35 research outputs found
Multi-miRNA panel of tumor-derived extracellular vesicles as promising diagnostic biomarkers of early-stage breast cancer
Extracellular vesicles (EV) have been emerging as potential biomarkers for disease monitoring. In particular, tumor-derived EV (TDE) are known to carry oncogenic miRNA, so they can be used for diagnosis of early cancer by analyzing the expression levels of EV-miRNA circulating in the blood. Here, using our novel microfluidic device, we rapidly and selectively isolate cancerous EV expressing breast cancer-derived surface markers CD49f and EpCAM within 2 minutes. Based on seven candidates of miRNA nominated from The Cancer Genome Atlas (TCGA) database, the expression levels of miRNA in TDE were validated in a total of 82 individuals, including 62 breast cancer patients and 20 healthy controls. Among seven candidates, four miRNAs (miR-9, miR-16, miR-21, and miR-429) from the EV were highly elevated in early-stage breast cancer patients compared with healthy donors. The combination of significant miRNAs from specific EV has high sensitivities of 0.90, 0.86, 0.88, and 0.84 of the area under the receiver operating characteristic curve (AUC) in each subtype (luminal A, luminal B, HER-2, and triple-negative) of early-stage breast cancer. Our results suggest that the combination of four miRNA signatures of specific EV could serve as a sensitive and specific biomarker and enable early diagnosis of breast cancer using liquid biopsy.ope
Tumor-Specific miRNA Signatures in Combination with CA19-9 for Liquid Biopsy-Based Detection of PDAC
Pancreatic ductal adenocarcinoma (PDAC) is considered one of the most aggressive malignancies and has high mortality and poor survival rates. Therefore, there is an urgent need to discover non-invasive biomarkers for early detection before PDAC reaches the incurable stage. We hypothesized that liquid biopsy of PDAC-derived extracellular vesicles (PDEs) containing abundant microRNAs (miRNAs) could be used for early diagnosis of PDAC because they can be selectively enriched and because they are biologically stable. We isolated PDEs by immunocapture using magnetic beads, and we identified 13 miRNA candidates in 20 pancreatic cancer patients and 20 normal controls. We found that expression of five miRNAs, including miR-10b, miR-16, miR-155, miR-429, and miR-1290, was markedly higher in PDEs. Furthermore, the miRNA signatures along with serum carbohydrate antigen 19-9 (CA19-9) were optimized by logistic regression, and the miRNA signature and CA19-9 combination markers (CMs) were effective at differentiating PDAC patients from normal controls. As a result, the CMs represented a high sensitivity (AUC, 0.964; sensitivity, 100%; specificity, 80%) and a high specificity (AUC, 0.962; sensitivity, 85.71%; specificity, 100%). These findings suggest that five miRNAs expressed in PDEs and CA19-9 are valuable biomarkers for screening and diagnosis of pancreatic cancer by liquid biopsy.ope
μ€μ¦ μΈμμμ λ³μ μ¬λ§κ³Ό μ₯μ μ λν νμ₯ μ μ°μμ¦κ³Ό μ νμμ κ΅νΈ μμ©
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : μκ³Όλν μμμκ³Όνκ³Ό, 2019. 2. μ μλ.μ€μ¦ μΈμμ λνλ―Όκ΅μμ 44μΈ μ΄ν νμμμ μ¬λ§ μμΈ 1μλ₯Ό μ°¨μ§νκ³ μλ€. 맀λ
30,000λͺ
μ λμ νμκ° μΈμμΌλ‘ μΈν΄μ μ¬λ§νκ³ μμΌλ©°, λ§μ νμ μ₯μ λ₯Ό λ¨κΈ°κ² λλ€. μ€μ¦ μΈμ νμμμ μ¬λ§κ³Ό νμ μ₯μ λ₯Ό μ€μ΄κ³ μ΄λ₯Ό μ‘°κΈ°μ νκ°νκΈ° μν΄μ λ³μ μ λ¨κ³μΈ μ¬κ±΄ νμ₯μμ ꡬκΈλμ μν΄ μΈ‘μ λ° κΈ°λ‘λλ λ³μλ€μ λν λΆμμ΄ νμν¨. μ΄μ€ νμ₯μμ μΈ‘μ λ μ μ°μμ¦ μ¬λΆκ° μ¬λ§λ₯ λ° νμ μ₯μ μ¨μ μν₯μ λ―ΈμΉλμ§μ λν λΆμμ μ§ννμμ. λν νμ₯μμμ μ νμ μ¬λΆμ λ°λΌμ μ΄λ¬ν μ μ°μμ¦μ μν₯μ΄ μ¦κ°νλμ§ ν¨κ» λΆμνμμ.
