17β-雌二醇 (Estradiol) 被认为是女性真正的卵巢激素。它是最有效的雌激素，包括雌酮和雌三醇。雌二醇负责生殖上皮细胞、乳房生长、长骨成熟和第二女性性征的发育。血浆中的雌二醇水平用于测定女孩的生育能力、闭经和性早熟。
测定原理
这是用于定量分析的 ELISA生物体液中的雌二醇水平。该检测试剂盒基于激素偶联物与样品中的雌二醇竞争抗体包被板上有限数量的结合位点。首先将样品或标准溶液添加到微孔板中。接下来，加入稀释的激素偶联物，将混合物摇动并在室温下孵育一小时。在孵化过程中，竞争结合位点的转变正在发生。然后洗涤板以去除所有未结合的物质。结合的激素缀合物通过添加底物进行检测，底物在 30 分钟后产生最佳颜色。定量测试结果可以通过用酶标仪在 450nm 或 650nm 处测量和比较样品孔的吸光度读数与标准品来获得。显色程度与样品或标准品中雌二醇的含量成反比。例如，样品中不含雌二醇会导致亮蓝色，而存在雌二醇会导致显色减少或不显色。如果使用尿液作为样品，建议运行我们的
肌酐测定试剂盒 (CR01)
与您的样本结合使用，因为尿肌酐水平可用于使其他分析物的排泄率正常化。
Product DocumentsMSDS EA70 SDSSpec Sheet EA70 Spec相关产品sProductCAT.#SizePriceCortisol EIA Kit睾酮EIA KitEA74Kit$330.00黄体酮EIA KitEA70Kit$330.00雌二醇EIA KitEA68Kit$245.00组胺EIA KitEA31Kit$610.00返回顶部显示 21 - 29 件，共 29 件产品CAT.#SizePriceCypExpress 2D6, 250 mgCE2D6.250250 mg$225.00CypExpress 2E1, 1.0 GramCE2E1.11.0 Gram$500.00CypExpress 2E1, 10.0 GramsCE2E1.1010.0克$3,760.00CypExpress 2E1, 250 毫克CE2E1.250250 毫克$225.00CypExpress 3A4, 1.0 GramCE3A4.11.0 Gram$500.00CypExpress 3A4, 10 GramsCE3A4.1010.0 Grams$3,760.00CypExpress 3A4, 250 mgCE3A4.250250 mg$225.00CypExpress Control, 1.0 GramCENull.11.0 Gram$250. 00CypExpress Control, 10.0 GramsCENull.1010 Grams$2,070.00分页首页« 第一页上一页‹ 上一页1页2当前页3 Drug repurposing for Alzheimer鈥檚 disease based on transcriptional profiling of human iPSC-derived cortical neurons AbstractAlzheimer鈥檚 disease is a complex disorder encompassing multiple pathological features with associated genetic and molecular culprits. However, target-based therapeutic strategies have so far proved ineffective. The aim of this study is to develop a methodology harnessing the transcriptional changes associated with Alzheimer鈥檚 disease to develop a high content quantitative disease phenotype that can be used to repurpose existing drugs. Firstly, the Alzheimer鈥檚 disease gene expression landscape covering severe disease stage, early pathology progression, cognitive decline and animal models of the disease has been defined and used to select a set of 153 drugs tending to oppose disease-associated changes in the context of immortalised human cancer cell lines. The selected compounds have then been assayed in the more biologically relevant setting of iPSC-derived cortical neuron cultures. It is shown that 51 of the drugs drive expression changes consistently opposite to those seen in Alzheimer鈥檚 disease. It is hoped that the iPSC profiles will serve as a useful resource for drug repositioning within the context of neurodegenerative disease and potentially aid in generating novel multi-targeted therapeutic strategies. IntroductionGlobal gene expression profiling can be thought of as a high content quantitative phenotypic measure characterising tissue1, cell type in, for example, the heterogeneous context of the brain2,3,4 and revealing diversity within a previously thought homogeneous population5. Further, biological state dynamics can be modelled through temporal patterns of expression6. In the therapeutic context, it has been established that disease-associated expression changes can distinguish between disease states and are consistent across independent data sets, thus facilitating the identification of robust biomarkers7. Disease-associated gene changes point to modulated pathways and affected cell types, thus providing valuable insights into mechanisms8. Interestingly, the quantitative nature of the transcriptional phenotype has allowed for a direct mapping of disease to potential therapeutic9,10,11,12. Here the obvious hypothesis is that drugs tending to reverse the expression changes seen in the disease state may act to reverse the biological changes associated with the disease itself. An important caveat here is that some expression changes associated with Alzheimer鈥檚 disease (AD) may in fact be compensatory and beneficial. Drug repurposing or repositioning has resulted in successful initiatives across several maladies13,14,15,16,17,18,19. Further, and of more specific interest to the present project, drugs with profiles showing significant anti-correlation to AD gene changes have been shown to be conspicuous for their reported neuroprotective activities12. In a recent development, disease-associated gene expression changes have begun to be inferred from genomic risk variant data with the Genotype-Tissue Expression repository20 and harnessed to predict repurposing candidates for major psychiatric conditions21. Although there is some intriguing psychotherapeutic association of the candidate drugs in this approach, the predicted transcriptional perturbation does not have an overlap with that seen in diseased brain tissue [G. Williams, unpublished observation]. In the absence of further validation of the predicted gene changes, one must fall back on data from patient samples.There are no established disease-modifying drugs for the treatment of AD, there have been no new symptomatic treatments licensed