DNA methylation shows genome-wide association of NFIX, RAPGEF2 and MSRB3 with gestational age at
DNA methylation shows genome-wide association of NFIX ,RAPGEF2and MSRB3with gestational age at birth
Hwajin Lee,1,2y Andrew E Jaffe,1,3,4y Jason I Feinberg,1Rakel Tryggvadottir,1Shannon Brown,3Carolina Montano,1,2Martin J Aryee,1,5Rafael A Irizarry,1,4Julie Herbstman,3,6Frank R Witter,7Lynn R Goldman,8,9Andrew P Feinberg 1,2,4*y and M Daniele Fallin 1,2,3,4y
1
Center for Epigenetics,Johns Hopkins School of Medicine,Baltimore,MD,USA,2Department of Medicine,Johns Hopkins School of Medicine,Baltimore,MD,USA,3Department of Epidemiology,Johns Hopkins Bloomberg School of Public Health,Baltimore,MD,USA,4Department of Biostatistics,Johns Hopkins Bloomberg School of Public Health,Baltimore,MD,USA,5Division of Biostatistics and Bioinformatics,Department of Oncology,Johns Hopkins School of Medicine,Baltimore,MD,USA,6Department of Environmental Health,Columbia Mailman School of Public Health,New York,NY,USA,7Department of Obstetrics and
Gynecology,Johns Hopkins School of Medicine,Baltimore,MD,USA,8George Washington University School of Public Health,Washington DC,USA and 9Department of Environmental Health Sciences,Johns Hopkins Bloomberg School of Public Health,Baltimore,MD,USA
*Corresponding author.Andrew P Feinberg,MD,MPH,Professor,John Hopkins School of Medicine,855N.Wolfe Street,Rangos 570,Baltimore,MD 21205,USA.E-mail:afeinberg@https://www.sodocs.net/doc/c03119077.html, y
These authors contributed equally to this work.
Background Gestational age at birth strongly predicts neonatal,adolescent and
adult morbidity and mortality through mostly unknown mechan-isms.Identification of specific genes that are undergoing regulatory change prior to birth,such as through changes in DNA methylation,would increase our understanding of developmental changes occur-ring during the third trimester and consequences of pre-term birth (PTB).Methods
We performed a genome-wide analysis of DNA methylation (using microarrays,specifically CHARM 2.0)in 141newborns collected in Baltimore,MD,using novel statistical methodology to identify gen-omic regions associated with gestational age at birth.Bisulphite pyrosequencing was used to validate significant differentially methylated regions (DMRs),and real-time PCR was performed to assess functional significance of differential methylation in a subset of newborns.
Results
We identified three DMRs at genome-wide significance levels adja-cent to the NFIX,RAPGEF2and MSRB3genes.All three regions were validated by pyrosequencing,and RAGPEF2also showed an inverse correlation between DNA methylation levels and gene expression levels.Although the three DMRs appear very dynamic with gesta-tional age in our newborn sample,adult DNA methylation levels at these regions are stable and of equal or greater magnitude than the oldest neonate,directionally consistent with the gestational age results.
Conclusions We have identified three differentially methylated regions asso-ciated with gestational age at birth.All three nearby genes play important roles in the development of several organs,including skeletal muscle,brain and haematopoietic system.Therefore,they
Published by Oxford University Press on behalf of the International Epidemiological Association ?The Author 2012;all rights reserved.International Journal of Epidemiology 2012;41:188–199
doi:10.1093/ije/dyr237
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may provide initial insight into the basis of PTB’s negative health
outcomes.The genome-wide custom DNA methylation array tech-
nology and novel statistical methods employed in this study could
constitute a model for epidemiologic studies of epigenetic variation. Keywords Epigenetic epidemiology,differentially methylated regions,pre-term birth,gestational age,genome-wide DNA methylation
Introduction
Gestational age is the most important indicator of peri-natal mortality in developed countries,1and also con-tributes to childhood and adult morbidity and mortality.2–4In2005,approximately13%of infants in the USA were born pre-term(<37weeks),a rise from<10%in1990.5The mechanism by which pre-term birth(PTB)increases morbidity and mortality is largely unknown.Recognition of specific genes that are still undergoing regulatory change prior to birth would not only increase our understanding of the developmental changes that are occurring during late pregnancy,but also it would aid in identifying genetic, epigenetic and environmental factors that could lead to PTB.The risks of negative public health conse-quences of PTB are many,including mortality,learning disabilities and respiratory illnesses.6Identification of environmental and epigenetic factors has the potential to prevent or ameliorate these adverse impacts.
From the point of view of a developmental change that is associated with health risk and environmental mediators,epigenetic changes in the fetus are poten-tially important,since epigenetic information affects gene expression,and its function varies within an in-dividual across developmental stages.A significant challenge in understanding the role of epigenetic changes in epidemiology is integrating novel molecu-lar,epidemiological and biostatistical tools at a genome-scale level.Unlike classical genome sequence analyses,the methods and study designs for whole-genome epigenetic epidemiology are not yet well established.The approach we have taken here is to design a genome-scale epidemiological analysis a priori from this joint conceptual perspective.We focused on DNA methylation because it is a key pri-mary epigenetic process,with a well-established mechanism for propagating non-sequence-based in-formation during cell division.The DNA methylation analysis presented here can serve as a paradigm for other epidemiological studies intending to character-ize epigenetic profiles in specimen repositories,in which DNA methylation but not other epigenetic marks(e.g.histone modifications)are preserved. We have applied a significant technological exten-sion of our previously described comprehensive high-throughput array-based relative methylation (CHARM)approach7that can now detect5.2million cytosine–guanine dinucleotide(CpG)sites which can be subject to DNA methylation.We also formally
define an epigenetic variable,termed differentially methylated region(DMR),which we have used pre-
viously,but now have advanced its genome-wide de-
tection to include novel statistical strategies to
improve signal to noise detection,as well as the con-
cept of regional methylation detection(Jaffe et al., companion paper8).
While one would expect large-scale epigenetic
changes to occur between early embryogenesis and
the end of gestation,at present nothing is known
about epigenetic changes in the fetus that occur rela-
tively late in pregnancy,covering intervals relevant to
the variation in gestational ages at birth that repre-
sent dramatic changes in health outcomes.Epigenetic
changes in placental samples across gestation have
been observed,implying the importance of such modifications for support of a growing fetus,9but
genome-scale site-specific methylation data on the
fetus itself,and with respect to the late gestational
ages associated with most births,have not yet been
reported to our knowledge.For these reasons,we
performed a genome-scale comprehensive analysis of
DNA methylation on141newborns to identify regions
of the genome with DNA methylation levels correlated
to gestational age at birth.We then validated these
microarray results via bisulphite sequencing and fur-
ther characterized the relationship between develop-
mental age and DNA methylation at the DMRs by
comparing these newborn results to the same regions
among adult DNA samples.
Methods
Study sample
Cord blood samples were obtained from the Baltimore
Tracking Health Related to Environmental Exposures [THREE]Study.10THREE is a cross-sectional sample
of newborns born at the Johns Hopkins Hospital in Baltimore,MD,between November2004and March
2005.Of the603children delivered during that time
window,300were eligible(24twin births removed,
291did not have any or ample cord blood available).
