Journal articles: 'L1000'

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Liu, Chenglin, Jing Su, Fei Yang, Kun Wei, Jinwen Ma, and Xiaobo Zhou. "Compound signature detection on LINCS L1000 big data." Molecular BioSystems 11, no. 3 (2015): 714–22. http://dx.doi.org/10.1039/c4mb00677a.

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The Library of Integrated Network-based Cellular Signatures (LINCS) L1000 big data provide gene expression profiles induced by over 10 000 compounds, shRNAs, and kinase inhibitors using the L1000 platform.

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Qiu, Yue, Tianhuan Lu, Hansaim Lim, and Lei Xie. "A Bayesian approach to accurate and robust signature detection on LINCS L1000 data." Bioinformatics 36, no. 9 (January 31, 2020): 2787–95. http://dx.doi.org/10.1093/bioinformatics/btaa064.

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Abstract Motivation LINCS L1000 dataset contains numerous cellular expression data induced by large sets of perturbagens. Although it provides invaluable resources for drug discovery as well as understanding of disease mechanisms, the existing peak deconvolution algorithms cannot recover the accurate expression level of genes in many cases, inducing severe noise in the dataset and limiting its applications in biomedical studies. Results Here, we present a novel Bayesian-based peak deconvolution algorithm that gives unbiased likelihood estimations for peak locations and characterize the peaks with probability based z-scores. Based on the above algorithm, we build a pipeline to process raw data from L1000 assay into signatures that represent the features of perturbagen. The performance of the proposed pipeline is evaluated using similarity between the signatures of bio-replicates and the drugs with shared targets, and the results show that signatures derived from our pipeline gives a substantially more reliable and informative representation for perturbagens than existing methods. Thus, the new pipeline may significantly boost the performance of L1000 data in the downstream applications such as drug repurposing, disease modeling and gene function prediction. Availability and implementation The code and the precomputed data for LINCS L1000 Phase II (GSE 70138) are available at https://github.com/njpipeorgan/L1000-bayesian. Supplementary information Supplementary data are available at Bioinformatics online.

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Zihler, Annina, Mélanie Gagnon, Christophe Chassard, Anita Hegland, Marc J. A. Stevens, Christian P. Braegger, and Christophe Lacroix. "Unexpected consequences of administering bacteriocinogenic probiotic strains for Salmonella populations, revealed by an in vitro colonic model of the child gut." Microbiology 156, no. 11 (November 1, 2010): 3342–53. http://dx.doi.org/10.1099/mic.0.042036-0.

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New biological strategies for the treatment of Salmonella infection are needed in response to the increase in antibiotic-resistant strains. Escherichia coli L1000 and Bifidobacterium thermophilum RBL67 were previously shown to produce antimicrobial proteinaceous compounds (microcin B17 and thermophilicin B67, respectively) active in vitro against a panel of Salmonella strains recently isolated from clinical cases in Switzerland. In this study, two three-stage intestinal continuous fermentation models of Salmonella colonization inoculated with immobilized faeces of a two-year-old child were implemented to study the effects of the two bacteriocinogenic strains compared with a bacteriocin-negative mutant of strain L1000 on Salmonella growth, as well as gut microbiota composition and metabolic activity. Immobilized E. coli L1000 added to the proximal colon reactor showed a low colonization, and developed preferentially in the distal colon reactor independent of the presence of genetic determinants for microcin B17 production. Surprisingly, E. coli L1000 addition strongly stimulated Salmonella growth in all three reactors. In contrast, B. thermophilum RBL67 added in a second phase stabilized at high levels in all reactors, but could not inhibit Salmonella already present at a high level (>107 c.f.u. ml−1) when the probiotic was added. Inulin added at the end of fermentation induced a strong bifidogenic effect in all three colon reactors and a significant increase of Salmonella counts in the distal colon reactor. Our data show that under the simulated child colonic conditions, the microcin B17 production phenotype does not correlate with inhibition of Salmonella but leads to a better colonization of E. coli L1000 in the distal colon reactor. We conclude that in vitro models with complex and complete gut microbiota are required to accurately assess the potential and efficacy of probiotics with respect to Salmonella colonization in the gut.

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Samaraweera, Hasara, Samadhi Nawalage, R. M. Oshani Nayanathara, Chathuri Peiris, Tharindu N. Karunaratne, Sameera R. Gunatilake, Rooban V. K. G. Thirumalai, Jilei Zhang, Xuefeng Zhang, and Todd Mlsna. "In Situ Synthesis of Zero-Valent Iron-Decorated Lignite Carbon for Aqueous Heavy Metal Remediation." Processes 10, no. 8 (August 21, 2022): 1659. http://dx.doi.org/10.3390/pr10081659.

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Lignite’s large abundance, physicochemical properties and low cost are attractive for industrial wastewater remediation. However, directly applying lignite for wastewater treatment suffers low efficiency. Here, we synthesize highly efficient zero-valent iron (ZVI)-decorated lignite carbon through the in-situ carbonization of a lignite and FeCl2 mixture for heavy metal removal. The effect of carbonization temperature on the morphology, structure and crystallite phases of ZVI-decorated lignite carbons (ZVI-LXs) was investigated. At an optimized temperature (i.e., 1000 °C), ZVI particles were found evenly distributed on the lignite matrix with the particles between 20 to 190 nm. Moreover, ZVI particles were protected by a graphene shell that was formed in situ during the carbonization. The synthesized ZVI-L1000 exhibited higher Cu2+, Pb2+ and Cd2+ stripping capacities than pristine lignite in a wide pH range of 2.2–6.3 due to the surface-deposited ZVI particles. The maximum Langmuir adsorption capacities of ZVI-L1000 for Cd2+, Pb2+ and Cu2+ were 38.3, 55.2 and 42.5 mg/g at 25 °C, respectively, which were 7.8, 4.5 and 10.6 times greater than that of pristine lignite, respectively. ZVI-L1000 also exhibited a fast metal removal speed (~15 min), which is ideal for industrial wastewater treatment. The pseudo-second-order model fits well with all three adsorptions, indicating that chemical forces control their rate-limiting adsorption steps. The reduction mechanisms of ZVI-L1000 for heavy metals include reduction, precipitation and complexation.

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Kort, Eric J., and Stefan Jovinge. "Streamlined analysis of LINCS L1000 data with the slinky package for R." Bioinformatics 35, no. 17 (January 10, 2019): 3176–77. http://dx.doi.org/10.1093/bioinformatics/btz002.

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Abstract Summary The L1000 dataset from the NIH LINCS program holds the promise to deconvolute a wide range of biological questions in transcriptional space. However, using this large and decentralized dataset presents its own challenges. The slinky package was created to streamline the process of identifying samples of interest and their corresponding control samples, and loading their associated expression data and metadata. The package can integrate with workflows leveraging the BioConductor collection of tools by encapsulating the L1000 data as a SummarizedExperiment object. Availability and implementation Slinky is freely available as an R package at http://bioconductor.org/packages/slinky

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McDermott, Matthew B. A., Jennifer Wang, Wen-Ning Zhao, Steven D. Sheridan, Peter Szolovits, Isaac Kohane, Stephen J. Haggarty, and Roy H. Perlis. "Deep Learning Benchmarks on L1000 Gene Expression Data." IEEE/ACM Transactions on Computational Biology and Bioinformatics 17, no. 6 (November 1, 2020): 1846–57. http://dx.doi.org/10.1109/tcbb.2019.2910061.

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Lin, Kequan, Lu Li, Yifei Dai, Huili Wang, Shuaishuai Teng, Xilinqiqige Bao, Zhi John Lu, and Dong Wang. "A comprehensive evaluation of connectivity methods for L1000 data." Briefings in Bioinformatics 21, no. 6 (November 27, 2019): 2194–205. http://dx.doi.org/10.1093/bib/bbz129.

