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Institution

Karolinska Institutet

EducationStockholm, Sweden
About: Karolinska Institutet is a education organization based out in Stockholm, Sweden. It is known for research contribution in the topics: Population & Cancer. The organization has 46212 authors who have published 121142 publications receiving 6008130 citations.


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01 Jan 1955

793 citations

Journal ArticleDOI
TL;DR: It is reported that genome editing by CRISPR–Cas9 induces a p53-mediated DNA damage response and cell cycle arrest in immortalized human retinal pigment epithelial cells, leading to a selection against cells with a functional p53 pathway, suggesting that p53 inhibition may improve the efficiency of genome editing of untransformed cells.
Abstract: Here, we report that genome editing by CRISPR–Cas9 induces a p53-mediated DNA damage response and cell cycle arrest in immortalized human retinal pigment epithelial cells, leading to a selection against cells with a functional p53 pathway. Inhibition of p53 prevents the damage response and increases the rate of homologous recombination from a donor template. These results suggest that p53 inhibition may improve the efficiency of genome editing of untransformed cells and that p53 function should be monitored when developing cell-based therapies utilizing CRISPR–Cas9. CRISPR–Cas9-induced DNA damage triggers p53 to limit the efficiency of gene editing in immortalized human retinal pigment epithelial cells.

793 citations

Journal ArticleDOI
TL;DR: Up-regulation of this human cathelicidin gene in inflammatory skin disorders is demonstrated, whereas in normal skin no induction was found, and a protective role for LL-37 is proposed, when the integrity of the skin barrier is damaged, participating in the first line of defense, and preventing local infection and systemic invasion of microbes.

792 citations

Journal ArticleDOI
TL;DR: Extracellular vesicles are emerging as potent genetic information transfer agents underpinning a range of biological processes and with therapeutic potential.
Abstract: Exosomes and microvesicles are extracellular nanovesicles released by most but not all cells. They are specifically equipped to mediate intercellular communication via the transfer of genetic information, including the transfer of both coding and non-coding RNAs, to recipient cells. As a result, both exosomes and microvesicles play a fundamental biological role in the regulation of normal physiological as well as aberrant pathological processes, via altered gene regulatory networks and/or via epigenetic programming. For example, microvesicle-mediated genetic transfer can regulate the maintenance of stem cell plasticity and induce beneficial cell phenotype modulation. Alternatively, such vesicles play a role in tumor pathogenesis and the spread of neurodegenerative diseases via the transfer of specific microRNAs and pathogenic proteins. Given this natural property for genetic information transfer, the possibility of exploiting these vesicles for therapeutic purposes is now being investigated. Stem cell-derived microvesicles appear to be naturally equipped to mediate tissue regeneration under certain conditions, while recent evidence suggests that exosomes might be harnessed for the targeted delivery of human genetic therapies via the introduction of exogenous genetic cargoes such as siRNA. Thus, extracellular vesicles are emerging as potent genetic information transfer agents underpinning a range of biological processes and with therapeutic potential.

792 citations

Journal ArticleDOI
TL;DR: A subset of 64 genes was found to give an optimal separation of patients with good and poor outcomes, and the signature associated with prognosis and impact of adjuvant therapies was identified.
Abstract: Adjuvant breast cancer therapy significantly improves survival, but overtreatment and undertreatment are major problems. Breast cancer expression profiling has so far mainly been used to identify women with a poor prognosis as candidates for adjuvant therapy but without demonstrated value for therapy prediction. We obtained the gene expression profiles of 159 population-derived breast cancer patients, and used hierarchical clustering to identify the signature associated with prognosis and impact of adjuvant therapies, defined as distant metastasis or death within 5 years. Independent datasets of 76 treated population-derived Swedish patients, 135 untreated population-derived Swedish patients and 78 Dutch patients were used for validation. The inclusion and exclusion criteria for the studies of population-derived Swedish patients were defined. Among the 159 patients, a subset of 64 genes was found to give an optimal separation of patients with good and poor outcomes. Hierarchical clustering revealed three subgroups: patients who did well with therapy, patients who did well without therapy, and patients that failed to benefit from given therapy. The expression profile gave significantly better prognostication (odds ratio, 4.19; P = 0.007) (breast cancer end-points odds ratio, 10.64) compared with the Elston–Ellis histological grading (odds ratio of grade 2 vs 1 and grade 3 vs 1, 2.81 and 3.32 respectively; P = 0.24 and 0.16), tumor stage (odds ratio of stage 2 vs 1 and stage 3 vs 1, 1.11 and 1.28; P = 0.83 and 0.68) and age (odds ratio, 0.11; P = 0.55). The risk groups were consistent and validated in the independent Swedish and Dutch data sets used with 211 and 78 patients, respectively. We have identified discriminatory gene expression signatures working both on untreated and systematically treated primary breast cancer patients with the potential to spare them from adjuvant therapy.

792 citations


Authors

Showing all 46522 results

NameH-indexPapersCitations
Meir J. Stampfer2771414283776
Albert Hofman2672530321405
Guido Kroemer2361404246571
Eric B. Rimm196988147119
Scott M. Grundy187841231821
Jing Wang1844046202769
Tadamitsu Kishimoto1811067130860
John Hardy1771178171694
Marc G. Caron17367499802
Ramachandran S. Vasan1721100138108
Adrian L. Harris1701084120365
Douglas F. Easton165844113809
Zulfiqar A Bhutta1651231169329
Judah Folkman165499148611
Ralph A. DeFronzo160759132993
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023101
2022500
20217,763
20206,922
20196,057
20185,548