Auswahl der wissenschaftlichen Literatur zum Thema „Elling O. Eide library“

A Review of: Mei, X. Y., Aas, E., & Eide, O. (2020). Applying the servicescape model to understand student experiences of a Norwegian academic library. Library & Information Science Research, 42(4), Article 101051. https://doi.org/10.1016/j.lisr.2020.101051 Abstract Objective – To understand how the physical environment of an academic library influences user behaviour. Design – Qualitative explorative. Setting – An academic library at a large university in Norway. Subjects – Twelve bachelor’s and master’s students at a business school. Methods – The researchers used a two-step approach, with the servicescape model from the marketing discipline serving as a theoretical framework. Subjects completed several tasks involving drawing and elaborating on their usage of the library space, utilizing a bird’s-eye floor plan. This was followed by semi-structured interviews to explore how subjects use and experience the library facilities. Main Results – Students found it important to be physically comfortable and to have enough room for the items they need while studying. The library in this study was seen more as a place for studying than for social interactions, and while some subjects reported being motivated by seeing students around them studying, others said they are distracted by having other students in their sightline. The time of the semester appeared to influence user experience and satisfaction with the library space, with spaces conducive to group work desired at some points in the semester and with single seating preferred when individual exams are taking place. Conclusion – The library’s physical environment triggers cognitive and emotional responses in users. These responses influence how frequently they visit the library and how well they are able to concentrate while there. Because academic library spaces have an impact on student learning, it is important to design libraries with user comfort in mind. Libraries should accommodate the different ways students work throughout the semester by providing flexible study space configurations.

Abstract CRISPR-mediated genome editing is a powerful approach to understanding disease biology, including identifying genes essential for cancer cell proliferation, immune evasion and survival. We routinely use the technology to identify novel therapeutic targets for cancer using both in vitro and in vivo tumor models, focusing on targets that are synthetic lethal with tumor suppressor gene loss. Large-scale CRISPR screens are often conducted using in vitro cell culture systems as in vivo ‘drop-out’ screens face several bottlenecks resulting in poor sgRNA library representation and low signal to noise ratio. These inherent limitations of in vivo screens occur because only a small fraction of the injected cells contribute to xenograft tumor formation, and because of the uneven clonal expansion due to heterogenous growth conditions in the tumor microenvironment. Hence, many in vivo screens are underpowered for statistical analysis, resulting in a high rate of ‘false negatives’ or ‘false positives’ unless a large number of animals are used or the size of the sgRNA library is greatly reduced to overcome sampling noise. Here, we validate a novel in vivo screening technology, CRISPR-StAR (Stochastic Activation by Recombination), that overcomes these challenges by (i) activating the sgRNA library in established tumors and (ii) generating internally matched-pair controls for each sgRNA using molecular barcodes to capture the history of each clone within the tumor. Using this clonal information, we developed a robust computational pipeline that extracts meaningful target sgRNA-level data from individual tumors, despite the random under-representation of the larger library. Statistical (down-sampling) analysis revealed that CRISPR-StAR has a resolution of 1,000 sgRNAs per tumor which reduces the number of animals required by 7-fold to traditional approaches. Consequently, we have performed several druggable genome screens (~30,000 sgRNA) using just 30-40 individual tumors and identified a catalog of tumor suppressor genes that, when lost, strongly promote tumor growth in vivo without affecting cell proliferation in vitro. This group of genes is enriched with epigenetic modifiers, in particular multiple members of the COMPASS family and the SWI/SNF chromatin remodeling complexes. These results validate CRISPR-StAR as a powerful in vivo functional genomics platform for high throughput target discovery screens. Citation Format: Silvia Fenoglio, James Tepper, Lauren Grove, Esther CH Uijttewaal, Alborz Bejnood, Yi Yu, Hsin-Jung Wu, Annabel Devault, Shangtao Liu, Binzhang Shen, Samuel R Meier, Ashley H Choi, Tenzing Khendu, Hannah Stowe, Minjie Zhang, Brian B Haines, Alan Huang, Jannik N Andersen, Xuewen Pan, Ulrich Elling, Teng Teng. Inducible activation of sgRNA libraries in tumor xenografts empowers large-scale in vivo target discovery screens [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C048.