2012λ
, 2013λ
λμ ꡬκΈλλ₯Ό ν΅ν΄ μ κ΅ 700μ¬κ° λ³μμ λ΄μν μ±μΈ μ€μ¦ μΈμνμ μ€ 17,406λͺ
μ ꡬκΈκΈ°λ‘ λ° μ무기λ‘μ μμ§νμ¬ λΆμμ μ§ννμμ. ꡬκΈλμ μν΄ μΈ‘μ λ μ μ°μμ¦ μ¬λΆ(μ°μν¬νλ 94% λ―Έλ§)λ₯Ό λ
립λ³μλ‘ μ€μ νμκ³ , μ€μ¦ μΈμνμμ λ³μ λ΄ μ¬λ§λ₯ λ° νμ μ₯μ μ¨μ κ²°κ³Ό λ³μλ‘μ μ€μ νμλ€. νμ μ₯μ λ°μ μ¬λΆλ Glasgow Outcome scaleμ 2μ μ΄μμ κ°μλ‘ μ μνμλ€. κ΅λμμΈ λ³΄μ μ μν΄ λ€λ³λ νκ·λΆμ λ° interaction λͺ¨λΈμ μ¬μ©νμ¬ μ μ°μμ¦ λ° μ νμ μ¬λΆμ μ€μ¦ μΈμνμμ μ¬λ§λ₯ , νμ μ₯μ μ¨μ μ€μ¦λΉμ 95% μ 뒰ꡬκ°μ κ³μ°νμλ€.
2012-2013λ
μ€μ¦μΈμνμ 17,406λͺ
μ΄ λΆμλμμ. μ΄ μ€ 14.9%μ ν΄λΉνλ 2,598 λͺ
μ΄ μ¬λ§νμκ³ 21.5%μ ν΄λΉνλ 3,292λͺ
μμ νμ μ₯μ κ° λ°μνμλ€. λν μ€μ¦μΈμνμ μ€ 16.7%μ ν΄λΉνλ 2,922λͺ
μ νμμμ μ¬κ³ νμ₯μμ μΈ‘μ μ μ°μν¬νλ 94% λ―Έλ§μ μ μ°μμ¦μ΄ νμΈλμμ.
μ€μ¦ μΈμνμ μ€ μ μ°μμ¦μ΄ νμΈλ νμμ μ¬λ§λ₯ (35.7%)μ΄ κ·Έλ μ§ μμ νμμ μ¬λ§λ₯ (10.7%) λ³΄λ€ λκ² νμΈλμκ³ νμ μ₯μ λ°μ λΉμ¨λ κ°κ° 51.2%μ 15.9%λ‘ μ μ°μμ¦μ΄ νμΈλ νμμμ λ λκ² νμΈλμλ€. μ€μ¦μΈμνμμμ μ μ°μμ¦ μ¬λΆμ νμ μ¬λ§ μνμ λν μ€μ¦λΉλ 2.15(1.92-2.40) μ΄μμ. λν μ μ°μμ¦ μ¬λΆμ νμ μ₯μ μνμ λν μ€μ¦λΉλ 1.97(1.75-2.21) λ‘ νμΈλμλ€.
