal bronchioles. The respiratory zone contains the respiratory bronchioles, the alveolar ducts, and the alveoli. The conducting zone is made up of airways, has no gas exchange with the blood, and is reinforced with cartilage in order to hold open the airways. The conducting zone humidifies inhaled air and warms it to 37° C. (99° F.). It also cleanses the air by removing particles via cilia located on the walls of all the passageways. The respiratory zone is the site of gas exchange with blood.
In one embodiment, the protein “capable of inducing respiratory sensitization” is a protein capable of inducing and triggering a Type I immediate hypersensitivity reaction at a site of lung epithelium in a mammal. Preferably, the site of lung epithelium is in the respiratory zone of the lung, but may alternatively or additionally be in the conductive zone of the lung.
In an additional or alternative embodiment, the method is for identifying food proteins which are allergenic in a mammal. For example, the expression of the two or more biomarkers measured in step (c) may be indicative of the allergenicity of the food protein. Preferably, the allergenicity of the food protein is due to a Type 1 hypersensitivity response.
The mammal may be any domestic or farm animal. Preferably, the mammal is a rat, mouse, guinea pig, cat, dog, horse or a primate. Most preferably, the mammal is human.
In a further aspect of the invention there is provided the use of a population of dendritic-like cells according to the first or second aspect for determining the sensitizing effect and/or the allergenicity of a test agent.
In a further aspect of the invention there is provided an analytical kit comprising:
i. an array comprising one or more binding moieties; and
ii. one or more cell or a cell culture as defined in the first or second aspect; and
iii. (optionally) one or more control agent; and
iv. (optionally) instructions for use.
In a further aspect of the invention there is provided an analytical kit for use in a method of the invention comprising:
i. an array comprising one or more binding moieties as defined herein; and
ii. one or more cell or a cell culture as defined in the first or second aspects; and
iii. (optionally) one or more control agent; and
iv. (optionally) instructions for performing the method of the invention.
The skilled person will appreciate that all non-conflicting embodiments may be used in combination. Hence, embodiments from one aspect of the invention may equally be applied to another aspect of the invention.
The listing or discussion of an apparently prior-published document in the specification should not necessarily be taken as an acknowledgment that the document is part of the state of the art or common general knowledge.
Preferred, non-limiting examples which embody certain aspects of the invention will now be described, with reference to the following figures:
Allergic contact dermatitis (ACD) is an inflammatory skin disease that affects a large proportion of the population. It is caused by the repeated exposure to skin sensitizers and results in symptoms such as eczema. The sensitization phase of ACD requires a skin sensitizer to activate an immune response leading to the production of allergen specific effector and memory T-cells (1). The known mechanisms for sensitization have been well described (1-4). In short, skin sensitizers gain access to the viable dermis where it reacts with proteins to form hapten-protein complexes. These hapten-protein complexes can be recognized by different immune cells or structural cells that exert inflammatory signals. Activated dendritic cells process the hapten-protein complexes and migrate to lymph nodes where they present them to naïve T-cells on MHC-molecules. Subsequently, effector and memory T-cells are produced that upon renewed exposure to the same sensitizer elicits inflammatory functions, giving rise to the symptoms of ACD (3). Conventional methods for assessing chemicals' abilities to induce allergic reactions at skin contact have mainly been conducted using animal models (1). Legislations and trends drive research to come up with better and more ethical in-vitro methods for the evaluation of chemical sensitizers (5-7).
The Genomic Allergen Rapid Detection (GARD) is an in vitro assay developed at Department of Immunotechnology, Lund University, for the assessment of chemical sensitizers. It uses transcriptional profiles from a dendritic cell-like cell line (8), to predict the sensitizing potential of chemicals (9). The transcriptional profiles consist of genes that were identified as having the greatest predicting performance in distinguishing sensitizers from non-sensitizers. The genes were identified using statistical data mining methods on transcription data originating from Affymetrix microarrays. No a priori information of the genes in the GARD Prediction Signature (GPS) were used for their identification, which could have made the signature specific for the cell line (10).
The inventors now provide a non-naturally occurring dendritic-like myeloid leukemia cell line “SenzaCell” (ATCC Patent Deposit Designation PTA-123875; deposited at ATCC on 9 Mar. 2017) which is suitable for use in the GARD assays. SenzaCell has been compared to the MUTZ-3 dendritic-like cell line which may also be used in GARD methods. The comparison included a phenotypic analysis where the expression of a panel of biomarkers were compared, a transcriptional analysis where the transcription levels of the cell lines were compared, a comparison of their DNA sequences and a functional analysis, which revealed many quantifiable differences in the phenotype and transcriptional profiles.
Materials and Methods
Cell Maintenance
MUTZ-3 (DSMZ, Braunschweig, Germany (www.dsmz.de; DSMZ No. ACC 295)) and SenzaCell cells were cultivated in minimum essential medium alpha (α-MEM) (GE Healthcare Life Sciences, Logan, Utah) supplemented with 20% (V/V) fetal calf serum (FCS) (Thermo Fisher Scientific, Waltham, Mass.) and 40 ng/ml granulocyte macrophage colony stimulating factor (GM-CSF) (Miltenyi Biotec, Bergisch Gladbach, Germany). Throughout this report the media is referred to as complete media. Cells were maintained at a concentration of 200 000 cells/ml and the media was renewed every 3-4 days. The cells were maintained at 37° C. and 5% CO2.
Phenotypic Characterization
Growing cells were harvested, counted and seeded at a concentration of 200 000 cells/ml in complete media. To prepare the cells for staining, 1 ml of cell suspension was transferred to FACS tubes. The cells were washed in wash buffer, PBS (GE Healthcare Life Sciences) with 1% BSA (Saveen & Werner, Limhamn, Sweden) (w/V), twice. All washing steps were performed by the addition of 1 ml of wash buffer, centrifugation at 1200 rpm at 4° C. and removal of the supernatant. After the second wash, the cells were resuspended in 50 μl wash buffer. The antibodies; isotype PE/FITC, CD40 FITC, CD54 PE, CD86 FITC (BD Pharmingen, San Diego, Calif.); CD1a FITC, CD5 FITC, CD14 PE, CD19 PE (DAKO); CD13 PE, CD123 PE, OX40L PE (Pharmingen, San Diego, Calif.); CD11b PE, CD34 FITC, CD32 FITC, CD80 PE, HLA-DR FITC, CD137 PE, CD16 PE, CD64 PE (BD, Franklin Lakes, N.J.); BDCA-3 APC (Miltenyi, Bergisch Gladbach, Germany); CD209 PE (R&D Systems, Minneapolis, Minn.); BDCA-1 APC (eBioscience, San Diego, Calif.); OX40 PE (BD Bioscience, San Jose, Calif.); CD15 FITC (Milteny); TLR2 PE, TLR4 PE (biolegend, San Diego, Calif.), were added to the resuspended cells and incubated for 15 min at 4° C. in the dark. Following staining, the cells were washed in wash buffer once more and then resuspended in 200 μl of wash buffer. The cells were kept in 4° C. until analyzed with flow cytometry. The flow cytometry was performed on a FACS CANTO II (BD Bioscience) with BD DIVA software (BD Bioscience) as the data acquisition program where 10 000 events were recorded for each sample. The data was analyzed using FACS Express V3 (De Novo Software, Los Angeles, Calif.). Appropriate gates were set using isotype controls and unstained cells. The measurements were performed at three separate experiments with technical duplicates at each, generating 6 measurements for each marker and cell line.
