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A comprehensive profile of DNA

EXPERIMENTAL and MOLECULAR MEDICINE,Vol.41,No.9,618-628,September2009

A comprehensive profile of DNA copy number variations in a Korean population: identification of copy number invariant regions among Koreans

Jae-Pil Jeon1*, Sung-Mi Shim1*, Jongsun Jung2, Hye-Young Nam1, Hye-Jin Lee1, Bermseok Oh2, Kuchan Kimm2, Hyung-Lae Kim2

and Bok-Ghee Han1,3

1Division of Biobank for Health Sciences

2Center for Genome Science

Korea National Institute of Health

Korea Centers for Disease Control and Prevention

Seoul 122-701, Korea

3Corresponding author: Tel, 82-2-380-1522;

Fax, 82-2-354-1078; E-mail, bokghee@https://www.wendangku.net/doc/5b580011.html,

*These authors contributed equally to this work.

DOI 10.3858/emm.2009.41.9.068

Accepted 20 April 2009

Abbreviations: CNVs, copy number variations; CNVR, copy number variation region; LCL, lymphoblastoid cell line; QMPSF, quantitative multiplex PCR of short fluorescent fragment

Abstract

To examine copy number variations among the Korean population, we compared individual genomes with the Korean reference genome assembly using the publicly available Korean HapMap SNP 50 k chip data from 90 individuals. Korean individuals exhibited 123 copy number variation regions (CNVRs) covering 27.2 mb, equivalent to 1.0% of the genome in the copy number variation (CNV) analysis using the combined criteria of P value (P＜0.01) and standard deviation of copy numbers (SD≥0.25) among study subjects. In con-trast, when compared to the Affymetrix reference ge-nome assembly from multiple ethnic groups, consid-erably more CNVRs (n=643) were detected in larger proportions (5.0%) of the genome covering 135.1 mb even by more stringent criteria (P＜0.001 and SD≥0.25), reflecting ethnic diversity of structural varia-tions between Korean and other populations. Some CNVRs were validated by the quantitative multiplex PCR of short fluorescent fragment (QMPSF) method, and then copy number invariant regions were detected among the study subjects. These copy number in-variant regions would be used as good internal con-trols for further CNV studies. Lastly, we demonstrated that the CNV information could stratify even a single ethnic population with a proper reference genome as-sembly from multiple heterogeneous populations. Keywords:gene dosage; genetic variation; hap-lotypes; Korea; polymorphism; single nucleotide

Introduction

Human genetic variations comprise various types of structural genomic changes and single nucleotide polymorphisms (SNPs). Large microscopic changes affect more than tens of millions of bases (mb) in the genome, and are rare in healthy individuals, but smaller structural variations ranging from 1 kb to hundreds of kb are frequent and widespread even in normal individuals, contributing to human genetic diversity or disease susceptibility (Feuk et al., 2006; Freeman et al., 2006). Such submicroscopic gen-omic variations have been defined in terms of copy number variations (CNVs) and include large-scale copy number variants (LCVs) (Iafrate et al., 2004), copy number polymorphisms (CNPs) (Sebat et al., 2004), and intermediate-sized variants (ISVs) (Tuzun et al., 2005), as well as other types of genomic variations such as low copy repeats (LCRs) (Lupski and Stankiewicz, 2005), multisite variants (MSVs) (Fredman et al., 2004), and paralogous sequence variants (PSV) (Eichler 2001). However, by con-vention, genomic variations do not include variants that arise from the insertion/deletion of transposable elements (Freeman et al., 2006).

Various experimental platforms and analytical tools such as array-based methods (SNP geno-typing array, BAC- and oligonucleotide-array CGH) and clone-based large scale sequencing approa-ches have been utilized to study structural genomic variations in humans as well as other species (Li et al., 2004; Newman et al., 2005; Perry et al., 2006; Dumas et al., 2007; Graubet et al., 2007; Human Genome Structural Variation Working Group 2007). In humans, multiple studies, including the interna-tional HapMap project, have so far annotated CNVs to more than 4000 distinct regions spanning 600 mb, though their abundance and size are likely to be overestimated due to variability of methods

A comprehensive CNV profile of Korean population 619

P -based (P ＜0.01)

CN-based (SD ≥0.25)

Combined P and CN values ( P ＜0.01 and SD ≥0.25)

T otal numbers of detected CNVRs 435595123Numbers of tested CNVRs 63912Numbers of validated CNVRS 3189c

Validation rate (%)

P indicates GSA_P value (genome-smoothed analysis of the P -value). b CN indicates GSA_CN values (genome-smoothed analysis of the copy number). c

% of validated CNVRs out of tested CNVRs.

Table 1. PCR validation of the CNVRs detected by different CNV calls using the Korean reference set.

and fewer cross-platform validations (Cooper et al ., 2007). Since most CNV detection technologies rely on a comparison to a reference genome, CNVs are determined when cross-referenced to disease-af-fected individuals or different ethnic populations (Rodriguez-Revenga et al ., 2007). Thus, absolute copy number information, which is especially im-portant for clinical diagnosis or assessment of dise-ase susceptibility, cannot be easily determined by current quantitative assays except Fiber-FISH. None-theless, recent studies have shown that DNA copy number variations are implicated in human disea-ses including glomerulonephritis (FCGR3B) (Aitman et al ., 2006), HIV-1/AIDS (CCL3L1) (Gonzalez et al ., 2005), bipolar disorder and schizophrenia (GLUR7, CACNG2 and AKAP5) (Wilson et al ., 2006), muscular atrophy (SMN) (Kesari et al ., 2005) and neoplasia (14q12) (Braude et al ., 2006). On the other hand, recent reports also suggest that different ethnic groups may represent different profiles of CNVs that are stratified in the human population (Redon et al ., 2006; Kidd et al ., 2007). In our previous BAC array CGH study, Korean copy number variants were discovered when com-pared to reference DNA from different ethnic gro-ups (Jeon et al ., 2007). In an attempt to obtain a standard CNV profile for the Korean population, which would facilitate association studies of CNVs with disease susceptibility as well as population genetic diversity, we analyzed a comprehensive CNV profile of 90 Korean individuals using the publicly available Korean HapMap SNP 50 k chip data sets and tested its application to population stratification.

Results

To generate CNV profiles of Koreans, we extracted DNA copy number information from the publicly available Korean HapMap SNP 50 k chip data (https://www.wendangku.net/doc/5b580011.html,), and then conducted either P value-based or copy number-based CNV ana-lyses as well as the combined P value and copy

number-based CNV analysis using two different copy number reference genome assembly sets. Basically, two different reference sets were used to detect CNVs from study subjects (n =90); 1) the Korean reference set generated from all the genomes of 90 individuals, 2) the Affymetrix refer-ence set provided as a copy number reference from multiple ethnic groups by Affymetrix Inc. We tested the validity of different CNV calling criteria by the quantitative multiplex PCR of short fluo-rescent fragment (QMPSF) experiments. The best validation rate was observed in the combined CNV calls with P and SD values.

P value-based CNV analysis (cutoff P ＜0.01 or P ＜0.001)

Our P value-based CNV analysis using the Korean reference set showed that 90 Korean individuals represented 435 copy number variation regions (CNVRs) covering 123 mb equivalent to 4.1% of the genome using a cutoff of P ＜0.01, while the choice of a more stringent cutoff of P ＜0.001 allowed detection of less CNVRs (n =126) cove-ring 35 mb (1.2%) (Supplemental Data Table S1). In contrast, when the Affymetrix reference set from multiple ethnic groups was used to detect CNVRs from Korean individuals, the more stringent cutoff of P ＜0.001 was chosed because this cutoff of P ＜0.001 provided enough stringency in CNV cal-ling to get a CNV profile of a reasonable number of CNVRs. Indeed, even stringent criteria of CNV cal-ling detected more CNVRs (n =2034) covering 594 mb equivalent to 19.8% of the genome (Supple-mental Data Table S1). The proportion of CNVRs on a given chromosome varies from 11.3% on chromosome 14 to 44% on chromosome 12, with the mean proportion of 19.8% on average for all chromosomes. Our P value-based CNV analysis using the Affymetrix reference set (P ＜0.001) showed that CNVRs were uniformly distributed across the human chromosomes, and the popula-tion-wide occurrence of particular CNVRs ranged from zero to 72 out of 90 individuals (data not

620 Exp.Mol.Med.Vol.41(9),618-628,2009

Chromosome

Compared with the Korean reference set

(P ＜0.01 and SD ≥0.25)

Compared with the Affymetrix reference set

(P ＜0.001 and SD ≥0.25)a

No.