μ€μ¦μΈμνμμμ μμΆκΈ°νμ 90mmHg λ―Έλ§μ μ νμμ΄ λλ°λμμ κ²½μ° μ μ°μμ¦ μ¬λΆμ νμ μ¬λ§ μνμ λν μ€μ¦λΉλ 2.66(2.32-3.04)μ΄μλ€. λν μ μ°μμ¦ μ¬λΆμ νμ μ₯μ μνμ λν μ€μ¦λΉλ 2.17(1.87-2.53)λ‘ νμΈλμλ€.
κ²°λ‘ μ μΌλ‘, μ€μ¦ μΈμ νμμμ μ μ°μμ¦ μ¬λΆκ° νμμ μ¬λ§ μν λλ νμ μ₯μ μνμ λμ΄λ©°, μ νμμ΄ λλ°λ κ²½μ° κ·Έ μνλκ° λμ± μ¦κ°νλ―λ‘, ꡬκΈλ¨κ³μμ μ°μν¬νλ λ° νμ λ±μ μ체μ§νλ₯Ό νμΈνμ¬ νμμ μ€μ¦λλ₯Ό νκ°νκ³ μ ν©ν μΉλ£λ₯Ό λ°μ μ μλλ‘ νμ¬μΌ ν¨.Background
It is unclear whether effect size of the hypoxia is different on in-hospital mortality and disability according to hypotension status in the field.
Methods
Adult severe trauma (ST) patients during 2012-2013 who were treated by emergency medical services (EMS) and had abnormal revised trauma scores in the field or who had positive trauma triage criteria were analyzed. Exposure was hypoxia (<94%) measured by EMS. End points were hospital mortality and disability defined as a Glasgow Outcome Scale that decreased by 2 points or more. Multivariable logistic regression with interaction model between hypoxia and hypotension was used for outcomes to calculate the adjusted odds ratios (AOR) with 95% confidence intervals (95%CIs) after adjusting for potential confounders.
Results
A total of 17,406 EMS-ST patients were analyzed. Of those, 2,598 (14.9%) died, and 3,292 (21.5%) were considered disabled at discharge. The total hypoxia group showed higher mortality and disability indices (35.7% and 51.2%) than the non-hypoxia group (10.7% and 15.9%), (each p-value <0.0001). The AOR of hypoxia was 2.15 (1.92-2.40) for mortality and was 1.97 (1.75-2.21) for disability. In the interaction model, AORs for mortality by hypoxia in the hypotensive and non-hypotensive groups were 2.66 (2.32-3.04) and 1.74 (1.61-1.87), respectively (P<0.0001 for interaction). The AORs for disability in the hypotensive and non-hypotensive groups were 2.17 (1.87-2.53) and 1.55 (1.42-1.69), respectively (P<0.0001 for interaction).
Conclusions
The effect of hypoxia was much greater in the hypotensive group than in the non-hypotensive group both in terms of mortality and disability.1. Background β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 1
2. Methods β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 4
2-1) Study setting β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 4
2-2) Study design and data source β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 6
2-3) Study population β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 8
2-4) Data variables β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 8
2-5) Outcome measure β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 10
2-6) Statistical analysis β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 11
3. Results β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 13
3-1) Demographic findings β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 13
3-2) Main analysis β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 19
3-3) Interaction analysis β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 19
3-4) Sensitivity analysis for different study population β¦20
4. Discussion β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 27
5. Limitation β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 33
6. Conclusion β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 35
7. Disclosure β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 36
8. Acknowledgment β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 36
9. Reference β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 37
10. κ΅λ¬Έ μ΄λ‘ β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 42Maste
λΉκ· μΌ κ²©μκ³μμμ νμνμ₯ μ ν체μ κΈ°λ²μ κ°μ μ κ΄ν μ°κ΅¬
The focus of the present study is to improve the numerical accuracy of wavenumber-extended finite volume scheme in non-uniform grid system. The basic idea of the present study is to optimize the dispersion-relation at a local cell postion. An optimization procedure is based on grid-optimized finite difference scheme in finite difference framework, and is extended to conservative approximation in finite volume framework. An optimization procedure at a local cell position obtains the numerical accuracy on wave propagation in non-uniform grid system. In addition, to eliminate excessive numerical dissipation in the continuous region, a distinguishing step is modified to predict the suitable approximation value in non-uniform grid system.