The expression levels were compared using t-tests where the generated p-values were corrected with the Benjamini Hochberg method to control the false discovery rate. A clustering algorithm was performed to compare the cell lines. The clustering was performed using R (15). A bootstrap algorithm was performed to assess the stability of the discovered clusters using the fpc (16) package. The bootstrap algorithm was run for 5000 iteration and the calculated mean Jaccard coefficients was used as an indication on cluster stability.
DNA—Whole Genome Sequencing
DNA from both MUTZ-3 and SenzaCell cells was isolated using Quick-gDNA™ Miniprep (Zymo Research, Irvine Calif.). The isolation was performed as follows; 5*10{circumflex over ( )}6 cells were harvested and centrifuged and the supernatant was removed. The remaining cell pellets were lysed in Quick-gDNA™ genomic lysis buffer and incubated for 10 minutes. The lysed cells were loaded to the supplied spin columns and washed. DNA was eluted in RNAse/DNase free water. DNA concentration and DNA purity were determined using NanoDrop, and the quality was evaluated by gel electrophoresis. The DNA whole genome sequencing was performed by SciLifeLabs (Stockholm, Sweden). The services from SciLifeLabs included library preparation, sequencing and best practice basic analysis. The DNA libraries were prepared with Illumina TruSeq PCR-free, 350 bp and the sequencing were performed with Illumina HiSeq X v2.5, PE 2×150 bp at 30× coverage.
The best practice basic analysis included mapping of the reads to the human assembly build 37 using bwa-mem. The Genome Analysis Toolkit (GATK) workflow (17, 18) for best practice analysis were followed for variant calling and genomic variant call format (gVCF) files were delivered from SciLifeLabs. The obtained gVCF files were merged and genotyped using GATK genotypeGVCFS. The variant were further processed using GATK Variant Quality Score Recalibration (VQSR) as recommended by the GATK workflow. The VQSR calculates new variant quality scores and filters variants that are predicted to be false positive discoveries using machine learning. The recalibrated variant call format (VCF) file was annotated and the variant effects were predicted using snpEff (19) to the human assembly build GRCh37.75. The variants that were unique to either of the cell lines were identified using SnpSift (20) casecontrol and the other variants were filtered using SnpSift filter. The list of unique variants was further filtered by their genotype quality scores were variants with a score <20 was removed (21). Finally variants predicted to have a high or moderate impact were retained for further analysis. PANTHER classification system (22) was used for classification of the molecular function of the genes which were identified to be impacted by the variants.
RNA—RNAseq
Total RNA was isolated from both MUTZ-3 and SenzaCell cell lines in three experiments to generate triplicate RNAseq samples. 200,000 cells were isolated and lysed in TRIzol reagent (Thermo Scientific, Waltham, Mass.). RNA was isolated using Direct-zol™ RNA Miniprep (Zymo research). The lysed samples were mixed with ethanol and added to the spin columns. The RNA was bound and washed with the supplied wash buffers. RNA was eluted in RNase/DNase free water and quality controlled with Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, Calif.). The samples were stored at −80° C. until shipped on dry ice to SciLifeLabs for RNA-seq. The libraries were prepared using Illumina TruSeq stranded mRNA, Poly-A selection. The sequencing was performed using Illumina HiSeq Rapid mode v2, SR 1×50 bp. The best practice basic analysis was performed by SciLifeLabs which included mapping of the reads using topHat v2.0.4 (23), sorting of the generated bam files with samtools (24), marking duplicates with piccard-tools and quantifying the counts using HTSeq (25).
The obtained count table were loaded into R and differential expression analysis was performed using edgeR v3.14.0 (26) and DESeq2 v1.12.4 (27), which performs normalization and fitting of negative binomial models to the count data, enabling effective differential expression analysis. The recommended workflows for differential expression (DE) analysis were followed for both packages. The model matrix used for hypothesis testing were designed to incorporate the experiment batch in the model due to batch effects which were discovered when analyzing PCA plots of the samples. A transcript was considered differentially expressed if both edgeR and DESeq called it significantly differentially expressed with a false discovery rate below 0.05. The molecular functions and the biological processes that the identified transcripts were mapped to were analyzed using PANTHER classification system. To identify gene ontology (GO) terms that were over- or underrepresented among the differentially expressed transcripts an overrepresentation test was performed, also using PANTHER. The background reference list was submitted as all the non-zero counts that were obtained in the count table, which is the same list that was entered into the DE analysis. The p-values of the identified GO terms were corrected with the Bonferroni method. GO terms with a corrected p-value below 0.05 were considered significantly over- or underrepresented. Additionally, pathway analysis was performed using Signaling Pathway Impact Analysis v2.24.0 (SPIA) (28) and KEGG pathways (29, 30) release 79.0. The list of significant DE transcripts was entered with accompanying log2-fold change values and all the non-zero transcripts were entered as background. The log2-fold change values were calculated using DESeq2 v1.12.4.
Finally a concordance analysis was made for the results of the phenotype analysis and the RNA-seq analysis. The genes corresponding to the measured surface markers were compared to the surface marker expression. The comparison was made similar to an evaluation of a classification with the classes significantly upregulated/expressed, significantly downregulated/expressed or no significant difference between the two cell lines. Therefore, if both the RNA-seq analysis and the phenotype analysis calls the transcript level or surface expression significantly higher, this would be considered a concordant result. Cohens Kappa (31) was calculated as an indication of the concordance between the analysis methods.
Functional Analysis
MUTZ-3 was used in the GARD assay to assess its ability to distinguish skin sensitizers from non-sensitizers using the GARD prediction signature as compared to SenzaCell. The GARD standard operating procedures were followed which has previously been described in detail (9). In short, the cells (MUTZ-3 or SenzaCell) were seeded in 24-well plates at a volume of 1.8 ml and a cell concentration of 222 000 cells/ml. The chemicals used for the stimulation were (2, 4)-dinitrochlorobenzene (DNCB) and 2-hydroxyethylacrylate. An unstimulated sample was also included as a negative control. DNCB was dissolved in DMSO to a concentration of 4 mM and 2-hydroxyethylacrylate in water to a concentration of 100 mM. Both chemicals were then diluted, in α-MEM supplemented with 20% FCS, 100× and finally diluted once more 10× as 200 μl was added to the wells. The in-well concentration for the stimulations was 4 μM and 100 μM for DNCB and 2-hydroxyethylacrylate, respectively. 200 μl of MEM-alpha was added to the unstimulated cells, giving an in-well cell concentration of 200 000 cells/ml for every sample. The cells were incubated with the chemicals for 24 h at 37° C. at 5% CO2. Following incubation, cells were harvested into RNase free Eppendorf tubes and lysed in TRIzol. Cell viability was assessed by flow cytometry using propidium iodide (PI) (Thermo Fisher Scientific) staining. RNA was extracted and purified using Direct-zol RNA kits (Zymo research). RNA was extracted as described and quality controlled using Agilent Bioanalyzer 2100 Samples with an RNA integrity numbers (RIN) ≥8 and an RNA concentration ≥20 ng/μl was used for quantification. The genes in the GPS (see Table A below) were quantified using NanoString nCounter systems (NanoString Technologies, Seattle, Wash.), using protocols and reagents supplied by the manufacturer. The raw counts of the genes were exported from NanoString and normalized by a count per total counts normalization, where each gene's count is divided by the sum of counts for a single sample. The classification was performed by Support vector machine from the R package e1071 (32) with a training set consisting of gene profiles of 40 chemical stimulations in replicates (10). The data was aligned with the training data prior to the classification by calculating the shifts in the first 4 principal components for the unstimulated samples in both data sets. The calculated shifts were then used to adjust every sample from the test samples.
