Dug: a semantic search engine leveraging peer-reviewed knowledge to query biomedical data repositories.

MOTIVATION: As the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets utilizing evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned. RESULTS: Developed through the National Heart, Lung and Blood Institute's (NHLBI) BioData Catalyst ecosystem, Dug has indexed more than 15 911 study variables from public datasets. On a manually curated search dataset, Dug's total recall (total relevant results/total results) of 0.79 outperformed default Elasticsearch's total recall of 0.76. When using synonyms or related concepts as search queries, Dug (0.36) far outperformed Elasticsearch (0.14) in terms of total recall with no significant loss in the precision of its top results. AVAILABILITY AND IMPLEMENTATION: Dug is freely available at https://github.com/helxplatform/dug. An example Dug deployment is also available for use at https://search.biodatacatalyst.renci.org/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Workshop report: Identifying opportunities for global integration of toxicogenomics databases, 26-27 June 2013, Research Triangle Park, NC, USA.

A joint US-EU workshop on enhancing data sharing and exchange in toxicogenomics was held at the National Institute for Environmental Health Sciences. Currently, efficient reuse of data is hampered by problems related to public data availability, data quality, database interoperability (the ability to exchange information), standardization and sustainability. At the workshop, experts from universities and research institutes presented databases, studies, organizations and tools that attempt to deal with these problems. Furthermore, a case study showing that combining toxicogenomics data from multiple resources leads to more accurate predictions in risk assessment was presented. All participants agreed that there is a need for a web portal describing the diverse, heterogeneous data resources relevant for toxicogenomics research. Furthermore, there was agreement that linking more data resources would improve toxicogenomics data analysis. To outline a roadmap to enhance interoperability between data resources, the participants recommend collecting user stories from the toxicogenomics research community on barriers in data sharing and exchange currently hampering answering to certain research questions. These user stories may guide the prioritization of steps to be taken for enhancing integration of toxicogenomics databases.

Optimizing androgen receptor prioritization using high-throughput assay-based activity models.

Introduction: Computational models using data from high-throughput screening assays have promise for prioritizing and screening chemicals for testing under the U.S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP). The purpose of this work was to demonstrate a data processing method for the determination of optimal minimal assay batteries from a larger comprehensive model, to provide a uniform method of evaluating the performance of future minimal assay batteries compared with the androgen receptor (AR) pathway model, and to incorporate chemical cluster analysis into this evaluation. Although several of the assays in the AR pathway model are no longer available through the original vendor, this approach could be used for future evaluations of minimal assay models for prioritization and screening. Methods: We compared two previously published models and found that an expanded 14-assay model had higher sensitivity for antagonists, whereas the original 11-assay model had slightly higher sensitivity for agonists. We then investigated subsets of assays in the original AR pathway model to optimize overall testing strategies that minimize cost while maintaining sensitivity across a broad chemical space. Results and Discussion: Evaluation of the critical assays across subset models derived from the 14-assay model identified three critical assays for predicting antagonism and two critical assays for predicting agonism. A minimum of nine assays is required for predicting agonism and antagonism with high sensitivity (95%). However, testing workflows guided by chemical structure-based clusters can reduce the average number of assays needed per chemical by basing the assays selected for testing on the likelihood of a chemical being an AR agonist, according to its structure. Our results show that a multi-stage testing workflow can provide 95% sensitivity while requiring only 48% of the resources required for running all assays from the original full models. The resources can be reduced further by incorporating in silico activity predictions. Conclusion: This work illustrates a data-driven approach that incorporates chemical clustering and simultaneous consideration of antagonism and agonism mechanisms to more efficiently screen chemicals. This case study provides a proof of concept for prioritization and screening strategies that can be utilized in future analyses to minimize the overall number of assays needed for predicting AR activity, which will maximize the number of chemicals that can be tested and allow data-driven prioritization of chemicals for further screening under the EDSP.

Chemistry domain of applicability evaluation against existing estrogen receptor high-throughput assay-based activity models.