Abstract Background Many CML patients treated with tyrosine kinase inhibitors (TKIs) eventually develop resistance as a result of ABL1 kinase domain (KD) mutations, and sequential treatment with different TKIs may select for multiple BCR-ABL1 mutations. Whether multiple mutations arise in distinct clones (in trans, or polyclonal mutations) or instead are present within the same BCR-ABL1 molecule (in cis, or compound mutations), has been shown to have important implications with respect to TKI sensitivities (Eide, C.A. et al., Blood 2011). Distinguishing between polyclonal and compound mutations, or mutation phasing, for the ABL1 KD has not been clinically practical with standard mutation detection methods. Here we have developed a highly sensitive next-generation sequencing (NGS) assay on the Ion Torrent PGM along with a proprietary data analysis pipeline that together enable deep sequencing of the BCR-ABL1 KD and neighboring domains with a 1% limit of detection and quantitative reporting of mutation phasing. Methods RT and long range PCR was performed to amplify BCR-ABL1 e1a2/3, e13a2/3, and e14a2/3 fusion transcripts and the PCR products were enzymatically randomly fragmented and ligated with Ion Torrent sequencing adaptors. Size-selected libraries were quantified, pooled, amplified with the OneTouch system and sequenced with the Ion Torrent PGM using 400 bp sequencing chemistry. Sequencing data were analyzed with Torrent Suite 3.4.2 with variant frequency cutoff adjusted to 1%. Variants were further annotated with a proprietary analysis pipeline and variant report was produced after manually reviewing variants by Integrative Genomics Viewer. If more than one non-synonymous variant was reported in a sample, a proprietary phasing analysis pipeline was applied to report the mutation spectrum of all of the combinations of multiple mutations in the sample. Results To validate the accuracy of the sequencing method which employs 400 bp sequencing chemistry, we compared this assay with our previously validated BCR-ABL1 NGS assay based on Ion Torrent 200 bp sequencing chemistry for a set of clinical specimens from CML patients previously treated with TKI. Results were highly concordant and similarly sensitive, with 11/11 variants (frequencies ranging from 2% to 100%) identified with comparable frequencies by both methods. To evaluate the specificity of the phasing analysis, an artificial sample was created by mixing two samples (b2_10 and b2_6) with 6 distinct variants present at ratio of 1:19. Because variants are unique in each sample, any compound mutation composed of b2_10 variant and b2_6 variant identified would be false positive. The false positive error rate (percentage of b2_6 variant as compound mutation with b2_10 variant and vice versa) ranged from 0 – 0.6%, which was consistent with sequencing error rate. We conservatively define a compound mutation as true if it is present in at least 5% of any one of the component variants in the compound mutation. Mutation detection and phasing analysis were reproducible on different chips (314 v2 and 318 v2) and different library preps from the same long range PCR product of BCR-ABL1. Table 1 shows the mutation spectrum from sample b2_6. Of the four variants detected, L248V and G250E were mutually exclusive (in trans), while T315I and M351T were present as compound mutations with each other and, separately, with either L248V or G250E. Notably, >86% of the molecules harbored single mutations, and no compound mutations containing more than 2 variants were observed. Conclusions We have developed and validated a sensitive NGS assay that enables deep sequencing of the BCR-ABL1 KD and neighboring domains along with quantitative mutational phasing. This method has been applied in evaluating >250 clinical specimens for a clinical trial of a third-generation TKI(results reported separately). The ability to easily determine the mutation phasing of a CML patients’ mutation profile using this assay will allow for investigations into compound mutation-based resistance mechanisms and may be used to better guide treatment decisions. Disclosures: Li: MolecularMD: Employment. Yan:MolecularMD: Employment. Darwanto:MolecularMD: Employment. Fang:MolecularMD: Employment. Liu:MolecularMD: Employment. Drafahl:MolecularMD: Employment. Toplin:MolecularMD: Employment. Spittle:MolecularMD: Employment. Galderisi:MolecularMD: Employment.