Of these,187contributed a cord blood clot from which
DNA could be isolated for this epigenetic project.Clots
were saved during the second half of the data collection
period.Those with available cord blood clots are similar
to the rest of the study population with respect to
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gestational age,birthweight and maternal age,race, body mass index and smoking status(data not shown).Study personnel abstracted data from maternal and infant medical records and study clinicians re-viewed a10%random sample for accuracy;gestational age was taken as the best obstetrical estimate. Information on potential confounders was based on clinical records.Women who reported smoking during pregnancy or had an umbilical cord serum cotinine measurement410ng/ml were considered active smo-kers;the remainder were considered passive smokers or non-smokers(not reporting smoking and cotinine <1ng/ml).11Copper(previously found to be associated with gestational age in this population)was measured in umbilical cord serum using inductively coupled plasma dynamic reaction cell mass spectrometry(ICP–DRC–MS)12at Centers for Disease Control and Prevention (CDC)laboratories,with4mg/dl as the limit of detec-tion.The THREE study was reviewed and approved by the Johns Hopkins School of Medicine Institutional Review Board.
For comparison of newborn methylation results with adult samples,CHARM 2.0data were available on 156adult samples obtained as unrelated controls for a schizophrenia case–control epigenetics consortium.13–15 This sample was40%male and had a broad age range of between16and89years(interquartile range31–55 years).DNA was obtained from the Rutgers University Cell&DNA Repository(RUCDR).DNA had been isolated from whole blood using Qiagen Autopure LS and pellets were hydrated in1?Tris-EDTA(TE)buffer.Sample con-centration and integrity were verified locally using NanoDrop and gel electrophoresis.DNA methylation was measured using the CHARM2.0assay. Laboratory analyses
CHARM DNA methylation
DNA was isolated from cord blood clot samples using the DNeasy?Blood&Tissue kit(Qiagen),following the manufacturer’s instructions.From the187fetal cord blood clot samples available,167(89.3%)yielded enough DNA for methylation array analysis.DNA methylation was measured via the CHARM 2.0 assay,a customized microarray method extended from our previous CHARM procedure,7a genome-scale microarray technique for DNA methylation that identifies differential DNA methylation without assumptions regarding where such changes would be, interrogating all CpG islands,as well as CpG island ‘shores’.16CHARM 2.0now includes 2.1million probes,which cover5.2million CpGs arranged into probe groups(where consecutive probes are within 300bp of each other)that tile regions of at least mod-erate CpG density.It includes all annotated and non-annotated promoters and microRNA sites on top of the features that are present in the original CHARM method.The design specifications are freely available on our website(https://www.sodocs.net/doc/c03119077.html,).For the CHARM 2.0assay,5m g of purified genomic DNA was sheared,digested,purified,amplified,labeled as described,17but hybridized onto our new CHARM2.0 array.We dropped26arrays with<80%of their probes above background intensities,resulting in 141samples for DNA methylation analysis.We then filtered probes where signal was below background in <25%of arrays(542055)and removed sex chromo-somes(39454)to improve the batch correction methods,leaving1569888autosomal probes covering 4254946CpGs spread across114984probe groups. Subsequent pre-processing,normalization and correc-tion for batch effects are described in the Statistical Methods subsection.CHARM hybridization and pro-cessing for these samples were performed across5 separate days,with the following numbers of samples per day:40,36,38,21,6,reflecting a potential source of batch effects that was addressed through the sur-rogate variable analysis(SVA)described in the Statistical Methods subsection.
Bisulphite pyrosequencing
Individual CpGs inside the DMRs meeting our signifi-cance threshold were chosen for validation based on MethPrimer software.18Of the141samples for which CHARM data were generated,139had ample DNA for subsequent pyrosequencing.Genomic DNA (200ng)from each sample was bisulphite treated using an EZ DNA Methylation-Gold TM Kit(Zymo research)according to the manufacturer’s instruc-tions.Bisulphite-treated genomic DNA was PCR amplified using unbiased nested primers,and DNA methylation was subsequently assessed quantitatively by pyrosequencing using a PSQ HS96(Biotage). Quantitative measurements(percentage methylation at each CpG)from the pyrosequencing results were determined using the Q-CpG methylation software (Biotage).Control titration standards of0,25,50, 75and100%methylated samples were generated using appropriate mixtures of Whole Genome Amplified(WGA)Human Genomic DNA:Male (Promega)using a REPLI-g Mini Kit(Qiagen)and SSs I-treated WGA DNA.Primer sequences used for the bisulphite pyrosequencing reactions can be found in Supplementary Table S2available as Supplementary Data at IJE online.
Quantitative real-time PCR
To examine the correlation between DNA methylation and gene expression in cord blood clots for each of the top three DMRs,we performed real-time PCR assays.Primers were designed to determine the mRNA expression of the gene closest to each DMR. Since this analysis required isolation of mRNA from cord blood clots,we were only able to perform these expression analyses on a subset of newborns with cord blood clots available.This included10babies with gestational age at birth<35weeks,15with ges-tational age at40weeks and17with gestational ages 541weeks.For isolation of RNA,fetal cord blood clot
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samples were treated with TRIzol(Invitrogen)and RNA was purified using a PureLink TM RNA Mini Kit (Invitrogen)according to the manufacturer’s instruc-tions.cDNA was synthesized using a QuantiTect Reverse Transcription Kit(Qiagen)and random hex-amers.Real-time PCR amplification was performed by using a Fast SYBR?Green Master Mix(Applied Biosystems),and transcript levels were quantified using an ABI7900Sequence Detection Systems (Applied Biosystems).Relative expression level for each gene was calculated based on the standard curve and normalized by the relative expression of b-actin.Primer sequences used for the real-time PCR reactions are in Supplementary Table S3avail-able as Supplementary Data at IJE online. Statistical analyses
Descriptive statistics(median or percentage)for ges-tational age at birth and potential confounders were calculated and compared using chi-squared tests for categorical variables and Mann–Whitney U-tests for continuous variables.
The CHARM microarray data were pre-processed and normalized as previously described.19,20We employed a novel statistical approach(see companion paper,Jaffe et al.8)for identifying regions of the epigenome associated with gestational age in days. Briefly,we fit a linear model predicting methylation at each probe as a function of gestational age at birth, adjusted for surrogate variables,estimated via SVA21,22to account for unmeasured potential con-founding often due to batch effects.SVA identifies combinations of probes in the data associated with heterogeneity of DNA methylation,conditioned on the covariate of interest,in this case,gestational age,and then constructs a‘surrogate variable’for each set.A value for each individual based on each surrogate variable can then be used for adjustment in subsequent regression.Measured variables in our data set most associated with these surrogate variables(as-sessed through pruned regression trees of all possible variables)were array quality control score and hybrid-ization date/batch.We did not adjust for sex,but did remove sex chromosome probes from the initial genome-wide screen.The estimated regression coefficients from these linear models for gestation-al age at each probe were then smoothed within the CHARM array’s pre-defined probe groups. Consecutive smoothed slopes above a fixed cut-off of the99.5th percentile of all smoothed slopes were summed into a region-level statistic reflecting the area of the DMR(see companion paper,Jaffe et al.8).We then ranked DMRs by their areas and calculated two measures of statistical uncertainty,a P-value and q-value,for each DMR by permutation that accounts for genome-wide testing.Gestational ages were per-muted1000times,and each time,the above regres-sion,smoothing,and thresholding procedure was repeated exactly as on the observed data to get1000sets of declared DMRs that occurred solely by chance.