A structural change of wavenumber-extended finite volume scheme is able to enhance the numerical accuracy of caculation of aeroacoustic problems in non-uniform grid system. An adoption of modified distinguishing step is able to implement e-MLP in non-uniform grid system.
Through numerous test cases such as spherical wave propagation, shock/sine wave interaction, acoustic pulse scattering, moving vortex preservation, and shock vortex interaction problems are carried out in non-uniform grid system. The proposed scheme is proven to have better numerical accuracy than the original wavenumber-extended finite volume scheme.λ³Έ μ°κ΅¬μ λͺ©νλ κΈ°μ‘΄μ κ°λ°λ νμνμ₯ μ ν체μ κΈ°λ²μ μ νλλ₯Ό κ°μ νκΈ° μν¨μ΄λ€. λΉκ· μΌ κ²©μκ³μμ λ°μνλ μμΉμ μΈ μ€μ°¨λ₯Ό μ΅μν νκΈ° μνμ¬ λ³Έ μ°κ΅¬μμλ κ° νΉμ μ§μ μμμ μ΄μ°κ΄κ³ 보쑴μ κ·Όκ±°νμ¬ μ΅μ νλ₯Ό μννμλ€. λ³Έ μ°κ΅¬μμ μ μ©λ μ΅μ ν κΈ°λ²μ μ νμ°¨λΆλ²μ κΈ°μ΄ν 격μ μ΅μ ν μ΄μ°κ΄κ³λ³΄μ‘΄λ²μ μμ©νμ¬ μ΄λ₯Ό μ ν체μ λ²μ μ μ©νμ¬ κ° μ ν 체μ μ μ
μ κ²½κ³λ©΄κ·Όμ¬μ μ μ©νμλ€. μ΄λ¬ν μ΅μ ν κΈ°λ²μ λͺ¨λ μ
μ κ° κ²½κ³λ©΄μ λν΄μ μ΄λ£¨μ΄μ§λ©° μ΄λ₯Ό ν΅ν΄ λΉκ· μΌ κ²©μμμμ μ νλ¬Έμ μ μ νλλ₯Ό ν₯μμμΌ°λ€. μ ν μμμμμ μ νμλ‘ μΈν΄ λΆνμν μμΉμ μΈ κ°μλ₯Ό λ°°μ νκΈ° μν΄μ μ λμ νΉμ±μ κ²°μ νλ κ²μ¦ ν¨μλ₯Ό λΉκ· μΌ κ²©μμ μ ν©νλλ‘ μ‘°μ νμλ€.
κΈ°μ‘΄ νμνμ₯ μ ν 체μ κΈ°λ²μ ꡬ쑰μ μΈ λ³νλ₯Ό ν΅ν΄μ λΉκ· μΌ κ²©μκ³μμμ 곡λ ₯μμ λ¬Έμ μ λν μμΉμ μ νλλ₯Ό ν₯μμν¬ μ μμμΌλ©°, λν μ μ ν κ²μ¦ν¨μλ₯Ό μ μ©ν¨μΌλ‘ λΉκ· μΌ κ²©μκ³μμλ κ°μ λ λ€μ°¨μ μ νμλ₯Ό λμ
μ΄ κ°λ₯νμλ€.