Introduction: The U. S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP) Tier 1 assays are used to screen for potential endocrine system-disrupting chemicals. A model integrating data from 16 high-throughput screening assays to predict estrogen receptor (ER) agonism has been proposed as an alternative to some low-throughput Tier 1 assays. Later work demonstrated that as few as four assays could replicate the ER agonism predictions from the full model with 98% sensitivity and 92% specificity. The current study utilized chemical clustering to illustrate the coverage of the EDSP Universe of Chemicals (UoC) tested in the existing ER pathway models and to investigate the utility of chemical clustering to evaluate the screening approach using an existing 4-assay model as a test case. Although the full original assay battery is no longer available, the demonstrated contribution of chemical clustering is broadly applicable to assay sets, chemical inventories, and models, and the data analysis used can also be applied to future evaluation of minimal assay models for consideration in screening. Methods: Chemical structures were collected for 6,947 substances via the CompTox Chemicals Dashboard from the over 10,000 UoC and grouped based on structural similarity, generating 826 chemical clusters. Of the 1,812 substances run in the original ER model, 1,730 substances had a single, clearly defined structure. The ER model chemicals with a clearly defined structure that were not present in the EDSP UoC were assigned to chemical clusters using a k-nearest neighbors approach, resulting in 557 EDSP UoC clusters containing at least one ER model chemical. Results and Discussion: Performance of an existing 4-assay model in comparison with the existing full ER agonist model was analyzed as related to chemical clustering. This was a case study, and a similar analysis can be performed with any subset model in which the same chemicals (or subset of chemicals) are screened. Of the 365 clusters containing >1 ER model chemical, 321 did not have any chemicals predicted to be agonists by the full ER agonist model. The best 4-assay subset ER agonist model disagreed with the full ER agonist model by predicting agonist activity for 122 chemicals from 91 of the 321 clusters. There were 44 clusters with at least two chemicals and at least one agonist based upon the full ER agonist model, which allowed accuracy predictions on a per-cluster basis. The accuracy of the best 4-assay subset ER agonist model ranged from 50% to 100% across these 44 clusters, with 32 clusters having accuracy ≥90%. Overall, the best 4-assay subset ER agonist model resulted in 122 false-positive and only 2 false-negative predictions compared with the full ER agonist model. Most false positives (89) were active in only two of the four assays, whereas all but 11 true positive chemicals were active in at least three assays. False positive chemicals also tended to have lower area under the curve (AUC) values, with 110 out of 122 false positives having an AUC value below 0.214, which is lower than 75% of the positives as predicted by the full ER agonist model. Many false positives demonstrated borderline activity. The median AUC value for the 122 false positives from the best 4-assay subset ER agonist model was 0.138, whereas the threshold for an active prediction is 0.1. Conclusion: Our results show that the existing 4-assay model performs well across a range of structurally diverse chemicals. Although this is a descriptive analysis of previous results, several concepts can be applied to any screening model used in the future. First, the clustering of the chemicals provides a means of ensuring that future screening evaluations consider the broad chemical space represented by the EDSP UoC. The clusters can also assist in prioritizing future chemicals for screening in specific clusters based on the activity of known chemicals in those clusters. The clustering approach can be useful in providing a framework to evaluate which portions of the EDSP UoC chemical space are reliably covered by in silico and in vitro approaches and where predictions from either method alone or both methods combined are most reliable. The lessons learned from this case study can be easily applied to future evaluations of model applicability and screening to evaluate future datasets.

Building a collaborative cloud platform to accelerate heart, lung, blood, and sleep research.

Research increasingly relies on interrogating large-scale data resources. The NIH National Heart, Lung, and Blood Institute developed the NHLBI BioData CatalystⓇ (BDC), a community-driven ecosystem where researchers, including bench and clinical scientists, statisticians, and algorithm developers, find, access, share, store, and compute on large-scale datasets. This ecosystem provides secure, cloud-based workspaces, user authentication and authorization, search, tools and workflows, applications, and new innovative features to address community needs, including exploratory data analysis, genomic and imaging tools, tools for reproducibility, and improved interoperability with other NIH data science platforms. BDC offers straightforward access to large-scale datasets and computational resources that support precision medicine for heart, lung, blood, and sleep conditions, leveraging separately developed and managed platforms to maximize flexibility based on researcher needs, expertise, and backgrounds. Through the NHLBI BioData Catalyst Fellows Program, BDC facilitates scientific discoveries and technological advances. BDC also facilitated accelerated research on the coronavirus disease-2019 (COVID-19) pandemic.