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Abstract The methodologies for evaluating similarities between gene expression profiles of different perturbagens are the key to understanding mechanisms of actions (MoAs) of unknown compounds and finding new indications for existing drugs. L1000-based next-generation Connectivity Map (CMap) data is more than a thousand-fold scale-up of the CMap pilot dataset. Although several systematic evaluations have been performed individually to assess the accuracy of the methodologies for the CMap pilot study, the performance of these methodologies needs to be re-evaluated for the L1000 data. Here, using the drug–drug similarities from the Drug Repurposing Hub database as a benchmark standard, we evaluated six popular published methods for the prediction performance of drug–drug relationships based on the partial area under the receiver operating characteristic (ROC) curve at false positive rates of 0.001, 0.005 and 0.01 (AUC0.001, AUC0.005 and AUC0.01). The similarity evaluating algorithm called ZhangScore was generally superior to other methods and exhibited the highest accuracy at the gene signature sizes ranging from 10 to 200. Further, we tested these methods with an experimentally derived gene signature related to estrogen in breast cancer cells, and the results confirmed that ZhangScore was more accurate than other methods. Moreover, based on scoring results of ZhangScore for the gene signature of TOP2A knockdown, in addition to well-known TOP2A inhibitors, we identified a number of potential inhibitors and at least two of them were the subject of previous investigation. Our studies provide potential guidelines for researchers to choose the suitable connectivity method. The six connectivity methods used in this report have been implemented in R package (https://github.com/Jasonlinchina/RCSM).

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Suter, Robert, Anna Jermakowicz, Vasileios Stathias, Luz Ruiz, Matthew D'Antuono, Simon Kaeppeli, Grace Baker, et al. "EPCO-14. ISOSCELES: AN INTEGRATIVE FRAMEWORK FOR THE PREDICTION OF TREATMENT RESISTANT GLIOBLASTOMA CELLS." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii118. http://dx.doi.org/10.1093/neuonc/noac209.449.

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Abstract Glioblastoma (GBM) remains the most common and lethal adult primary brain cancer. Two of the most significant issues preventing the development of effective GBM treatments are inter- and intra-tumor heterogeneity. To address these issues, we developed a novel platform termed ISOSCELES (Inferred cell Sensitivity Operating on the integration of Single-Cell Expression and L1000 Expression Signatures). ISOSCELES integrates single-cell gene expression data in individual GBM tumors with perturbation-response data derived from the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) L1000 dataset to predict sensitive and resistant tumor cell populations. Importantly, we analyzed the predictive power of ISOSCELES in an in vivo xenograft model and demonstrated that ISOSCELES reveals the GBM cell identities primed for lineage expansion during treatment with the aurora kinase inhibitor alisertib. These studies suggest that ISOSCELES can be used to identify sensitive and resistant cell populations to targeted therapies in GBM, which can inform treatment decisions in ongoing and future clinical trials.

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Wen, Huaming, Ryan A. Gallo, Xiaosheng Huang, Jiamin Cai, Shaoyi Mei, Ammad Ahmad Farooqi, Jun Zhao, and Wensi Tao. "Incorporating Differential Gene Expression Analysis with Predictive Biomarkers to Identify Novel Therapeutic Drugs for Fuchs Endothelial Corneal Dystrophy." Journal of Ophthalmology 2021 (June 28, 2021): 1–8. http://dx.doi.org/10.1155/2021/5580595.

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Purpose. Based on the differential gene expression analysis for predictive biomarkers with RNA-Sequencing data from Fuchs endothelial corneal dystrophy (FECD) patients, we are aiming to evaluate the efficacy of Library of Integrated Network-based Cellular Signatures (LINCS) perturbagen prediction software to identify novel pharmacotherapeutic targets that can revert the pathogenic gene expression signatures and reverse disease phenotype in FECD. Methods. A publicly available RNA-seq dataset was used to compare corneal endothelial specimens from controls and patients with FECD. Based on the differential gene expression analysis for predictive biomarkers, we evaluated the efficacy of LINCS perturbagen prediction software to identify novel therapeutic targets that can revert the pathogenic gene expression signatures and reverse disease phenotypes in FECD. Results. The RNA-seq dataset of the corneal endothelial cells from FECD patients revealed the differential gene expression signatures of FECD. Many of the differential expressed genes are related to canonical pathways of the FECD pathogenesis, such as extracellular matrix reorganization and immunological response. The expression levels of genes VSIG2, IL18, and ITGB8 were significantly increased in FECD compared with control. Meanwhile, the expression levels of CNGA3, SMOX, and CERS1 were significantly lower in the FECD than in control. We employed LINCS L1000 Characteristic Direction Signature Search Engine (L1000-CDS2) to investigate pathway-based molecular treatment. L1000-CDS2 predicted that small molecule drugs such as histone deacetylase (HDAC) inhibitors might be a potential candidate to reverse the pathological gene expression signature in FECD. Conclusions. Based on differential gene expression signatures, several candidate drugs have been identified to reverse the disease phenotypes in FECD. Gene expression signature with LINCS small molecule prediction software can discover novel preclinical drug candidates for FECD.

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Wang, Zichen, Neil R. Clark, and Avi Ma’ayan. "Drug-induced adverse events prediction with the LINCS L1000 data." Bioinformatics 32, no. 15 (April 1, 2016): 2338–45. http://dx.doi.org/10.1093/bioinformatics/btw168.

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Subramanian, Aravind, Rajiv Narayan, Steven M. Corsello, David D. Peck, Ted E. Natoli, Xiaodong Lu, Joshua Gould, et al. "A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles." Cell 171, no. 6 (November 2017): 1437–52. http://dx.doi.org/10.1016/j.cell.2017.10.049.

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Amano, Hiroshi, Nobuaki Watanabe, Norikatsu Koide, and Isamu Akasaki. "Room-Temperature Low-Threshold Surface-Stimulated Emission by Optical Pumping from Al0.1Ga0.9N/GaN Double Heterostructure." Japanese Journal of Applied Physics 32, Part 2, No. 7B (July 15, 1993): L1000—L1002. http://dx.doi.org/10.1143/jjap.32.l1000.

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Yoo, Sang-Im, Masato Murakami, Naomichi Sakai, Takamitsu Higuchi, and Shoji Tanaka. "Enhanced $\mbi T_{\bf c}$ and Strong Flux Pinning in Melt-Processed $\bf NdBa_{2}Cu_{3}O_{\ninmbi y}$ Superconductors." Japanese Journal of Applied Physics 33, Part 2, No. 7B (July 15, 1994): L1000—L1003. http://dx.doi.org/10.1143/jjap.33.l1000.

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Zhu, YaBin, Masahiro Ikeda, Yoshihiro Murakami, Atsushi Tsukazaki, Tomoteru Fukumura, and Masashi Kawasaki. "Low-Temperature Growth of Highly Crystalline Superconducting ZrN Thin Film onc-GaN Layer by Pulsed Laser Deposition." Japanese Journal of Applied Physics 46, No. 41 (October 19, 2007): L1000—L1002. http://dx.doi.org/10.1143/jjap.46.l1000.

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Manabe, Takaaki, Wakichi Kondo, Susumu Mizuta, and Toshiya Kumagai. "Effects of Annealing Conditions and Substrate Materials on the Superconducting Properties of Ba2YCu3O7-yFilms Prepared by the Dipping-Pyrolysis Process at 750°C." Japanese Journal of Applied Physics 30, Part 2, No. 6A (June 1, 1991): L1000—L1002. http://dx.doi.org/10.1143/jjap.30.l1000.

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Schwarz, Margaret A., Fangrong Zhang, John E. Lane, Susan Schachtner, Yangsun Jin, Gail Deutsch, Vaughn Starnes, and Bruce R. Pitt. "Angiogenesis and morphogenesis of murine fetal distal lung in an allograft model." American Journal of Physiology-Lung Cellular and Molecular Physiology 278, no. 5 (May 1, 2000): L1000—L1007. http://dx.doi.org/10.1152/ajplung.2000.278.5.l1000.