Methodology for distinguishing skin sensitizers from non-sensitizers using the GARD prediction signature is also described in detail in WO 2012/056236; Johansson et al. (2017) Evaluation of the GARD assay in a blind Cosmetics Europe study. ALTEX Online first Feb. 17, 2017; Forreryd et al. (2016) From genome-wide arrays to tailor-made biomarker readout—Progress towards routine analysis of skin sensitizing chemicals with GARD. Toxicolgy In Vitro; Johansson et al. (2014) GARD in-house validation—A proof of concept. Tox Sci; Johansson et al., (2011) A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests. BMC Genomics, 2011), each incorporated herein by reference.
Table A legend. Genes were annotated, using the NetAffx database from Affymetrix (www.affymetrix.com, Santa Clara USA). When found, the Unigene (www.ncbi.nlm.nih.gov/UniGene/) ID was chosen as the gene identifier. In the cases where no Unigene ID was reported the best alternative ID was given.
Results
Phenotype Analysis
To investigate any indications of phenotypic differences in SenzaCell compared to MUTZ-3, the expression of a panel of surface markers, Table I, were measured in both cell lines. The selected surface markers are associated with dendritic cells, myeloid cells, hematopoietic cells or cell activation. The populations of positive cells were calculated and the population sizes were compared using t-tests. Out of the 26 surface markers that were measured, 12 were significantly differentially expressed between the cell lines when considering a false discovery rate <0.05 significant, see rows shaded in Table I. The differentially expressed surface markers were CD1a, CD11b, CD14, CD34, CD15, CD32, CD40, CD54, HLA-DR, CD64, Toll like receptor (TLR) 4 and TLR-2. The expression of CD54 is however nearly identical in the cell lines and close to the highest obtainable value, and should be interpreted with caution. The expression was also considered as binary, + if the surface marker was detected and − otherwise. The binary expression of the surface markers were very similar between the cell lines. The only surface markers that showed a different binary expression was CD80 and possibly OX40L. A hierarchical clustering algorithm was performed to further compare the cell lines, see
Whole Genome Sequencing
Whole genome sequencing was performed to identify indicators of genetic differences between SenzaCell and MUTZ-3. Indicators could be represented by variants only discovered in either of the cell lines. The investigated variants consisted of single nucleotide polymorphisms (SNPs), insertions or deletions. The number of raw variants that were discovered in the cell lines was 5,080,918 and 5,073,307 in SenzaCell and MUTZ-3 respectively. The gVCF files were merged and genotyped and the variant quality scores in the outputted VCF file were recalibrated. Variants with low quality scores were filtered and removed from further analysis. The similarity between the cell line genomes and the reference genome was calculated to 99.86% for both cell lines, resulting in a variant frequency of 1 variant per ˜720 bp. Variants unique to either of the cell lines were identified by finding positions where one cell line had an identified variant and the other cell line were homozygous to the reference genome. Further filtering was performed to remove variants where the certainty of the homozygous calls was low, by filtering on the genotype quality scores. The obtained variant list consisted of 7,977 variants that could only be found in the SenzaCell and 5,672 variants unique to the MUTZ-3 cell line. Though the list of variants had been decreased significantly, further filtering was necessary to identify mutations that could have an impact on the cells. Variants were therefore filtered to remove low impact mutations. Mutations that were predicted to have a high or moderate impact in SenzaCell can be seen in Table 2 and the identified variants for MUTZ-3 can be seen in Table 3. 14 variants were discovered in SenzaCell that matched the described criteria and 28 were found in MUTZ-3. The predicted effects ranged from amino acid substitutions to more severe mutations such as gaining a new stop codons or changes in splice sites. The biological processes that the genes that were impacted by the variants could be mapped to were determined using PANTHER, see
Finally, a similar filtering strategy was performed to determine the number of common variants that could be identified in the cell lines, which resulted in the identification of 4,298,116 variants.
RNA-Seq Analysis
The transcriptomes of the cell lines were compared to assess the cell states during normal growth and identify any differences between them. An initial analysis of the data was performed by creating a PCA plot of the samples and performing a hierarchical clustering analysis,
The regulation of the genes selected in the GPS are important for the identification and separation of sensitizers and non-sensitizers in the GARD assay. The genes in the GPS were therefore compared to the DE transcripts which revealed that 43 transcripts were found in both lists. This demonstrated that almost 22% of the genes in the GPS were differentially expressed between the cell lines.
A comparison between the results in the phenotype and the RNA-seq data was performed to investigate the analysis methods and possibly give more validity to the findings. The panel of biomarkers and their corresponding genes were compared in a concordance analysis. The comparison was performed as an evaluation of a classification task with the groups significantly higher, significantly lower or not different in the MUTZ-3 compared to SenzaCell. The analysis resulted in a Cohens Kappa value of 0.44, suggesting concordance between the two methods, see expression for each marker in Table 1. 8 of the 10 surface markers that showed an increased expression also showed an increased expression in the RNAseq analysis. CD54 shows an increased gene expression in MUTZ-3 which is opposed to the surface marker expression. The gene for TLR2 is not called as differentially expressed. 8 of the 14 surface markers that were not call significant did not have differentially expressed genes either. The gene expression levels for the remaining 6 markers were however found to be differentially expressed. CD34 was the only marker that was found to have significantly lower expression levels in the MUTZ-3 in both transcription levels and surface expression. HLA-DR was left out of this comparison due to its complex genetics.
Functional Analysis
MUTZ-3 and SenzaCell's ability to distinguish skin sensitizers when analyzing the expression of the genes in the GARD prediction signature (GPS) after chemical exposure was assessed by using the cells in the well-established assay workflow. 2-hydroxyethylacrylate and DNCB were used to stimulate the cells and an unstimulated sample was used as negative control. RNA was quantified and the samples were classified using a SVM model trained on genetic profiles from SenzaCell. The chemicals were correctly classified as sensitizers and the unstimulated sample as non-sensitizer using both MUTZ-3 and SenzaCell,
Allergic contact dermatitis is a condition with increasing prevalence that causes significant costs for society. Contact with skin sensitizers can trigger an immune response to attack the complexes that are formed between the chemical and biomolecules, resulting in an attack on healthy tissue (1). Assessment of a chemicals' ability to induce ACD is important to reduce the number of people that are affected by ACD. The gold standard for sensitization testing, the local lymph node assay (LLNA) (33), rely on animal testing and is being phased out by more ethical in vitro assays (6, 7, 34). The GARD assay is an in-vitro assay that distinguishes between sensitizers and non-sensitizers by analyzing the gene expression of the genes in the GARD prediction signature. The transcripts are isolated from a dendritic cell-like cell line that has been exposed to test chemicals. The assay relies on the innate decision making of the cell line to recognize sensitizers and adapt its gene expression, making it possible to quantify the changes and learn to recognize sensitizers (9). MUTZ-3 and SenzaCell are suitable cells for this task due their ability to resemble dendritic cells, which are important for recognizing antigens and orchestrate immune responses.