for AD for 20 years and the pipeline of emerging therapies is very limited. Target-based drug research in AD has led to many insights into the disease and provided the research community with useful tool compounds. However, the promising results seen in the laboratory have so far failed to be carried over to the clinic and this has led to researchers casting around for novel, non-target-based approaches22. The main aim of transcription-based drug discovery is not target discovery, but rather the discovery of drugs that have a disease-modulating effect based on their global transcriptional activity. A particularly attractive aspect of the approach is that it naturally lends itself to repositioning existing drugs thereby bypassing the hurdles that novel entities must overcome on the road to the clinic. AD has been extensively studied in relation to the expression changes following pathological and cognitive decline23,24,25,26. The wealth of data points to consistent and characteristic changes associated with the disease and thereby makes a repositioning strategy particularly attractive.The application of gene expression profiling to drug repositioning is limited at present by the fact that full drug profiles are available only on a restricted set of immortalised human cell lines. This data is provided by the Broad Institute connectivity map project (CMAP)11. A more extensive drug set has been profiled on a variety of induced pluripotent stem cell (iPSC)-derived cells, including neural stem cells and differentiated cortical neurons. However, this data constituting the LINCS project27 is based on profiling a set of 1000 landmark genes and then using an optimised linear mapping to generate full profiles. This motivated the present initiative to define the full expression profiles of the CMAP candidate drugs in the more AD relevant cell type of iPSC-derived cortical neurons. The new phenotypes can then be compared to the CMAP profiles and more pertinently scored against the disease profiles to see whether they preserve or enhance their anti-correlation with AD. In this context, iPSC-derived cortical neurons have now been established as a model system for the study of neurological diseases especially the tracing of the effects of disease-related genetic variants28,29,30,31. This model provides for an efficient moderate throughput platform to assess the transcriptional effects of the candidate drugs in a more neurological context. It must be remembered, however, that AD is a complex pathology also involving multiple cell types, such as microglia and astrocytes. In this context, assaying drug perturbations within isolated iPSC cultures facilitates an important but limited insight into the disease.The motivation for the work presented here is to generate a neuronal-specific transcriptional database of compounds with a view to drug repositioning in AD and other neurodegenerative conditions. The initial compound set was assembled based on CMAP profiles that showed a tendency to reverse AD-associated expression changes observed across a variety of independent studies. The drug candidates were then profiled for their transcriptional effects on iPSC-derived human cortical neurons. The results indicate that at the global level there is a degree of correspondence between the CMAP and iPSC profiles. Furthermore, 51 of the drugs have profiles that drive transcription changes counter to those in AD. The consistently regulated genes correspond to those implicated in AD. It is hoped that the transcriptional data for these drugs will be of use to the wider community of researchers interested in neurodegenerative conditions and facilitate further repositioning efforts.Materials and methodsThe AD-associated transcriptional landscapeThe NCBI GEO database32 was queried for series containing samples derived from postmortem AD patient brains for various stages of the disease. Similarly, murine AD model brain samples were also collected based on relevant query key words: 5xFAD, 3xTG, Alzheimer鈥檚 disease+mouse. Profiles were generated based on relative levels of non-disease and disease state sample averages, with the scaled fold level defined as (f = frac{{{langle}drangle - {langle} crangle}}{{{langle}drangle + {langle}crangle}}), where the brackets indicate averages of the control (c) and disease (d) samples. The statistical significance is measured by Student鈥檚 t test and those folds falling below the 95% confidence interval were dropped as were those with folds of 20%. The human disease versus control AD set comprises 21 profiles derived from 13 series (NCBI GEO accession: GSE8442224, GSE3726333, GSE3698034, GSE3942035, GSE129723, GSE2937836, GSE4835037, GSE1522225, GSE2697238, GSE3726439, GSE2814640, GSE528141, GSE1321442) showing intra-profile consistency based on the regression scores for