length (bp)(%)

No.length (bp)

(%)

1111,809,671 (0.80)449,571,203 (4.25) 28740,862 (0.31)449,307,153 (3.92) 3132,608,112 (1.34)4613,744,688 (7.06) 43692,651 (0.37)7017,683,664 (9.44) 59535,305 (0.30)518,116,219 (4.57) 63616,173 (0.37)344,984,122 (2.98) 74219,083 (0.14)318,623,642 (5.57) 845,245,791 (3.68)253,070,941 (2.15) 952,591,933 (2.15)247,821,941 (6.50)109727,149 (0.55)333,896,498 (2.96)114117,351 (0.09)334,647,951 (3.54)127808,965 (0.62)409,592,415 (7.36)13244,468 (0.05)215,498,948 (5.75)142308,220 (0.35)141,912,037 (2.17)155939,313 (1.15)193,196,319 (3.93)1683,467,237 (4.40)254,805,622 (6.09)17102,797,790 (3.60)234,823,744 (6.20)18259,776 (0.08)101,390,765 (1.86)1971,824,426 (3.27)183,418,799 (6.13)202285,843 (0.48)182,718,443 (4.57)2118,661 (0.03)71,875,996 (5.49)224703,331 (2.02)134,393,331 (12.59)T otal

123

27,152,111 (1.01)643

135,094,441 (5.04)

Average length per CNVR

220,748

210,100

Numbers of CNVRs. b Length of CNVRs in the corresponding chromosome.

Table 2. Summary of population-wide CNVRs from a Korean population (n =90).

shown). According to the results of QMPSF ex-periments for the CNV calls detected by P value- based CNV analysis using the Korean reference set, the validation rate was approximately 50% (3 out of 6 CNVRs) of tested CNVRs (Table 1, see also Supplemental Data Materials for CNV vali-dation).

Copy number-based CNV analysis (cutoff SD ≥0.25)

We also employed the standard deviation (SD ≥0.25) of copy numbers of each probe for the 90 individuals as the criteria of CNV calling in the copy number-based CNV analysis, which detected the population-wide CNVRs among the Korean popul-ation. This copy number-based CNV analysis dete-cted 595 CNVRs (8.9% of the genome) and 790 CNVRs (11.8%) from 90 individuals, using the Korean reference and the Affymetrix reference sets, respectively (Supplemental Data Table S1). The average length of CNVRs was approximately 448kb when using both reference genomes. The validation rate was approximately 46% (18 out of 39 CNVRs) of tested CNVRs (Table 1, and see also Supplemental Data Materials for CNV vali-dation).

Combined CNV analysis with P value (P ＜0.01 or 0.001) and copy numbers (SD ≥0.25)

When compared with the Korean reference set using the combined criteria of P value (P ＜0.01) and standard deviation of copy numbers (SD ≥0.25) of given probes among study subjects, Korean individuals (n =90) exhibited 123 CNV regions (CNVRs) encompassing 27.2 mb, equi-valent to 1.0% of the genome (Table 2, and see also Supplemental Data Table S4 for CNVR list). In contrast, when compared with the Affymetrix reference set, the combined CNV analysis (P ＜0.001 and SD ≥0.25) detected more CNVRs (n =643) encompassing 135.1 mb in larger proportions (5.0%) of the genome (Table 2, and see also Supplemental Data Table S5 for CNVR list). The proportion of copy number gains was lower than that of copy number losses when compared with the Korean reference set, whereas the ratio of gains to losses was higher when compared with the Affymetrix reference set (Figure 1).

Standard deviation of copy numbers allowed us to detect only population-wide CNVRs among the study subjects (n =90) which could not provide the number of CNVRs per person, while the P value-

A comprehensive CNV profile of Korean population

621

Figure 1. Composition of population- wide CNVRs. (A) Population-wide CNVRs were defined for two or more consecutive probes among study subjects (n =90) using the combined CNV analysis of P and SD values. High or low copy numbers of given genomic regions in relative to a refer-ence genome assembly set were classified into gain or loss of CNVRs. The mixed type of CNVRs was de-fined when a genomic region encom-passing two or more consecutive probes represented both types of gain and loss of CNVRs among the study population. (B) Composition of CNVR types. CNVRs were detected by the combined CNV analysis using the Korean and Affymetrix reference sets, and then were classified into three CNVR types (gains, losses, or mixed types).

based CNV analysis could detect individual-based CNVRs. Therefore, the combined criteria of P and SD values could detect both population-wide and individual-based CNVRs, enhancing the reliability of CNV calls. Indeed, the validation rate was app-roximately 75% (9 out of 12 CNVRs) of tested CNVRs, which was higher than those of other CNV calls (Table 1 and Supplemental Data Materials for CNV validation).

Properties of Korean CNVs

According to the results of the combined CNV analysis, an average number of CNVRs per person was 2.1±5.0 (ranging from 0 to 32 CNVRs) when compared to the Korean reference set (Figure 2).

Thirteen individuals exhibited 5 or more CNVRs, and the top three highest numbers of CNVRs were detected in individuals of KR-41 (32 CNVRs), KR- 72 (27 CNVRs) and KR-70 (15 CNVRs). Fifty indi-viduals did not represent CNVRs when compared to the Korean reference set using the combined criteria of P and SD values (Figure 2). In contrast to the Korean reference set, the Affymetrix reference set allowed detection of more CNVRs in the combined CNV analysis (P ＜0.001 and SD ≥0.25). An average number of CNVRs per person was 33.1±26.6 (ranging from 2 to 124 CNVRs) (Figure 2). Thus, the average number of CNVRs per person was much higher in the com-bined CNV analysis using the Affymetrix reference than the Korean reference. This observation may

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2009

Figure 2. Numbers of CNVRs per person. The CNVRs were detected by the combined CNV analysis with P and SD values using the Korean and Affymetrix reference set. The average number of CNVRs per person was 2.1±5.0 (mean±STD) (up to 32 CNVRs at KR-41 sample) when compared with the Korean reference set, and 33.1±26.6 (mean±STD) (up to 124 CNVRs at KR-41 sample) when compared with the Affymetrix reference set.

be ascribed to ethnic diversity between the Korean reference set from a single population and the Affymetrix reference set from the multiple ethnic groups. On the other hand, KR-2 and KR-4 sam-ples represented lowest numbers of CNVRs when referenced to both the Korean and Affymetrix reference sets, while KR-41 and KR-72 samples represented highest numbers of CNVRs. The ave-rage length of individual-based CNVRs exhibited no big difference in the combined CNV analysis between the Korean reference set (128 kb per CNVR) and the Affymetrix reference set (142 kb per CNVR).

The most frequent CNVR (KC16-T01) was de-tected in 7 individuals (7.8%) out of the study subjects (n=90) in the combined CNV analysis using the Korean reference set (Supplemental Data Table S4). Seventy CNVRs (57%) of 123 CNVRs in total were occurred in single individuals while 53 CNVRs (43%) were occurred in two or more individuals (7 individuals at most). The most frequent CNVR (KC16-T01) was localized in 16p12.1 with a higher occurrence (n=7) than that of a CNVR (n=4) at the Ig locus, suggesting that this frequent CNVR may be one of highly sus-ceptible CNV targets as much as Ig loci.

Chromosome 21 and 18 represented the least proportion (0.03% and 1.86% of the corresponding chromosome) of CNVRs using the Korean refer-ence set and the Affymetrix reference set, respec-tively (Table 2). This observation suggests that chromosome 21 may be relatively conserved for genomic structural variations within the Korean population. When the two criteria (P＜0.01 and SD≥0.25) were combined in CNV calling using the Korean reference set, 64 CNVRs (52.1%) of 123 CNVRs were known while 59 CNVRs (47.9%) were unknown according to the Database of Geno-mic Variants (http://projects.tcag.ca/variation) (Sup-plemental Data Table S4).

Identification of copy number invariant regions and qPCR validation of CNVRs

Based on the result of copy number-based CNV analysis, we first selected eleven copy number in-variant regions showing the lowest standard devia-tion (SD) of GSA_CN values (Table 3). These re-gions exhibited almost no variation in DNA copy numbers among all individuals when compared to the self-including Korean reference set. One of them (2q36.1) was validated for no copy number varia-tion in reference to copy numbers of Factor VIII gene among all study subjects (n=90) (Figure 3).