SenzaCell was compared to MUTZ-3 by characterizing their phenotype, genotype and assessing the cell line's ability to distinguish sensitizers from non-sensitizers when used in the assay. The expression levels of a panel of surface markers were determined for both cell lines. The results demonstrated that 12 out of 26 surface markers had a different expression levels in MUTZ-3 compared to SenzaCell. A hierarchical clustering algorithm with bootstrapping also showed the possibility to group the cell lines into different stable clusters, implying that the cell lines are more similar to themselves than the other cell line over the course of the experiments. The surface markers that had higher expression values in MUTZ-3 were CD1a, CD11b, CD14, CD15, CD32, CD40, CD64, TLR2 and TLR4 while CD34, CD54 and HLA-DR had lower expression values. MUTZ-3 cells can, based on CD14 and CD34 expression, be divided into three subpopulations; CD34+ CD14−, CD34− CD14− and CD34− CD14+. Previous analysis of the populations revealed that the CD34+ CD14− is the proliferating population which give rise to the other populations (35). Some of the differences between SenzaCell and MUTZ-3 could therefore possibly be explained by the differences between the subpopulations, e.g. the increased amount of CD14 + cells in MUTZ-3 could possibly explain the increased CD11b+ expression (35). However, considering the available data, the different levels in expression cannot be explained solely due to different sizes of the subpopulations. CD1a is expressed in both cell lines, which has preciously not been recorded in undifferentiated MUTZ-3 (8, 35, 36). The expression of CD1a could therefore indicate differentiation of the MUTZ-3 cells towards a more dendritic cell like phenotype. MUTZ-3 displayed a larger population of CD1a positive cells, which could indicate that it is more differentiated than SenzaCell. Evidence that further implies that MUTZ-3 could be more differentiated comes from the increased CD40 expression, which is a marker that increases in expression as cells differentiate towards a DC phenotype (37). Although both CD1a and CD40 are expressed in higher levels in MUTZ-3, the expression of other surface markers show evidence of the contrary. HLA-DR is another marker whose expression has been shown to moderately increase with differentiation which is more expressed in SenzaCell, making it difficult to draw any conclusions regarding unequal levels of differentiation between the cell lines (37). Further on, neither of the cell lines' morphology exhibits the characteristic dendrites that are formed as MUTZ-3 differentiates (data not shown). Considering the roles of the surface markers that are differentially expressed, many are related to myeloid cell differentiation, e.g. the FC-gamma receptors CD32 and CD64 (38), the pathogen recognition receptors (PRR) TLR2 and TLR4 (39-41) or the carbohydrate adhesion molecule CD15 (42). This could indicate that there is some form of differentiation that is responsible for the observed differences; however the nature of the differentiation process is difficult to determine using only the obtained expression values.
The whole genome sequencing was performed to compare the genomes of the cell lines and search for indications of genetic differences. The number of identified variants lie within the expected range as a typical person differs at 4.1 million to 5 million sites compared to the reference genome (43). Variants that were unique to either of the cell lines were identified and their predicted impacts were assessed. In total, 7,977 unique variants were discovered in the SenzaCell and 5,672 in MUTZ-3. Comparing the number of discovered unique variants to the number of common variants makes the observed differences seem small. However when filtering variants with low impact and assessing variants predicted to have a moderate or high impact, the situation was reversed. 14 variants were discovered in SenzaCell and 28 mutations in MUTZ-3. One of the reasons for these results could be due to the insensitivity of the method used to identify variants. Whole genome sequencing is a high throughput method which generates billions of reads. The process of transforming the reads into interpretable results is difficult and an ongoing challenge. However, if assuming that the discovered variants are unique to the cell lines and not the artifacts from detection, they could be the product of other biological processes. Genetic differences can be caused by selective pressure as the cell lines grow. If MUTZ-3 contains subpopulations with different variants in its genome, it is possible that some of them have been lost in SenzaCell. Genetic heterogeneity has previously been shown in cell lines (44). Another possibility is that MUTZ-3 has been expanded in parallel to SenzaCell, which could have generated the discovered variants. A parallel expansion of MUTZ-3 cell line could result in finding unique variants that are absent in the SenzaCell cells, but would require the mutations to be of high or moderate impact with higher frequency. A continued parallel expansion could possibly introduce additional differences between the cell lines. The biological processes that were mapped to the genes predicted to be impacted by the mutations are general terms and difficult to draw functional conclusions from. This is made even harder when considering the impact that a mutation has, e.g. the effect of a missense variant where an amino acid in the resulting protein is changed to another amino acid. However, studying the gene lists reveals interesting variants. The high impact variant found in MUTZ-3 in the MYB gene could be a relevant finding. The MYB is an identified oncogene that normally has important functions in the regulation of stem- and progenitor cells (45). Another interesting observation is the mutation in the WISP1 gene, also in the MUTZ-3. WISP1 functions in the WNT signaling pathways which has important roles in mediating cell-cell interactions and stem cell regulation and control (46-48). Both of these mutations could be important for the function and regulation of the cell lines, and the lack of these variants in SenzaCell could have a functional impact.
Analysis of the cell transcriptomes were performed by quantification of transcription levels using RNA-seq during ordinary cultivation. A comparison of the expression levels revealed that 3644 transcripts were differentially expressed, which is close to 10% of all the transcripts that were analyzed after removing zero count transcripts. The high number of differentially expressed genes suggests that large differences between the cell lines can be observed at molecular level. An overrepresentation test and a topological pathway analysis were performed to get a better understanding of the potential impacts that the identified genes could have on the cells. 94 biological process GO terms were identified as over- or underrepresented amongst the identified transcripts. Due to the comparative analysis of identifying differences between the cell lines, overrepresented terms are of the greatest importance. Underrepresented terms can be interesting in other types of analyses, e.g. to identify key biological processes necessary for cell function. However, most of the identified GO terms were overrepresented. To briefly mention some of the GO terms, myeloid leukocyte activation and leukocyte chemotaxis were identified as the biological processes with largest fold change between the expected and the observed number of transcripts. Both of these terms indicate response to external stimuli (49). Positive regulation of MAP kinase activity was also identified with many related GO terms. The MAP kinases are involved in signaling pathways that control important cellular processes such as cell differentiation, cell proliferation and cell death (50). Different inflammatory responses and immune processes were also identified as overrepresented, which could be of importance due to the functional role of SenzaCell in the GARD assay. Continuing the analysis of the RNA-seq data, a pathway analysis was performed to gain further knowledge about the differences between the cell lines. The pathway analysis discovered 19 signaling pathways that were significantly activated and 2 that were inhibited in the MUTZ-3 cells compared to SenzaCell. MAP kinase signaling pathway was identified as activated in MUTZ-3. MAP kinase activity was also identified as an overrepresented biological process, giving additional evidence of changes in cellular processes between the cell lines. NF-kappa B signaling is another interesting pathway that was discovered as activated, which is intimately linked to inflammatory responses (51). These observations together with the activated toll-like receptor signaling could be the result of an inflammatory response.