Empirical P-values,defined as the fraction of the
maximum areas from each permutation greater than
the observed area,were calculated(‘P max’)to compare
with a specified family-wise error rate control of10%.
False discovery rate(FDR)q-values were obtained by
pooling all areas across all permutations,calculating
the proportion of these‘null’areas greater than the
observed area,then converting this to a q-value for comparison to an FDR control of5%.23DMRs with
an empirical P max<0.10or an FDR q-value<0.05
were examined visually via plots of the methylation
curve within the DMR.Average methylation for each
newborn across all probes within a DMR was plotted
against gestational age at birth with slopes and
P-values estimated via linear regression and Wald
statistics.
Univariate relationships between potential confoun-
ders and methylation at DMRs were also estimated
via linear regression.Although some potential con-
founding due to these variables may already be
addressed via the SVA adjustment,we also explicitly
estimated relationships between average DNA methy-
lation for each DMR and each confounder through
linear models adjusted for the same surrogate vari-
ables used in our discovery.To do this,we applied
SVA analysis to the methylation data first,then
took SVA-adjusted methylation as the methylation
metric for linear regression with the covariate,to
ensure the same SVA adjustment was applied in
each analysis.Also,as a sensitivity analysis to assess
the influence of sex on our list of identified DMRs,we
repeated the original DMR identification procedure
adjusting for sex.We further performed the original
discovery procedure after omitting samples with
mothers who had pregnancy-induced hypertension
(PIH),intrapartum fever or diabetes,separately,to
assess influence of these variables on our results.
For analysis of DNA methylation data from pyrose-quencing,we fit a linear model at every CpG predict-
ing DNA methylation as a function of gestational age.
We assessed the functional implications of differential methylation at each gene by fitting linear models at
each CpG assessing the linear association between
DNA methylation and gene expression.Heavily
skewed gene expression values were transformed to
log2scale.
Results
To identify epigenetic changes that occur throughout
later stages of gestation in an unselected population
of newborns,we performed the CHARM 2.0assay,
which now includes approximately one-third of all
single-copy CpG sites including all islands and
shores,as well as all annotated promoters and microRNAs.Bisulphite pyrosequencing and real-time
PCR were performed to validate DNA methylation
levels and functional significance of the DMRs
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associated with gestational age at birth.Of the 141newborns with CHARM data,there were 18PTBs (<37weeks)and the range of gestational ages in days was 208–292(see Supplementary Figure S1available as Supplementary Data at IJE online for full distribution).The pre-term newborns did not differ in the distributions of sex or maternal age,race or diabetes status compared with newborns born after 37weeks (Table 1).Birthweight differed strongly between the two groups,as did smoking and serum copper levels,which had been previously reported for the full study sample of 300newborns.24Previous research indicates that increasing gesta-tional ages at birth through 39–41weeks is advanta-geous for neurodevelopment 25,26and confers a lower risk of respiratory morbidity,27suggesting the need to study gestational age on a continuum.Thus,treating gestational age as a continuous variable in linear re-gression,compared with pre-term and term birth categories can be https://www.sodocs.net/doc/c03119077.html,ing this approach,we identified 8611candidate DMRs associated with ges-tational age at birth (Supplementary Table S1avail-able as Supplementary Data at IJE online),of which the top three DMRs met our genome-wide threshold of protecting family-wise error rates <10%and false discovery rates <5%(Table 2).The first of these DMRs was found to be positioned in the first intron of the nuclear factor I/X (NFIX )gene,encoding a transcription factor known to be responsible for fetal-specific transcription regulation during skeletal muscle development.28Another was positioned in the first intron of the alternative transcript of the Rap guanine nucleotide exchange factor (RAPGEF2)gene,which encodes one of the RAS protein family activators that maintains the GTP-bound state of RAS.Although this DMR was not located at the promoter of the canonical gene,the DMR contains strong DNase I hypersensitive sites and a number of strong transcription factor-binding sites including Gata-2and PU.1,which are the critical transcription factors in haematopoiesis.29,30The third DMR was located next to the promoter region of the methionine-S -sulphoxide reductase 3(MSRB3)gene,which encodes the enzyme involved in the methionine cycle and is responsible for antioxidant repairing by converting methionine sulphoxide to methionine.31Two of the three DMRs are located at the CpG island shore,sug-gesting that these DMRs may be associated with al-ternative transcription or splicing.16
The methylation values at each probe for each of these DMRs are shown in Figure 1according to ges-tational age in weeks (calculated from days).Smoothed lines indicate the average methylation curve for each week of gestational age at birth,and show a dose–response trend between gestational age and methylation levels across all weeks for each DMR.To further illustrate this point,Figure 1also shows the relationship between the average methylation across all probes in the DMR and gestational age,and the linear fit of this relationship (see insets in each panel).For the DMR near NFIX ,DNA methyla-tion levels of each probe are greater in high
Table 1Characteristics of THREE study newborns included in this epigenetics project
Pre-term
(<37weeks),N ?18Term/post-term (537weeks),N ?123P -value*
Male sex (%)
56520.98
Maternal age,median (IQR)(years)28(22–30)24(20–29)0.14Maternal race (%)0.32
Caucasian 3321AA 6773Asian
06
Maternal smoking (%)<0.01
Non-smoker 5675Passive smoker 011Active smoker
44
14
Birthweight,median (IQR)(g)2422(2102–2689)3279(2906–3648)<0.01Pregnancy-induced hypertension (%)2260.04Intrapartum fever (%)
6
8
1.00Serum copper,median (IQR)(m g/dl)26(22–34)41(30–55)<0.01Elected delivery (%)
a
44
37
0.59a
Caesarean section or induced delivery.
*P -values based on chi-squared tests for categorical variables and Mann–Whitney tests for quantitative variables.IQR ?interquartile range;AA =African American.
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gestational age neonates when compared with low gestational age neonates (Figure 1a ),and the average DNA methylation level of each sample in the DMR exhibits a linear correlation with gestational age,with an estimated increase of 1.57%DNA methylation per week of gestation [95%confidence interval (CI)1.02–2.12],or an increase of 7.85between Weeks 35and 40,roughly corresponding to late pre-term vs term births (P ?8.6?10à8for Wald statistic;see Figure 1a,inset).In contrast,the DMRs at RAPGEF2and MSRB3show lower DNA methylation levels of each probe in higher gestational age neonates when com-pared with lower gestational age neonates (Figure 1b and c),and the average DNA methylation levels of each sample in these DMRs exhibit inverse linear cor-relation with gestational age.For RAPGEF2,there is a 1.33decrease in %DNA methylation (95%CI à1.76to à0.9)per week of gestation or a decrease of 6.65between Weeks 35and 40;(Wald P ?9.9?10à9)and for MSRB3,a 2.08decrease (95%CI à2.51to à1.64)per week or 10.4between Weeks 35and 40(Wald P ?1.3?10à16;see Figure 1b and c insets).Also note the progressive change in DNA methylation within each gestational age bin,a dose–response relationship consistent with a functional relationship between methylation and gestational age.