significant (Student鈥檚 t test p鈥?lt;鈥?.05) correlations, see Supplementary Table 1. To capture brain region variability, the number of profiles is greater than the number of series. In Supplementary Table 2, the extent of intra- versus inter-series AD profile correlation scores are given showing that in many cases the variability in brain region profiles is greater than that between independent series. Cognitive decline was based on decline in Mini-Mental State Examination (MMSE)43 represented by two profiles from two independent series and Clinical Dementia Rating (CDR)44 profiles from one series. Similarly, series corresponding to murine models of AD were gathered from 5xFAD and 3xTG mice resulting in seven profiles from three series (NCBI GEO accession: GSE5052145, GSE11975646, GSE10114447, GSE7757448) for the 5xFAD set and nine profiles from eight series (NCBI GEO accession: GSE31624, GSE1512849, GSE36237, GSE9292650, GSE60460, GSE6091151, GSE3698134, GSE35210) for the 3xTG set. Series corresponding to BRAAK stage progression (NCBI GEO accession: GSE1297, GSE84422, GSE48350, GSE10624152) were generated with a linear mixed model analysis, by fitting the gene expression level across the samples in the series to a linear function of the BRAAK stage with categorical calls on cell type and gender as covariates. The resulting residual correlation Z score for gene expression against BRAAK stage constituted the BRAAK profile. Profiles corresponding to full BRAAK progression were not considered to be sufficiently different to the overt disease profiles derived from the same series, where disease assignment is also based on BRAAK staging. However, gene expression changes driving mild BRAAK pathology should capture early disease biology invisible in the overt profiles. In total, six profiles corresponding to mild BRAAK pathology, level 0 to level 3, formed the mild BRAAK AD set. Similar profiles were generated for psychiatric measures MMSE and CDR (NCBI GEO accession: GSE48350, GSE1297, GSE84422). In the case of the MMSE profile, the regression signs were reversed as MMSE scores decrease with disease progression, see Table 1 for an overall comparison of the profile sets.Table 1 The AD sets show varying degrees of overlapFull size tableRepresentative profiles for each set were based on genes showing consistent changes across the constituent profiles. In particular, the sense changes (upregulation and downregulation calls) for significantly regulated genes were summed over the profiles and only those genes retained that had an absolute regulation fraction of 20% and with a significant regulation statistic measured by Student鈥檚 t test of p鈥?lt;鈥?.05. Owing to the categorical nature of the representative profiles, correlation with the iPSC profiles was based on an enrichment analysis. The enrichment score was generated based on a binomial probability sum with gene probabilities scaled according to their frequencies in SPIED53.CMAP profilesCMAP data were downloaded from the Broad connectivity map site (www.broadinstitute.org/connectivity-map-cmap) 11. This consisted of probe sets for each sample ranked according to expression level relative to batch control. The data consist of 6100 samples covering 1260 drugs and 4 cell types. The relative probe expression ranks, defined as (1 - 2frac{{R - R_{{mathrm{min}}}}}{{R_{{mathrm{max}}} - R_{{mathrm{min}}}}}), where R in the rank of a given gene鈥檚 expression change (Rmax being the highest and Rmin being the lowest ranks), were averaged over replicates ignoring cell type and filtered based on significance using a one-sample Student鈥檚 t test. For genes with multiple probes, the probe with the largest significant change was mapped to the gene. This resulted in a unique profile for each drug in CMAP. The compound data can be queried through SPIED53.iPSC profilesFollowing the dominant CMAP treatment protocol, cell cultures were treated for 6鈥塰 and at compound concentrations of 10鈥壩糓. The iPSC expression samples were generated on the Affymetrix Human Genome U133 Plus 2.0 Array platform from ThermoFisher Scientific.Human iPSC-derived cerebral cortical neurons (HyCCNs; Ax0026) were cultured as per the manufacturer鈥檚 guidelines (www.axolbio.com/page/neural-stem-cells-cerebral-cortex). Each drug treatment at a concentration of 10鈥壩糓 for 6鈥塰 was performed on 3 independent HyCCN cultures (average density 300鈥塊/cm2) and RNA from each treated well extracted by direct cell lysis and recovery using the Absolutely RNA Microprep Kit (Agilent, as per the manufacturer鈥檚 guidelines). Each drug-treated plate also consisted of a vehicle-only control set of triplicate cultures. Integrity of total RNAs was determined using an Agilent Bioanalyser as per the manufacturer鈥檚 instructions and only samples with RNA integrity number 7 were progressed to transcriptome