μ΄λ¬ν μμΉμ κΈ°λ²μ κ°μ μ μ κ²μ¦νκΈ° μν΄μ ꡬ면ν λ°©μ¬, 좩격ν-μ¬μΈν μνΈμμ©, μν₯ν μ ν νμ, μλ₯ 보쑴 νμ, λ° μΆ©κ²©ν-μλ₯ μνΈμμ©μ λ¬Έμ μ κ°μ κ²μ¦λ¬Έμ λ₯Ό λΉκ· μΌ κ²©μκ³μμμ ν΄μνμλ€. μ΄λ₯Ό ν΅ν΄ κ°μ λ μμΉκΈ°λ²μ κΈ°μ‘΄ νμνμ₯ μ ν 체μ κΈ°λ²μ λΉν΄ λ³΄λ€ λμ μ νμ±μ κ°μ§μ νμΈν μ μμλ€.Docto
Predicting Subway Passenger Flows By Spatio-temporal Modeling
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ μμ°κ³Όνλν ν΅κ³νκ³Ό, 2017. 8. μμ±μ.μ§νμ² μ μ μμ±κ³Ό νΈλ¦¬ν¨μΌλ‘ μΈν΄ λ§μ μ¬λλ€μ΄ μ΄μ©νλ κ΅ν΅μλ¨μ΄λ€.
ν΅κ³μ λͺ¨ννλ₯Ό ν΅ν΄ μκ°λλ³ μ§νμ² μΉμ°¨μΈμμ μμΈ‘νκ³ μ μ΄ λ
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λ°κ³ μμΌλ©° μμΉμ 보μ μν μμ‘΄μ±μ κ³ λ €ν κ³΅κ° ν΅κ³ λ°©λ²λ‘ μ΄ λ°μ νκ³
μλ€. μμΈμ κ° μ§νμ² μμ μμΉμ 보λ₯Ό λΆμ¬νκ³ κ³΅κ°μ μμ‘΄μ±κ³Ό μκ°μ
λ°λ₯Έ μμ‘΄μ±μ ν¨κ» κ³ λ €ν μκ³΅κ° λͺ¨ννλ₯Ό ν΅ν΄ μ§νμ² μΉμ°¨μΈμμ μμΈ‘νλ €
νλ€. μ§νμ² μΉμ°¨μΈμμ μ κ·λΆν¬λ‘ κ°μ νκ³ λͺ κ°μ§ 곡λ³λκ³Ό B-μ€νλΌμΈ
곑μ μΌλ‘ νκ· κ΅¬μ‘°λ₯Ό ꡬμ±νλ€. 곡λΆμ°κ΅¬μ‘°λ μκ°μ λ°λ₯Έ μμ‘΄μ±κ³Ό 곡κ°μ
μν μμ‘΄μ±μ΄ λΆλ¦¬κ°λ₯νλ€κ³ κ°μ νμ¬ μ§μλͺ¨νμ μ μ©νλ©° μ΅μ’
λͺ¨νμ
λͺ¨μλ μ΅λμ°λ μΆμ λ²μΌλ‘ μΆμ λλ€.1 μλ‘ (Introduction) 1
1.1. κ³΅κ° λ°μ΄ν° (Spatial data) 1
1.2. μ곡κ°νλ₯ κ³Όμ (Spatio-temporal Stohastic Process) 2
1.3. μ°κ΅¬ λͺ©ν 2
2 λ°μ΄ν° μ€λͺ
(data description) 4
2.1. λ°μ΄ν° μ μ²λ¦¬ (data pre-processing) 5
2.2. λ°μ΄ν° μκ°ν (data visualizing) 6
3 μμ‘΄μ±μ νμ (Exploring dependencies) 10
3.1. 배리μ€κ·Έλ¨ (Variogram) 11
3.2. 배리μ€κ·Έλ¨ κ΅¬λ¦ (Variogram cloud) 12
3.3. κ²½νμ 배리μ€κ·Έλ¨ (Empirical variogram) 13
3.4. λͺ¨μ 배리μ€κ·Έλ¨-곡λΆμ° λͺ¨ν μ ν© (Parametric variogramcovariance model fitting) 16
4 μκ³΅κ° ν¬λ¦¬κΉ
λͺ¨ν (Spatio-Temporal Kriging Model) 20
4.1. μκ³΅κ° κ°μ°μμ κ³Όμ (Gaussian Process) 20
4.2. νκ· κ΅¬μ‘° (Mean Structure) 21
4.3. 곡λΆμ° ꡬ쑰 (Covariance Structure) 22
4.4. μ΅λμ°λμΆμ (Maximum Likelihood Estimator) 23
4.5. μΌλ° ν¬λ¦¬κΉ
(Universal Kriging) 25
5 λ°μ΄ν° λΆμ (Data Analysis) 27
5.1. μμΈ‘ κ²°κ³Ό 27
6 κ²°λ‘ (Conclusion) 33Maste
λμ₯κ· μμμ CRISPR/Cas9 μμ€ν λ° λΌμ΄λ³΄μμμ μ΄μ©ν λ€μ€ κ²λ νΈμ§ λ°©λ² κ΅¬μΆ λ° μμ©
νμλ
Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : 곡과λν νλκ³Όμ λ°μ΄μ€μμ§λμ΄λ§μ 곡, 2018. 8. κΉλ³κΈ°.Engineering cellular metabolism for improved production of valuable products requires extensive modulation of bacterial genome to explore complex genetic spaces. In order to introduce genetic modifications for rebalancing the metabolic flux, target genes to be modulated should be determined, and the expression of target genes should be optimized. However, it is difficult to select the target genes precisely because it is very labor-intensive to identify all the effects of the expression changes of the all genes involved in the metabolic pathway on the production of the target product. In addition, even if several genes were selected to be modulated, it is difficult to optimize the metabolic pathway because the combination of modulation may cause detrimental effects on the strain. Therefore, in order to overcome the drawbacks described above, it is reasonable to conduct the metabolic engineering using a combinatorial approach. A combinatorial approach is to screen strain with the best phenotype among the various mutation library expected to improve the desired phenotype. Therefore, in order to generate the mutant library, the tool to introduce mutations at multiple loci is required.
In this thesis, we established the multiple sgRNA generation strategy to enable the metabolic pathway optimization using the CRISPR/Cas system. The dCas9-Ο, which fused the transcriptional activator domain to inactivated cas9 (dCas9), can activate or repress target gene expression depending on the design of the sgRNA. Therefore, if multiple sgRNAs can be generated, the expression of several genes can be modulated simultaneously without genetic modification. For this reason, the strategy to generate multiple sgRNAs was required, and we constructed a strategy to produce several sgRNAs from one primary transcript using the self-cleavage property of Rz. By combining multiple sgRNA production strategies with the dCas9-Ο system, target genes that have a strong effect on phenotypic enhancement can be efficiently identified.