To validate these findings on a separate platform,we designed bisulphite pyrosequencing assays for CpGs within each DMR (indicated as red blocks in Figure 1).The individual CpG results within each DMR were correlated (average pair-wise correlation for neighbouring CpG methylation was 0.85for NFIX ,0.68for RAPGEF2and 0.82for MSRB3)and confirmed the CHARM differences in methylation by gestational age.For NFIX ,four CpGs were assayed (see Figure 1for locations),each showing an incremental increase in methylation with increase in gestational age at birth,consistent with the pattern detected in CHARM (Figure 2a ).All three of the CpGs assayed in RAPGEF2(see Figure 1for locations)showed
greater
Figure 1Methylation plots for three identified DMRs for gestational age at birth.(a)NFIX ,(b)RAPGEF2,(c)MSRB3.Top half of panels show individual methylation levels at each probe by genomic position,with coloured lines reflecting the average methylation curve for samples binned by gestational age—gestational ages in weeks were split into equal sized bins,and the average age for each bin is shown in the legend.Bottom half of panels show location of CpG dinucleotides (black tick marks)and CpGs validated by bisulphite pyrosequencing (black tick marks contained in red box)as well as the CpG density by position (black curve)and the location of refseq annotated genes (bar,tand àrepresent the direction of the gene,green bar indicates CpG island).Vertical lines represent boundaries of the DMR.Inset box:linear regression plot of average methylation across the DMR (Avg %M)per sample by gestational age (GA)
Table 2Differentially methylated regions for gestational age identified via CHARM 2.0Chr Nearest gene DMR area P-value q-value DMR
start position DMR end position Location relative to gene
19NFIX 0.3430.0010.0121313068613133039Inside intron
4RAPGEF20.2230.0470.029160026138160028079Upstream,intron of alt.transcript 12
MSRB3
0.197
0.098
0.041
65671230
65672140
Promoter
P -values and q -values based on comparison of observed DMR area ranks to ranks among 1000permutations of gestational age values.All coordinates are based on hg19/build 37.Chr,Chromosome;Alt.,Alternative.
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methylation with early gestational age at birth,con-sistent with the CHARM results (Figure 2b ).For MSRB3,all five CpGs assayed showed greater methyla-tion in earlier gestational age samples as seen in CHARM (Figure 2c ).Thus,these DNA methylation analyses on an independent measurement plat-form confirmed the differential methylation by gesta-tional age for each of the three genes identified via CHARM.
Since the three DMRs we identified reflect variabil-ity in methylation corresponding to late-stage devel-opment in utero ,we considered whether adult DNA methylation at these same sites would show any vari-ability and whether adult levels would be similar to those of full-term births.We compared CHARM 2.0data for each DMR among healthy adult blood DNA samples with our newborn sample results.Although the three DMRs appear very dynamic and progressive with gestational age in the newborn sample,these exact same regions have little variability in the adult population.Given the span of adult ages represented,this suggests that these sites are stable in adulthood.The magnitude of adult DNA methylation levels is similar to or more extreme than those of the latest gestational ages in a direction consistent with the newborn sample correlations to gestational age (Figure 3).These results provide compelling support for maturation-related changes in DNA methylation at these loci,and also indicate that the process con-tinues beyond birth,but reaches a maximum at some time at or before adult life.
To address potential confounding by sex,maternal age,race,maternal smoking,presence of PIH,intra-partum fever,maternal smoking and serum copper levels,we estimated the linear relationships between each of these variables and gestational age at birth.Consistent with the general characteristics comparing pre-term babies to the rest of the newborns,maternal smoking,PIH and serum copper were associated with gestational age (Table 3).To further address whether these potential confounders were associated with methylation at the identified DMRs,we estimated the linear relationship between these variables and the average methylation value per DMR as well.PIH and serum copper were also associated with methyla-tion at each of these DMRs (Table 3),suggesting the potential for confounding.However,the strong asso-ciation between methylation and gestational age remained even after adjusting for PIH and copper in both CHARM and pyrosequencing data.For example,in the CHARM data,the coefficient for gestational age at birth in linear models predicting average methylation at each DMR with and without adjust-ment for copper (which had a stronger effect
than
(a (b (c Figure 2Bisulphite pyrosequencing results for each DMR.(a)NFIX ,(b)RAPGEF2,(c)MSRB3.Circles represent methy-lation values (y -axis)at individual CpGs for their corresponding gestational age in weeks (x -axis).Lines represent predicted values from linear regression.Reconstitution controls (represented as black dots)with explicitly designed %methylation (x -axis)are located at the right of each panel (Recon).The numbers on the bottom of each figure represent effect size/slope estimate from the regression of methylation on gestational age and P -value for a Wald test of this slope
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PIH)changed from 1.57to 1.37for NFIX ,à1.33to à1.17for RAPGEF2and à2.08to à1.87for MSRB3,and all remained statistically significant.We in fact examined the potential influence of each potential confounder on the detected associations with these three DMRs and saw no substantial change in effect sizes after adjustment for any of these covariates (Supplementary Table S4available as Supplementary Data at IJE online).
Birthweight was also correlated with both gestational age and with methylation at each of the three DMRs.This is expected given the strong relationship between gestational age and birthweight.Gestational age is the best indicator of maturation of the newborn including growth parameters.Since birthweight is largely a con-sequence of gestational age,removing birthweight variability would almost completely restrict variability for gestational age in our analyses,so we did not con-dition on birthweight for these analyses.When we con-sidered birthweight for gestational age as a separate phenotype,we saw no relationship to methylation at the three DMRs (Supplementary Table S5available as Supplementary Data at IJE online).
To explore the functional significance of the differ-ential methylation,we measured the expression of the NFIX ,RAPGEF2and MSRB3using real-time PCR.RAPGEF2showed an inverse linear correlation between expression and DNA methylation levels in two of the three CpGs at this DMR (CpG1:P ?0.37;CpG2:P ?0.013,CpG3:P ?0.014,Supplementary Figure S2available as Supplementary Data at IJE online).
Table 3Co-efficient (95%CIs)of linear relationship between potential confounders and gestational age at birth or average methylation at each of the identified differentially methylated regions
GA (in weeks)
NFIX a
RAPGEF2a
MSRB3a
Male sex à0.12(à0.84to 0.60)à5.72(à8.16to à3.27) 1.85(à0.21to 3.91) 1.07(à1.32to 3.46)Maternal age
à0.06(à0.13to à0.00)à0.17(à0.40to 0.05)0.17(à0.01to 0.35)0.24(0.04to 0.44)Caucasian race
(vs African American)à0.38(à1.26to 0.49)à1.09(à4.27to 2.10)0.53(à2.01to 3.07)0.14(à2.76to 3.04)Maternal smoking b à0.62(à1.06to à0.18)0.86(à0.83to 2.54)0.39(à0.95to 1.73)à0.21(à1.75to 1.33)PIH
à2.25(à3.54to à0.96)à8.41(à13.09to à3.73) 5.01(1.23to 8.79) 6.77(2.46to 11.09)Intrapartum fever 0.60(à0.7to 1.91)à1.93(à6.76to 2.9)à0.24(à4.1to 3.62)0.13(à4.28to 4.55)Serum copper (m g/dl)0.04(0.02to 0.06)0.13(0.06to 0.20)à0.10(à0.16to à0.05)à0.15(à0.21to à0.08)Birthweight (kg) 2.07(1.63to 2.51) 2.86(0.87to 4.84)à2.89(à4.43to à1.34)à3.97(à5.71to à2.22)Elected delivery
à0.12(à0.84to 0.61)
à1.84(à4.50to 0.82)
1.10(à1.03to 3.22)
à0.56(à3.00to 1.88)
Bold indicates statistical significance at P <0.05.a
Average residual DNA methylation across DMR adjusted for the same surrogate variables from SVA used in the primary analysis.b
Ordinal variable:0?non-smoker,1?passive smoker,2?active smoker.