The functional analysis showed that MUTZ-3 was able to produce predictions similar to what is achieved when using SenzaCell in the assay. The sensitizers DNCB and 2-hydroxyethylacrylate were correctly predicted as sensitizers by assessing the transcriptional levels of the genes in the GPS. The predicted decision values also showed a similar pattern between the two cell lines. The ability to separate the chemicals do however not directly give any measure of similarity. It is possible that the cells react similar to stimuli and that the GPS captures these changes well.
To summarize the comparisons, we have completed a phenotypic analysis which revealed that 12 out of 26 surface markers were differentially expressed between MUTZ-3 and SenzaCell. Cluster analysis on the expression values forms two separate clusters which separates the cell lines. Whole genome sequencing identified more unique variants in the SenzaCell cell line when assessing all the discovered variants. When filtering out low impact variants, the MUTZ-3 cell lines contained 28 variants compared to 14 in the SenzaCell cell line. Some of the identified variants could have important impacts in the functions of the cells. The RNA-seq analysis found 3644 transcripts that were differentially expressed. An overrepresentation test and a pathway analysis revealed that many biological processes and signaling pathways were differentially activated. Finally, MUTZ-3 and SenzaCell have been used in the GARD assay to evaluate their ability to recognize sensitizers and produce predictions. The classifications of the samples resulted in correct predictions for all the chemicals, indicating that both cell lines could be used in the assays, despite observed differences.
In conclusion, we have identified numerous differences between SenzaCell and MUTZ-3 in phenotypic and genotypic analyses. Collectively the differences indicate that SenzaCell has unique expression of surface molecules and cellular functions, yet both cell lines can be used in the GARD assays.
The SenzaCell cell line may also be used together with a genomic biomarker signature for classification of respiratory sensitizers using the GARD platform, i.e. for identifying agents capable of inducing respiratory sensitization in a mammal.
The methods may be performed in line with the methodology discussed in the functional analysis section of Example 1 above for identifying skin sensitizers but adapted to use the biomarker signature of Table B and/or Table C below. See also the detailed methodology described specifically for identifying agents capable of inducing respiratory sensitization described in WO 2013/160882; WO 2016/083604; and Forreryd et al. (2015) Prediction of chemical Respiratory sensitizers using GARD, a novel in vitro assay based on a genomic biomarker signature. PLoS One 10(3)).
Table B. Genes are annotated with Entrez Gene ID where found (www.ncbi.nlm.nih.gov/gene). The Affymetrix Probe Set ID for the Human ST 1.0 Array are provided. The validation call frequency (%) is the occurrence of each gene in the 20 Validation Biomarker Signatures obtained during cross-validation.
The table shows predictor genes in GRPS, identified by one-way ANOVA p-value filtering and Backward elimination. When possible, the Ensembl transcript ID was used as gene identifier. The Affymetrix Probe Set ID for the Human ST 1.0 Array are provided.
1Validation call frequency (%) describes the occurrence of each predictor transcript among the 20 biomarker signatures obtained by cross validation.
The SenzaCell cell line may also be used for classification of the potency of skin sensitizers using the GARD platform, i.e. for determining the skin sensitizing potency of test agents.
Such methods may be performed in line with those described in the functional analysis section of Example 1 above for identifying skin sensitizers but adapted to use the biomarker signature of Table D in order to determine potency. See also the specific methodology described in detail in Zeller et al. (2017) The GARD platform for potency assessment of skin sensitizing chemicals. ALTEX Online first published Apr. 12, 2017, version 2 https://doi.org/10.14573/altex.1701101, and also described in WO 2017/162773.
The SenzaCell cell line may also be used for identifying proteins which are allergenic in a mammal.
The methods may be performed in line with the functional analysis section of Example 1 above for identifying skin sensitizers but adapted to use the biomarker signature of Table E in order to identify allergenic proteins. See also the methodology described in Zeller et al. An alternative biomarker-based approach for the prediction of proteins known to sensitize the respiratory tract. Toxicol In Vitro, 2017 Oct. 7; 46:155-162.
A possible method of performing allergenic predictions on proteins is provided as follows. It is based on the GARD Protein Allergen Prediction Signature of Table E (herein referred to as “GARD PAPS”). The readout of GARD is a set of genomic predictors, referred to as the GARD Prediction Signature (GPS).
The genetic material of the cells are isolated from cell samples stimulated with the test substances. The transcriptional levels of the GARD PAPS are quantified and compared to a reference data set by the use of multivariate statistical prediction models. Each sample is assigned a decision value based on its transcriptional levels of the GARD PAPS, as measured by Affymetrix microarray technology. Final predictions are based on the mean value from biological triplicate samples.
All proteins are screened for cytotoxic effects and the GARD input concentration established for each protein.
LPS may be used as a negative control, ensuring that observed signals generated by samples are not due to endotoxin contaminants. Endotoxin contents of the samples may be quantified using a LAL test. The LPS concentration used as a negative control may be set to correspond to the highest endotoxin concentration present in a sample.
All test proteins and substances are assayed in biological triplicates. All replicates of test substances are assigned decision values using the GARD PAPS prediction model, as described (see materials and methods below).
Materials & Methods
The comprehensive materials and methods for the GARD testing strategy, used to generate data according to this example, is included below.
Deviations from Standard Protocols
The cytotoxic effects of the test proteins may be monitored in the concentration range 1-25 μg/ml. 25 μg/ml may be used as the GARD input concentration.
When stimulating the cells with the test substances, the proteins may first be dissolved in PBS to a concentration of 1000 μg/ml. 50 μl of the dissolved proteins then added to 1.95 ml of seeded cells. LPS may be diluted in PBS to a final concentration of 0.1 μg/ml and 2 μl added to 1.998 ml of cell suspension.
The cells are stimulated for 24 h after which they are lysed in TRIzol reagent. RNA is purified, labeled and hybridized to Affymetrix arrays.
The quantified transcription levels are single chain array normalized (SCAN) and the GARD PAPS extracted from the data set. Unstimulated samples, from the test samples and the reference samples used to build the prediction model, may be used to remove batch effects between data sets.
Final classifications are made using a support vector machine (SVM) which had been trained on the reference samples used to establish the GARD PAPS.
Cell Line Maintenance and Seeding of Cells for Stimulation
The SenzaCell cell line may be maintained in α-MEM (Thermo Scientific Hyclone, Logan, Utah) supplemented with 20% (volume/volume) fetal calf serum (Life Technologies, Carlsbad, Calif.) and 40 ng/ml rhGM-CSF (Bayer HealthCare Pharmaceuticals, Seattle, Wash.), as described (Johansson et al., 2011). A media change during expansion is performed every 3-4 days, or when cell-density exceeds 5-600.000 cells/ml. Proliferating progenitor cells are used for the assay, with no further differentiation steps applied. During media exchange, cells are counted and suspended to 200.000 cells/ml. Working stocks of cultures are grown for a maximum of 20 passages or two months after thawing. For chemical stimulation of cells, 1.8 ml is seeded in 24-well plates at a concentration of 222.000 cells/ml. The compound to be used for stimulation is added in a volume of 200 μl, diluting the cell density to 200.000 cells/ml during incubation.