Decision trees for evaluating skin and respiratory sensitizing potential of chemicals in accordance with European regulations.

Guidance for determining the sensitizing potential of chemicals is available in EC Regulation No. 1272/2008 Classification, Labeling, and Packaging of Substances; REACH guidance from the European Chemicals Agency; and the United Nations Globally Harmonized System (GHS). We created decision trees for evaluating potential skin and respiratory sensitizers. Our approach (1) brings all the regulatory information into one brief document, providing a step-by-step method to evaluate evidence that individual chemicals or mixtures have sensitizing potential; (2) provides an efficient, uniform approach that promotes consistency when evaluations are done by different reviewers; (3) provides a standard way to convey the rationale and information used to classify chemicals. We applied this approach to more than 50 chemicals distributed among 11 evaluators with varying expertise. Evaluators found the decision trees easy to use and recipients (product stewards) of the analyses found that the resulting documentation was consistent across users and met their regulatory needs. Our approach allows for transparency, process management (e.g., documentation, change management, version control), as well as consistency in chemical hazard assessment for REACH, EC Regulation No. 1272/2008 Classification, Labeling, and Packaging of Substances and the GHS.

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research.

Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to "TAME" data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health.

Catalyzing Knowledge-Driven Discovery in Environmental Health Sciences through a Community-Driven Harmonized Language.

Harmonized language is critical for helping researchers to find data, collecting scientific data to facilitate comparison, and performing pooled and meta-analyses. Using standard terms to link data to knowledge systems facilitates knowledge-driven analysis, allows for the use of biomedical knowledge bases for scientific interpretation and hypothesis generation, and increasingly supports artificial intelligence (AI) and machine learning. Due to the breadth of environmental health sciences (EHS) research and the continuous evolution in scientific methods, the gaps in standard terminologies, vocabularies, ontologies, and related tools hamper the capabilities to address large-scale, complex EHS research questions that require the integration of disparate data and knowledge sources. The results of prior workshops to advance a harmonized environmental health language demonstrate that future efforts should be sustained and grounded in scientific need. We describe a community initiative whose mission was to advance integrative environmental health sciences research via the development and adoption of a harmonized language. The products, outcomes, and recommendations developed and endorsed by this community are expected to enhance data collection and management efforts for NIEHS and the EHS community, making data more findable and interoperable. This initiative will provide a community of practice space to exchange information and expertise, be a coordination hub for identifying and prioritizing activities, and a collaboration platform for the development and adoption of semantic solutions. We encourage anyone interested in advancing this mission to engage in this community.

The Data Use Ontology to streamline responsible access to human biomedical datasets.

Human biomedical datasets that are critical for research and clinical studies to benefit human health also often contain sensitive or potentially identifying information of individual participants. Thus, care must be taken when they are processed and made available to comply with ethical and regulatory frameworks and informed consent data conditions. To enable and streamline data access for these biomedical datasets, the Global Alliance for Genomics and Health (GA4GH) Data Use and Researcher Identities (DURI) work stream developed and approved the Data Use Ontology (DUO) standard. DUO is a hierarchical vocabulary of human and machine-readable data use terms that consistently and unambiguously represents a dataset's allowable data uses. DUO has been implemented by major international stakeholders such as the Broad and Sanger Institutes and is currently used in annotation of over 200,000 datasets worldwide. Using DUO in data management and access facilitates researchers' discovery and access of relevant datasets. DUO annotations increase the FAIRness of datasets and support data linkages using common data use profiles when integrating the data for secondary analyses. DUO is implemented in the Web Ontology Language (OWL) and, to increase community awareness and engagement, hosted in an open, centralized GitHub repository. DUO, together with the GA4GH Passport standard, offers a new, efficient, and streamlined data authorization and access framework that has enabled increased sharing of biomedical datasets worldwide.

Calcium influx mechanisms underlying calcium oscillations in rat hepatocytes.