(a )(c Figure 3Methylation plots for three identified DMRs for gestational age at birth with adult methylation results included.Individual adult methylation levels are represented as grey lines,and the black line represents mean adult methylation level.(a)NFIX ,(b)RAPGEF2and (c)MSRB3
DNA METHYLATION AND GESTATIONAL AGE 195
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Discussion
Using a genome-wide custom DNA methylation array technology and novel statistical methods,we have identified three differentially methylated regions associated with gestational age at birth.Array-based methylation results for all three regions were validated via bisulphite pyrosequencing.These regions target areas of the genome likely to be under developmental regulation in late gestation,which may have implica-tions for understanding the reasons for immediate as well as long-term health effects of gestational age at birth.The observed incremental progression between methylation and gestational age at birth is further supported by the observation that adults are not vari-able at these DMRs,but rather appear to be stable at levels similar to or more extreme than newborns with the latest gestational ages at birth.
The genes nearest the identified DMRs may play important roles in late-stage fetal development.NFIX is known to be responsible for regulating skeletal muscle,28brain and bone development,32–34which show substantial growth during late gestation.This finding offers face validity that our approach can iden-tify epigenomic regions relevant to late gestational development.RAPGEF2plays a critical role in embryonic haematopoiesis35and brain development (https://www.sodocs.net/doc/c03119077.html,missures).36Although this DMR was not located at the promoter of the canonical gene,the DMR contains strong DNase I hypersensitive sites and a number of strong transcription factor-binding sites including Gata-2and PU.1,which are the critical transcription factors in haematopoiesis.29,30In utero,a fetus has a higher haematocrit(given lower available oxygen in utero),low B-cell function(given ready transplacental passage of maternal antibodies)as well as lower platelet counts than seen in babies (born at term).This methylation change with gesta-tional age could be involved in the ontogeny of the haematopoietic system and the switch from production of erythrocytes to increased production of B-lymphocytes and megakaryocytes in preparation for birth and,respectively,secretion of antibodies in response to antigenic assaults as well as production of platelets to prepare for possible birth trauma. Furthermore,anaemia of prematurity is known to cause morbidity in pre-term infants;disruption of regulation of this system may contribute to anaemia of prematurity,due to higher haematocrits and restricted erythropoiesis at birth.The differential methylation detected in our newborn sample did cor-relate with expression of RAPGEF2in cord blood cells, lending support for involvement in development of the haematopoietic system.Finally,MSRB3encodes a methionine sulphate reductase enzyme involved in antioxidant repair,converting methionine sulphoxide to methionine.This specific reductase has been found to be present in many tissues including the human lens and the cochlea and has been suspected to be involved in cataracts caused by oxidative damage to lens cells.31Most congenital cataracts are idiopathic; however,PTB and the administration of certain drugs in utero have been identified as risk factors,37pointing to a possible role for oxidative stress for cataract for-mation in infants as well as adults.Generally,a number of morbid conditions associated with term birth have been tied to oxidative stress,from admin-istration of oxygen,including retinopathy of prema-turity,bronchopulmonary dysplasia,necrotizing enterocolitis and intraventricular haemorrhage.38 MSRB3and other Methionine Sulfoxide Reductases (MSRs)may play a role in this sensitivity to oxidative stress.Mutations in MSRB3also cause hereditary deafness39and variants in this gene have been asso-ciated with primary tooth development during infancy in a recent genome-wide association study.40
These results do not appear to be sensitive to con-founding by measured variables.Furthermore,it is possible that methylation may be part of the mechan-ism relating factors to gestational age at birth.In this case,one would not want to adjust for such factors in analysis.Thus,we were conservative in our approach to adjustment.Nonetheless,inclusion of potential confounders in our models did not attenuate the relationship between methylation and gestational age at these DMRs.Our use of SVA to reduce the impact of measurement issues,such as batch effects, may also have adjusted for potential residual con-founding not captured by a measured variable.It is worth noting that serum copper levels have previously been shown in this sample to be related to gestational age at birth and,therefore,a potential confounder. Nonetheless,the relationship between DMR methyla-tion and gestational age did not attenuate after adjustment for copper.We did,however,observe a relationship between copper levels and methylation in these adjusted models,suggesting an independent effect of copper on methylation,consistent with the growing interest in environmental impacts on the epi-genome and their implications for human health.41,42 Also,although we saw a relationship between birth-weight and these DMRs,this appeared to be a func-tion of the relationship between gestational age and birthweight,rather than specific to birthweight itself. Although a recent report did see a relationship between global DNA methylation and birthweight for gestational age,43we did not see an association with these particular DMRs when considering birth-weight adjusted for gestational age(Supplementary Table S5available as Supplementary Data at IJE online).
An important caveat in this study is that we mea-sured DNA methylation from a surrogate tissue, blood,for which methylation changes may not reflect those of tissues undergoing developmental epigenetic changes.Despite this,one of these genes,RAPGEF2, showed the expected inverse relationship of DNA methylation and gene expression.Consistent with this idea,RAPGEF2regulates embryonic
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haematopoiesis,35whereas NFIX and MSRB3play in the development of organs such as brain,tooth,skel-etal muscle and bone.32,33,40,44Thus,differential ex-pression by methylation patterns of the latter two genes may not be detectable in cord blood,or these DMRs may regulate the enhancer function of distal genes or focally modify the high-order chromatin structure and thus not manifest a change in cord blood expression of NFIX or MSRB3.These results are quite encouraging for epigenetic epidemiology in general,since they indicate that DNA methylation dif-ferences may be widespread,and methylation profiles in blood may be a useful indicator of developmental change even in tissues that do not utilize the differ-entially methylated genes in normal developmental processes.