Next, we evaluated the reliability of CNV calls from P value-based and copy number-based anal-yses, as well as the combined P value and copy number-based analysis in order to determine the better CNV calling rule. Since our Korean refer-ence set was generated from averaged copy num-bers of a given probe for all of 90 individuals, the coefficient of variation (CV) was used as a statisti-cal criteria of copy number difference among the tested subjects (n=90). Therefore, when a parti-cular CNVR displayed over 8% of the coefficient of variation of copy number measurements from the QMPSF results, we considered the particular CNVR to be validated. As a result, the validation rate of CNVRs was higher in the combined CNV analysis than P value-based or copy number-ba-sed CNV analyses (Table 1, and Supplemental Data Materials for CNV validation).

A comprehensive CNV profile of Korean population 623

No.

Start

End

Cyto

No. SNP c

Reg-ional Ave

Reg-ional Std

Gene symbol (name)

Position

dbSNP number Position dbSNP number 1221779509SNP_A-1749529221892884SNP_A-17422992q36.1 7 2.07 0.041 None

241581651SNP_A-169059541586232SNP_A-17216383p22.1 5 2.07 0.037 ULK4 (unc-51-like

kinase 4)

19987547

SNP_A-1679990

20234635

SNP_A-1686904

6p22.3

2.07 0.045 LOC729105, MBOAT1

(membrane bound O- acyltransferase domain containing 1)

436617191SNP_A-166135036648891SNP_A-17481617p14-p12 3 2.08 0.045 AOAH (acyloxyacyl

hydrolase, neutrophil)

32432492

SNP_A-174227932477521SNP_A-16743319p22-p12 3 2.06 0.040 ACO1 (aconitase 1), DDX58

(DEAD box polypeptide 58)

6110469745SNP_A-1743757110607849SNP_A-16960929p31.312 2.07 0.045 None

7120035655SNP_A-1664044120159930SNP_A-171487510q26.1110 2.06 0.045 C10orf84 (chromosome 10

open reading frame 84)

8105445579SNP_A-1736373105445860SNP_A-175432013q33.2 2 2.08 0.045 None

932530580SNP_A-172801132558105SNP_A-166647818q12.218 2.08 0.045 FHOD3 (formin homology 2

domain containing 3)

101421397SNP_A-17446991422405SNP_A-171211920p1320 2.08 0.043 None 11

8714796

SNP_A-1726550

8724160

SNP_A-1727573

20p12.3

2.07 0.045 PLCB1 (phospholipase C,

beta 1)

Cytogenetical locations of each non-CNVRS. b Numbers of SNPs within each non-CNVRs. c and d Average copy numbers and standard deviations of all probes within the corresponding region.

Table 3.

Copy number invariant regions (non-CNVRS).

Figure 3. Validation of copy nu-mber invariant regions. DNA copy numbers of a copy number invari-ant region (CN2-2) at 2q36.1 was quantitated for all study subjects (n =90) by the QMPSF method. Relative copy numbers of CN2-2 to Factor VIII was 1.32±0.08 with the coefficient of variation (6.3%) among the test subjects. Error bars indicate standard deviations from three independent QMPSF meas-urements performed in triplicate.

Refining the population stratification of 90 individuals

CNV profiles identified in this study were applied to the principle component analysis to stratify the 90 individuals. Multi-dimensional scaling showed that the first and second eigenvectors explained 21% and 9% of the distance variables, respectively (Figure 4). When referenced to the Affymetrix reference, the 90 individuals were dispersed in a plot of eigenvetors, suggesting that the Korean population can be stratified into subgroups accor-

ding to DNA copy number variations. In contrast, most of the 90 individuals were centered around an intersecting point of two eigenvectors when referenced to the self-including Korean reference genome. This result suggests that a reference genome assembly from the more heterogeneous population gives more informative data for popula-tion stratification studies using CNV information. Thus, if an appropriate copy number reference genome is selected, genomic structural variation would provide a valuable source for refining stratifi-cation of ethnic groups within a single population.

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Figure 4. Plots of two eigenvectors for 90 unrelated individuals from the P value-based analysis of DNA copy numbers. The first and second eigenvectors explain 21% and 9% of the distance variables, respectively. The Affymetrix reference that was generated from more heterogeneous populations resulted in a more widely dispersed plot than the self-including Korean reference that was generated from a single ethnic population.

Discussion

We analyzed the profiles of DNA CNVs of 90 Korean individuals using publicly available Korean HapMap SNP 50k chip data. Different CNV refe-rence genome assembly sets (either the Korean reference or Affymetrix reference sets) were used for the combined CNV analysis with P and SD values, as well as P value-based and copy num-ber-based analyses. As results of QMPSF expe-riments, the validation rate of CNVRs was higher in the combined CNV analysis than P value-based or copy number-based CNV analyses. Thus, here we finally generated the CNV profile of a Korean population by the combined CNV analysis. The Korean HapMap samples (n=90) represented five times less CNVRs in total when referenced to the Korean reference set (123 CNVRs) than the Affymetrix reference set (643 CNVRs), reflecting an ethnic difference in CNV profiles between the Korean population and other ethnic groups. In fact, it was reported that copy numbers of particular genes (e.g., CCL3L1, MAPT) varied among diffe-rent ethnic groups (Gonzalez et al., 2005). Thus, this result suggests that each ethnic population has a distinct CNV profile applicable to various popu-lation genetic studies.

On the other hand, in the combined CNV analy-sis with both P and SD values, no CNVR was detected in 50 individuals when referenced to the self-including Korean reference set. These 50 indi-viduals would represent the standard genome of the Korean population in terms of structural geno-mic variations because they did not exhibit a copy number difference in relative to the Korean refer-ence genome. Thus, genetic information of these 50 individuals could be a good standard reference for further population genetic studies.

Some array CGH studies detected 11 CNVs (Sebat et al., 2004) or 12.4 CNVs (Iafrate et al., 2004) on average in each person. Generally, array CGH assays identifies a smaller number of CNVRs comprising large-insert clones, which result in the overestimation of CNV length (Redon et al., 2006). Moreover, the different choice of reference genome sets can affect total and average numbers of CNVRs in normal healthy individuals. In this study, the Korean population exhibited 123 CNVRs in total and 2.1±5.0 per person (up to 32 CNVRs) when compared with the Korean reference geno-me set in the combined CNV analysis with cutoffs of P＜0.01 and SD≥0.25. These CNV calls were made in reference to the self-including Korean genome assembly which was generated from the average copy numbers for the study subjects. Therefore, the choice of self-including reference genome assembly set might contribute to small numbers of CNV calls.

Generally, the term polymorphic is used to de-note variants that occur in ＞1% of the population (Strachen and Read, 1999). CNVRs identified in this study are considered to be potentially poly-morphic rather than rare mutations in the Korean population, because those CNVRs were detected at least once more in 90 individuals. Furthermore, these structural genomic variations provide an add-itional dimension for human genetic diversity and disease susceptibility along with SNPs. Thus, it would be interesting to study the simultaneous association of these potentially polymorphic CNVRs and SNPs of particular genes with human complex diseases including diabetes and hypertension.

A comprehensive CNV profile of Korean population 625

Many CNV analysis tools have been developed to extract accurate copy number information from probe intensities of SNP genotyping arrays (Lin et al., 2004; Nannya et al., 2005; Price et al., 2005; Slater et al., 2005; Fiegler et al., 2006; Hu et al., 2006). However, the detection of real CNVs still needs additional experiments for validation, such as PCR-based methods (e.g. quantitative real-time PCR, QMPSF, MAPH, MLPA) and hybridization- based methods (e.g., Fiber-FISH, Southern blo-tting) (Feuk et al., 2006). According to the QMPSF results, the validation rates of CNV calls from our different CNV analyses were 50%, 46% and 75% for P value-based, copy number-based analysis, and the combined CNV analysis with P and SD values, respectively. The P value-based CNV ana-lysis could detect individual-based CNVRs wher-eas the copy number-based CNV analysis with SD values could detect population-wide CNVRs. Thus, our combined CNV analysis enabled to detect pop-ulation-wide as well as invidividual-based CNVRs with the higher validation rate (75%) of CNV calls. Redon group estimated false positive rate using singleton CNVs (called in only a single individual) (Redon et al., 2006). They found an initial vali-dation failure rate of 24% and then finally estima-ted a false positive rate of 8% by extrapolating these validation rates across the entire data set (24% multiplied by the frequency of singleton CNVs called on only one plate form).