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Neovascularization is crucial to lung morphogenesis; however, factors determining vessel growth and formation are poorly understood. The goal of our study was to develop an allograft model that would include maturation of the distal lung, thereby ultimately allowing us to study alveolar development, including microvascular formation. We transplanted 14-day gestational age embryonic mouse lung primordia subcutaneously into the back of nude mice for 3.5–14 days. Lung morphogenesis and neovascularization were evaluated by light microscopy, in situ hybridization, and immunohistochemical techniques. Embryonic 14-day gestational age control lungs had immature structural features consistent with pseudoglandular stage of lung development. In contrast, 14 days after subcutaneous transplantation of a 14-day gestational age lung, the allograft underwent significant structural morphogenesis and neovascularization. This was demonstrated by continued neovascularization and cellular differentiation, resulting in mature alveoli similar to those noted in the 2-day postnatal neonatal lung. Confirmation of maturation of the allograft was provided by progressive type II epithelial cell differentiation as evidenced by enhanced local expression of mRNA for surfactant protein C and a threefold ( P < 0.008) increase in vessel formation as determined by immunocytochemical detection of platelet endothelial cell adhesion molecule-1 expression. Using the tyrosine kinase Flk-1 receptor ( flk-1) LacZ transgene embryos, we determined that the neovascularization within the allograft was from the committed embryonic lung endothelium. Therefore, we have developed a defined murine allograft model that can be used to study distal lung development, including neovascularization. The model may be useful when used in conjunction with an altered genetic background (knockout or knock in) of the allograft and has the further decided advantage of bypassing placental barriers for introduction of pharmacological agents or DNA directly into the lung itself.

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Miyake, Hideto, Atsushi Motogaito, and Kazumasa Hiramatsu. "Effects of Reactor Pressure on Epitaxial Lateral Overgrowth of GaN via Low-Pressure Metalorganic Vapor Phase Epitaxy." Japanese Journal of Applied Physics 38, Part 2, No. 9A/B (September 15, 1999): L1000—L1002. http://dx.doi.org/10.1143/jjap.38.l1000.

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Dyaduysha, Andrey, Anatolii Khizhnyak, Tatyana Marusii, Victor Reshetnyak, YuriyReznikov, and Woo-Sang Park. "Peculiarity of an Oblique Liquid Crystal Alignment Induced by a Photosensitive Orientant." Japanese Journal of Applied Physics 34, Part 2, No. 8A (August 1, 1995): L1000—L1002. http://dx.doi.org/10.1143/jjap.34.l1000.

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Kiyomura, Takakazu, and Manabu Gomi. "Room-Temperature Epitaxial Growth of Ni-Zn Ferrite Thin Films by Pulsed Laser Deposition in High Vacuum." Japanese Journal of Applied Physics 36, Part 2, No. 8A (August 1, 1997): L1000—L1002. http://dx.doi.org/10.1143/jjap.36.l1000.

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Nakamura, Minoru. "Order of the Formation Reaction and the Origin of the Photoluminescence W Center in Silicon Crystal." Japanese Journal of Applied Physics 40, Part 2, No. 10A (October 1, 2001): L1000—L1002. http://dx.doi.org/10.1143/jjap.40.l1000.

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Akahane, Tadashi, Masumi Kondoh, Kenji Hashimoto, and Masahiro Nakagawa. "Heat Pulse Method for Determination of Thermal Diffusivity of Liquid Crystals." Japanese Journal of Applied Physics 26, Part 2, No. 6 (June 20, 1987): L1000—L1003. http://dx.doi.org/10.1143/jjap.26.l1000.

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Suzuki, Kazunori, Yasuo Kimura, and Masataka Nakazawa. "An 8 mW cw Er3+-Doped Fiber Laser Pumped by 1.46 µm InGaAsP Laser Diodes." Japanese Journal of Applied Physics 28, Part 2, No. 6 (June 20, 1989): L1000—L1002. http://dx.doi.org/10.1143/jjap.28.l1000.

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Li, Chan, Zeyu Zhang, Qian Xu, and Ruizheng Shi. "Comprehensive Analyses of miRNA-mRNA Network and Potential Drugs in Idiopathic Pulmonary Arterial Hypertension." BioMed Research International 2020 (July 4, 2020): 1–10. http://dx.doi.org/10.1155/2020/5156304.

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Introduction. Idiopathic pulmonary arterial hypertension (IPAH) is a severe cardiopulmonary disease with a relatively low survival rate. Moreover, the pathogenesis of IPAH has not been fully recognized. Thus, comprehensive analyses of miRNA-mRNA network and potential drugs in IPAH are urgent requirements. Methods. Microarray datasets of mRNA and microRNA (miRNA) in IPAH were searched and downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMIs) were identified. Then, the DEMI-DEG network was conducted with associated comprehensive analyses including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis, while potential drugs targeting hub genes were investigated using L1000 platform. Results. 30 DEGs and 6 DEMIs were identified in the lung tissue of IPAH. GO and KEGG pathway analyses revealed that these DEGs were mostly enriched in antimicrobial humoral response and African trypanosomiasis, respectively. The DEMI-DEG network was conducted subsequently with 4 DEMIs (hsa-miR-34b-5p, hsa-miR-26b-5p, hsa-miR-205-5p, and hsa-miR-199a-3p) and 16 DEGs, among which 5 DEGs (AQP9, SPP1, END1, VCAM1, and SAA1) were included in the top 10 hub genes of the PPI network. Nimodipine was identified with the highest CMap connectivity score in L1000 platform. Conclusion. Our study conducted a miRNA-mRNA network and identified 4 miRNAs as well as 5 mRNAs which may play important roles in the pathogenesis of IPAH. Moreover, we provided a new insight for future therapies by predicting potential drugs targeting hub genes.

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Angeli, Davide, Maurizio Fanciulli, and Matteo Pallocca. "Reverse Engineering Cancer: Inferring Transcriptional Gene Signatures from Copy Number Aberrations with ICAro." Cancers 11, no. 2 (February 22, 2019): 256. http://dx.doi.org/10.3390/cancers11020256.

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The characterization of a gene product function is a process that involves multiple laboratory techniques in order to silence the gene itself and to understand the resulting cellular phenotype via several omics profiling. When it comes to tumor cells, usually the translation process from in vitro characterization results to human validation is a difficult journey. Here, we present a simple algorithm to extract mRNA signatures from cancer datasets, where a particular gene has been deleted at the genomic level, ICAro. The process is implemented as a two-step workflow. The first one employs several filters in order to select the two patient subsets: the inactivated one, where the target gene is deleted, and the control one, where large genomic rearrangements should be absent. The second step performs a signature extraction via a Differential Expression analysis and a complementary Random Forest approach to provide an additional gene ranking in terms of information loss. We benchmarked the system robustness on a panel of genes frequently deleted in cancers, where we validated the downregulation of target genes and found a correlation with signatures extracted with the L1000 tool, outperforming random sampling for two out of six L1000 classes. Furthermore, we present a use case correlation with a published transcriptomic experiment. In conclusion, deciphering the complex interactions of the tumor environment is a challenge that requires the integration of several experimental techniques in order to create reproducible results. We implemented a tool which could be of use when trying to find mRNA signatures related to a gene loss event to better understand its function or for a gene-loss associated biomarker research.

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Liu, Tsang-Pai, Yao-Yu Hsieh, Chia-Jung Chou, and Pei-Ming Yang. "Systematic polypharmacology and drug repurposing via an integrated L1000-based Connectivity Map database mining." Royal Society Open Science 5, no. 11 (November 2018): 181321. http://dx.doi.org/10.1098/rsos.181321.

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Drug repurposing aims to find novel indications of clinically used or experimental drugs. Because drug data already exist, drug repurposing may save time and cost, and bypass safety concerns. Polypharmacology, one drug with multiple targets, serves as a basis for drug repurposing. Large-scale databases have been accumulated in recent years, and utilization and integration of these databases would be highly helpful for polypharmacology and drug repurposing. The Connectivity Map (CMap) is a database collecting gene-expression profiles of drug-treated human cancer cells, which has been widely used for investigation of polypharmacology and drug repurposing. In this study, we integrated the next-generation L1000-based CMap and an analytic Web tool, the L1000FWD, for systematic analyses of polypharmacology and drug repurposing. Two different types of anti-cancer drugs were used as proof-of-concept examples, including histone deacetylase (HDAC) inhibitors and topoisomerase inhibitors. We identified KM-00927 and BRD-K75081836 as novel HDAC inhibitors and mitomycin C as a topoisomerase IIB inhibitor. Our study provides a prime example of utilization and integration of the freely available public resources for systematic polypharmacology analysis and drug repurposing.

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Clayman, Carly L., Satish M. Srinivasan, and Raghvinder S. Sangwan. "K-means Clustering and Principal Components Analysis of Microarray Data of L1000 Landmark Genes." Procedia Computer Science 168 (2020): 97–104. http://dx.doi.org/10.1016/j.procs.2020.02.265.

