TOPO TA Cloning – Adding 3’A Overhangs

The TOPO TA cloning kits for subcloning offer an easy way to subclone effectively, provided you can get it to work for you.  The topoisomerase I in which the kit relies on requires the presence of 3’A overhangs on the DNA inserts in order to catalyze the reaction joining insert to vector. Ironically, the enzymes and other components required to add the 3’A overhangs are not supplied with the kits and the protocol provided in the instruction manual is not what I consider ideal.

I have personally never followed the 3’A overhang procedure set out in the product manuals provided; instead, I used my own, which I believe works out more efficiently.

The following is a quick and general run-down of how I clone using the TOPO TA subcloning kits. The focus will be on the addition of 3’A overhangs.

Insert Preparation

* Setup PCR reactions to amplify your insert. Use a proofreading DNA polymerase.

* Run your DNA gels and cut out your insert. If you do not want any possibility of point mutations or DNA breakage, try excising your bands without exposure to any UV.

* Gel purify your gel cut-outs. I recommend using a kit such as Qiagen's QIAquick Gel Extraction Kit. Elute/resuspend the DNA in nuclease-free water.

Adding 3’A Overhangs

Proofreading DNA polymerases have 5' to 3' polymerization and exonuclease activity as well as 3’ to 5’ exonuclease activity (proofreading). It is the 3’ to 5’ exonuclease activity of a proofreading DNA polymerase which enables it to remove any base-pair mismatch, including any overhanging bases, thereby generating blunt-end PCR products. In contrast, Taq DNA polymerases lack the 3' to 5' exonuclease activity, so while Taq enzymes are not suitable for generating inserts for cloning, they are useful for TA cloning for the addition of 3’A overhangs.

* You will need a Taq DNA polymerase which does not have 3’ to 5’ exonuclease activity. Check the product information sheet. An example of such a Taq is Thermo Scientific's Red Hot Taq DNA Polymerase.

* Using the Red Hot Taq as an example, set up the following:

For x1 reaction:

10x PCR Buffer à 2.5ul

MgCl₂ à 2ul

dATP (10mM stock) à 0.5ul

Red Hot Taq à 0.1ul

Insert DNA (from gel extraction) à 19.9ul

Incubate in a PCR thermal cycler à 72 degrees for 30 minutes. Do not cycle. Cool on ice or 4 degrees when complete.

DNA Precipitation

Cool your reaction on ice and proceed to precipitate your DNA inserts.

* Take the above 25ul reaction and add 2.5ul (which is 1/10^th volume) of 3M pH5.2 NaAc (sodium  acetate). Tap or gently vortex to mix.

* Add 62.5ul (which is 2.5 volumes of ice cold absolute ethanol). Tap or gently vortex to mix.

* Incubate the entire reaction on ice for 30 minutes.

* Centrifuge at 16200xg for 20 minutes.

* Aspirate the supernatant and wash the pellet with 500ul of 70% ethanol.

* Centrifuge at 16200xg for 5 minutes.

* Aspirate the supernatant, air dry the pellet and resuspend in 10ul nuclease-free water.

* Use 4ul for TOPO cloning reaction.

Things To Consider When Ordering Published Primer Sequences

When you see a set of primers in the materials and methods section of a journal publication, it is tempting to order the sequences provided. Before doing so, it is good practice to check the primer sequences to make sure that they are suitable for your needs. In other words, make sure the primer sequences published are suitable for your needs in regards to:

* Cell type – some cell types may have little to no expression of your GOI.

* Tissue type – as with cell type, there may be little to no expression of your GOI.

* Species and percent homology if the species are different.

* Whether the region targeted in the transcript will give you the expected product size when PCR products are run on a gel.

* Primer direction – sometimes the primer sequences provided in a journal article may not be in the correct format for ordering. For instance, if a reverse sequence rather than the reverse complement sequence is provided in the publication, you will have serious issues when you start experimenting with the primers.

* Purpose of the primer – it is important to see what experiments the sequences published were actually used for. A primer set used for expression cloning will not be suitable for real-time PCR.

* The primer sequence published – you want to make sure that the sequences provided are accurate so check by blasting the sequence. It is not uncommon for authors to make typos when entering in their sequences during manuscript preparation.  

Data Analysis – T-Test vs Mann Whitney U

For those unfamiliar with statistics, it can often be confusing when deciding which test to apply to analyse data in order to determine whether changes observed are indeed statistically significant (i.e. p<0.05)

The following will provide some guidance on how to analyse data between two groups (e.g. placebo vs drug treatment, normal vs diseased, light vs dark, etc).

What Are The Differences?

The t-test is a test between population means. They are parametric tests and should only be applied to data that is normally distributed. In contrast, the Mann-Whitney U (MWU) test is a test of differences in medians as well as the shape and spread of the data. It is a non-parametric test that can be used as an alternative to the t-test on data that is not normally distributed. However, it should be noted that the MWU test can be applied to normally distributed data.

The number of data points or sample size can also affect the choice in tests. If you have large datasets that have a normal distribution, a t-test can be very powerful. But if you have a small number of data points (e.g. less than 6 data points), a MWU test would be preferable to a t-test since the data is unlikely to have a normal distribution.

What Do I Use?  

To determine which test to apply, you will first need to establish whether your experimental data is normally distributed. For illustrative purposes, a mock dataset (below) will be used. The dataset below is from two groups (normal vs diseased). The sample type is coded and blind to the experimenter.

There are a number of normality tests for you to choose from, but if you have a set of data with less than 2000 data points, as above, try using the Shapiro-Wilk normality test. The null hypothesis is that your data points belong to a normal distribution; reciprocally, your alternative hypothesis is that your data points do not belong to a normal distribution. If p<0.05, your data does not have a normal distribution. For the above dataset, the results of running the Shapiro-Wilk test is as follows:

n = 24

Mean = 66.33333333333333

SD = 21.25142791451429

W = 0.9437512788610762

Threshold (p=0.01) = 0.8840000033378601

Threshold (p=0.05) = 0.9160000085830688

Threshold (p=0.10) = 0.9300000071525574

Here, p>0.05 at all thresholds. Accordingly, the alternative hypothesis is rejected and we conclude that the data points have a normal distribution.

** The results above were calculated using an online Shapiro-Wilk calculator.   

For sample sizes greater than 2000, you can use the Kolmogorov-Smirnov test. If you prefer to visualize the data in graphical form, try using a normal probability plot or quantile-quantile plot.

Other Ways To Test Normality

Aside from the normality tests mentioned above, there are other normality tests available. For their description, please refer to the following link.