analysis. Transcriptome changes driven by exposure to the candidate drugs were determined using the Nugen Ovation V2 labelling system (https://www.nugen.com/products) followed by Human U133 Plus 2 GeneChips as per the manufacturer鈥檚 instructions (www.thermofisher.com/order/catalog/product/900466).The NCBI GEO hosts 145,000 samples on this platform, making it the most popular array chip. The relative expression levels of probes were collected for the GEO data and the iPSC control data. The ranks were scaled to lie between zero for the highest expression probe and unity for the lowest. The relative rank of each probe was defined as (frac{{r_0 - r}}{{r_0}}) for r鈥?lt;鈥?i>r0 and (frac{{r_0 - r}}{{1 - r_0}}) for r鈥?lt;鈥?i>r0, where r and r0 are the average probe ranks over the iPSC samples and the set of samples deposited on GEO, respectively. Probes were then mapped to genes and, in the case of degeneracy, the probe with the largest relative rank mapping to the gene. The gene rank profile was taken to be related to the relative gene expression characterising iPSCs.Drug treatment profiles were based on statistically filtered ratios of drug-treated and control groups. These were generated based on a combined set of 554 samples, which were robust multiarray averaging normalised. The samples were distributed over 23 plates with the corresponding dimethyl sulfoxide controls. Transcriptional profiles for the 153 drugs were generated based on normalising to the plate control and multiple plate drug replicates kept as separate profiles. The drug set is enriched for CMAP based anti-AD potential (153). Rapamycin, which has a well-defined transcriptional signature, served as a positive control. The expression changes were either measured as scaled folds filtered for significance with Student鈥檚 t test or as Z scores, with significance based on the magnitude of Z. Degenerate probes were mapped to genes based on the dominant probe responses.ResultsAD-associated expression changesTo capture as much as possible of the transcriptional landscape of AD, different categories were defined based on overt disease versus healthy profiles, profiles following early pathological and cognitive measures, together with those from animal models, as described in 鈥楳aterials and methods鈥? There is a good degree of overlap between the overt AD profiles and those following cognitive decline, see Table 1, but it was reasoned that there is sufficient variability to give rise to unique drug candidates, see section on 鈥楥MAP candidates鈥? The early BRAAK stage profiles show little overlap with overt or cognitive decline profiles, see Table 1, and thus it is anticipated that these profiles may shed light on distinct early stage pathology and early therapeutic intervention. The animal model data naturally separates into those based on the 5xFAD, which is consistent with AD as can be seen in Supplementary Table 3, and those based on 3xTG, showing little overlap with AD profiles or internal consistency. A similar analysis also including rat models of AD has been carried out by Hargis and Blalock54. Animal model data were included in this study because the expression changes seen in the model systems have established causes, i.e. the inserted mutations, 5xFAD or 3xTG in our case. Consequently, candidate drugs reversing these changes may have more focused mechanisms of action. Furthermore, the evidence for neuroprotection is to a large extent derived from experiments in animal models.CMAP candidatesIn general, transcription-based repositioning results in tens of candidates out of a total of just over a thousand drugs constituting CMAP13,14,15,16,17,18,19. The relatively small number of compounds that are put forward for rigorous bio-assaying to establish firmer evidence for a disease-modulating potential of course reflects the experimental resource required. The basis of the present project was to select candidates to populate a database of iPSC profiles for drugs biased towards their predicted anti-AD and wider neuroprotective activities. It was therefore reasoned that the thresholds for deeming a drug a repositioning candidate had to be relaxed to allow for over a hundred candidates to be taken forward. To this end, five AD-based profile sets that capture distinct aspects of the disease were separately queried against CMAP and three selection criteria were applied. In the first instance, data were gathered on the anti-correlation rank of each compound, with compounds showing a high rank in either of the profiles considered as candidates, see Supplementary Table 4 for the complete candidate list. A second selection was based on consistency of the anti-correlation across profiles in each set, and finally some compounds with conspicuously high anti-correlations with individual profiles were added to the set. The full