UNLABELLED: The process of capacitative or store-operated Ca(2+) entry has been extensively investigated, and recently two major molecular players in this process have been described. Stromal interacting molecule (STIM) 1 acts as a sensor for the level of Ca(2+) stored in the endoplasmic reticulum, and Orai proteins constitute pore-forming subunits of the store-operated channels. Store-operated Ca(2+) entry is readily demonstrated with protocols that provide extensive Ca(2+) store depletion; however, the role of store-operated entry with modest and more physiological cell stimuli is less certain. Recent studies have addressed this question in cell lines; however, the role of store-operated entry during physiological activation of primary cells has not been extensively investigated, and there is little or no information on the roles of STIM and Orai proteins in primary cells. Also, the nature of the Ca(2+) influx mechanism with hormone activation of hepatocytes is controversial. Hepatocytes respond to physiological levels of glycogenolytic hormones with well-characterized intracellular Ca(2+) oscillations. In the current study, we have used both pharmacological tools and RNA interference (RNAi)-based techniques to investigate the role of store-operated channels in the maintenance of hormone-induced Ca(2+) oscillations in rat hepatocytes. Pharmacological inhibitors of store-operated channels blocked thapsigargin-induced Ca(2+) entry but only partially reduced the frequency of Ca(2+) oscillations. Similarly, RNAi knockdown of STIM1 or Orai1 substantially reduced thapsigargin-induced calcium entry, and more modestly diminished the frequency of vasopressin-induced oscillations. CONCLUSION: Our findings establish that store-operated Ca(2+) entry plays a role in the maintenance of agonist-induced oscillations in primary rat hepatocytes but indicate that other agonist-induced entry mechanisms must be involved to a significant extent.

Identifying and Prioritizing Chemicals with Uncertain Burden of Exposure: Opportunities for Biomonitoring and Health-Related Research.

BACKGROUND: The National Institutes of Health's Environmental influences on Child Health Outcomes (ECHO) initiative aims to understand the impact of environmental factors on childhood disease. Over 40,000 chemicals are approved for commercial use. The challenge is to prioritize chemicals for biomonitoring that may present health risk concerns. OBJECTIVES: Our aim was to prioritize chemicals that may elicit child health effects of interest to ECHO but that have not been biomonitored nationwide and to identify gaps needing additional research. METHODS: We searched databases and the literature for chemicals in environmental media and in consumer products that were potentially toxic. We selected chemicals that were not measured in the National Health and Nutrition Examination Survey. From over 700 chemicals, we chose 155 chemicals and created eight chemical panels. For each chemical, we compiled biomonitoring and toxicity data, U.S. Environmental Protection Agency exposure predictions, and annual production usage. We also applied predictive modeling to estimate toxicity. Using these data, we recommended chemicals either for biomonitoring, to be deferred pending additional data, or as low priority for biomonitoring. RESULTS: For the 155 chemicals, 97 were measured in food or water, 67 in air or house dust, and 52 in biospecimens. We found in vivo endocrine, developmental, reproductive, and neurotoxic effects for 61, 74, 47, and 32 chemicals, respectively. Eighty-six had data from high-throughput in vitro assays. Positive results for endocrine, developmental, neurotoxicity, and obesity were observed for 32, 11, 35, and 60 chemicals, respectively. Predictive modeling results suggested 90% are toxicants. Biomarkers were reported for 76 chemicals. Thirty-six were recommended for biomonitoring, 108 deferred pending additional research, and 11 as low priority for biomonitoring. DISCUSSION: The 108 deferred chemicals included those lacking biomonitoring methods or toxicity data, representing an opportunity for future research. Our evaluation was, in general, limited by the large number of unmeasured or untested chemicals. https://doi.org/10.1289/EHP5133.

Laying a Community-Based Foundation for Data-Driven Semantic Standards in Environmental Health Sciences.