Overall,the results obtained here by genome-wide DNA methylation analysis are encouraging for the field of epigenetic epidemiology,since they indicate that DNA methylation differences are detectable with this strategy.Specifically,this work identifies epigenetic changes associated with gestational age at birth.The underlying reason for this correlation cannot be determined in this cross-sectional study, but there are at least two implications of these find-ings for the epidemiology of PTB.First,regions of the genome that are still undergoing DNA methylation variation late in gestation may be functionally related to the health consequences of PTB,and our findings can inform new epidemiologic research and biological mechanisms towards understanding the reasons for negative outcomes in premature babies and lessening these negative infant,childhood or even adult health consequences related to gestational age at birth. Secondly,it is possible that these results reflect involvement of DNA methylation in the aetiology of PTB.There are a number of mechanisms(including infections leading to inflammation,45preeclampsia46 and stress47)and risk factors(African American race,bacterial vaginosis,cigarette smoking and low maternal pregnancy body mass index48,49)associated with PTB,which could be associated with epigenetic changes themselves,although this explanation is less consistent with the function of the particular genes identified in our study.In addition,the use of assisted reproductive technology and nutritional deficiencies have been identified as possible risk factors for PTB50,51and also have the potential to alter the epigenome.52,53Identification of epigenetic changes associated with PTB potentially could be useful for identifying,among the many factors associated with PTB,which are most likely to be causal factors, although our design did not contain a large number of spontaneous PTBs and thus the relationship between methylation and causes of PTB may be best suited for subsequent studies in different samples.
Further work is required to determine whether the detection of DNA methylation in non-primary proxy tissues(in this instance,blood)indeed is a useful
indicator of developmental change in the primary
tissue for expression of affected genes.However,the
work presented here shows that DNA methylation
changes progressively during late fetal development,
and thus opens the door to studies of the epigenetic epidemiology of PTB.
Supplementary Data
Supplementary Data are available at IJE online.
Funding
National Institutes of Health(R01ES017646to M.D.F.
and A.F.,5R37CA054358-19to A.F.);Johns Hopkins Bloomberg School of Public Health,The THREE study
(to L.R.G.);the Maryland Cigarette Restitution
Program Research Grant(to R.U.H.);National
Institute of Environmental Health Sciences(grant
1R01ES015445to R.U.H.);Heinz Family Foundation
(to L.R.G.).
Acknowledgements
The authors thank the participants in the THREE
study,Hopkins Labor and Delivery staff,Benjamin Apelberg,PhD,Ellen Wells,PhD,and,from the US
Centers for Disease Control and Prevention,Robert
Jones,PhD and Kathleen Caldwell,PhD(copper ana-
lysis),John Bernert,PhD(cotinine analysis),and Rolf
Halden,PhD(THREE study).The authors thank
Walter Kaufmann MD for comments,and Eirikur
Briem and Unner Unnsteinsdottir for assisting in
the CHARM2.0data generation.We also thank the
principle investigators on the contributing studies of
the schizophrenia epigenetics consortium from which
adult comparison sample data were available:David
Braff,MD(UCSD),Rodney Go,PhD(UAB),Vishwajit
L.Nimgaonkar,MD,PhD(University of Pittsburgh),
Raquel Gur,MD,PhD(University of Pennsylvania).
H.L.performed DNA extraction and generated
gene-specific DNA methylation expression data.
A.E.J performed the genome-wide and site-specific
data analyses with assistance from R.A.I.J.I.F,and
R.T performed CHARM 2.0assays,with the array
designed by M.J.A.S.B.was a study coordinator.
C.M.provided unpublished region-specific methyla-