list of compounds is given in Supplementary Table 4 and consists of 153 compounds. Interestingly, among these drugs are established neuroprotective entities and AD therapeutics, see below.iPSC profilesAs a first step in establishing the phenotype of the model cell system, the overall iPSC transcriptional profile was queried against a database of publicly deposited gene expression profiles via SPIED12,53, see 鈥楳aterials and methods鈥? The top 1000 genes in the iPSC rank profile consists of 959 upregulated and 41 downregulated genes and this served as a query in the SPIED search. It is perhaps worth pointing out here that the level of gene expression unique to a given cell type will tend to be elevated relative to a background consisting of a variety of tissue types. An analogy would be in the context of division of labour one is characterised by what one does not by what one does not do. The top SPIED hits show a high correlation with human brain-derived samples, validating the cell鈥檚 lineage, see Supplementary Table 5.Comparison of iPSC and CMAP profilesThe extent to which an iPSC profile correlates with its CMAP equivalent can be assessed by querying the CMAP database with the iPSC profile and ranking the CMAP equivalent. The extensively studied perturbagen rapamycin served as a positive control and eight independent profiles were generated to assess the degree to which these profiles are consistent with each other and with the rapamycin profile in CMAP. The rapamycin profiles had consistently high overlaps among themselves, but less so with the CMAP profile, with only one returning rapamycin as a top hit, rank seven, in a CMAP query, see Supplementary Fig. 1. In Supplementary Fig. 2, iPSC and CMAP profile pairs with the four highest CMAP query ranks are shown. Overall, there are 30 significantly correlating and 8 anti-correlating pairs. The overall comparison of the iPSC and CMAP profiles can be framed in terms of an enrichment analysis for the rank of the equivalent compound hit and the significance can be assessed with Kolmogorov鈥揝mirnov (KS) statistic on the maximal deviation from the zero-enrichment diagonal line. The KS statistic furnishes an objective measure of the robustness of the iPSC profiles and suggest that iPSC profiles based on a Z score threshold of |Z|鈥?gt;鈥?, see 鈥楳aterials and methods鈥?for details, capture most of the compound-associated changes. The enrichment is that of the rank of a given iPSC compound score with itself in CMAP. The enrichment plot is shown in Fig. 1. The KS statistic is highly significant with the chance of a random compound association beating the enrichment maximum of p鈥?鈥?.1E鈭?.Fig. 1: The overall comparison between the iPSC profiles and those on the cancer cell lines can be framed as an enrichment analysis for the rank of iPSC queries against CMAP.For each drug, the correlation between iPSC and CMAP profiles are ranked against the remainder of the CMAP data set profiles. For a good agreement between the profiles, one would expect an enrichment in high rank scores and this is the case for iPSC profiles. The top plot shows the rank distributions in bins of 50 with a clear bias for high rank scores. The bottom plot is the cumulative distribution of ranks contrasted with the non-enriched diagonal. The significance is measured by an MC simulation randomising rank orders and counting the number of times peak deviation from the diagonal exceeds that in the original enrichmentFull size imageRelation of iPSC profiles to ADFurther to assessing the extent to which compounds orchestrate similar expression changes in the cancer cell lines and differentiated cortical neurons, it is critical to test whether the drugs also act in an anti-AD manner in the neuronal context. To this end, the drug profiles were scored against five representative AD reprofiles derived from the AD sets defined above, see 鈥楳aterials and methods鈥?for details. Table 2 lists the compounds with at least two significant anti-correlations with the representative AD profiles, which will be referred to as AD hit compounds (ADC). The ADC set show a relatively high degree of intra-profile correlation as compared to other iPSC profile pairs, see Fig. 2. The average correlations in terms of regression Z scores are: 2.43 for ADC pairs and 0.77 for all other pairs. It is therefore of interest to see to what extent the ADC set regulate a common set of transcripts. In Fig. 3, the common ADC target genes are shown demonstrating a high degree of consistency with a clearly defined set of upregulated and downregulated gene cohort. To get an idea of the underlying biological networks that are being perturbed by the ADC, a pathway enrichment analysis was performed on each of the profiles in the ADC set. The consistently positively and negatively regulated pathways defined by an