BACKGROUND: Despite increasing availability of environmental health science (EHS) data, development, and implementation of relevant semantic standards, such as ontologies or hierarchical vocabularies, has lagged. Consequently, integration and analysis of information needed to better model environmental influences on human health remains a significant challenge. OBJECTIVES: We aimed to identify a committed community and mechanisms needed to develop EHS semantic standards that will advance understanding about the impacts of environmental exposures on human disease. METHODS: The National Institute of Environmental Health Sciences sponsored the "Workshop for the Development of a Framework for Environmental Health Science Language" hosted at North Carolina State University on 15-16 September 2014. Through the assembly of data generators, users, publishers, and funders, we aimed to develop a foundation for enabling the development of community-based and data-driven standards that will ultimately improve standardization, sharing, and interoperability of EHS information. DISCUSSION: Creating and maintaining an EHS common language is a continuous and iterative process, requiring community building around research interests and needs, enabling integration and reuse of existing data, and providing a low barrier of access for researchers needing to use or extend such a resource. CONCLUSIONS: Recommendations included developing a community-supported web-based toolkit that would enable a) collaborative development of EHS research questions and use cases, b) construction of user-friendly tools for searching and extending existing semantic resources, c) education and guidance about standards and their implementation, and d) creation of a plan for governance and sustainability. CITATION: Mattingly CJ, Boyles R, Lawler CP, Haugen AC, Dearry A, Haendel M. 2016. Laying a community-based foundation for data-driven semantic standards in environmental health sciences. Environ Health Perspect 124:1136-1140; http://dx.doi.org/10.1289/ehp.1510438.

STIM1 is a calcium sensor specialized for digital signaling.

When cells are activated by calcium-mobilizing agonists at low, physiological concentrations, the resulting calcium signals generally take the form of repetitive regenerative discharges of stored calcium, termed calcium oscillations [1]. These intracellular calcium oscillations have long fascinated biologists as a mode of digitized intracellular signaling. Recent work has highlighted the role of calcium influx as an essential component of calcium oscillations [2]. This influx occurs through a process known as store-operated calcium entry, which is initiated by calcium sensor proteins, STIM1 and STIM2, in the endoplasmic reticulum [3]. STIM2 is activated by changes in endoplasmic reticulum calcium near the resting level, whereas a threshold of calcium depletion is required for STIM1 activation [4]. Here we show that, surprisingly, it is STIM1 and not STIM2 that is exclusively involved in calcium entry during calcium oscillations. The implication is that each oscillation produces a transient drop in endoplasmic reticulum calcium and that this drop is sufficient to transiently activate STIM1. This transient activation of STIM1 can be observed in some cells by total internal reflection fluorescence microscopy. This arrangement nicely provides a clearly defined and unambiguous signaling system, translating a digital calcium release signal into calcium influx that can signal to downstream effectors.

Phosphorylation of STIM1 underlies suppression of store-operated calcium entry during mitosis.

Store-operated Ca(2+) entry (SOCE) and Ca(2+) release-activated Ca(2+) currents (I(crac)) are strongly suppressed during cell division, the only known physiological situation in which Ca(2+) store depletion is uncoupled from the activation of Ca(2+) influx [corrected]. We found that the endoplasmic reticulum (ER) Ca(2+) sensor STIM1 failed to rearrange into near-plasma membrane puncta in mitotic cells, a critical step in the SOCE-activation pathway. We also found that STIM1 from mitotic cells is recognized by the phospho-specific MPM-2 antibody, suggesting that STIM1 is phosphorylated during mitosis. Removal of ten MPM-2 recognition sites by truncation at amino acid 482 abolished MPM-2 recognition of mitotic STIM1, and significantly rescued STIM1 rearrangement and SOCE response in mitosis. We identified Ser 486 and Ser 668 as mitosis-specific phosphorylation sites, and STIM1 containing mutations of these sites to alanine also significantly rescued mitotic SOCE. Therefore, phosphorylation of STIM1 at Ser 486 and Ser 668, and possibly other sites, underlies suppression of SOCE during mitosis.

Complex actions of 2-aminoethyldiphenyl borate on store-operated calcium entry.