Phenotypic Analysis
Prior to any chemical stimulation, a qualitative phenotypic analysis is performed to ensure that proliferating cells are in an immature stage. All cell surface staining and washing steps are performed in PBS containing 1% BSA (w/v). Cells are incubated with specific mouse monoclonal antibodies (mAbs) for 15 min at 4° C. The following mAbs are used for flow cytometry: FITC-conjugated CD1a (DakoCytomation, Glostrup, Denmark), CD34, CD86, and HLA-DR (BD Biosciences, San Diego, Calif.), PE-conjugated CD14 (DakoCytomation), CD54 and CD80 (BD Biosciences). Mouse IgG1, conjugated to FITC or PE are used as isotype controls (BD Biosciences) and propidium iodide (PI) (BD Biosciences) is used to assess cell viability. FACSDiva software is used for data acquisition with FACSCanto II instrument (BD Bioscience). 10,000 events are acquired, gates are set based on light scatter properties to exclude debris and non-viable cells, and quadrants are set according to the signals from isotype controls. Further data analysis is performed, using FCS Express V3 (De Novo Software, Los Angeles, Calif.). For a reference phenotype of unstimulated cells, see Johansson et al., 2011.
Chemical Handling and Assessment of Cytotoxicity
All chemicals are stored according to instructions from the supplier, in order to ensure stability of compounds. Chemicals are dissolved in water when possible or DMSO for hydrophobic compounds. As many chemicals will have a toxic effect on the cells, cytotoxic effects of test substances are monitored. Some chemicals are poorly dissolved in cell media; therefore the maximum soluble concentration is assessed as well. The chemical that is to be tested is titrated to concentrations ranging from 1 μM to the maximum soluble concentration in cell media. For freely soluble compounds, 500 μM is set as the upper end of the titration range. For cell stimulations, chemicals are dissolved in its appropriate solvent as 1000× stocks of target in-well concentration, called stock A. A 10× stock, called stock B, is prepared by taking 10 μl of stock A to 990 μl of cell media. 200 μl of stock B is then added to the wells containing 1.8 ml seeded cells. For the samples dissolved in DMSO, the in-well concentration of DMSO will thus be 0.1%. Following incubation for 24 h at 37° C. and 5% CO2, harvested cells are stained with PI and analyzed with a flow cytometer. PI-negative cells are defined as viable, and the relative viability of cells stimulated with each concentration in the titration range is calculated as
For toxic compounds, the concentration yielding 90% relative viability (Rv90) is used for the GARD assay, the reason being that this concentration demonstrates bioavailability of the compound used for stimulation, while not impairing immunological responses. For non-toxic compounds, a concentration of 500 μM is used if possible. For non-toxic compounds that are insoluble at 500 μM in cell media, the highest soluble concentration is used.
Whichever of these three criteria is met, only one concentration will be used for the genomic assay. The concentration to be used for any given chemical is termed the ‘GARD input concentration’.
Chemical Exposure of Cells for GARD
Once the GARD input concentration for chemicals to be assayed is established, the cells are stimulated again as described above, this time only using the GARD input concentration. All assessments of test substances are assayed in biological triplicates, performed at different time-points and using different cell cultures. Following incubation for 24 h at 37° C. and 5% CO2, cells from one well are lysed in 0.5 ml TRIzol reagent (Life Technologies) and stored at −20° C. until RNA is extracted. In parallel, a small sample of stimulated cells is taken for PI staining and analysis with flow cytometry, to ensure the expected relative viability of stimulated cells is reached.
Preparation of Benchmark Controls
In addition to any test substance(s) to be assayed within a campaign, a set of benchmark controls are performed, for the purpose of prediction model calibration and estimation of prediction performance. For details regarding benchmark controls used in each specific campaign, see the main document to which this appendix is attached.
Isolation of RNA and GPS Quantification Using Nanostring nCounter System
RNA isolation from lysed cells is performed using commercially available kits (Direct-Zol RNA MiniPrep, Zymo Research, Irvine, Calif.). Total RNA is quantified and quality controlled using BioAnalyzer equipment (Agilent, Santa Clara, Calif.). A total of 100 ng of RNA is used as sample input in a hybridization assay with GPS specific reporter probe CodeSet (Nanostring, Seattle, Wash.). The hybridized RNA-CodeSet sample is prepared on chip using nCounter Prepstation and individual transcripts of the GPS is quantified using Nanostring Digital Analyzer (Nanostring).
Data Acquisition and Normalization
Raw data is exported from the Digital Analyzer and counts of individual transcripts of the GPS are single-chip normalized with a count per total counts algorithm. Normalized data consists of a S by V matrix, where S denotes the number of samples in the GARD campaign, and V denotes the number of quantified transcripts of the GPS.
Data Analysis—Generation of Calibrated Support Vector Machine Decision Values
All further downstream analysis is performed using application-based software, developed in the open source statistical environment R. A support vector machine (SVM) is trained using historical data used for GPS establishment (Johansson et al., 2011). All samples from test substances and benchmark controls from the specific GARD campaign are predicted using the trained SVM, assigning each sample with a SVM decision value. The predictor performance is estimated by identification of the area under the receiver operating characteristic (ROC AUC) of the predicted class of benchmark controls.
GARD Classifications of Test Substance(s)
The GARD prediction model is defined as follows:
If the mean decision value of all available biological replicates of a test substance is greater than zero, the test substance is classified as a sensitizer.