enrichment in the upregulated and downregulated gene sets, respectively, are given in Supplementary Table 6, and these point to key processes associated with AD that underpin the potential therapeutic action of the drugs. The enrichment for the AD, Parkinson鈥檚 disease and mitochondrial pathways in the positively regulated gene sets is driven by the upregulation of cytochrome c oxidases, ubiquinone oxidoreductases and ATP synthases. These are all key players in mitochondrial function, which is known to be compromised in AD55,56, with growing evidence that gene variation affecting mitochondrial function may play a role in AD57,58. The downregulated set appears to be less consistent. Nonetheless, the enrichment of immune-associated pathways points to a possible anti-inflammatory activity of the candidate drugs. Interestingly, the following drugs have been reported to have neuroprotective activity: fluocinonide59, kawain60,61,62,63, allantoin64, dipyridamole65,66,67, estriol68, levamisole69, mycophenolic acid70, neostigmine71, probenecid72,73, chlorpromazine74, and phenoxybenzamine75, and xamoterol has been reported to ameliorate neuroinflammation and pathology in 5xFAD mice76 and shown to enhance cognition in a Down syndrome mouse model77. The atypical antipsychotic risperidone prescribed to manage psychosis in AD has demonstrated neuroprotection in animal models of ischemia78. Furthermore, cholinesterase inhibition is a therapeutic strategy for AD79 and there are two such inhibitors in the candidate list with galantamine as an established AD therapeutic80, while neostigmine exhibits poor blood鈥揵rain barrier penetrance and is therefore not in clinical use for AD. There does not appear to be any gene expression signature distinguishing compounds with reported neuroprotective activities from the other ADC compounds. This is to be expected as not all compounds have been assayed for neuroprotection and biological activity is not expected to be solely encoded in the transcriptome.Table 2 Compounds with iPSC profiles showing anti-correlation with at least two representative AD profiles, referred to as the ADC setFull size tableFig. 2: The ADC compounds have relatively high intra-profile correlations.The correlation Z scores are shown on a heat map with the ADC component split off to highlight the enhanced correlation. The average correlation for intra-ADC profiles is 2.43 as opposed to 0.77 for all other pairsFull size imageFig. 3: The gene expression heat map for genes consistently regulated by the ADC set.Genes were selected based on their having a sum sense change ratio 33%. Specifically, the sum sense change ratio is defined as (frac{1}{P}mathop {sum }nolimits_{i = 1, ldots ,P} {mathrm{sign}}left( {g_i} right)), where gi is the expression change of a gene in the ith profile. The compounds are clustered with the UPGMA algorithm and the corresponding dendrogram shown at leftFull size imageDiscussionNeurodegenerative diseases present a therapeutic challenge due to the difficulty in establishing a clear protein or mechanistic culprit for classic target-based intervention. Another hurdle is a consequence of the temporal extent of disease progression and the probable need to treat before overt symptom onset. This is a particular problem in designing clinical trials. With this in mind, alternatives to target-based approaches are increasingly being pursued. One recent report compared Parkinson鈥檚 disease (PD) incidence and chronic therapeutic use data from the Norwegian Prescription Database (www.norpd.no), showing that salbutamol use reduced PD risk81. A middle ground between target-based and epidemiological approaches is a methodology based on the disease phenotype gleaned from gene expression changes observed in pathological states. Underlying this approach is the observation that disease states can effectively be represented by characteristic expression changes, in the sense that these changes are consistent and can function as high content quantitative biomarkers. One avenue available to drug repositioning is to use these transcriptional phenotypes together with the hypothesis that an anti-correlation in phenotypes is indicative of the therapeutic potential of the compound. Whereas the transcriptional landscape of neurodegeneration and AD in particular has been well characterised, the corresponding data for compounds are either limited to full profiles defined on non-neuronal proliferating cells or partial profiles on iPSC-derived neuronal cells. The basis of the present study is to go some way to fill this gap in the compound-associated transcriptome with an emphasis on drugs with an anti-AD potential.In the context of defining the neurotherapeutic potential of candidate drugs, a further development would be to treat wild-type or mutant AD mice with the compounds and measure