How to stop Excel converting your SEPT1 gene to September1

Several scientists around the world use Microsoft Excel to visualize or process their data. Excel can be fun to use when you know the commands well. However, when it comes to sorting gene symbols or creating protein databases using Excel, it may cause several problems. Some of the gene symbols are always converted into dates. 

For example:

SEPT1 becomes Sep-01

MARC1 becomes Mar-01

MARCH1 becomes Mar-01

This is true for the SEPT, MARC1 and MARCH1 gene families. 

Most often, you don't know that this has happened and you are forced to erroneously conclude that you haven't identified these genes in your data.

You can manually correct these by adding a single quote (') symbol before the gene name. This is very tedious and troublesome for downstream data processing. To make it worse, I found out that Excel does not have an option to turn off this annoying transmogrification.

By trial and error, I found out  a perfect way to overcome this problem in Excel.

STEP1: Save your database file in CSV (Comma separated) or TSV (Tab separated) formats

STEP 2: Start the "Import Text File" wizard by going to the "Data" Tab and clicking "From Text"

STEP 3: Select your CSV or TSV file and click "Import".

STEP 4: Choose the "delimited" option and click "Next"

STEP 5: Select "Comma" (if you used CSV file) or "Tab" (if you used TSV file) and click "Next"

STEP 6: Select the "Gene Symbol" column in the "Data Preview" and select the option "Text" in the "Column data format"

STEP 7: Click "Finish" and then click "OK".

Now scroll back and check that SEPT1 has not been touched by Excel.

If you want a video on it, use this link

RefSeq72 is out. Time to update your databases

RefSeq72 has been now released. Follow this link for more information.

Phosphoproteomic analysis of breast cancer cells to identify targets for tamoxifen resistance

A new study on the phosphoproteomics analysis of tamoxifen-resistant breast cancer cells has been published from Dr. Akhilesh Pandey's lab in the recent issue of Molecular and Cellular Proteomics. 

In this study, MCF7 breast cancer cell lines were chronically treated with tamoxifen for 6 months to acquire resistance. SILAC-based quantitative phosphoproteomic profiling  of treated and untreated cells with a Orbitrap Velos mass spectrometer identified over 5,000 unique phosphopeptides, with over 2,000 peptides differentially phosphorylated between the two conditions. 

Focal adhesion pathway was found to be enriched by pathway analysis. Silencing FAK2 suppressed cell proliferation and tumor formation. Further, high FAK2 expression was found to correlate with shorter metastasis-free survival in tamoxifen-treated breast cancer patients. 

The authors suggest that FAK2 could serve as a potential therapeutic target for management of hormone refractory breast cancers.

An optimized workflow for quantitative lysine acetylome

There are dozens of post-tranlational modifications out there, but there aren't enough techniques to study them all. Lysine acetylation has been studied before, but few labs work on this modification as opposed to phosphorylation due to lack of optimized protocols. A new paper from Steve Carr's lab on characterization of lysine acetylome  using an optimized protocol is an effort in this direction. The authors combined 7 monoclonal antibodies with specificity for lysine acetylated peptides and used this for enrichment. They used this method with Jurkat cells and combined it with a SILAC approach for quantitation. The authors identified over 10,000 lysine acetylated peptides from over 3,000 proteins!!

They applied this method to breast cancer xenografts and used TMT and iTRAQ for quantitation. Over 6,700 acetylated peptides from over 2,300 proteins were identified.

A combined RNAi and SILAC approach to characterize tyrosine kinase-regulated proteome in breast cancer

Tyrosine kinases are important players in cancer progression and are considered as good therapeutic targets. Knocking down the multiple kinases in cancer and studying the consequences has been done before. But in a new study published in Molecular and Cellular Proteomics, they took this to a whole new level by silencing 65 tyrosine kinases in breast cancer cell lines and studying the proteome using a SILAC-based approach and an Orbitrap Velos mass spectrometer.

The authors identified 10 signaling clusters in breast cancers and potential markers of drug sensitivity and resistance in breast cancer. The clusters also showed correlation with different breast cancer subtypes. In addition, the data revealed redundancy in signaling, explaining why tyrosine kinases can be sometimes ineffective because the cancers can proliferate through alternate signaling routes that are not blocked therapeutically.