There are possible explanations for low valida-tion rates of our CNV calls. First, our CNV calls were made in reference to the genome assembly set from multiple individuals (n=90) instead of the single genome, which could not appropriately pro-vide a single reference DNA sample for QMPSF experiments. Second, QMPSF primers might not exactly target authentic CNV regions because our CNV profile was generated from the 50 k Affyme-trix chip data which contained a relatively low den-sity of SNP probes, contributing to detect a low resolution of CNVRs in the present study. Third, an alternative explanation is that some CNVRs might be multi-allelic or complex CNVRs that were diffi-cult to be validated by conventional PCR based methods. Rather, hybridization based methods (e.g., Fiber-FISH, Southern blotting) or a whole genome sequencing approach would detect such multi-all-elic or complex CNVRs (Rodriguez-Revenga et al., 2007). Taken together, the validation rate would be increased if CNV-affected samples were compared with a single genome reference in validation ex-periments of CNVRs. Moreover, more advanced chip platforms with a higher density of probes would give a higher-resolution map of CNVRs, which supports better experimental validation. The longest CNVR detected by P value-based CNV anlaysis was rather a microscopic genome change encompassing approximately 31 mb at 4q13.14q22.13, which was validated in the cor-responding EBV-transformed lymphoblastoid cell line (LCL) by the QMPSF method. The copy num-ber change of this region was not found in blood DNA from the same donor as the LCL DNA (data not shown), suggesting that this copy number change was acquired during EBV-mediated B cell transformation. The LCL strain containing this CNVR would provide a valuable resource for a hap-loinsufficiency study of corresponding genes at this locus.

An immunoglobulin locus (22q11.22) was also detected as CNV-affected regions with the occur-rence of four (4/90) in the combined CNV analysis using the Korean reference set. Previous reports suggested that polymorphic gene duplication may frequently occur in Ig loci including 14q32.33 (Ig heavy variable cluster) and 22q11.2 (Ig heavy chain constant region and Ig lamda) (Sasso et al., 1995; van der Burg et al., 2002; Buckland et al., 2003). It is likely that the copy number variation of the Ig loci may not be from the germ line but a de novo CNV enriched during LCL generation. In fact, Ig locus CNVs were excluded in CNV analyses (Redon et al., 2006).

Recent reports suggest that as much as 40% of the known CNVs occur in gene deserts while the other CNVs are enriched for genes involved in immunity and environmental responses (Derti et al., 2006; Rodriguez-Revenga et al., 2007). Copy number invariant regions may be evolutionary con-served for DNA copy numbers because of the potential effects of gene dosage. In addition, with respect to genome stability, it would be interesting to know whether individuals who contain more CNVRs have a higher risk of cancer than indivi-duals who contain less CNVRs. Therefore, CNVRs and non-CNVRs identified in this study would be a good starting point of further CNV studies to deter-mine their clinical relevance in complex traits or disease susceptibility in the Korean population. Methods

DNA samples

We used the Affymetrix GeneChip Mapping 50 k array data set obtained from the Korean HapMap project for which DNA samples were selected to include 90 unrelated healthy Korean individuals with an equal sex ratio and age 40-69 years for the Korean Health and Genome Epide-miology Study (http://cgs.cdc.go.kr), as described in pre-vious reports (Kim et al., 2006; Yoo et al., 2006). Genomic

626 Exp.Mol.Med.Vol.41(9),618-628,2009

DNA was extracted from EBV-transformed B lympho-blastoid cell lines (LCLs) provided by the National Biobank of Korea, Korea National Institute of Health.

DNA copy number analysis

The Affymetrix GeneChip Mapping 50k_XBA240 array data for the Korean HapMap project (Korean HapMap 50 k) were generated according to manufacturer's instruc-tions, as reported elsewhere (Herbert et al., 2006). Ave-rage call rate of the Korean HapMap 50 k data set was 98.4% with a standard deviation of 0.92%, ranging from 95.1% to 99.4%. For CNV detection, the Korean HapMap 50 k data were analyzed using Affymetrix GeneChip Chromosome Copy Number Analysis Tool (CNAT) 3.0. DNA copy numbers of individual Koreans (n=90) were compared to two different copy number reference genome assembly sets: 1) the Affymetrix Mapping 50k_XBA240 reference data (hereinafter called the Affymetrix reference set) from three different ethnic groups (42 African Ameri-cans, 20 Asians, and 42 Caucasians) and the fourth group (24 PD panel) in which the 20 Asians did not include Koreans, 2) the self-including copy number reference genome assembly from 90 Korean individuals of the Korean HapMap 50 k (hereinafter called the Korean re-ference set).

With regard to CNV calling rules, we annotated CNVRs to particular chromosomal regions if the region was more than 1kb in size, encompassed two or more consecutive probes, and met either of the following cutoff criteria: 1) P

＜0.001 from the GSA_pVal (genome-smoothed analysis of the P-value) when compared with the Affymetrix reference, 2) P＜0.01 from the GSA_pVal when compared with the self-including Korean reference, 3) a standard deviation (SD≥0.25) of GSA_CN values (genome-smoothed analysis of the copy number) in copy number-based analysis, 4) the combined criteria of P value (P＜0.01 or P＜0.001) and standard deviation (SD≥0.25) of copy numbers. DNA copy number variations of the sex chromosomes were not analyzed in this study because of different sex ratios of test samples to reference genome assemblies.

CNV validation

DNA copy numbers were validated by the QMPSF method using fluorescein-labeled forward primers, as described before (Charbonnier et al., 2002; Vaurs-Barriere et al., 2006). Briefly, 100 ng of DNA template was added to make 25 μl of PCR reaction mixture including 1× PCR Gold buffer, 2 mM MgCl2, 0.2 mM dNTP, appropriate PCR pri-mers at concentrations of 0.04-0.09 μM, and 3 units of Ampli-Taq Gold (Applied Biosystems). One of copy number invariant regions identified among the 90 individuals in this study (CN2-2) or the coagulation factor VIII gene was used as an internal reference DNA for the normalization of input DNA (Levine et al., 2005; Jeon et al., 2007). Primer sequ-ences of reference genes were as follows. CN2-2: forward, 5'-CTTAGGTTCCCACGGTTTGA-3’; reverse 5'-GCACTT-GAAAGGTGCCTAGC-3’, Factor VIII: forward, 5’-TACCAT-CCAGGCTGAGGTTTAT-3’, reverse, 5’-AAAGAGTTGTAA-CGCCACCATT-3’. Hot start PCR was carried out at 95o C for 10 min for a denaturation step, followed by 21 cycles of 94o C for 30 s, 60o C for 30 s, and then further extended at 72o C for 50 min. Ethanol precipitated PCR products were dissolved in 5 μl of water. One microliter of the purified PCR products was mixed with 1 μl of Gene Scan-500 LIZ size standards and 14 μl of HiDi formamide, and then run on the ABI3730 capillary sequencer (Applied Biosystems). Data analysis was performed using the GeneMapper soft-ware (Applied Biosystems). In order to obtain experimental copy numbers of CNVRs, the relative peak height of a given CNVR was devided by the peak height of a refer-ence of Factor VIII or CN2-2 from the QMPSF chroma-togram, which resulted in relative copy numbers of CNVRs (see Supplemental Data Materials for QMPSF experiments in details).

Multi-dimensional scaling (MDS)

After CNV profiles of the 90 individuals were obtained by the P value-based analysis in reference to the Affymetrix (P＜0.001) or Korean (P＜0.01) reference genome ass-embly sets, loss and gain of DNA copy numbers were coded to “1” and “2” for each probe, respectively. Probes with no copy number change were coded to zero “0”. These codes of copy number patterns were used in principle component analysis. Briefly, the pairwise similarity coefficients (s ij) of CNV for individuals were computed as;

∑∑∑

===

ijk

111

where c ijk is 1 (or 0) depending on the copy number change (or no change) between two individuals (i and j) at a marker position(k), N is the number of markers and L (= M) is the number of subjects. In particular, the diagonal matrix elements (s ii) are not normalized to be the same among individuals since the number of accumulated copy number changes in a whole genome could be different individually. Using the similarity coefficient (s ij) Jacobi tran-sformation was performed to calculate eigenvectors (Press et al., 1988).

Supplemental data

Supplemental Data include two figures and six table and can be found with this article online at http://e-emm.or.kr/ article/article_files/SP-41-9-02.pdf.

Acknowledgements

This work was supported by an intramural grant from Korea National Institute of Health, Korea Centers for Disease Control and Prevention (2007-N00353-00).