Store-operated Ca2+ entry (SOCE) is likely the most common mode of regulated influx of Ca2+ into cells. However, only a limited number of pharmacological agents have been shown to modulate this process. 2-Aminoethyldiphenyl borate (2-APB) is a widely used experimental tool that activates and then inhibits SOCE and the underlying calcium release-activated Ca2+ current (I CRAC). The mechanism by which depleted stores activates SOCE involves complex cellular movements of an endoplasmic reticulum Ca2+ sensor, STIM1, which redistributes to puncta near the plasma membrane and, in some manner, activates plasma membrane channels comprising Orai1, -2, and -3 subunits. We show here that 2-APB blocks puncta formation of fluorescently tagged STIM1 in HEK293 cells. Accordingly, 2-APB also inhibited SOCE and I(CRAC)-like currents in cells co-expressing STIM1 with the CRAC channel subunit, Orai1, with similar potency. However, 2-APB inhibited STIM1 puncta formation less well in cells co-expressing Orai1, indicating that the inhibitory effects of 2-APB are not solely dependent upon STIM1 reversal. Further, 2-APB only partially inhibited SOCE and current in cells co-expressing STIM1 and Orai2 and activated sustained currents in HEK293 cells expressing Orai3 and STIM1. Interestingly, the Orai3-dependent currents activated by 2-APB showed large outward currents at potentials greater than +50 mV. Finally, Orai3, and to a lesser extent Orai1, could be directly activated by 2-APB, independently of internal Ca2+ stores and STIM1. These data reveal novel and complex actions of 2-APB effects on SOCE that can be attributed to effects on both STIM1 as well as Orai channel subunits.

Calcium inhibition and calcium potentiation of Orai1, Orai2, and Orai3 calcium release-activated calcium channels.

The recent discoveries of Stim1 and Orai proteins have shed light on the molecular makeup of both the endoplasmic reticulum Ca(2+) sensor and the calcium release-activated calcium (CRAC) channel, respectively. In this study, we investigated the regulation of CRAC channel function by extracellular Ca(2+) for channels composed primarily of Orai1, Orai2, and Orai3, by co-expressing these proteins together with Stim1, as well as the endogenous channels in HEK293 cells. As reported previously, Orai1 or Orai2 resulted in a substantial increase in CRAC current (I(crac)), but Orai3 failed to produce any detectable Ca(2+)-selective currents. However, sodium currents measured in the Orai3-expressing HEK293 cells were significantly larger in current density than Stim1-expressing cells. Moreover, upon switching to divalent free external solutions, Orai3 currents were considerably more stable than Orai1 or Orai2, indicating that Orai3 channels undergo a lesser degree of depotentiation. Additionally, the difference between depotentiation from Ca(2+) and Ba(2+) or Mg(2+) solutions was significantly less for Orai3 than for Orai1 or -2. Nonetheless, the Na(+) currents through Orai1, Orai2, and Orai3, as well as the endogenous store-operated Na(+) currents in HEK293 cells, were all inhibited by extracellular Ca(2+) with a half-maximal concentration of approximately 20 mum. We conclude that Orai1, -2, and -3 channels are similarly inhibited by extracellular Ca(2+), indicating similar affinities for Ca(2+) within the selectivity filter. Orai3 channels appeared to differ from Orai1 and -2 in being somewhat resistant to the process of Ca(2+) depotentiation.

Role of the store-operated calcium entry proteins Stim1 and Orai1 in muscarinic cholinergic receptor-stimulated calcium oscillations in human embryonic kidney cells.

We have investigated the nature of the Ca2+ entry supporting [Ca2+]i oscillations in human embryonic kidney (HEK293) cells by examining the roles of recently described store-operated Ca2+ entry proteins, Stim1 and Orai1. Knockdown of Stim1 by RNA interference (RNAi) reduced the frequency of [Ca2+]i oscillations in response to a low concentration of methacholine to the level seen in the absence of external Ca2+. However, knockdown of Stim1 did not block oscillations in canomical transient receptor potential 3 channel (TRPC3)-expressing cells and did not affect Ca2+ entry in response to arachidonic acid. The effects of knockdown of Stim1 could be reversed by inhibiting Ca2+ extrusion with a high concentration of Gd3+, or by rescuing the knockdown by overexpression of Stim1. Similarly, knockdown of Orai1 abrogated [Ca2+]i oscillations, and this was reversed by use of high concentrations of Gd3+; however, knockdown of Orai1 did not affect arachidonic acid-activated entry. RNAi targeting 34 members of the transient receptor potential (TRP) channel superfamily did not reveal a role for any of these channel proteins in store-operated Ca2+ entry in HEK293 cells. These findings indicate that the Ca2+ entry supporting [Ca2+]i oscillations in HEK293 cells depends upon the Ca2+ sensor, Stim1, and calcium release-activated Ca2+ channel protein, Orai1, and provide further support for our conclusion that it is the store-operated mechanism that plays the major role in this pathway.