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Sendama, Wezi. "L1000 connectivity map interrogation identifies candidate drugs for repurposing as SARS-CoV-2 antiviral therapies." Computational and Structural Biotechnology Journal 18 (2020): 3947–49. http://dx.doi.org/10.1016/j.csbj.2020.11.054.

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Duan, Qiaonan, Corey Flynn, Mario Niepel, Marc Hafner, Jeremy L. Muhlich, Nicolas F. Fernandez, Andrew D. Rouillard, et al. "LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures." Nucleic Acids Research 42, W1 (June 6, 2014): W449—W460. http://dx.doi.org/10.1093/nar/gku476.

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Wu, Xinxing, and Qiang Cheng. "Fractal Autoencoders for Feature Selection." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10370–78. http://dx.doi.org/10.1609/aaai.v35i12.17242.

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Feature selection reduces the dimensionality of data by identifying a subset of the most informative features. In this paper, we propose an innovative framework for unsupervised feature selection, called fractal autoencoders (FAE). It trains a neural network to pinpoint informative features for global exploring of representability and for local excavating of diversity. Architecturally, FAE extends autoencoders by adding a one-to-one scoring layer and a small sub-neural network for feature selection in an unsupervised fashion. With such a concise architecture, FAE achieves state-of-the-art performances; extensive experimental results on fourteen datasets, including very high-dimensional data, have demonstrated the superiority of FAE over existing contemporary methods for unsupervised feature selection. In particular, FAE exhibits substantial advantages on gene expression data exploration, reducing measurement cost by about 15% over the widely used L1000 landmark genes. Further, we show that the FAE framework is easily extensible with an application.

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Konuma, Takahiro, Kotaro Ogawa, and Yukinori Okada. "Integration of genetically regulated gene expression and pharmacological library provides therapeutic drug candidates." Human Molecular Genetics 30, no. 3-4 (February 1, 2021): 294–304. http://dx.doi.org/10.1093/hmg/ddab049.

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Abstract Approaches toward new therapeutics using disease genomics, such as genome-wide association study (GWAS), are anticipated. Here, we developed Trans-Phar [integration of transcriptome-wide association study (TWAS) and pharmacological database], achieving in silico screening of compounds from a large-scale pharmacological database (L1000 Connectivity Map), which have inverse expression profiles compared with tissue-specific genetically regulated gene expression. Firstly we confirmed the statistical robustness by the application of the null GWAS data and enrichment in the true-positive drug–disease relationships by the application of UK-Biobank GWAS summary statistics in broad disease categories, then we applied the GWAS summary statistics of large-scale European meta-analysis (17 traits; naverage = 201 849) and the hospitalized COVID-19 (n = 900 687), which has urgent need for drug development. We detected potential therapeutic compounds as well as anisomycin in schizophrenia (false discovery rate (FDR)-q = 0.056) and verapamil in hospitalized COVID-19 (FDR-q = 0.068) as top-associated compounds. This approach could be effective in disease genomics-driven drug development.

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HOLSTEIN, P. A., F. CHALAND, C. CHARPIN, J. M. DUFOUR, H. DUMONT, J. GIORLA, L. HALLO, et al. "Evolution of the target design for the MJ laser." Laser and Particle Beams 17, no. 3 (July 1999): 403–13. http://dx.doi.org/10.1017/s0263034699173087.

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In the previous design, the maximum drive radiation temperature was 4 MK or 350 eV (Holstein 1996). Different beam configurations gave roughly the same uniformity with the NIF-size cavity. Our best configuration used four cones of beams illuminating three rings. An integrated 2D simulation pointed out that the symmetry was good enough to reach a gain of ten. Two evolutions took place in the design of our MJ laser. We moved from a capsule adapted to 4 MK (L1000) to another one adapted to 3.5 MK (L1215) in order to minimize the parametric instabilities (the cavity size is almost the same). This new capsule also has a better hydrostability according to the “classical modelling” (Lindl 1995). The second evolution is a simplification of the target chamber. We restricted ourselves to two major configurations for indirect drive (two-ring and three-ring configurations). Therefore, only three cones of beams are necessary instead of five cones in the first design. Finally, the number of holes in the chamber is 80 instead of 100.

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Petralia, Maria Cristina, Katia Mangano, Maria Catena Quattropani, Vittorio Lenzo, Ferdinando Nicoletti, and Paolo Fagone. "Computational Analysis of Pathogenetic Pathways in Alzheimer’s Disease and Prediction of Potential Therapeutic Drugs." Brain Sciences 12, no. 7 (June 24, 2022): 827. http://dx.doi.org/10.3390/brainsci12070827.

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Background. Alzheimer’s disease (AD) is a chronic and progressive neurodegenerative disease which affects more than 50 million patients and represents 60–80% of all cases of dementia. Mutations in the APP gene, mostly affecting the γ-secretase site of cleavage and presenilin mutations, have been identified in inherited forms of AD. Methods. In the present study, we performed a meta-analysis of the transcriptional signatures that characterize two familial AD mutations (APPV7171F and PSEN1M146V) in order to characterize the common altered biomolecular pathways affected by these mutations. Next, an anti-signature perturbation analysis was performed using the AD meta-signature and the drug meta-signatures obtained from the L1000 database, using cosine similarity as distance metrics. Results. Overall, the meta-analysis identified 1479 differentially expressed genes (DEGs), 684 downregulated genes, and 795 upregulated genes. Additionally, we found 14 drugs with a significant anti-similarity to the AD signature, with the top five drugs being naftifine, moricizine, ketoconazole, perindopril, and fexofenadine. Conclusions. This study aimed to integrate the transcriptional profiles associated with common familial AD mutations in neurons in order to characterize the pathogenetic mechanisms involved in AD and to find more effective drugs for AD.

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Halle, Mari K., Ane Cecilie Munk, Birgit Engesæter, Saleha Akbari, Astri Frafjord, Erling A. Hoivik, David Forsse, et al. "A Gene Signature Identifying CIN3 Regression and Cervical Cancer Survival." Cancers 13, no. 22 (November 16, 2021): 5737. http://dx.doi.org/10.3390/cancers13225737.

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The purpose of this study was to establish a gene signature that may predict CIN3 regression and that may aid in selecting patients who may safely refrain from conization. Oncomine mRNA data including 398 immune-related genes from 21 lesions with confirmed regression and 28 with persistent CIN3 were compared. L1000 mRNA data from a cervical cancer cohort was available for validation (n = 239). Transcriptomic analyses identified TDO2 (p = 0.004), CCL5 (p < 0.001), CCL3 (p = 0.04), CD38 (p = 0.02), and PRF1 (p = 0.005) as upregulated, and LCK downregulated (p = 0.01) in CIN3 regression as compared to persistent CIN3 lesions. From these, a gene signature predicting CIN3 regression with a sensitivity of 91% (AUC = 0.85) was established. Transcriptomic analyses revealed proliferation as significantly linked to persistent CIN3. Within the cancer cohort, high regression signature score associated with immune activation by Gene Set enrichment Analyses (GSEA) and immune cell infiltration by histopathological evaluation (p < 0.001). Low signature score was associated with poor survival (p = 0.007) and large tumors (p = 0.01). In conclusion, the proposed six-gene signature predicts CIN regression and favorable cervical cancer prognosis and points to common drivers in precursors and cervical cancer lesions.

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Lefever, Daniel E., Mark T. Miedel, Fen Pei, Johanna K. DiStefano, Richard Debiasio, Tong Ying Shun, Manush Saydmohammed, et al. "A Quantitative Systems Pharmacology Platform Reveals NAFLD Pathophysiological States and Targeting Strategies." Metabolites 12, no. 6 (June 7, 2022): 528. http://dx.doi.org/10.3390/metabo12060528.