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植物基因的克隆｜植物基因克隆的基本步骤

植物基因的克隆 08医用二班姚桂鹏0807508245 简介克隆（clone）是指一个细胞或一个生物个体无性繁殖所产生的后代群体。通常所说的基因克隆是指基于大肠埃希菌的DNA片段（或基因）的扩增，主要过程包括目标DNA的获取、重组载体的构建、受体细胞的转化以及重组细胞的筛选和繁殖等。本文主要介绍植物基因的特点、基因克隆的载体、基因克隆的工具酶、基因克隆的策略以及植物目的基因的分离克隆方法等内容。关键词植物基因基因克隆载体工具酶克隆策略分离克隆方法 Plant gene cloning Introduction Cloning (clone) refers to a cell or an individual organisms asexual reproduction produced offspring. Usually said cloning genes means

based on escherichia coli segment of DNA (or genes), including the main course target DNA, restructuring of the carrier, transformation of receptor cells and reorganization of screening and reproductive cells. This paper mainly introduces the characteristics of plant gene and gene cloning and carrier, gene clone tool enzyme, gene cloning and plant gene strategy of separation cloning method, etc. Keywords Plant gene cloning tool enzyme gene cloning vector method of separation of cloning strategy 一、植物基因的结构和功能基因（gene）是核酸分子中包含了遗传信息的遗传单位。一般来说，植物基因都可分为转录区和非转录的调控区两部分。（一）植物基因的启动子启动子（promoter）是指在位于结构基因上游决定基因转录起始的区域，植物积阴德启动子包括三个较重要的区域，一时转录起始位点，而是转录起始位点上游25~40bp的区域，三是转录起始位点上游-75bp处或更远些的区域。（二）植物基因的增强子序列

整个基因克隆实验流程(完整)

一、组织总RNA的提取相关试剂：T rizol；氯仿；苯酚；异丙醇；75%乙醇；RNase-free水相关仪器：制冰机；液氮&研钵/生物样品研磨仪；高速离心机；移液器（1ml、200μl、100μl/50μl）；涡旋振荡仪；恒温金属浴。相关耗材：解剖工具，冰盒，离心管，离心管架，吸头（1ml，200μl/300μl），一次性手套，实验手套。实验步骤 1.取暂养草鱼，冰上放置一段时间，然后解剖，剪取肠道50~100mg，放入研钵中，加入液氮迅速研磨，然后加入1ml 预冷TRIzol试剂，充分研磨至无颗粒物存在。 2.转移到离心管中，室温放置5min，使细胞充分裂解； 3.按1ml Trizol加入200μl氯仿，盖上盖子，迅速充分摇匀15s，然后室温放置3min； 4.4℃，,12000g 离心15min；此时混合物分为三层，下层红色的苯酚氯仿层，中间层和上层无色水相；RNA存在于无色水相中； 5.小心吸取上清液，千万不要吸取中间界面，否则有DNA污染；转移至一个新的离心管，加入等体积的异丙醇，轻轻混匀； 6.室温放置10min；4℃，,12000g 离心10min； 7.弃上清，加入1ml 75%乙醇洗涤；涡旋，悬浮沉淀；4℃，,12000g 离心5min； 8.弃上清；可以再次用75%乙醇洗涤沉淀； 9.弃上清；用移液器轻轻吸取管壁或管底的残余乙醇，注意不要吸取沉淀；室温放置5min 晾干沉淀；（RNA样品不要过于干燥，否则极难溶解） 10.沉淀中加入30μl RNase-free水，轻弹管壁，使RNA溶解。 RNA质量检测相关试剂：溴酚蓝，TEB/TAE电泳缓冲液，溴乙锭（EB）相关仪器：（超微量分光光度计，移液器（2.5μl 或2μl 规格，10μl规格），电子天平，电泳仪，电泳槽，凝胶成像仪，微波炉，制冰机）相关耗材：（无菌无绒纸，吸头，离心管架，PCR管，PCR管架，锥形瓶，烧杯，一次性手套，实验手套，冰盒）（1）RNA纯度的检测：测定其OD260和OD280的值，根据其OD260/ OD280的比值，当其比值在1.9~2.1之间，说明提取的总RNA纯度比较高，没有蛋白质和基因组的污染。（2）RNA完整性的检测：取2μlRNA，与2μl溴酚蓝混匀，用1%的琼脂糖进行凝胶电泳，20min后，在凝胶成像系统中观察效果。当28S与18S条带清晰，且亮度比大约是2:1时，5S条带若隐若现，而且没有其它条带时，说明完整性不错，可以用于下游逆转录实验。

绿色荧光蛋白基因克隆及表达结果分析

3 结果与分析 3.1质粒提取用醋酸铵法提取pET-28a 和pEGFP-N3质粒后，进行琼脂糖电泳检测质粒是否提取成功。得到电泳结果，如图一所示，3、4号泳道有明显清晰的条带说明pEGFP-N3提取成功。1、2泳道同样有明显清晰的条带，说明pET-28a 提取成功。 3.2 双酶切用BamH1和Not1分别对pEGFP-N3和pET-28a 双酶切。1、2号泳道为pEGFP-N3的酶切结果，如图二所示，电泳会得到两条带，说明pEGFP-N3酶切成功。4号泳道为pET-28a 的酶切产物的电泳有明显条带，证明酶切成功。 3.3 抗性筛选通过氯化钙法制备DH5α感受态细胞，用热激发将pET-28a-GFP 转入DH5α感图 1 pET-28a 和pEGFP-N3质粒提取电泳图 1、2泳道为pET-28a 电泳结果 3、4号泳道为pEGFP-N3电泳结果图 2 BamH1、Not1双酶切 pEGFP-N3和pET-28a 1、2号泳道为pEGFP-N3酶切产物 3号泳道为pEGFP-N3原始质粒 4号泳道为pET-28a 酶切产物 5号用泳道为pET-28a 原使质粒

受态细胞。转化重组质粒后涂平板，进行重组质粒的抗性筛选。因为28a中含有抗卡那基因，所以筛选后可以得到含28a的重组质粒。从图中可以看出1号平板长出较多菌落，说明DH5α感受态细胞存活。2号平板无菌落生长，说明DH5α中不含抗卡那基因。3号板生长出较少菌落，证明卡那有活性。4号板无菌落生长。失败原因其一可能是在倒了第一个平板加入卡那后，由于倒平板速度太慢，导致培养基凝固，影响了卡那的浓度和活性。其二可能是在转化过程中，离心后，弃上清的过程中，将沉淀和上清混在了一起，影响了溶液的浓度。图3重组质粒转化DH5α感受态细胞 1号图为不含卡那的阴性对照 2号图为含卡那的阴性对照 3号图为含卡那的自提pET-28a的阳性对照 4号图为含卡那的连接产物结果 3.4PCR鉴定经PCR扩增后，进行琼脂糖凝胶电泳检测是否扩增成功，得到电泳结果如图四所示，结果表明，1、2泳道的条带约为700bp，说明成功扩增出含有GFP的基因。DNA电泳检验扩增片段,选出能够得到700bp左右片段的阳性克隆。图4阳性重组菌的PCR鉴定 1、2号泳道为重组质粒转化结果