Scripts
Listed below are details of the script, written in R code, which may be used to perform the method:
Homo sapiens tripartite motif
Homo sapiens ASAP mRNA,
Homo sapiens similar to 40S
Homo sapiens gastrin-releasing
Homo sapiens gastrin-releasing
Homo sapiens cDNA FLJ10788
Homo sapiens basonuclin2
Homo sapiens cDNA FLJ51913
Homo sapiens surfactant
Homo sapiens surfactant
Homo sapiens surfactant
Homo sapiens surfactant protein
Homo sapiens B lymphocyte
Homo sapiens cDNA FLJ51913
Homo sapiens surfactant
Homo sapiens surfactant
Homo sapiens surfactant
Homo sapiens surfactant protein
Homo sapiens retinal pigment
Homo sapiens mRNA; cDNA
Homo sapiens cDNA FLJ44313
Homo sapiens beta-netrin mRNA,
Homo sapiens signal transducer
Homo sapiens signal
Homo sapiens AIM-1 protein
Homo sapiens cDNA FLJ43862
Homo sapiens mRNA; cDNA
Homo sapiens gycosyltransferase
Homo sapiens PRO2613 mRNA,
Homo sapiens cDNA FLJ46251
Homo sapiens kallikrein-related
Homo sapiens cDNA FLJ46257
Homo sapiens cDNA FLJ46460
Homo sapiens ST8 alpha-N-
Homo sapiens ST8 alpha-N-
Homo sapiens cDNA FLJ43063
Homo sapiens cDNA FLJ43862
Homo sapiens mRNA; cDNA
Homo sapiens chromosome 12
Homo sapiens chromosome 12
Homo sapiens reprimo, TP53
Homo sapiens olfactory receptor,
Homo sapiens cDNA clone
Homo sapiens leucine rich repeat
Homo sapiens leucine rich
H. sapiens mitogen inducible gene
Homo sapiens mRNA; cDNA
Homo sapiens receptor
Homo sapiens testis-development
Homo sapiens hypothetical
Homo sapiens cytochrome P450
Homo sapiens testis-specific
Homo sapiens G protein-coupled
Homo sapiens cDNA FLJ38419
Homo sapiens Cbp/p300-
Homo sapiens PX
Homo sapiens hGnTVb mRNA for
Homo sapiens hypothetical
Homo sapiens microtubule-
Homo sapiens testis-specific
Homo sapiens cytochrome P450,
Homo sapiens similar to
Homo sapiens ATPase, Class I,
Homo sapiens ATPase, Class I,
Homo sapiens telomeric repeat
Homo sapiens WW domain
Homo sapiens WW domain
Homo sapiens WW domain
Homo sapiens WW domain
Homo sapiens WW domain
Homo sapiens cDNA FLJ46155
Homo sapiens similar to CG4995
Homo sapiens cDNA FLJ13161
Homo sapiens miR-1247 stem-
Homo sapiens deiodinase,
Homo sapiens HSPDE11A mRNA
Homo sapiens early growth
Homo sapiens cDNA FLJ90371
Homo sapiens chromosome 9
Homo sapiens chromosome 9
Homo sapiens ChaC, cation
Homo sapiens schizophrenia
Homo sapiens cDNA FLJ46250
Homo sapiens interferon, alpha 7
Homo sapiens cDNA FLJ76724
Homo sapiens POM121-like
Homo sapiens B lymphocyte
Homo sapiens pleckstrin and
Homo sapiens EMI domain
Homo sapiens MAM domain-
Homo sapiens miR-15a stem-loop
Homo sapiens maltase-
Homo sapiens maltase-
Homo sapiens Niemann-Pick C1-
Homo sapiens NPC1 (Niemann-
Homo sapiens nuclear receptor
Homo sapiens nuclear receptor
Homo sapiens cDNA clone
Homo sapiens mRNA; cDNA
Homo sapiens opsin 5 (OPN5),
Homo sapiens arylacetamide
Homo sapiens arylacetamide
Homo sapiens chromosome 12
Homo sapiens testis-specific
Homo sapiens WW domain
Homo sapiens Spred3 mRNA,
Homo sapiens microtubule-
Homo sapiens progesterone
Homo sapiens prestin (PRES)
Homo sapiens solute carrier
Homo sapiens cDNA FLJ78115
Homo sapiens C-reactive protein,
Homo sapiens C-reactive
Homo sapiens WD repeat domain
Homo sapiens S100 calcium
Homo sapiens S100 calcium
Homo sapiens casein alpha s1,
Homo sapiens casein alpha s1
Homo sapiens adaptor-related
Homo sapiens mRNA; cDNA
Homo sapiens FXYD domain
Homo sapiens calcium-
Homo sapiens Ca++-dependent
Homo sapiens RING finger
Homo sapiens ring finger protein
Homo sapiens ring finger protein
Homo sapiens ankyrin repeat and
Homo sapiens ankyrin repeat
Homo sapiens arginine
Homo sapiens cDNA:FLJ23109
Homo sapiens Cas-Br-M (murine)
Homo sapiens cDNA FLJ54911
Homo sapiens FBJ murine
Homo sapiens FBJ murine
Homo sapiens cDNA FLJ60307
Homo sapiens clone DNA98565
Homo sapiens brevican (BCAN),
Homo sapiens pro-platelet basic
Homo sapiens interleukin-1
Homo sapiens interleukin 1
Homo sapiens nuclear DNA-
Homo sapiens nuclear DNA-
Homo sapiens PRAME family
Homo sapiens PRAME family
Homo sapiens FK506 binding
Homo sapiens miR-145 stem-loop ///
Homo sapiens cDNA FLJ36638
Homo sapiens BRAL1 mRNA for
Homo sapiens hyaluronan and
Homo sapiens PRAME family
Homo sapiens PRAME family
Homo sapiens clone DNA172197
Homo sapiens microtubule-
Homo sapiens PRAME family
Homo sapiens PRAME family
Homo sapiens solute carrier
Homo sapiens uncharacterized
Homo sapiens miR-375 stem-loop
Homo sapiens cDNA FLJ43917
Homo sapiens bombesin-like
Homo sapiens coiled-coil domain
Homo sapiens one cut homeobox
Homo sapiens T-box 18, mRNA
Homo sapiens T-box 18
Homo sapiens cDNA FLJ41669
Homo sapiens cDNA clone
Homo sapiens chromosome 9
Homo sapiens chromosome 9
Homo sapiens myosin heavy
Homo sapiens myosin, heavy
Homo sapiens calsequestrin 1
Homo sapiens calsequestrin 1
Homo sapiens dual specificity
Homo sapiens dual specificity
Homo sapiens cDNA FLJ43587
Homo sapiens zinc finger protein
Homo sapiens nuclear DNA-
Homo sapiens nuclear DNA-
Homo sapiens NAG-7 protein
Homo sapiens cDNA FLJ30336
Homo sapiens melatonin receptor
Homo sapiens transmembrane
Homo sapiens urocanase domain
Homo sapiens CLLL6 protein
Homo sapiens chromosome 13
Homo sapiens chromosome 13
Homo sapiens chromosome 13
Homo sapiens UDP-glucose
Homo sapiens UDP-glucose
Homo sapiens cDNA FLJ76724
Homo sapiens POM121-like
Homo sapiens cDNA FLJ38773
Homo sapiens leucine rich repeat
Homo sapiens cDNA FLJ16056
Homo sapiens cDNA FLJ38723
Homo sapiens heat shock protein
Homo sapiens calreticulin 3,
Homo sapiens calreticulin 3
Homo sapiens mRNA for
Homo sapiens suppression of
Homo sapiens cyclooxygenase 2b
Homo sapiens NPCEDRGP
Homo sapiens mRNA for
Homo sapiens tetratricopeptide
Homo sapiens myeloid cell
Homo sapiens mRNA for Phenol-
Homo sapiens immunoglobulin
Homo sapiens cDNA FLJ38426
Homo sapiens family with
Homo sapiens pendrin (PDS)
Homo sapiens cDNA FLJ54489
Homo sapiens 2′-
Homo sapiens solute carrier
Homo sapiens solute carrier
Homo sapiens phospholipid
Homo sapiens tumor protein,
Homo sapiens tumor protein,
Homo sapiens small nucleolar
Homo sapiens cDNA clone
Homo sapiens cDNA FLJ37118
Homo sapiens cDNA FLJ37833
