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Genome-Wide shRNA Screen for DNA Damage Response Regulators in Human Hematopoietic Stem and Progenitor Cells

2011· article· en· W2977813434 on OpenAlexaff
Olga I. Gan, Michael Milyavsky, Mark F. van Delft, Alla Buzina, Irina Kalatskaya, Troy Ketela, Lincoln Stein, Jason Moffat, John E. Dick

Bibliographic record

VenueBlood · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsUniversity Health NetworkUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsDNA damageHaematopoiesisGene knockdownBiologyProgenitor cellStem cellBone marrowGenome instabilitySmall hairpin RNADNA repairCancer researchMolecular biologyApoptosisCell biologyGeneDNAGeneticsImmunology

Abstract

fetched live from OpenAlex

Abstract Abstract 1289 Hematopoietic stem cells due to their life-long function should protect genome integrity to avoid accumulation of genetic aberrations leading to malignant transformation or bone marrow failure. We reported recently that human HSC of cord blood origin are exquisitely sensitive to DNA damage-induced apoptosis. Thus, following 3Gy of ionizing radiation HSC show evidence of persistent DNA damage response and greater p53-dependent apoptosis in comparison with commited myeloid progenitors. To elucidate the molecular basis of these observations we carried out a genome-wide loss-of-function genetic screen using a library of 80 000 shRNA vectors targeting more than 16 000 human genes. A screen was performed on immortalized (by TLS-ERG infection) cord blood cells (TEX), which radio-sensitivity is similar to early hematopoietic cells. To find out the radio-protective hits we exposed infected cells to four rounds of 4Gy irradiation in three independent experiments. TEX cells exhibited steady increase of their proliferative potential after each irradiation exposure. Cells were gathered after each irradiation round and their DNA was subjected to sequencing to determine protective hits. Upon the analysis we recognized known regulators of DNA damage response (e.g. p53) and identified many genes that previously were not connected to genotoxic stress response. Thus, the knockdown of these genes was at least as effective as p53 knockdown in protection of TEX cells against gamma-irradiation. The validation of the chosen hits on TEX cells showed that about half of them indeed mediate the protection against irradiation. Further validation included real-time PCR of infected cells to ensure the absence of off-target effect along with Western blot analysis of affected protein. We followed up the investigation of chosen hits on primary human lineage-negative cord blood cells and observed that only few candidates were mediating the same effect on HSC/progenitors cells as on TEX cells. To further validate the effect of the most prominently effective hits we employed several shRNA vectors for the same target gene. Following this step of validation, we choose to investigate the knockdown of CHEK2, the gene with reported role in DNA damage response in several murine tissues. Our preliminary studies in long-term cytokine-supplemented cultures demonstrated that CHEK2 is involved in DNA damage response of human HSC and progenitors. The further investigation of the effect of CHEK2 knockdown on repopulating human HSC is currently underway. An integrated analysis of our observations revealed several putative CHEK2-centered molecular networks, which connect DNA damage response to HSC function. Disclosures: No relevant conflicts of interest to declare.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.242
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2011
Admission routes1
Has abstractyes

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