蛋白酪氨酸激酶综述

蛋白酪氨酸激酶综述目前至少已有近六十种分属20个家族的受体酪氨酸激酶被子识别。所有受体酷氨酸激酶都属于I型膜蛋白，其分子具有相似的拓朴结构：糖基化的胞外配体结合区，疏水的单次跨膜区，以及胞内的酪氨酸激酶催化结构域及调控序列。不同受体酪氨酸激酶结合，将导致受体发生三聚化，并进一步使受体胞内区特异的受体酪氨酸残基发生自身磷酸化或交叉磷酸化，从而激活下游的信号转导通路。许多肿瘤的发生、发展都与酪氨酸激酶的异常表达有着极其密切的联系，下面将对几类与肿瘤的发生发展最为密切的受体酪氨酸激酶的研究迸展做一简介。一、表皮生长因子受体（Epidermal grovth factor receptor, EGFR）家族 EGFRPE包括EGFR、ErbB2、ErbB4等4个成员，其家族受体酪氨酸激酶（RTK）以单体形式存在，在结构上由胞外区、跨膜区、胞内区3个部分组成，胞外区具有2个半氨酸丰富区，胞内区有典型的ATP结合位点和酪氨酸激酶区，其酪氨酸激酶活性在调节细胞增殖及分化中起着至关重要的作用。人的egfr基因定位于第7号染色体的短臂（7p12.3-p12.1），它编码的产物EGFR由1210个氨基酸组成，蛋白分子量约为170kDa，其中，712-979位属于酪氨酸激酶区。EGFR的专一配体有EGF、TGF、amphiregulin,与其他EGFR家庭成员共有的配体有（cellulin(BTC)、heparin-binding EGF(HB-EGF)、Epiregulin(EPR) ）等。 EGFR在许多上皮业源的肿瘤细胞中表达，如非小细胞性肺癌，乳腺癌、头颈癌，膀胱癌，胃癌，前列腺癌，卵巢癌、胶质细胞瘤等。另外，在一些肿瘤如恶性胶质瘤、非小细胞性肺癌、乳腺癌、儿童胶质瘤、成神经管细胞瘤及卵巢癌等中还可检测到EGFR缺失。最为常见的EGFR缺失突变型是EGFRⅧ，EGFR Ⅷ失去了配体结合区，但是可自身活化酪氨酸激酶，刺激下游信号通路的激活，而不依赖于与其配全结合。EGFR在许多肿瘤中的过表达和/或突变，借助信号转导至细胞生长失控和恶性化。另外，EGFR的异常表达还与新生血管生成，肿瘤的侵袭和转移，肿瘤的化疗抗性及预后密切相关。EGFR高表达的肿瘤患者，肿瘤恶性程度高，易发生转移，复发间期短，复发率高，患者的存活期短。 ErbB2，又名HER-2/neu，是EGFR家族的第二号成员，ErbB2通过与EGFR家族中其它三位成员构成异源二聚体，而发挥生物学作用，尚未发现能与其直接结合的配体。编码ErbB2的基因neu最早从大鼠神经母细胞瘤中分离得到，人类体细胞内neu基因的同源基因，又称为HER-2或erbB2，位于人第17号染色体的长臂（17q21.1），它编码的产物ErbB2由1255个氨基酸组成，蛋白分子量约为185Kda，其中，720-987位属于酪氨酸激酶区。 ErbB2通常只在胎儿时期表达，成年以后只在极少数组织内低水平表达。然而在多种人类肿瘤中却过度表达，如乳腺癌（25-30%）、卵巢癌（25-32%、肺静癌（30-35%）、原发性肾细胞癌（30-40%）等。过度表达的原因主要是ErbB2基因扩增（95%）或转录增多（5%）。 1987年，Slamon等人首行先报道了ErbB2扩增和乳腺癌临床预后不良之间的显著关系，其显著性高于雌激素、孕激素等指标，并在以后的研究中得到大量证实。随后，ErbB2表达水平和乳腺癌治疗效果间的关系得到广泛研究，人们发现ErbB2高表达乳腺癌患者对他莫昔芬（tamoxifen）治疗、单独的激素疗法、以及环磷酰胺、甲氨喋呤、5-氟脲嘧啶联合化疗产生耐受。研究还表明，ErbB2在细胞的恶性转化中发挥重要作用，并能促进恶性肿瘤转移。ErbB2受体过度表达往往提示乳腺癌恶性程度高，转移潜力强，进展迅速，化疗缓解期短，易产生化疗和激素治疗抗性，生存率和生存期短，复发率高。和ErbB4对肿瘤的作用目前尚不清楚，但在肿瘤形成模型的临床前研究发现，ErbB3、Erb3与EGFR、ErbB2共表达后会使肿瘤恶性程度明显增加。二、血管内皮细胞生长因子受体（Vascular endothelial growth factor receptor, VEGFR）家族VEGFR家族的成员包括：VEGFR1（Flt-1）、VEGFR2（KDR/Flk-1）、VEGFR3（Flt-4）,这一家族的受体在细胞外存在着7个免疫球蛋白样的结构域，在胞内酪氨酸激酶区则含有一段亲水手插入序列。

蛋白酪氨酸激酶简介

蛋白酪氨酸激酶简介癌症极大威胁人类健康，抗肿瘤研究是当今生命科学中极富挑战性且意义重大的领域。目前，临床上常用的抗肿瘤药物主要是细胞毒类药物，这类抗癌药具有难以避免的选择性差、毒副作用强、易产生耐药等缺点。近年来，随着生命科学研究的飞速进展，恶性肿瘤细胞内的信号转导、细胞周期的调、细胞凋亡的诱导、血管生成以及细胞与胞外基质的相互作用等各种基本过程正在被逐步阐明。以一些与肿瘤细胞分化增殖相关的细胞信号转导通路的关键酶作为药物筛选靶点，发现选择性作用于特定靶点的高效、低毒、特异性强的新型抗癌药物已成为当今抗肿瘤药物研究开发的重要方向。蛋白酪氨酸激酶是一类具有酪氨酸激酶活性的蛋白质，可分为受体型和非受体型两种，它们能催化ATP上的磷酸基转移到许多重要蛋白质的酪氨酸残基上，使其发生磷酸化。蛋白酪氨酸激酶在细胞内的信号转导通路中占据了十分重要的地位，调节着细胞体内生长、分化、死亡等一系列生理化过程。蛋白酪氨酸激酶功能的失调则会引发生物体内的一系列疾病。已有的资料表明，超过50%的原癌基因和癌基因产物都具有蛋白酪氨酸激酶活性，它们的异常表达将导致细胞增殖调节发生紊乱，进而导致肿瘤发生。此外，酪氨酸基酶的异常表达还与肿瘤的侵袭和转移，肿瘤新生血管的生成，肿瘤的化疗抗性密切相关。因此，以酪氨酸激酶为靶点进行药物研发成为国际上抗肿瘤药物研究的热点，为此投入的研究经费也是其它任何一个非传统的肿瘤靶点所无法匹敌的。目前为止，已有十多种蛋白酪氨酸激酶抑制剂和抗体进入I-Ⅱ期临床试验阶段，个别的已经上市，并取得了令人鼓舞的治疗结果。基中，Genentech公司和罗氏药厂联合研究和生产的HerceptinTM(Trastuzumab)是一种抗酪氨酸激酶受体HER2/neu的人源化的单克隆抗体。1998年，美国食品的药物管理局（Food and Drug Administration, FDA）正式批准Herceptin用于治疗某些HER2阳性的转移性乳腺癌。2001年5月，N ovartis公司研发的针对酪氨酸激酶Bcr-Abl的抑制剂GleevecTM (imatinib mesylate)由于对治疗慢性髓样白血病（chronic myelogenous leukemia,CML）具有非常好的疗效，尚未完成Ⅲ期临床就被FDA批准提前上市，用于治疗费城染色体呈阳性（Philadelphia chromosome – positive, Ph+）的慢性髓样白血病患者，引起了巨大的轰动。GleevecTM是第一个在了解癌症的病因后鸽是设计开发，并取得了显著成效和的肿瘤治疗药物，它的研发成功可以说是癌症治疗的一个里程碑。这一重大成就被美国《科学》杂志列入2001年度十大科技新闻。纽约《时代》杂志将其作为杂志的封面，称GleevecTM 开创了药物研发的新时代。2002年2月，美国FDA又批准GleevecTM 用于胃肠基质瘤（gastrointestinal stromal tumors, GLST）的治疗。2002年7月，AstraZeneca公司研发的IressaTM (ZD1839又被美国FDA批准用于治疗经过标准含铂类方案和紫杉萜化疗后仍然继续恶化的终未期非小细胞肺癌患者，这也是第一种用于实体瘤治疗的针对特定靶点挑战分子酪氨酸激酶抑制剂。Herceptin，Gleevec以及Iressa的上市进一步证明了以特定靶点尤其是以酪氨酸激酶为靶点进行抗肿瘤药物的研发是21世纪最有可能获得突破性进展的抗肿瘤药物领域，具有十分广阔的前景。

拟南芥基因克隆的策略与途径

拟南芥基因克隆的策略与途径拟南芥（Arabidopsis thaliana）是一种模式植物，具有基因组小（125 Mbp）、生长周期短等特点，而且基因组测序已经完成（The Arabidopsis Genomic Initiative, 2000）。同时，拟南芥属十字花科（Cruciferae），具有高等植物的一般特点，拟南芥研究中所取得成果很容易用于其它高等植物包括农作物的研究，产生重大的经济效益，特别是十字花科中还有许多重要的经济作物，与人类的生产生活密切相关，因此目前拟南芥的研究越来越多地受到国际植物学及各国政府的重视。基因(gene)是遗传物质的最基本单位,也是所有生命活动的基础。不论要揭示某个基因的功能,还是要改变某个基因的功能,都必须首先将所要研究的基因克隆出来。特定基因的克隆是整个基因工程或分子生物学的起点。本文就基因克隆的几种常用方法介绍如下。 1、图位克隆 Map-based cloning, also known as positional cloning, first proposed by Alan Coulson of the University of Cambridge in 1986, Gene isolated by this method is based on functional genes in the genome has a relatively stable loci, in the use of genetic linkage analysis or chromosomal abnormalities of separate groups will queue into the chromosome of a specific location, By constructing high-density molecular linkage map, to find molecular markers tightly linked with the aimed gene, continued to narrow the candidate region and then clone the gene and to clarify its function and biochemical mechanisms. 图位克隆（map-based clonig）又称定位克隆（positoinal cloning），1986年首先由剑桥大学的Alan Coulson提出。用该方法分离基因是根据功能基因在基因组中都有相对较稳定的基因座，在利用分离群体的遗传连锁分析或染色体异常将基因伫到染色体的1个具体位置的基础上，通过构建高密度的分子连锁图，找到与目的基因紧密连锁的分子标记，不断缩小候选区域进而克隆该基因，并阐明其功能和生化机制。用该方法分离基因是根据目的基因在染色体上的位置进行的，无需预先知道基因的DNA序列，也无需预先知道其表达产物的有关信息。它是通过分析突变位点与已知分子标记的连锁关系来确定突变表型的遗传基础。近几年来随着拟南芥基因组测序工作的完成，各种分子标记的日趋丰富和各种数据库的完善，在拟南芥中克隆一个基因所需要的努力已经大大减少了（图1）。