tion data.J.H., F.R.W.and L.R.G.were involved
with aspects of conception and design of the THREE
study and collection of THREE data. A.P.F.and
M.D.F.designed and oversaw the study,and wrote
the paper with H.L.and A.E.J.
Conflict of interest:None declared.
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KEY MESSAGES
There is a need for established statistical methodology for performing genome-wide DNA methylation studies in epidemiologic samples.
Using a novel statistical approach for DNA methylation micro-array data,we identify three regions of the genomic containing methylation levels that are associated with gestational age at birth in a sample of141newborns.
Differential methylation by gestational age at NFIX is consistent with the role of this gene in skeletal and brain development;at RAPGEF2may implicate the haematopoietic system in ways relevant to anaemia of prematurity;and at MSRB3may relate to the role of this gene in protection from oxidative damage,which may have implications for several health consequences of PTB.
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酪氨酸激酶异常活化在恶性血液病发病中的作用(1)(精)
酪氨酸激酶异常活化在恶性血液病发病 中的作用(1) 】蛋白酪氨酸激酶（protein tyrosine kinase, PTK）在调节细胞生长、活化和分化的信号转导中起着重的作用。基因突变（多半由染色体移位）或者激酶的过度表达可使PTK活力异常增高，并介导异常的信号转导途径，在多种恶性血液病的发生发展中起着主的作用。在慢性骨髓增殖性疾病（CMPD）、急性髓性白血病（AML）和间变性大细胞淋巴瘤的发病中，均存在着PTK的异常活化。进一步研究PTK相关的恶性血液病的发病机理，可以加快特异性的分子靶向治疗的研究进展。 【关键词】酪氨酸激酶恶性血液病；基因突变 Abnormal Activation of Tyrosine Kinases and Its Role in the Pathogenesis of Hematological Malignancies ——Review Abstract Protein tyrosine kinases are key participants in signal transduction pathways that regulate cellular growth, activation and differentiation. Aberrant PTK activity resulting from gene mutation (often accompanying chromosome translocation) or overexpression of these enzymes plays an etiologic role in several clonal hematopoietic malignancies. Other than the causative effect of PTK product of the bcr/abl fusion gene on chronic myelogenous leukemia (CML), more evidence suggests that mutated tyrosine kinases are pivotal in the pathogenesis of most of other chronic myeloproliferative disorders, such as chronic myelomonocytic leukemia (CMML) and hypereosinophilic syndrome (HES). And the exciting results in several dependent groups in 2005 showed that a single nucleotide JAK2 somatic mutation (JAK2V617F mutation) was found to be involved in the pathogenesis of polycythemia vera (PV), essential thrombocythemia (ET) and chronic idiopathic myelofibrosis (CIMF). In the leukogenesis of acute myeloid leukemias (AML), the losing of the control of the proliferation of hematopoietic progenitor cells was principally the results of the aberrant PTK activity, such as FLT3 and C kit overexpression. It works together with the loss of function mutation genes in promoting progenitor cell differentiation to confer AML's phenotypes. These upregulated PTK molecules represent attractive disease specific
基因克隆、假病毒操作步骤
实验名称：基因克隆 实验器材:荧光定量PCR仪、摇床、离心机、生工PCR产物纯化试剂盒、恒温加热器、 NEB连接体系、灭菌纯水、JM109感受态、冰、LB培养基、酒精灯、涂棒、氨苄、氨苄抗性平板、甘油等； 操作步骤： 1、可通过PCR进行拼接获得目的基因的，过柱纯化（生工试剂盒根据说明书进行纯化， 在最后一步的洗脱可以用预热的灭菌纯水洗脱，在加灭菌纯水洗脱的时候一定要加在纯化柱子的膜中间）； 2、选择合适的载体（EZ-T）用连接酶进行连接，NEB体系，16℃过夜连接 T4lages 1.0 10×T4buffer 2.0 EZ-T 1.0 目的基因8.0 DdH2O 8.0 ＿＿＿＿＿＿＿＿＿ 20ul 3、取100μl摇匀后的JM109感受态细胞悬浮液（如是冷冻保存液，则需化冻后马上进行下 面的操作），加入10μl连接产物，轻轻摇匀，冰上放置30min后，于42度水浴中保温90s，然后迅速在冰上冷却2min； 4、加入500μl LB液体培养基，混匀于37℃振荡培养45min使受体菌恢复正常生长状态并 使转化体产生抗药性； 5、将恢复培养的菌体5000rpm离心3min，移去上层LB培养基，用余下的200μl重悬菌体， 并用灭菌玻璃推子(酒精灯上烧后冷却)，均匀涂布于琼脂凝胶表面（氨苄抗性），37℃倒置培养12~16小时； 6、挑取多个单克隆菌落分别接种到1ml含有抗生素（氨苄）的LB液体培养基中，37℃振 荡培养3h； 7、培养1-2小时即可以利用PCR（定量或定性）进行鉴定； 8、选取初步鉴定阳性的菌液送测序，测序正确后甘油保存（甘油的浓度为30%-50%），充 分混匀，-80℃保存；
红豆杉中MYB家族基因克隆及表达分析 开题报告 于凯
毕业设计/论文 开题报告 课题名称红豆杉中MYB家族基因克隆及表达分析类别毕业论文 系别城市建设学院 专业班生物工程0701班 姓名于凯 评分 指导教师 华中科技大学武昌分校
华中科技大学武昌分校学生毕业论文开题报告