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Non-alcoholic fatty liver disease (NAFLD) has a high global prevalence with a heterogeneous and complex pathophysiology that presents barriers to traditional targeted therapeutic approaches. We describe an integrated quantitative systems pharmacology (QSP) platform that comprehensively and unbiasedly defines disease states, in contrast to just individual genes or pathways, that promote NAFLD progression. The QSP platform can be used to predict drugs that normalize these disease states and experimentally test predictions in a human liver acinus microphysiology system (LAMPS) that recapitulates key aspects of NAFLD. Analysis of a 182 patient-derived hepatic RNA-sequencing dataset generated 12 gene signatures mirroring these states. Screening against the LINCS L1000 database led to the identification of drugs predicted to revert these signatures and corresponding disease states. A proof-of-concept study in LAMPS demonstrated mitigation of steatosis, inflammation, and fibrosis, especially with drug combinations. Mechanistically, several structurally diverse drugs were predicted to interact with a subnetwork of nuclear receptors, including pregnane X receptor (PXR; NR1I2), that has evolved to respond to both xenobiotic and endogenous ligands and is intrinsic to NAFLD-associated transcription dysregulation. In conjunction with iPSC-derived cells, this platform has the potential for developing personalized NAFLD therapeutic strategies, informing disease mechanisms, and defining optimal cohorts of patients for clinical trials.

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Szalai, Bence, Vigneshwari Subramanian, Christian H. Holland, Róbert Alföldi, László G. Puskás, and Julio Saez-Rodriguez. "Signatures of cell death and proliferation in perturbation transcriptomics data—from confounding factor to effective prediction." Nucleic Acids Research 47, no. 19 (September 25, 2019): 10010–26. http://dx.doi.org/10.1093/nar/gkz805.

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Abstract Transcriptional perturbation signatures are valuable data sources for functional genomics. Linking perturbation signatures to screenings opens the possibility to model cellular phenotypes from expression data and to identify efficacious drugs. We linked perturbation transcriptomics data from the LINCS-L1000 project with cell viability information upon genetic (Achilles project) and chemical (CTRP screen) perturbations yielding more than 90 000 signature–viability pairs. An integrated analysis showed that the cell viability signature is a major factor underlying perturbation signatures. The signature is linked to transcription factors regulating cell death, proliferation and division time. We used the cell viability–signature relationship to predict viability from transcriptomics signatures, and identified and validated compounds that induce cell death in tumor cell lines. We showed that cellular toxicity can lead to unexpected similarity of signatures, confounding mechanism of action discovery. Consensus compound signatures predicted cell-specific drug sensitivity, even if the signature is not measured in the same cell line, and outperformed conventional drug-specific features. Our results can help in understanding mechanisms behind cell death and removing confounding factors of transcriptomic perturbation screens. To interactively browse our results and predict cell viability in new gene expression samples, we developed CEVIChE (CEll VIability Calculator from gene Expression; https://saezlab.shinyapps.io/ceviche/).

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Won, Shen-Jeu, Hsing-Chih Wu, Kuan-Ting Lin, Cheng-Hao Yu, Yi-Ting Chen, Chi-Shiuan Wu, Chi-Ying F. Huang, Hsiao-Sheng Liu, Chun-Nan Lin, and Chun-Li Su. "Discovery of molecular mechanisms of lignan justicidin A using L1000 gene expression profiles and the Library of Integrated Network-based Cellular Signatures database." Journal of Functional Foods 16 (June 2015): 81–93. http://dx.doi.org/10.1016/j.jff.2015.04.025.

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Lee, Hanbi, and Wankyu Kim. "Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data." Pharmaceutics 11, no. 8 (August 2, 2019): 377. http://dx.doi.org/10.3390/pharmaceutics11080377.

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Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important question is which features for targets are most relevant to DTI prediction. As an early attempt to answer this question, we objectively compared three canonical target features extracted from: (i) the expression profiles by gene knockdown (GEPs); (ii) the protein–protein interaction network (PPI network); and (iii) the pathway membership (PM) of a target gene. For drug features, the large-scale drug-induced transcriptome dataset, or the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset was used. All these features are closely related to protein function or drug MoA, of which utility is only sparsely investigated. In particular, few studies have compared the three types of target features in DNN-based DTI prediction under the same evaluation scheme. Among the three target features, the PM and the PPI network show similar performances superior to GEPs. DNN models based on both features consistently outperformed other machine learning methods such as naïve Bayes, random forest, or logistic regression.

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Suter, Robert, Vasileios Stathias, Anna Jermakowicz, Hari Pradhyumnan, Maurizio Affer, Florence Guglielmetti, Michael Ivan, et al. "EPCO-18. A MESENCHYMAL CELL POPULATION MEDIATES RESISTANCE TO AURORA KINASE INHIBITORS IN GLIOBLASTOMA." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi5. http://dx.doi.org/10.1093/neuonc/noab196.017.

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Abstract Glioblastoma (GBM) remains the most common adult brain cancer, with a dismal average patient survival of less than two years. No new treatments have been approved for GBM since the introduction of the alkylating agent temozolomide in 2005. Even then, temozolomide treatment only increases the average survival of GBM patients by a few months. Thus, novel therapeutic options are direly needed. The aurora kinases A and B are targetable and overexpressed in GBM, and their expression is highly correlated with patient survival outcomes. Our lab has found that small molecule aurora kinase inhibition reduces GBM tumor growth in vitro and in vivo, however, eventually tumors still grow. Computational analysis integrating compound transcriptional response signatures from the LINCS L1000 dataset with the single-cell RNA-sequencing data of patient GBM tumors resected at the University of Miami predicts that aurora inhibition targets a subset of cells present within any GBM tumor. Results of in vivo single-cell perturbation experiments with the aurora kinase inhibitor alisertib coincide with our predictions and reveal a cellular transcriptional phenotype resistant to aurora kinase inhibition, characterized by a mesenchymal expression program. We find that small molecules that are predicted to target different cell populations from alisertib, including this resistant mesenchymal population, synergize with alisertib to kill GBM cells. As a whole, we have identified the cellular population resistant to aurora kinase inhibition and have developed an analytical framework that identifies synergistic small molecule combinations by identifying compounds that target transcriptionally distinct cellular populations within GBM tumors.

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Soufan, Othman, Jessica Ewald, Charles Viau, Doug Crump, Markus Hecker, Niladri Basu, and Jianguo Xia. "T1000: a reduced gene set prioritized for toxicogenomic studies." PeerJ 7 (October 29, 2019): e7975. http://dx.doi.org/10.7717/peerj.7975.

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There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to identify reduced gene sets. Though several gene sets have been defined for toxicological applications, few of these were purposefully derived using toxicogenomics data. Here, we developed and applied a systematic approach to identify 1,000 genes (called Toxicogenomics-1000 or T1000) highly responsive to chemical exposures. First, a co-expression network of 11,210 genes was built by leveraging microarray data from the Open TG-GATEs program. This network was then re-weighted based on prior knowledge of their biological (KEGG, MSigDB) and toxicological (CTD) relevance. Finally, weighted correlation network analysis was applied to identify 258 gene clusters. T1000 was defined by selecting genes from each cluster that were most associated with outcome measures. For model evaluation, we compared the performance of T1000 to that of other gene sets (L1000, S1500, Genes selected by Limma, and random set) using two external datasets based on the rat model. Additionally, a smaller (T384) and a larger version (T1500) of T1000 were used for dose-response modeling to test the effect of gene set size. Our findings demonstrated that the T1000 gene set is predictive of apical outcomes across a range of conditions (e.g., in vitro and in vivo, dose-response, multiple species, tissues, and chemicals), and generally performs as well, or better than other gene sets available.

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Lin, Tsung-Chieh. "DDX3X is Epigenetically Repressed in Renal Cell Carcinoma and Serves as a Prognostic Indicator and Therapeutic Target in Cancer Progression." International Journal of Molecular Sciences 21, no. 8 (April 20, 2020): 2881. http://dx.doi.org/10.3390/ijms21082881.