基因克隆及转基因方法

基因克隆及转基因一、基因克隆及转基因过程 1、设计引物软件是，用到里面的PrimerSelect和EditSeq。一般原则：1、长度：18-25； 2、GC含量：40-60％，正反向引物相差不要大于5%； 3、Tm值：55以上（到65），实在不行50以上也可以，正反向引物相差不要大于5； 4、3’端结尾最好是GC，其次是T，不要A； 5、正反向引物连续配对数小于4； 6、在NCBI上的Primer Blast上看引物特异性如何；（如果克隆的话不能满足条件也没办法。）不是必须条件，但可以考虑：多个基因设计引物时，可尽量使Tm值相似，方便PCR。步骤：一、打开PrimerSelect和EditSeq。二、在EditSeq中输入你的序列。引物有一对F和R 1、对于F是从5’到3’，在序列的前部分选择长度为18-25bp的碱基，如果你是要验证就随便选，如果你是要克隆就在最开始选，不符合原则就只能在你选的后边增或减碱基。 2、将选择的F引物输入到PrimerSelect中，在File中选择Enter New Primer，复制，OK，然后可以看到引物的情况，看看长度、Tm、GC含量是不是符合标准，不符合就继续选。 3、对于R是从3’到5’，选中序列，在EditSeq的Goodies中选择第一个“反向互补”，此时序列已反向互补，按照前面F的方法搜索R的引物。、 4、注意你想要的目的带的大小，比如序列是1000bp，你想PCR出来800大小的目的带，那

就要看看F和R之间的长度在你想要的范围内。可以将R反向互补，在正向的序列中搜索R 在的位置，就是在EditSeq中选择Search，点击第一个Find，开始搜寻。 5、搜索完引物在PrimerSelec中的Report中选择前两个查看二聚体情况。 6、在NCBI上的Primer Blast上看引物特异性如何。 7、因为是克隆，所以引物要有酶切位点，酶切位点的加入主要考虑所用到的表达载体，在NEBcutter网站中输入总序列查看可用的酶切位点。在引物上游加入酶切位点，注意加入时载体的表达的方向，前面的酶切位点在引物F上，后面的酶切位点在引物R上。一般在引物上游还要加上两个保护碱基。 2、提取醋栗DNA 3、PCR扩增与目的基因回收 PCR先找合适的退火温度，找到后回收时就可以多PCR几管，一般我们用20ul的体系，PCR5管就可以回收，就是琼脂糖凝胶回收，将目的基因用刀片切下来，用试剂盒回收。回收完可以再跑电泳检测一遍。 PCR： 20ul体系：灭菌水，若模板为质粒灭菌水；； 10乘Taq ；引物F、R各；模板1ul；若为质粒就够；最后加入Taq酶，Taq酶现用现拿。过程：我们用的是预变性94℃3min；然后进入循环（要进行克隆的话循环数最好不要超过30个），循环过程：变性94℃30s，退火退火温度下30s，延伸72℃时间根据目的基因长度和酶而定，一般Taq酶30s可以延伸1kb；循环完成后，此时尚有正处于合成过程中的dna链,为了保证充分的得率,所以再增加7分钟的72℃延伸；最后4℃保存待用。

PCR技术克隆目的基因全过程

实验：目的基因克隆（PCR技术）【课前预习】 PCR (polymerase chain reaction) 反应的基本原理。【目的要求】 1．学习和掌握PCR 反应的基本原理与实验技术方法。 2．认真完成每一步实验操作，详细记录实验现象和结果并加以分析和总结。【基本原理】类似于DNA 的天然复制过程，其特异性依赖于与靶序列两端互补的寡核苷酸引物。PCR 由变性--退火--延伸三个基本反应步骤构成：①模板DNA的变性：模板DNA 经加热至93℃左右一定时间后，使模板DNA双链或经PCR 扩增形成的双链DNA 解离，使之成为单链，以便它与引物结合，为下轮反应作准备；②模板DNA 与引物的退火(复性)：模板DNA 经加热变性成单链后，温度降至55℃左右，引物与模板DNA 单链的互补序列配对结合；③引物的延伸：DNA 模板--引物结合物在TaqDNA 聚合酶的作用下，以dNTP为反应原料，靶序列为模板，按碱基配对与半保留复制原理，合成一条新的与模板DNA 链互补的半保留复制链重复循环变性--退火--延伸三过程，就可获得更多的“半保留复制链”，而且这种新链又可成为下次循环的模板。每完成一个循环需2～4 分钟，2～3 小时就能将待扩目的基因扩增放大几百万倍。到达平台期(Plateau)所需循环次数取决于样品中模板的拷贝。【实验用品】 1．材料：重组质粒DNA作为模板 2．器材和仪器：移液器及吸头，硅烷化的PCR 小管，DNA扩增仪(PE 公司)，琼脂糖凝胶电泳所需设备(电泳槽及电泳仪)，台式高速离心机 3．试剂： ①10×PCR 反应缓冲液：500mmol/L KCl, 100mmol/L Tris·Cl, 在25℃下, pH9.0, 1.0％Triton X-100。 ②MgCl2 ：25mmol/L。 ③ 4 种dNTP 混合物:每种 2.5mmol/L。 ④Taq DNA聚合酶5U/μl。 ⑤T4 DNA连接酶及连接缓冲液：

酪氨酸激酶体系顺口溜

酪氨酸激酶体系：胰岛素，生长激素，促红细胞生成素记忆：为了生计老暗算别人一刀，简直是个畜生血沉ESR加快：纤维蛋白质，球蛋白，胆固醇记忆：单纯（胆固醇）少女求（球蛋白）签（纤维蛋白）问红尘（红细胞血沉）单核细胞：3-8%,中性粒50-70%,淋巴20-40% 记忆：单身三八，中年无（5）妻（7），聆听儿诗红细胞生成素调节：BPA(爆式促进激活物)，促红细胞生成素，性激素，生长激素，甲状腺素记忆：一个畜生（促红细胞生成素）居然对生（生长激素）怀六甲（甲状腺素）的女人实施性（性激素）暴力，性激素：雄激素是正性，雌激素负性铅中毒：动力性肠梗阻，卟啉症记忆：铅可抑制ALA脱水酶和亚铁鳌合酶的活性蛋白质大部分是由肝脏合成的，除了Y-球蛋白是由浆细胞合成的记忆：将（浆）在外（Y）,君令有所不受失读症：角回受损-->独角戏（歌名）

感觉性失语症：颞上回受损-->驱赶余孽运动性失语症：Broca区-->洞与B联系起来，或Bro像brother,brother爱运动啊快痛-->传入纤维是A*纤维，慢痛C纤维，记忆：形状像豆芽，豆芽长的快，C纤维-->chronic是慢的意思凝血因子：IV->钙离子，V->易变因子记忆：武艺盖世凝血辅因子：IV，V，III，VIII 记忆：我师傅三八被消耗的因子：II，V，VII，VIII 记忆：浩霸占我妻儿内源性凝血：启动因子12,慢，单独凝血因子12,11,9,8,4，外源性凝血：启动因子3,快，单独凝血因子，3,4,7 记忆：123,3+4=7