癌活性，对于治疗卵巢癌、乳腺癌等疗效突出。但是由于含量少、提取困难等诸多因素，高纯度紫杉醇价格昂贵，每公斤200万元人民币左右。因此，近年来国内外许研究人员、实验室和公司一直试图通过生物合成、化学合成、微生物提取、组织和细胞培养、寻找类似物等途径来解决紫杉醇的药源短缺问题。 研究紫杉醇的生物合成，尤其一些限速反应步骤机理的阐明对于人为定向的提高合成效率，克隆重组形成关键酶基因从而提高紫杉醇的产量意义重大。从理论上来说这是一个好方法，但是紫杉醇的合成途径非常复杂，涉及到多种酶以及很多分支途径，单纯依靠转化一、两种限速酶基因，只能保证转入的限速酶表达量提高，使之不再是限速因素，但其它阶段对于最终产量的限制依然存在，而且同时转入多种基因的可行性非常低，这种方法的缺陷很明显。 若采用化学合成，如从红豆杉植物中分离得到的巴卡亭Ⅲ经过四步化学过程可合成紫杉醇，为合成紫杉醇提供了新途径[5]。但化学合成从实质意义上说还没有取得彻底的突破，目前还不具备应用价值。 如果从共生真菌中直接提取紫杉醇，能够利用真菌生长速度快的优势，但目前分离的菌株无论从种类还是数量上都远不够工业化的要求，而且还存在很多不确定因素[1]。生产紫杉醇的微生物大多是与红豆杉共生的真菌，其紫杉醇含量极微，并且这些真菌的培养和大规模发酵困难，菌株衰退也是一个难题。 另外，红豆杉愈伤组织和细胞培养生产紫杉醇是研究的热点之一，是工厂化大规模生产紫杉醇的重要手段之一。但运用植物组织、细胞培养技术生产紫杉醇仍处在实验室阶段，如何获得高含量、产紫杉醇稳定的愈伤组织一直都是组织培养、细胞培养生产紫杉醇的关键。 1.1.3关于MYB基因 ①MYB基因 目前，在几乎所有的真核生物中都发现了与禽类逆转录病毒癌基因和细胞原癌基因c-MYB相似的基因，它们的编码产物在结构和功能上具有高度保守的DNA结合域，是一类转录因子[6]。在植物中首先从玉米中克隆了含有MYB结构域的转录因子C1基因，之后在植物中发现的MYB相关基因的数量迅速增加[7]。
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。
细胞色素C氧化酶亚基Ⅱ可溶性结构域的结构改变、转换和功能研究
细胞色素C氧化酶亚基Ⅱ可溶性结构域的结构改变、转换和功能 研究 细胞色素 c 氧化酶是哺乳动物线粒体或细菌呼吸链上的末端酶，催化电子从细胞色素 c 到 O2 的转移并使后者还原为水。该酶是一个复合的膜蛋白，包括多个亚基及不同的金属活性中心。 其中，亚基II中的 CuA 中心被认为是电子从细胞色素 c 进入该酶的入口，在生理过程中起着重要作用。细胞色素 c 氧化酶的缺损或不足可导致许多疾病，在一些退行性疾病 (如：老年痴呆症) 或癌症研究中已发现了与其相关的基因突变。 本工作以该酶的亚基 II 可溶区为研究对象，成功表达并获得了Paracoccus versutus 的细胞色素 c 氧化酶亚基II可溶区重组蛋白 (128-280位的氨基酸序列)；通过基因工程方法构建突变体蛋白，研究了 Trp121 和 Val162氨基酸残基在该酶中的重要地位，报道了突变和其它物理化学因素引起的蛋白结构改变、转换，以及与此相关的性质和功能变化。主要工作和结果如下：(一) 细胞色素 c 氧化酶亚基II可溶区蛋白的体外表达、纯化及性质表征以大肠杆菌BL21 (DE3) 为表达载体，建立并优化了表达条件，获得了细菌Paracoccus versutus 的细胞色素 c 氧化酶亚基II可溶区重组蛋白。 该蛋白大量存在于包涵体中，通过体外复性，金属中心重组和FPLC纯化，获得了水溶性的紫色蛋白，在SDS-PAGE中表现为单一条带。经电喷雾质谱测定分子量，蛋白的成功制备得到了进一步确证。 采用紫外-可见和园二色谱表征了蛋白的金属活性中心和二级结构。紫外可见光谱显示出明显的 CuA 特征，360，480，530 和 810 nm 的吸收带是双核混
细胞色素C氧化酶亚基Cox4的克隆及高效表达
苏明慧，汪一荣，杨艳梅，等．细胞色素Ｃ氧化酶亚基Ｃｏｘ４的克隆及高效表达［Ｊ］．江苏农业科学，２０１８，４６（１５）：３１－３４．ｄｏｉ：１０．１５８８９／ｊ．ｉｓｓｎ．１００２－１３０２．２０１８．１５．００８ 细胞色素Ｃ氧化酶亚基Ｃｏｘ４的克隆及高效表达 苏明慧，汪一荣，杨艳梅，商巾杰 （南京师范大学生命科学学院，江苏南京２１００４６） 摘要：Ｃｏｘ４是电子传递链上复合体Ⅳ的１个关键亚基，实现Ｃｏｘ４的高效表达以及制备其抗体，对于线粒体功能研究至关重要。根据裂殖酵母序列信息数据库（Ｓ．ｐｏｍｂｅ＿ＧｅｎｅＤＢ）中ｃｏｘ４的基因序列（登录号：ＳＰＡＣ１２９６．０２）设计引物，以粟酒裂殖酵母的ｃＤＮＡ序列为模板，通过ＰＣＲ扩增技术得到目标蛋白的基因，然后通过克隆构建到含有Ｔ７强启动子的表达载体ｐＥＴ－２８ａ（＋）上，并将其转入大肠杆菌ＢＬ２１（ＥｓｃｈｅｒｉｃｈｉａｃｏｌｉＢＬ２１）中实现重组工程菌株的构建。结果表明，通过一系列条件优化实现了对重组工程菌株中Ｃｏｘ４的高效表达，并纯化得到大量相应蛋白，最后以纯化的蛋白为抗原成功制备了Ｃｏｘ４抗体。Ｃｏｘ４抗体的成功制备为粟酒裂殖酵母中线粒体功能研究奠定了基础。 关键词：线粒体；粟酒裂殖酵母；细胞色素Ｃ氧化酶；Ｃｏｘ４ 中图分类号：Ｑ７８５ 文献标志码：Ａ 文章编号：１００２－１３０２（２０１８）１５－００３１－０３ 收稿日期：２０１７－０３－１５ 基金项目：国家自然科学基金（编号：３１４０００３２）；江苏省高校自然科学研究项目（编号： １３ＫＪＢ１８００１０）。作者简介：苏明慧（１９９１—），女，河南周口人，硕士研究生，从事粟酒裂殖酵母线粒体蛋白结构与功能研究。Ｅ－ｍａｉｌ：１５７９２５７４８４＠ｑｑ．ｃｏｍ 。通信作者：商巾杰，博士，副教授，从事裂殖酵母中与人类疾病相关基因的结构和功能研究。Ｅ－ｍａｉｌ：ｓｈａｎｇｊｉｎｊｉｅｙ＠１６３．ｃｏｍ。 线粒体有自己的基因组，是一种半自主性的细胞器［１－２］ ，几乎存在于所有真核生物中。线粒体能够进行氧化磷酸化和 三磷酸腺苷（ ＡＴＰ）的合成，是有氧呼吸的主要场所［３－４］ ，可为细胞的生命活动提供能量，被称为细胞内的发动机［５］ 。同 时，线粒体还参与调控细胞的信息传递、细胞的凋亡和细胞的分化等一系列生理过程，并且还对细胞生长和细胞周期进行 调控［ ６］，因此，线粒体对细胞的生长至关重要［５］ 。氧化磷酸化是一个电子传递过程，依赖于线粒体的电子传递链。线粒 体的电子传递链对于线粒体功能的正常发挥十分重要［７］ ，一旦电子传递链受损会引起很多疾病的发生［８－９］。在粟酒裂殖 酵母中，电子传递链由４种复合体组成，而组成这些复合体的蛋白是由核基因组和线粒体基因组共同编码的。细胞色素Ｃ氧化酶（复合物Ⅳ）是线粒体中电子传递链的末端酶，催化电 子从细胞色素Ｃ转移到氧［１０］，核编码的细胞色素Ｃ氧化酶亚 基４（Ｃｏｘ４）是其中１个关键亚基。由此可见，在对线粒体功能进行研究时，不可避免地需要对线粒体中构成这些复合体的蛋白水平进行检测，因此制备这些蛋白的相应抗体就显得尤为重要。本研究主要是将粟酒裂殖酵母中构成复合体Ⅳ的细胞色素Ｃ氧化酶亚基Ｃｏｘ４在大肠杆菌中进行高效表达，并制备相应的抗体。１ 材料与方法１．１ 试验材料 １．１．１ 菌株与质粒 粟酒裂殖酵母单倍体菌株ｙＨＬ６３８１［ｈ＋，组氨酸生物合成缺陷型（ｈｉｓ３－Ｄ１），亮氨酸生物合成缺 陷型（ｌｅｕ１－３２），尿嘧啶生物合成缺陷型（ｕｒａ４－Ｄ１８），腺嘌呤生物合成缺陷型（ ａｄｅ６－Ｍ２１０）］，由南京师范大学微生物所实验室保存。质粒ｐ ＥＴ－２８ａ（＋）为笔者所在实验室保存。１．１．２ 试剂 ｒＴａｑＤＮＡ聚合酶、限制性内切酶、ＳｏｌｕｔｉｏｎⅠ（货号为６０２２）、蛋白ｍａｒｋｅｒ，购自ＴａＫａＲａ公司；ＤＮＡｍａｒｋｅｒ，购自北京全式金生物技术有限公司；ＤＮＡ割胶回收试剂盒、ＰＣＲ过柱纯化试剂盒，购自北京博大泰克生物基因技术有限责任公司；十二烷基硫酸钠（ｓｏｄｉｕｍｄｏｄｅｃｙｌｓｕｌｆａｔｅ，简称ＳＤＳ）、琼脂粉、４－羟乙基哌嗪乙磺酸（ＨＥＰＥＳ）、山梨醇（ｓｏｒｂｉｔｏｌ）、三羟甲基氨基甲烷（Ｔｒｉｓ）、３－（Ｎ－吗啉基）丙磺酸（ ＭＯＰＳ）、３０％丙烯酰胺和乙二胺四乙酸（ＥＤＴＡ），购自索莱宝公司；酵母粉，购自ＯＸＯＩＤ公司；腺嘌呤、尿嘧啶、精氨酸、组氨酸、卡那霉素（Ｋａｎ），购自Ｓｉｇｍａ公司；ＲＮＡ提取试剂盒，ＯＭＥＧＡ（货号为Ｒ６８７０－００），购自南京贝纳生物技术有限公司；ｉＳｃｒｉｐｔｃＤＮＡ合成试剂盒（货号为１７０８８９０），购自南京润亚生物科技发展有限公司； ＰＣＲ引物，由南京思普金生物工程技术服务有限公司合成；常用试剂为分析纯级，购自国药集团化学试剂有限公司和生工生物工程（上海）股份有限公司。 １．１．３ 培养基 （１）ＬＢ培养基配方（１００ｍＬ）：１ｇ胰蛋白胨， ０．５ｇ酵母粉，１ｇ氯化钠，在固体培养基中添加２ｇ琼脂粉。（２）ＬＢ＋Ｋａｎ培养基：在ＬＢ培养基中添加卡那霉素至终浓度为５０ｍｇ／Ｌ。（３）ＹＥＳ培养基配方（１００ｍＬ）：３．０ｇ葡萄糖，０．５ｇ酵母粉，２２．５０ｍｇ腺嘌呤，２２．５０ｍｇ尿嘧啶，２２．５０ｍｇ亮氨酸，２２．５０ｍｇ组氨酸。１．２ 试验方法 １．２．１ 引物的设计 根据裂殖酵母序列信息数据库（Ｓ．ｐｏｍｂｅ＿ＧｅｎｅＤＢ）中登录号为ＳＰＡＣ１２９６．０２的基因序列，通过ｈｔｔｐｓ：／／ｉｈｇ．ｇｓｆ．ｄｅ／ｉｈｇ／ｍｉｔｏｐｒｏｔ．ｈｔｍｌ预测线粒体定位序列，将线粒体定位序列去掉后，设计上下游特异性引物，在正向引物的５′端加入ＮｄｅⅠ酶切位点，在互补链引物的５′端加入ＸｈｏⅠ酶切位点（下划线分别表示ＮｄｅⅠ、ＸｈｏⅠ酶切位点）：正向引物：５′－ＧＧＡＡＴＴＣＣＡＴＡＴＧＡＡＴＧＡＧＣＡＡＡＡＣＧＴＴＧＴＡＡＡＡＧＣＣ－ — １３—江苏农业科学 ２０１８年第４６卷第１５期
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.sodocs.net/doc/c03119077.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。