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DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, X-linked (DDX3X) is a member of the DEAD-box family of RNA helicases whose function has been revealed to be involved in RNA metabolism. Recent studies further indicate the abnormal expression in pan-cancers and the relevant biological effects on modulating cancer progression. However, DDX3X’s role in renal cell carcinoma (RCC) progression remains largely unknown. In this study, a medical informatics-based analysis using The Cancer Genome Atlas (TCGA) dataset was performed to evaluate clinical prognoses related to DDX3X. The results suggest that DDX3X is epigenetically repressed in tumor tissue and that lower DDX3X is correlated with the poor overall survival of RCC patients and high tumor size, lymph node metastasis, and distant metastasis (TNM staging system). Furthermore, knowledge-based transcriptomic analysis by Ingenuity Pathway Analysis (IPA) revealed that the SPINK1-metallothionein pathway is a top 1-repressed canonical signaling pathway by DDX3X. Furthermore, SPINK1 and the metallothionein gene family all serve as poor prognostic indicators, and the expression levels of those genes are inversely correlated with DDX3X in RCC. Furthermore, digoxin was identified via Connectivity Map analysis (L1000) for its capability to reverse gene signatures in patients with low DDX3X. Importantly, cancer cell proliferation and migration were decreased upon digoxin treatment in RCC cells. The results of this study indicate the significance of the DDX3Xlow/SPINK1high/metallothioneinhigh axis for predicting poor survival outcome in RCC patients and suggest digoxin as a precise and personalized compound for curing those patients with low DDX3X expression levels.

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Ferguson, Laura B., Shruti Patil, Bailey A. Moskowitz, Igor Ponomarev, Robert A. Harris, Roy D. Mayfield, and Robert O. Messing. "A Pathway-Based Genomic Approach to Identify Medications: Application to Alcohol Use Disorder." Brain Sciences 9, no. 12 (December 16, 2019): 381. http://dx.doi.org/10.3390/brainsci9120381.

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Chronic, excessive alcohol use alters brain gene expression patterns, which could be important for initiating, maintaining, or progressing the addicted state. It has been proposed that pharmaceuticals with opposing effects on gene expression could treat alcohol use disorder (AUD). Computational strategies comparing gene expression signatures of disease to those of pharmaceuticals show promise for nominating novel treatments. We reasoned that it may be sufficient for a treatment to target the biological pathway rather than lists of individual genes perturbed by AUD. We analyzed published and unpublished transcriptomic data using gene set enrichment of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to identify biological pathways disrupted in AUD brain and by compounds in the Library of Network-based Cellular Signatures (LINCS L1000) and Connectivity Map (CMap) databases. Several pathways were consistently disrupted in AUD brain, including an up-regulation of genes within the Complement and Coagulation Cascade, Focal Adhesion, Systemic Lupus Erythematosus, and MAPK signaling, and a down-regulation of genes within the Oxidative Phosphorylation pathway, strengthening evidence for their importance in AUD. Over 200 compounds targeted genes within those pathways in an opposing manner, more than twenty of which have already been shown to affect alcohol consumption, providing confidence in our approach. We created a user-friendly web-interface that researchers can use to identify drugs that target pathways of interest or nominate mechanism of action for drugs. This study demonstrates a unique systems pharmacology approach that can nominate pharmaceuticals that target pathways disrupted in disease states such as AUD and identify compounds that could be repurposed for AUD if sufficient evidence is attained in preclinical studies.

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Lim, Joe Jongpyo, Xueshu Li, Hans-Joachim Lehmler, Dongfang Wang, Haiwei Gu, and Julia Yue Cui. "Gut Microbiome Critically Impacts PCB-induced Changes in Metabolic Fingerprints and the Hepatic Transcriptome in Mice." Toxicological Sciences 177, no. 1 (June 16, 2020): 168–87. http://dx.doi.org/10.1093/toxsci/kfaa090.

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Abstract Polychlorinated biphenyls (PCBs) are ubiquitously detected and have been linked to metabolic diseases. Gut microbiome is recognized as a critical regulator of disease susceptibility; however, little is known how PCBs and gut microbiome interact to modulate hepatic xenobiotic and intermediary metabolism. We hypothesized the gut microbiome regulates PCB-mediated changes in the metabolic fingerprints and hepatic transcriptome. Ninety-day-old female conventional and germ-free mice were orally exposed to the Fox River Mixture (synthetic PCB mixture, 6 or 30 mg/kg) or corn oil (vehicle control, 10 ml/kg), once daily for 3 consecutive days. RNA-seq was conducted in liver, and endogenous metabolites were measured in liver and serum by LC-MS. Prototypical target genes of aryl hydrocarbon receptor, pregnane X receptor, and constitutive androstane receptor were more readily upregulated by PCBs in conventional conditions, indicating PCBs, to the hepatic transcriptome, act partly through the gut microbiome. In a gut microbiome-dependent manner, xenobiotic, and steroid metabolism pathways were upregulated, whereas response to misfolded proteins-related pathways was downregulated by PCBs. At the high PCB dose, NADP, and arginine appear to interact with drug-metabolizing enzymes (ie, Cyp1–3 family), which are highly correlated with Ruminiclostridium and Roseburia, providing a novel explanation of gut-liver interaction from PCB-exposure. Utilizing the Library of Integrated Network-based Cellular Signatures L1000 database, therapeutics targeting anti-inflammatory and endoplasmic reticulum stress pathways are predicted to be remedies that can mitigate PCB toxicity. Our findings demonstrate that habitation of the gut microbiota drives PCB-mediated hepatic responses. Our study adds knowledge of physiological response differences from PCB exposure and considerations for further investigations for gut microbiome-dependent therapeutics.

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Mookhtiar, Adnan K., Sarah Greenblatt, Na Man, Daniel Karl, Vasileios Stathias, Stephan Schurer, and Stephen D. Nimer. "CARM1 Inhibition: Evaluation of Response and Efficacy in Acute Myeloid Leukemia." Blood 132, Supplement 1 (November 29, 2018): 2719. http://dx.doi.org/10.1182/blood-2018-99-114981.

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Abstract Small molecule protein arginine methyltransferase inhibitors (PRMTi) are being actively pursued for the treatment of a variety of cancers; however, the mechanisms of response to PRMTi remain poorly understood. CARM1, also known as PRMT4, is significantly overexpressed in AML, as well as many solid tumors, and regulates myeloid differentiation. We have shown the dependency of AML cells, but not normal blood cells, on CARM1 activity, based on CARM1 knockout, CARM1 knockdown, and chemical inhibition (Greenblatt et al. Cancer Cell 2018). These experiments showed that CARM1 regulates essential processes in leukemia cells, and is critical for leukemic transformation. Although several small molecule inhibitors of CARM1 have been reported recently, many display a lack of selectivity for CARM1 or fail to produce a biological response. The recent discovery of potent and selective CARM1 inhibitors (Drew et al., 2017), has made it possible to investigate the implications of pharmacological inhibition of CARM1 in vitro and in vivo. In vitro, a selective CARM1 inhibitor, EPZ025654, reduced the methylation of a CARM1 substrate, BAF155, in a time and concentration-dependent manner, while the specific histone targets of CARM1 remained unchanged. Translocation (8;21) AML samples in the Eastern Cooperative Oncology Group cohort, have significantly higher CARM1 expression compared to normal CD34+ controls. This led us to hypothesize that CARM1 is a direct target of the AML1-ETO fusion protein. Therefore, we assessed whether EPZ025654 could inhibit AML1-ETO driven gene expression. AML1-ETO specific target genes showed significant changes in expression following EPZ025654 treatment. AML1-ETO positive patient samples also displayed decreased colony formation in methylcellulose and increased myeloid differentiation in response to CARM1 inhibition. We next evaluated EZM2302, a compound structurally related to EPZ025654, that is highly orally bioavailable and is well tolerated in mice (Drew et al., 2017). We generated AE9a-GFP primary transplantation mice and treated them with 100 mg/kg of EZM2302 or vehicle twice-daily (BID). The inhibitor treated mice showed significantly improved survival as well as fewer GFP+ cells in the peripheral blood over time. GFP+ AE9a bone marrow cells also showed decreased colony formation in vitro and induced macrophage differentiation in methylcellulose. GFP+ cells were isolated by FACS and submitted for RNA-sequencing. Flow cytometry analysis post-treatment revealed a significant downregulation of c-Kit and increased differentiation of hematopoietic stem and progenitor cells. Resistance to epigenetic targeted therapeutics has been observed, often through the induction of kinase signaling pathways. Therefore, we explored synergistic combinations with CARM1 inhibition using RNA-sequencing and proteomics analysis in leukemia cell lines. We used L1000 profiling (Subramanian et al., 2017) to simultaneously profile the transcriptional response of 18 AML cell line and CD34+ cells after 6 days of treatment. The AML1-ETO positive cell lines exhibited an IC50 in the 0.4-3 μM range, while CD34+ cells and several AML cell lines appeared to be resistant to CARM1 inhibition. While gene expression changes resulting from alterations in RNA stability were observed, the most significant differences between sensitive and resistant cell lines were genes associated with the regulation of cell cycle progression. Gene expression changes were evaluated over time in an AML1-ETO positive cell line, SKNO-1. SKNO-1 cell lines showed an upregulation of a gene expression signature associated with PI3K/AKT/mTOR signaling, with the most significant gene expression changes occurring 7-14 days post treatment. We simultaneously profiled these cells using multiplexed kinase inhibitor beads (MIBs) and quantitative mass spectrometry (MS) to compare kinase expression and activity in response to CARM1 inhibition over time. A comparison of this response to chemical perturbation signatures in the L1000 database, identified several chemical inhibitors of the PI3K/AKT/mTOR axis that could reverse the gene expression changes induced by CARM1 inhibition. This finding elucidated a response mechanism for CARM inhibition and a synergistic therapeutic strategy that has the potential to improve CARM1 directed therapy. Disclosures No relevant conflicts of interest to declare.