生理性抗凝血酶III：丝氨酸酶抑制物-->9-12因子记忆：3+9=12 VitA的活性形式：视黄醇，视黄醛，视黄酸记忆：看（视）黄色的电影是看A片几乎所有的血浆蛋白均为糖蛋白，除了清蛋白，清蛋白与胆红素结合记忆：因为清蛋白清高，不愿与糖为伍，清蛋白-->清扫有毒物质-->与胆红素结合-->肝脏-->胆红素与Y,Z蛋白结合对凝血酶敏感的凝血因子：I,V,VIII,XIII 蛋白质C系统：蛋白质C(PC),凝血酶调节蛋白，蛋白质S,蛋白C抑制物蛋白质C (PC):灭活Va,VIIIa，抑制Fx及凝血酶原激活记忆：我爸（5,8）嫖娼（P,C）蛋白C抑制物与PC形成APC 肝脏是储存维生素A,E,B12,K的主要场所记忆：干(肝)脆罢课（BAKE）氨基酸的记忆：中性氨基酸：中性氨基酸：谷氨酰胺，天冬酰胺，酪氨酸，丝氨酸，色氨酸，苏氨酸，胱氨酸，蛋氨酸记忆：(中)国(股东老实)好(色输)成穷(光蛋)

非受体型酪氨酸激酶Syk蛋白的提取与纯化

论著文章编号:1007-8738(2004)02-0230-04 非受体型酪氨酸激酶-Syk 蛋白的提取与纯化单保恩1,董青1,李宏芬1,陈晶1,董金琢2,马洪2 (1河北医科大学第四医院科研中心,河北石家庄050011;2美国MD 技术公司,Mo 63021,美国) 收稿日期:2003-06-07; 修回日期:2003-09-18基金项目:美国MD 技术公司合作课题基金资助(2002年)作者简介:单保恩(1962-),男,河北邯郸人,教授,博士生导师. Tel:(0311)6033941-290;Email:baoenshan@yahoo.c https://www.wendangku.net/doc/5b580011.html, Extraction and purification of non -recep -tor type PTKs -Syk SHAN Bao -en 1,DO NG Qing 1,LI Hong -fen 1 ,C HE N Jing 1,DO NG Jin -zhuo 2,MA Hong 2 1 Research Center ,The Fourth Hospital of Hebei Medical University,Shijiazhuang 050011,China;2MD Technologies Inc.845Pheasant Woods Drive Manchester,Mo 63021,USA Abstract AIM:To extract and purify Syk protein from Sf 21cells transfec-t ed by syk gene.METHODS:Sf 21cells were transfected with recom bi nant syk gene.After 48h of incubation at 28 ,the transfected cells were collected and sonicated with Sonfier son-i cator on ice.Filtered cell extract was loaded onto a R eactive Yellow -3res i n column and Toyopearl AF -H eparin -650M colum n respectively.The character of Syk protein in the fractions were i dentified by SDS -PAGE,W estern blotting and IEF.RESULTS:225m g of protein containing Syk were obtained from Sf 21cells (2.5 109)extract.There were two subpopulations in the elu -tion of Reactive Yellow -3resin column with the same relative molecular mass (M r )72 103.The two subpopulations were then applied on Toyopearl AF -H eparin -650M column and two pure proteins were obtained.The results of SDS -PAGE,W es-t ern blotting,and IEF showed the two proteins having the sam e relative molecular mass (72 103),corresponding to Syk,but with different pI.CONCLUSION:The y ield of Syk was 8m g from 2.5billion cells and the purity was >95%.The two purified Syk proteins have the sam e M r and different pI.The purified Syk protein can be applied to study Syk s m echanism,produce ant -i Syk antibody and invent Syk diagnosis kit,etc .Keywords:Syk;purification;chromatography;Sf 21cells 摘要目的:从转染syk 基因的Sf21细胞中提取、纯化免疫相关因子 Syk 蛋白。方法:将syk 基因转染Sf21细胞,于28 培养48h,收集细胞,用超声波破碎仪裂解细胞,提取裂解液中总蛋白,用Yellow -3凝胶和Toyopear-l AF -Heptin -650M 凝胶层析柱分离、纯化。层析液中的Syk 蛋白存在和性质,用SDS -PAGE 、免疫印迹实验和等电聚焦实验鉴定。结果:从25亿个Sf21细胞裂解液中提取了含有Syk 的225mg 蛋白质。经Yellow -3凝胶层析分离,得到两个亚种的Syk 蛋白,相对分子质量(M r )均为72 103。进一步用Toyopear-l AF -Heptin -650M 凝胶层析纯化后,得到两个纯的Syk 蛋白,SDS -PAGE 、免疫印迹实验结果显示,两种Syk 的M r 均为72 103,与Syk 的理论相对分子质量吻合。但等电聚焦实验显示,这两种Syk 蛋白成分具有不同的pI 值。结论:从25亿个转染syk 基因的Sf21细胞中纯化出8mg Syk 蛋白,纯度高于95%。这两种Syk 的M r 虽然相同,但具有不同的pI 值,是两个亚种。这些Syk 可用于研究Syk 的作用机制、抗Syk 抗体的制备和Syk 诊断试剂盒的制备等。关键词:Syk;分离纯化;凝胶层析;Sf21细胞中图分类号:R392.11 文献标识码:B 非受体型酪氨酸激酶(spleen tyrosine kinase,Syk),是一种B 细胞激活信号转导中最重要的激酶 [1] ,与T 细胞激活中的ZAP -70属于同一个PTK 家族,M r 为72 103。Syk 在T 细胞和B 细胞的成熟和活化过程中起关键作用[2]。该酶除有激酶活性中心SH1之外,还有两个SH2结构域,因而成为磷酸化I -TAM 招募的首选对象。被招募的Syk 立即成为Src 作用的第二个靶目标,进而启动B 细胞活化信号转导的三条主要途径(磷脂酰肌醇途径、MAP 激酶相关途径和磷酸肌醇3激酶途径),激活各种转录因子转位进入细胞核,与基因启动子区域中各种顺式作用元件或DNA 小盒结合,使相应基因发生转录激活和产物表达,调整B 细胞等细胞克隆的蛋白质表达、细胞分化或凋亡。研究发现,Syk 不但是免疫调节因子,在肿瘤发生发展中也发挥着重要作用。但是,用于研究Syk 的蛋白标准品、抗体和诊断用试剂很难取得。我们介绍从转染s yk 基因的Sf21细胞中提取、纯化免疫相关因子Syk 蛋白。

基因克隆步骤完整版

1总RNA提取 (1) 液氮研磨或冰上匀浆实验材料；先将1mlTrizol加到离心管中待用 (2) 将研磨好的样品加到离心管中混匀，室温放置5 min；打开离心机预冷 (3) 加200 μL氯仿，振荡15 sec，室温放置3 min，分层； (4) 4o C，12,000g，离心15 min； (5) 取上清，加500 μL异丙醇，混匀，室温放置10 min； (6) 4o C，12,000g，离心10 min； (7) 弃上清，加1 mL75%乙醇，漂浮洗涤沉淀，振荡充分；再用100%乙醇清洗 (8) 4o C，7,500g，离心5 min； (9) 弃上清，离心，用枪吸取多余液体，放在超净台里干燥后，加50 μL DEPC 水，－80o C保存。此操作中所用到的器皿均需经过DEPC灭活RNA酶处理。提取的总RNA 需经RNA电泳检测质量，并用紫外分光光度计测定浓度。OD260值为核酸的吸收值，OD280值为蛋白的吸收值，OD260/280值在1.8-2.0间一般说明该核酸蛋白含量在允许的范围内，可正常使用；此外还有OD230值为多糖和酚类的吸收值，比较干净的核酸OD260/230值能达到2.2左右。RNA浓度计算公式：总RNA 浓度（μg/mL）=A260×稀释倍数×40。

2反转录/cDNA第一链的合成纯化RNA以去除基因组DNA，操作按TaKaRa公司的PrimeScript RT reagent with gDNA Eraser(Perfect Real Time)说明书进行。其体系为： Total RNA 1μg 5×gDNA Eraser Buffer 2μL gDNA Eraser 1μL RNase Free dH2O 补齐至10μL 条件为：42o C，2min； RNA纯化后，即可进行反转录。其体系为： 5×PrimeScript Buffer 2（for Real Time）4μL PrimeScript RT enzyme mix Ⅰ1μL RT Primer Mix 1μL 上一步的反应液10μL RNase Free dH2O 补齐至20μL 操作条件为： (1) 37o C放置15 min；(2) 85o C，5 sec；(3) 4o C保存。 3 PCR 按TaKaRa公司的Premix Taq Version 2.0操作，，PCR反应体系如下： Premix Taq25μL 模板5μL 引物1 (10 μM) 1μL 引物2 (10 μM) 1μL ddH2O 18μL PCR反应条件为：94°C预变性5min；94°C变性30s，53°C退火30s，72°C 延伸30s，循环36次；72°C延伸10min。 PCR反应完毕，取5μL反应产物进行1%琼脂糖凝胶电泳（若割胶回收则用10μL反应产物）。