After identification of target genes to be modulated, genetic engineering should be performed to alter the expression of those genes. Modulating in the expression level of a single gene to fine tune the metabolic flux often results in a change in the overall flux. Therefore, when optimizing the expression levels of target genes to achieve the desired phenotype through the metabolic engineering, the combinatorial approach is more reasonable than the sequential approach. To do this, it is necessary to be able to construct a library of sufficient size that incorporates genetic modifications to various locations within the chromosome. However, the multiplexing methods reported in the literature have limitations in producing mutant libraries for applying the combinatorial approach because the recombination efficiency is very low and negative selection is not efficient. Therefore, we have designed a system that allows a combinatorial approach by using the CRISPR/Cas system to introduce a mutation library for one site in one cycle and accumulate a mutation library for multiple sites by repeating this cycle. Therefore, we have constructed a plasmid capable of obtaining high CFU with high editing efficiency through the CRISPR/Cas system, and confirmed that a strain in which mutations were introduced into three different target genes could be generated with high efficiency.Chapter 1. Introduction 1
1.1 Genome editing with CRISPR/Cas system 2
1.1.1 CRISPR/Cas system 2
1.1.2 CRISPRi system 8
1.2 Multiple sgRNA generation strategy 12
1.2.1 Conventional methods to generate multiple sgRNAs. 12
1.2.2 Multiple sgRNA generation by using Rz 16
1.2.3 Methods to calculate the Rz efficiency 16
1.3 Multiplex genome editing . 21
1.3.1 MAGE . 21
1.3.2 CRMAGE 23
1.4 The scope of thesis. 23
Chapter 2. Materials and methods. 25
2.1 Bacterial strains and culture conditions 26
2.2 Plasmid construction 30
2.2.1 Design of transcripts containing HHRzs 30
2.2.2 Plasmid construction to express transcripts containing HHRzs 32
2.2.3 Construction of sgRNA transcribing plasmid 36
2.2.4 plasmids construction for CRISPR/Cas system . 42
2.3 Extraction of RNA from E. coli 47
2.4 In vitro transcription 47
2.5 Reverse transcription. 47
2.6 Quantification of transcript. 48
2.7 Circularized RT-PCR 48
2.8 Genome editing using CRISPR/Cas system . 49
2.9 Measurement of the endonuclease activity of Cas9 protein 51
2.10 Plasmid curing . 51
2.11 Lycopene measurement 51
Chapter 3. Development of Quenching-qPCR (Q-Q) assay for measuring absolute intracellular cleavage efficiency of Rz. 53
3.1 Development of calculation method of absolute cleavage efficiency of HHRz . 54
3.2 Quenching method to deactivate the in vitro cleavage activity of HHRz by using asDNA and anti-asDNA 57
3.3 The calculated intracellular cleavage efficiency of Rz was constant regardless of the degree of mRNA degradation. 61
3.4 Conclusion . 65
Chapter 4. Multiple engineering in Escherichia coli by using CRISPR/cas system couple with Rz . 67
4.1 Multiple sgRNA generating strategy by using Rz 68
4.2 In vivo cleavage assay of Rz . 71
4.3 Mature sgRNAs cleaved by Rz have function as a navigator of dCas9 73
4.4 The number of functional sgRNAs was increased up to five by transcribing a primary transcript using strong promoter. 76
4.5 Efficient multiplex genome engineering in Escherichia coli via CRISPRz. 79
4.6 Application of CRISPRi system in lycopene production . 82
4.7 Conclusion . 85
Chapter 5. Enhancement of the CRISPR/Cas9 editing by improving the homologous recombination efficiency 88
5.1 Attempts to enable multiple genome editing through improved HR efficiency have not been successful. 89
5.2 Verification of endonuclease activity of Cas9 of pCASRec. 96
5.3 Verification of HR efficiency of pCASRec 99
5.4 Optimization of experimental conditions using pCASRec: Temperature 101
5.5 Optimization of experimental conditions using pCASRec: Arabinose induction . 103
5.6 Sequential mutagenesis using pCASRec allows multiple editing 105
5.7 Conclusion . 108
Chapter 6. Overall Conclusion and Further Suggestions 110
6 Overall conclusion and further suggestions. 111
References . 114
Abstract in Korean . 121Docto
Diminished rostral anterior cingulate activity in response to threat-related events in posttraumatic stress disorder : (a)functional magnetic resonance imaging study
νμλ
Όλ¬Έ(μμ¬) --μμΈλνκ΅ λνμ :μ¬λ¦¬νκ³Ό μμΒ·μλ΄μ¬λ¦¬ν μ 곡,2006.Maste
μνΌλμ€ μ§λ©΄μμμ κ΄λμμμ₯μ μ νμ λ°μ¬μ κ΄ν μμΉμ μ°κ΅¬
νμλ
Όλ¬Έ(μμ¬)--μμΈλνκ΅ λνμ :κΈ°κ³ν곡곡νλΆ,2006.Maste