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Halle, Mari Kyllesø, Erlend Hodneland, Erling Hoivik, Kari Wagner-Larsen, Njål G. Lura, Julie Dybvik, David Forsse, et al. "Abstract 513: Radiomic profiles revealing targets for therapy in cervical cancer." Cancer Research 82, no. 12_Supplement (June 15, 2022): 513. http://dx.doi.org/10.1158/1538-7445.am2022-513.

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Abstract Magnetic resonance imaging (MRI) has emerged as an important part of the diagnostic work-up in cervical cancer, with tumor volume and extent as important prognosticators. MRI radiomic tumor features may aid in prognostication and tailoring of treatment in cervical cancer. We extracted whole-volume radiomic texture features from 124 manually segmented tumors and performed unsupervised clustering yielding two distinct clusters. Overlapping clinicopathologic, genomic (whole exome sequencing, n=61), transcriptomic (L1000 arrays, n= 65) and molecular biomarker (n=82) data were applied to characterize the clusters. Independent of tumor size parameters, patients in cluster II had significantly reduced disease-specific survival as compared to those in cluster I (p>0.001), also within squamous cell carcinomas (n=96, p<0.001). Cluster II associated with high age (p=0.02), high FIGO-2009 stage (p<0.001), high BMI (p=0.03) and PR negative tumors (p=0.004). Distinct mutational and copy-number profiles were detected for the two clusters. By gene set enrichment analyses, gene sets associated to immune activation were enriched in cluster I tumors, whilst gene sets associated with oxidative phosphorylation and epithelial to mesenchymal transition associated to cluster II tumors. This study links radiomic signatures to distinct genomic profiles that may potentially aid in prognostication and tailoring of treatments in cervical cancer patients. Citation Format: Mari Kyllesø Halle, Erlend Hodneland, Erling Hoivik, Kari Wagner-Larsen, Njål G. Lura, Julie Dybvik, David Forsse, Bjørn I. Bertelsen, Camilla Krakstad, Ingfrid S. Haldorsen, Olivera Bozickovic, Kathrine Woie. Radiomic profiles revealing targets for therapy in cervical cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 513.

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Ruiz, Luz, and Nagi Ayad. "DDRE-36. INVESTIGATING MEDULLOBLASTOMA HETEROGENEITY AND PREDICTING COMPOUND RESPONSE." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi82. http://dx.doi.org/10.1093/neuonc/noab196.320.

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Abstract Medulloblastoma is the most common malignant brain tumor found in children. It is a cerebellar tumor that affects motor and cognitive processes such as coordination and movement. The standard of care is surgical removal, radiation, and chemotherapy. These treatments can be very damaging to the developing child, in that they can impair vision and walking, among other body functions. Due to this, new treatments are necessary. Treatment plans for children with medulloblastoma need to be tailored to the specific subtype that they have. Genetic studies have revealed that there are four subtypes of pediatric medulloblastoma: Group 3, Group 4, SHH, and WNT. Beyond these bulk-resolution subtypes, we hypothesize intratumor heterogeneity as a barrier to new effective treatments. I have mined single-cell RNA sequencing data to investigate cellular heterogeneity and predict compound response. I analyzed Medulloblastoma patient tumor data along with data obtained from a 10X Genomics Chromium single-cell RNA sequencing experiment performed in the laboratory from a Tg (Neurod-Smoothened*A1) mouse. We hypothesize that distinct cell populations within medulloblastoma should show different predicted compounds that would target them. We have ranked compound predictions to investigate whether compounds may selectively target any of these populations using transcriptional response signatures derived from the LINCS L1000 perturbagen-response dataset. We also hypothesize that Medulloblastoma tumors have distinct subtypes of cells that are preferentially sensitive to BET bromodomain, casein kinase, and ATM/ATR inhibitors. Our analysis identified ten transcriptionally distinct cell types across these medulloblastoma tumors as well as compounds predicted to target them in each transcriptional subtype. Furthermore, we identified bromodomain and casein kinase inhibitors as a potential combination therapy due to their predicted synergy at targeting all cell populations within medulloblastoma. Our studies show the importance of considering cellular heterogeneity when identifying new treatments for medulloblastoma and other brain cancers.

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Kar, Ranjeet D., and Johann K. Eberhart. "Predicting Modifiers of Genotype-Phenotype Correlations in Craniofacial Development." International Journal of Molecular Sciences 24, no. 2 (January 8, 2023): 1222. http://dx.doi.org/10.3390/ijms24021222.

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Most human birth defects are phenotypically variable even when they share a common genetic basis. Our understanding of the mechanisms of this variation is limited, but they are thought to be due to complex gene-environment interactions. Loss of the transcription factor Gata3 associates with the highly variable human birth defects HDR syndrome and microsomia, and can lead to disruption of the neural crest-derived facial skeleton. We have demonstrated that zebrafish gata3 mutants model the variability seen in humans, with genetic background and candidate pathways modifying the resulting phenotype. In this study, we sought to use an unbiased bioinformatic approach to identify environmental modifiers of gata3 mutant craniofacial phenotypes. The LINCs L1000 dataset identifies chemicals that generate differential gene expression that either positively or negatively correlates with an input gene list. These chemicals are predicted to worsen or lessen the mutant phenotype, respectively. We performed RNA-seq on neural crest cells isolated from zebrafish across control, Gata3 loss-of-function, and Gata3 rescue groups. Differential expression analyses revealed 551 potential targets of gata3. We queried the LINCs database with the 100 most upregulated and 100 most downregulated genes. We tested the top eight available chemicals predicted to worsen the mutant phenotype and the top eight predicted to lessen the phenotype. Of these, we found that vinblastine, a microtubule inhibitor, and clofibric acid, a PPAR-alpha agonist, did indeed worsen the gata3 phenotype. The Topoisomerase II and RNA-pol II inhibitors daunorubicin and triptolide, respectively, lessened the phenotype. GO analysis identified Wnt signaling and RNA polymerase function as being enriched in our RNA-seq data, consistent with the mechanism of action of some of the chemicals. Our study illustrates multiple potential pathways for Gata3 function, and demonstrates a systematic, unbiased process to identify modifiers of genotype-phenotype correlations.

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He, Bing, and Lana Garmire. "Prediction of repurposed drugs for treating lung injury in COVID-19." F1000Research 9 (June 15, 2020): 609. http://dx.doi.org/10.12688/f1000research.23996.1.

