LINCS Data Portal 2.0: next generation access point for perturbation-response signatures

Stathias, Vasileios; Turner, John Paul; Koleti, Amar; Vidović, Dušica; Cooper, Daniel J.; Fazel-Najafabadi, Mehdi; Pilarczyk, Marcin; Terryn, Raymond; Chung, Caty; Umeano, Afoma; Clarke, Daniel J B; Lachmann, Alexander; Evangelista, John Erol; Ma’ayan, Avi; Medvedovic, Mario; Schürer, Stephan C.

doi:10.1093/nar/gkz1023

Cited by 142 publications

(119 citation statements)

References 28 publications

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“…S 15b ). Investigation of the Library of Integrated Network-Based Cellular Signatures (LINCS) dataset 52 showed that compounds predicted to recapitulate the 3D tetra-culture signature included hypoxia inducible factor activators, caspase activators, and HDAC inhibitors, while RAF inhibitors and immunosuppressive agents may impair expression of this gene signature (Supplementary information, Fig. S 15c ).…”

Section: Resultsmentioning

confidence: 99%

Three-dimensional bioprinted glioblastoma microenvironments model cellular dependencies and immune interactions

et al. 2020

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Brain tumors are dynamic complex ecosystems with multiple cell types. To model the brain tumor microenvironment in a reproducible and scalable system, we developed a rapid three-dimensional (3D) bioprinting method to construct clinically relevant biomimetic tissue models. In recurrent glioblastoma, macrophages/microglia prominently contribute to the tumor mass. To parse the function of macrophages in 3D, we compared the growth of glioblastoma stem cells (GSCs) alone or with astrocytes and neural precursor cells in a hyaluronic acid-rich hydrogel, with or without macrophage. Bioprinted constructs integrating macrophage recapitulate patient-derived transcriptional profiles predictive of patient survival, maintenance of stemness, invasion, and drug resistance. Whole-genome CRISPR screening with bioprinted complex systems identified unique molecular dependencies in GSCs, relative to sphere culture. Multicellular bioprinted models serve as a scalable and physiologic platform to interrogate drug sensitivity, cellular crosstalk, invasion, context-specific functional dependencies, as well as immunologic interactions in a species-matched neural environment.

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Section: Resultsmentioning

confidence: 99%

Three-dimensional bioprinted glioblastoma microenvironments model cellular dependencies and immune interactions

et al. 2020

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“…Hypergeometric overlap of the ACE2-correlated signature was performed via tools available through the National Institutes of Health Library of Integrated Network-Based Cellular Signatures. 20 Comparison with gene lists related to viral infection was accomplished using Enrichr 21 to interrogate the ''Virus Perturbations from GEO'' data set. Metadata from gene lists that demonstrated significant overlap with P value less than .05 after correction for multiple testing were mined for semantic similarity using hierarchical clustering based on cosine similarity measurement, identifying SARS-CoV as the most common term.…”

Section: Overlap With Library Of Integrated Network-based Cellular Simentioning

confidence: 99%

Expression of SARS-CoV-2 receptor ACE2 and coincident host response signature varies by asthma inflammatory phenotype

Camiolo

Gauthier

Kaminski

et al. 2020

Journal of Allergy and Clinical Immunology

109

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Background: More than 300 million people carry a diagnosis of asthma, with data to suggest that they are at a higher risk for infection or adverse outcomes from severe acute respiratory syndrome coronavirus 2. Asthma is remarkably heterogeneous, and it is currently unclear how patient-intrinsic factors may relate to coronavirus disease 2019. Objective: We sought to identify and characterize subsets of patients with asthma at increased risk for severe acute respiratory syndrome coronavirus 2 infection. Methods: Participants from 2 large asthma cohorts were stratified using clinically relevant parameters to identify factors related to angiotensin-converting enzyme-2 (ACE2) expression within bronchial epithelium. ACE-2-correlated gene signatures were used to interrogate publicly available databases to identify upstream signaling events and novel therapeutic targets. Results: Stratifying by type 2 inflammatory biomarkers, we identified subjects who demonstrated low peripheral blood eosinophils accompanied by increased expression of the severe acute respiratory syndrome coronavirus 2 receptor ACE2 in bronchial epithelium. Genes highly correlated with ACE2 overlapped with type 1 and 2 IFN signatures, normally induced by viral infections. T-cell recruitment and activation within bronchoalveolar lavage cells of ACE2-high subjects was reciprocally increased. These patients demonstrated characteristics corresponding to risk factors for severe coronavirus disease 2019, including male sex, history of hypertension, low peripheral blood, and elevated bronchoalveolar lavage lymphocytes. Conclusions: ACE2 expression is linked to upregulation of viral response genes in a subset of type 2-low patients with asthma with characteristics resembling known risk factors for severe coronavirus disease 2019. Therapies targeting the IFN family and T-cell-activating factors may therefore be of benefit in a subset of patients.

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“…Recently, the next generation project of CMap was launched, termed the Library of Integrated Network-based Cellular Signatures (LINCS). 30,31) LINCS employs transcriptome profile data, although with added ingenuity to achieve high-throughput data acquisition. LINCS narrows target genes by data-driven analysis of 12063 transcriptome profile data points, so that the genes ultimately selected explain sufficient biological information about a specimen.…”

Section: Comparison Of Bridging Strategy Between a Disease And A Drugmentioning

confidence: 99%

Interesting Properties of Profile Data Analysis in the Understanding and Utilization of the Effects of Drugs

Mizuno

Morita

Kusuhara

2020

Biological & Pharmaceutical Bulletin

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Profile data is defined as data which describes the properties of an object. Omics data of a specimen is profile data because its comprehensiveness supports the idea that omics data is numeric information which reflects biological information of the specimen. In general, omics data analysis utilizes an existing body of biological knowledge, while some profile data analysis methods are independent of existing knowledge, which is suitable for uncovering unidentified aspects of a specimen of interest. The effects of a small compound, such as drugs, are multiple, and include unrecognized effects, even by the developers. To uncover such unrecognized effects, it is useful to employ profile data analysis independent of existing knowledge. In this review, we summarize what profile data is, properties of profile data analysis, and current applications of profile data in order to understand and utilize the effects of small compounds, in particular, in a recently developed method to decompose multiple effects of a drug.

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LINCS Data Portal 2.0: next generation access point for perturbation-response signatures

Cited by 142 publications

References 28 publications

Three-dimensional bioprinted glioblastoma microenvironments model cellular dependencies and immune interactions

Three-dimensional bioprinted glioblastoma microenvironments model cellular dependencies and immune interactions

Expression of SARS-CoV-2 receptor ACE2 and coincident host response signature varies by asthma inflammatory phenotype

Interesting Properties of Profile Data Analysis in the Understanding and Utilization of the Effects of Drugs

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