Homo sapiens chromosome 1
Homo sapiens CD2 molecule,
Homo sapiens CD2 molecule
Homo sapiens cDNA clone
Homo sapiens testis-specific
Homo sapiens dolichyl-phosphate
Homo sapiens cDNA FLJ60436
Homo sapiens dolichyl-phosphate
Homo sapiens dolichyl-phosphate
Homo sapiens cDNA FLJ42124
Homo sapiens N-acetylated
Homo sapiens N-acetylated
Homo sapiens mRNA; cDNA
Homo sapiens G protein-coupled
Homo sapiens mRNA for Sodium
Homo sapiens cDNA FLJ39563
Homo sapiens down-regulator of
Homo sapiens down-regulator
Homo sapiens elongation of very
Homo sapiens cDNA clone
Homo sapiens ligand for
Homo sapiens tumor necrosis
Homo sapiens FZD8 mRNA for
Homo sapiens ATP synthase
Homo sapiens ATP synthase
Homo sapiens chromosome 17
Homo sapiens N-acylsphingosine
Homo sapiens N-
Homo sapiens chromosome 3
Homo sapiens chromosome 3
Homo sapiens chromosome 3
Homo sapiens cerebellin 1
Homo sapiens cDNA FLJ55553
Homo sapiens patatin-like
Homo sapiens ATPase, Cu++
Homo sapiens similar to F-box
Homo sapiens nonspecific
Homo sapiens cDNA FLJ12955
Homo sapiens forkhead box J3,
Homo sapiens forkhead box J3
Homo sapiens aiolos isoform
Homo sapiens gamma-
Homo sapiens gamma-
Homo sapiens CGI-125 protein
Homo sapiens leucine rich repeat
Homo sapiens leucine rich
Homo sapiens leucine rich repeat
Homo sapiens clone FP7072
Homo sapiens cDNA FLJ75846
Homo sapiens cytochrome P450,
Homo sapiens cytochrome P450,
Homo sapiens cytochrome P450,
Homo sapiens zinc finger protein
Homo sapiens small ubiquitin-like
Homo sapiens SMT3 suppressor
Homo sapiens secretoglobin,
Homo sapiens secretoglobin,
Homo sapiens ribosomal protein
Homo sapiens mesoderm
Homo sapiens mesoderm
Homo sapiens ETS-family
Homo sapiens ERO1-like protein
Homo sapiens ERO1-like
Homo sapiens eukaryotic
Homo sapiens solute carrier
Homo sapiens solute carrier
Homo sapiens spermatogenesis
Homo sapiens
Homo sapiens natural cytotoxicity
Homo sapiens natural
Homo sapiens serine protease
Homo sapiens kallikrein-related
Homo sapiens kelch domain
Homo sapiens cDNA FLJ75833
Homo sapiens membrane-bound
Homo sapiens phenylalanine
Homo sapiens chromosome 4
Homo sapiens chromosome 4
Homo sapiens Hus1-like protein
Homo sapiens HUS1 checkpoint
Homo sapiens mRNA for dynein
Homo sapiens dynein, axonemal,
Homo sapiens cDNA FLJ27255
Homo sapiens SHINC3 (SHINC3)
Homo sapiens zeta-sarcoglycan
Homo sapiens cDNA FLJ40688
Homo sapiens cDNA FLJ20261
Homo sapiens gametocyte
Homo sapiens clone DNA84912
Homo sapiens testis transcript Y 9
Homo sapiens testis-specific
Homo sapiens microRNA pri-miR-
Homo sapiens testis transcript Y 9
Homo sapiens testis-specific
Homo sapiens even-skipped
Homo sapiens thromboxane A
Homo sapiens thromboxane A
Homo sapiens thromboxane A
Homo sapiens meltrin-L precursor
Homo sapiens clone 14 CD7
Homo sapiens CD7 molecule
Homo sapiens ataxin 10, mRNA
Homo sapiens ataxin 10
Homo sapiens zinc finger protein
Homo sapiens zinc finger
Homo sapiens cDNA FLJ46182
Homo sapiens solute carrier
Homo sapiens interleukin 8 (IL8),
Homo sapiens alcadein gamma
Homo sapiens mRNA for
Homo sapiens CoREST protein
Homo sapiens REST
Homo sapiens vacuolar protein
Homo sapiens miR-221 stem-loop
Homo sapiens ubiquitin-
Homo sapiens ubiquitin-
Homo sapiens cDNA FLJ44851
Homo sapiens dual specificity
Homo sapiens dual specificity
Homo sapiens cDNA FLJ32610
Homo sapiens kelch-like 33
Homo sapiens mRNA; cDNA
Homo sapiens miR-33a stem-loop
Homo sapiens protein tyrosine
Homo sapiens protein tyrosine
Homo sapiens cDNA FLJ46667
Homo sapiens tetratricopeptide
Homo sapiens heptacellular
Homo sapiens dapper, antagonist
Homo sapiens PTPRD mRNA for
Homo sapiens protein tyrosine
Homo sapiens CDC26 subunit of
Homo sapiens TSPY-like 3
Homo sapiens TSPY-like 3
Homo sapiens mRNA for G-
Homo sapiens cDNA FLJ13224
Homo sapiens cDNA FLJ46181
Homo sapiens small nucleolar
Homo sapiens hypoxanthine
Homo sapiens hypoxanthine
Homo sapiens mRNA for DEPP
Homo sapiens chromosome 10
Homo sapiens hypothetical protein
Homo sapiens nedasin s-form
Homo sapiens small nucleolar
Homo sapiens tyrosine 3-
Homo sapiens tyrosine 3-
Homo sapiens small nucleolar
Homo sapiens asparagine
Homo sapiens asparagine
Homo sapiens kelch-like 11
Homo sapiens tandem pore
Homo sapiens potassium
Homo sapiens mRNA; cDNA
Homo sapiens cDNA FLJ59270
Homo sapiens killer cell lectin-like
Homo sapiens cleavage and
Homo sapiens mRNA for
Homo sapiens pogo transposable
Homo sapiens channel kinase 2
Homo sapiens transient receptor
Homo sapiens chromosome 9
Homo sapiens chromosome 9
Homo sapiens RAB2A,
Homo sapiens RAB2A,
Homo sapiens neuron navigator
Homo sapiens neuron navigator 3
Homo sapiens solute carrier
Homo sapiens nucleosome
Homo sapiens nucleosome
Homo sapiens transforming
Homo sapiens transforming
Homo sapiens ribosomal protein
Homo sapiens mRNA for
Homo sapiens TP53 target 3
Homo sapiens junctional
Homo sapiens junctional
Homo sapiens cDNA FLJ77347
Homo sapiens cyclin A2
Homo sapiens arylacetamide
Homo sapiens arylacetamide
Homo sapiens transmembrane
Homo sapiens cDNA FLJ60555
Homo sapiens cDNA FLJ33683
Homo sapiens leucine rich repeat
Homo sapiens miR-34b stem-loop
Homo sapiens olfactory receptor,
Homo sapiens K-ras oncogene
Homo sapiens v-Ki-ras2 Kirsten
Homo sapiens cDNA FLJ53851
Homo sapiens clone 1 myosin
Homo sapiens clone DNA35663
Homo sapiens non imprinted in
Homo sapiens apoptosis related
Homo sapiens protein
Homo sapiens protein
Homo sapiens casein kinase 2,
Homo sapiens cDNA FLJ43862
Homo sapiens mRNA; cDNA
Homo sapiens myelin protein
Homo sapiens cDNA:FLJ23455
Homo sapiens immediate early
Homo sapiens coiled-coil domain
Homo sapiens coiled-coil
Homo sapiens DNA-damage-
Homo sapiens DNA-damage-
Homo sapiens cDNA FLJ61651
Homo sapiens SEC24 family,
| Number | Date | Country | Kind |
|---|---|---|---|
| 1715445. 1 | Sep 2017 | GB | national |
This application is a § 371 application of PCT/EP2018/075829, filed Sep. 24, 2018, which claims priority to GB 1715445.1, filed Sep. 25, 2017. The foregoing applications are incorporated by reference herein.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2018/075829 | 9/24/2018 | WO | 00 |