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Background: Coronavirus disease (COVID-19) is an infectious disease discovered in 2019 and currently in outbreak across the world. Lung injury with severe respiratory failure is the leading cause of death in COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there still lacks efficient treatment for COVID-19 induced lung injury and acute respiratory failure. Methods: Inhibition of angiotensin-converting enzyme 2 (ACE2) caused by the spike protein of SARS-CoV-2 is the most plausible mechanism of lung injury in COVID-19. We performed drug repositioning analysis to identify drug candidates that reverse gene expression pattern in L1000 lung cell line HCC515 treated with ACE2 inhibitor. We confirmed these drug candidates by similar bioinformatics analysis using lung tissues from patients deceased from COVID-19. We further investigated deregulated genes and pathways related to lung injury, as well as the gene-pathway-drug candidate relationships. Results: We propose two candidate drugs, COL-3 (a chemically modified tetracycline) and CGP-60474 (a cyclin-dependent kinase inhibitor), for treating lung injuries in COVID-19. Further bioinformatics analysis shows that 12 significantly enriched pathways (P-value <0.05) overlap between HCC515 cells treated with ACE2 inhibitor and human COVID-19 patient lung tissues. These include signaling pathways known to be associated with lung injury such as TNF signaling, MAPK signaling and chemokine signaling pathways. All 12 pathways are targeted in COL-3 treated HCC515 cells, in which genes such as RHOA, RAC2, FAS, CDC42 have reduced expression. CGP-60474 shares 11 of 12 pathways with COL-3 and common target genes such as RHOA. It also uniquely targets other genes related to lung injury, such as CALR and MMP14. Conclusions: This study shows that ACE2 inhibition is likely part of the mechanisms leading to lung injury in COVID-19, and that compounds such as COL-3 and CGP-60474 have potential as repurposed drugs for its treatment.

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He, Bing, and Lana Garmire. "Prediction of repurposed drugs for treating lung injury in COVID-19." F1000Research 9 (August 26, 2020): 609. http://dx.doi.org/10.12688/f1000research.23996.2.

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Background: Coronavirus disease (COVID-19) is an infectious disease discovered in 2019 and currently in outbreak across the world. Lung injury with severe respiratory failure is the leading cause of death in COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there still lacks efficient treatment for COVID-19 induced lung injury and acute respiratory failure. Methods: Inhibition of angiotensin-converting enzyme 2 (ACE2) caused by the spike protein of SARS-CoV-2 is the most plausible mechanism of lung injury in COVID-19. We performed drug repositioning analysis to identify drug candidates that reverse gene expression pattern in L1000 lung cell line HCC515 treated with ACE2 inhibitor. We confirmed these drug candidates by similar bioinformatics analysis using lung tissues from patients deceased from COVID-19. We further investigated deregulated genes and pathways related to lung injury, as well as the gene-pathway-drug candidate relationships. Results: We propose two candidate drugs, COL-3 (a chemically modified tetracycline) and CGP-60474 (a cyclin-dependent kinase inhibitor), for treating lung injuries in COVID-19. Further bioinformatics analysis shows that 12 significantly enriched pathways (P-value <0.05) overlap between HCC515 cells treated with ACE2 inhibitor and human COVID-19 patient lung tissues. These include signaling pathways known to be associated with lung injury such as TNF signaling, MAPK signaling and chemokine signaling pathways. All 12 pathways are targeted in COL-3 treated HCC515 cells, in which genes such as RHOA, RAC2, FAS, CDC42 have reduced expression. CGP-60474 shares 11 of 12 pathways with COL-3 and common target genes such as RHOA. It also uniquely targets other genes related to lung injury, such as CALR and MMP14. Conclusions: This study shows that ACE2 inhibition is likely part of the mechanisms leading to lung injury in COVID-19, and that compounds such as COL-3 and CGP-60474 have potential as repurposed drugs for its treatment.

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Suter, Robert, Vasileios Stathias, Anna Jermakowicz, Alexa Semonche, Michael Ivan, Ricardo Komotar, Stephan Schürer, and Nagi Ayad. "COMP-16. COMPREHENSIVE TRANSCRIPTOMIC ANALYSIS OF SINGLE CELLS FROM RECURRENT AND PRIMARY GLIOBLASTOMA TO PREDICT CELL-TYPE SPECIFIC THERAPEUTICS." Neuro-Oncology 21, Supplement_6 (November 2019): vi64. http://dx.doi.org/10.1093/neuonc/noz175.259.

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Abstract Glioblastoma (GBM) remains the most common adult brain tumor, with poor survival expectations, and no new therapeutic modalities approved in the last decade. Our laboratories have recently demonstrated that the integration of a transcriptional disease signature obtained from The Cancer Genome Atlas’ GBM dataset with transcriptional cell drug-response signatures in the LINCS L1000 dataset yields possible combinatorial therapeutics. Considering the extreme intra-tumor heterogeneity associated with the disease, we hypothesize that the utilization of single-cell RNA-sequencing (scRNA-seq) of patient tumors will further strengthen our predictive model by providing insight on the unique transcriptomes of the cellular niches present within these tumors, and into the transcriptional dynamics of these same cellular niches. By sequencing single-cell transcriptomes from recurrent GBM tumors resected from patients at the University of Miami, and integrating our datasets with previously published scRNA-seq data from primary GBM tumors, we are able to gain additional insight into the differences between these clinical distinctions. We have analyzed the differential expression of kinases both across and within distinct cell populations of primary and recurrent GBM tumors. This transcriptional map of kinase expression represents the heterogeneity of potential targets within individual tumors and between recurrent and primary GBM. Additionally, by generating disease signatures unique to each cellular population, and integrating these with transcriptional drug-response signatures from LINCS, we are able to predict compounds to target specific cell populations within GMB tumors. Additional computational techniques such as RNA velocity analysis and cell cycle scoring elucidate temporal insights to further prioritize these cell-type specific therapeutics, and reveal the intra-cellular dynamics present within these tumors. Collectively, our studies suggest that we have developed a novel omics pipeline based on the single cell RNA-sequencing of individual GBM cells that addresses intra-tumor heterogeneity, and may lead to novel therapeutic combinations for the treatment of this incurable disease.

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50

Kakkassery, Vinodh, Timo Gemoll, Miriam M. Kraemer, Thorben Sauer, Aysegül Tura, Mahdy Ranjbar, Salvatore Grisanti, Stephanie C. Joachim, Stefan Mergler, and Jacqueline Reinhard. "Protein Profiling of WERI-RB1 and Etoposide-Resistant WERI-ETOR Reveals New Insights into Topoisomerase Inhibitor Resistance in Retinoblastoma." International Journal of Molecular Sciences 23, no. 7 (April 6, 2022): 4058. http://dx.doi.org/10.3390/ijms23074058.

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Chemotherapy resistance is one of the reasons for eye loss in patients with retinoblastoma (RB). RB chemotherapy resistance has been studied in different cell culture models, such as WERI-RB1. In addition, chemotherapy-resistant RB subclones, such as the etoposide-resistant WERI-ETOR cell line have been established to improve the understanding of chemotherapy resistance in RB. The objective of this study was to characterize cell line models of an etoposide-sensitive WERI-RB1 and its etoposide-resistant subclone, WERI-ETOR, by proteomic analysis. Subsequently, quantitative proteomics data served for correlation analysis with known drug perturbation profiles. Methodically, WERI-RB1 and WERI-ETOR were cultured, and prepared for quantitative mass spectrometry (MS). This was carried out in a data-independent acquisition (DIA) mode. The raw SWATH (sequential window acquisition of all theoretical mass spectra) files were processed using neural networks in a library-free mode along with machine-learning algorithms. Pathway-enrichment analysis was performed using the REACTOME-pathway resource, and correlated to the molecular signature database (MSigDB) hallmark gene set collections for functional annotation. Furthermore, a drug-connectivity analysis using the L1000 database was carried out to associate the mechanism of action (MOA) for different anticancer reagents to WERI-RB1/WERI-ETOR signatures. A total of 4756 proteins were identified across all samples, showing a distinct clustering between the groups. Of these proteins, 64 were significantly altered (q < 0.05 & log2FC |>2|, 22 higher in WERI-ETOR). Pathway analysis revealed the “retinoid metabolism and transport” pathway as an enriched metabolic pathway in WERI-ETOR cells, while the “sphingolipid de novo biosynthesis” pathway was identified in the WERI-RB1 cell line. In addition, this study revealed similar protein signatures of topoisomerase inhibitors in WERI-ETOR cells as well as ATPase inhibitors, acetylcholine receptor antagonists, and vascular endothelial growth factor receptor (VEGFR) inhibitors in the WERI-RB1 cell line. In this study, WERI-RB1 and WERI-ETOR were analyzed as a cell line model for chemotherapy resistance in RB using data-independent MS. Analysis of the global proteome identified activation of “sphingolipid de novo biosynthesis” in WERI-RB1, and revealed future potential treatment options for etoposide resistance in RB.

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Journal articles on the topic 'L1000'

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