expression changes in the brain, along the lines of the DrugMatrix project82. This approach would have the advantage of including non-neuronal factors contributing to AD pathology such as inflammation. However, practical considerations limit whole-animal approaches to smaller drug sets and will therefore form part of a subsequent endeavour based on a more limited set of drug candidates selected based on the iPSC data.In the present work, we have established an AD transcriptional profile landscape and shown this to have a high degree of internal consistency. This disease-associated transcriptional landscape served as the basis for selecting a series of candidate drugs from the CMAP database of cancer cell line profiles, which were then assayed for their transcriptional effect on iPSC-derived cortical neurons. The iPSC profiles show a degree of overlap with the corresponding CMAP profiles, with a highly significant overall comparison in terms of the ranks observed for iPSC queries of CMAP. Out of the 153 iPSC drug profiles, 51, termed the ADC set, showed a high degree of anti-correlation with transcriptional changes seen in AD. A pathway enrichment analysis performed on each of the ADC set showed that pathways related to mitochondrial function were commonly upregulated while commonly downregulated pathways represented immune-associated pathways. Interestingly, these pathological features are found in multiple neurodegenerative disorders, such as PD and Huntington鈥檚 disease, and it would be of interest to investigate whether these compounds may have wider therapeutic potential. Notably, 18 of the ADC drugs already have established neuroprotective ability in published studies. Whereas we expect that initial CMAP filtering against AD profiles has led to increased likelihood of discovering compounds that tend to reverse AD-associated expression changes in the context of iPSC cultures, this can only be rigorously assessed by generating iPSC profiles for a series of compounds randomly selected from the CMAP database, which is outside the scope of the present study. In conclusion, approaches to identifying a broader range of candidate therapies for AD are urgently needed. It is therefore expected that the iPSC database will serve as a useful platform for drug repositioning across multiple neuropathological disorders as well as AD. 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Database (Oxford) 2016, baw052 (2016).Article聽 PubMed聽 PubMed Central聽Google Scholar聽 Download referencesAcknowledgementsThis work was funded by the Wellcome foundation: A systematic programme to develop and evaluate the best candidate treatments for repositioning as therapies for Alzheimer鈥檚 disease (SMART-AD) reference 102001/Z/13/Z.Author informationAffiliationsWolfson Centre for Age-Related Diseases, King鈥檚 College London, London Bridge, London, SE1 1UL, UKGareth Williams,聽Ariana Gatt,聽Earl Clarke,聽Jonathan Corcoran,聽Patrick Doherty聽 聽David ChambersCollege of Medicine and Health, University of Exeter, Exeter, EX1 2LU, UKClive BallardAuthorsGareth WilliamsView author publicationsYou can also search for this author in PubMed聽Google ScholarAriana GattView author publicationsYou can also search for this author in PubMed聽Google ScholarEarl ClarkeView author publicationsYou can also search for this author in PubMed聽Google ScholarJonathan CorcoranView author publicationsYou can also search for this author in PubMed聽Google ScholarPatrick DohertyView author publicationsYou can also search for this author in PubMed聽Google ScholarDavid ChambersView author publicationsYou can also search for this author in PubMed聽Google ScholarClive BallardView author publicationsYou can also search for this author in PubMed聽Google ScholarCorresponding authorCorrespondence to Gareth Williams.Ethics declarations Conflict of interest C.B. reports grants and personal fees from Lundbeck and Acadia and personal fees from Roche, Orion, GSK, Otusaka, Heptares and Lilly outside the submitted work. The other authors declare that they have no conflict of interest. Additional informationPublisher鈥檚 note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary information Steve Rodriguez, Clemens Hug, Petar Todorov, Nienke Moret, Sarah A. Boswell, Kyle Evans, George Zhou, Nathan T. Johnson, Bradley T. Hyman, Peter K. Sorger, Mark W. Albers Artem Sokolov Nature Communications (2021) Rammohan Shukla, Nicholas D. Henkel, Khaled Alganem, Abdul-rizaq Hamoud, James Reigle, Rawan S. Alnafisah, Hunter M. Eby, Ali S. Imami, Justin F Creeden, Scott A. Miruzzi, Jaroslaw Meller Robert E. Mccullumsmith Neuropsychopharmacology (2021) Clive Ballard, Dag Aarsland, Jeffrey Cummings, John O鈥橞rien, Roger Mills, Jose Luis Molinuevo, Tormod Fladby, Gareth Williams, Pat Doherty, Anne Corbett Janet Sultana Nature Reviews Neurology (2020)>>> 更多资讯详情请访问蚂蚁淘商城
