Research Article Analysis

The research article to the right is about growth control of the eukaryote cell, specifically yeast. Although the research is a lot more advanced than anything we will ever have to do in the biology lab, there are a number of significant similarities that are worth mentioning. Since part of the class focuses on cell biology, it is a quite relevant article because it is on growth control of yeast cells. Of course there are differences between our lab class and real biology research labs, but the methods and materials used are astonishingly similar to the methods and materials we use in class. For example, in the highlighted portion of the article, there is a section called, “Isotope tags for multiplexed relative and absolute quantification (iTRAQ) of proteins,” and the highlighted portions underneath show a measurement and a part of the paragraph that says, “Samples were then diluted…” The highlighted measurement is measured in microliters which can only be measured with a micropipette which we were taught how to use in class. Clearly, using a micropipette is going to be an invaluable lab technique that most students in the biology field are going to need in the future; my interview with Erin also supports this. Also, the dilution part is also important because knowing how to properly dilute solutions is important if one is going to be using high concentrations of chemicals. Those solutions need to be at the proper concentration in order for an experiment to run properly. These lab techniques are extremely important and learning them in lab class and seeing them show up in research articles is promising for the class.


Another invaluable asset a biologist needs is understanding how to observe, collect, and analyze data. In the article, the authors show the way they analyzed their data whether it be through means of a software program or using a mass spectral library. These tools helped them analyze their data properly and draw conclusions based on their data. We don’t necessarily use the same programs or use a mass spectral library but we have similar means of analyzing our data. For example, they used a hybrid mass spectrometer (highlighted under section LC-MS/MS analysis) and we use a spectrophotometer which is a similar device that measures the absorbance and wavelengths of light. A spectrometer helps you “view and analyze a range (or a spectrum) of a given characteristic for a substance, or a range of wavelengths as in absorption spectrometry.” (ChemWiki) These two instruments are somewhat different but they help you reach the same goal: analyzing your data. In one of our experiments we had to use a spectrophotometer to measure the absorption of light over time for enzyme activity, and this helped us see whether the enzyme was working to speed up a reaction. We were then able to analyze our absorption levels at different time intervals and conclude that it indeed was speeding up the reaction. Also, the article has a portion (highlighted under, “Determination of relative changes in translational control efficiencies”) where the authors show their use of an equation and how they were able to derive it based off their research. They then used this equation to define the efficiency of each mRNAi, and used that to analyze their data. Of course it is a lot more complicated than that, but the important thing is they used equations to analyze their data which is what we did in our lab. We collected our data in an experiment involving enzymes and had to use an equation to calculate the final concentration of a specific molecule in our solution. This helped us to draw up a graph and analyze our data even further.

Furthermore, the first portion highlighted, under the section “Transcriptional studies,” shows several different types of software the biologists used to analyze their data. They look like very advanced and foreign software to the average person, but when looking through our future lab experiments, I saw that we are going to be using a computer program to help us analyze DNA. These things are somewhat similar in that we are using computer software to help us analyze our data.

Finally, in the last portion of the article the authors talk about how they sought to minimize error. This is something that we have to learn in lab to ensure that we are creating the best conditions for our experiment. The last thing highlighted that says, “all samples were processed by the same specialist researcher,” is something that is vital in any lab because you ideally want to have the same person doing the same procedure so there aren’t any differences in measurements. For example, when we use the micropipettes, I try to make sure that either my lab partner or I do all the measurements so they are consistent. The similarities between our lab class and real research work are much more significant than one would expect, and these similarities highlighted here show that our lab class may be more valuable than I thought.

Also, the students need to know things from lecture to do the lab but sometimes the lab touches on things we haven’t learned in class yet because you need to know them in order to do the lab. For example, one of the labs involved knowing about mitosis and meiosis and we hadn’t been taught about those in class yet so Erin had to teach the class about it when she thinks she isn’t the best at explaining it. She said it should be the professor’s job to teach it.

Research Article

This is only a small portion of the actual article. There is no need to read this scholarly article. It is just meant to be referred to in my analysis on the left.

Isotope tags for multiplexed relative and absolute quantification (iTRAQ) of proteins

Protein samples were precipitated as follows. Chilled acetone (1.8 ml) was added to a 300 μl sample. The tubes were inverted three times and left at -20°C for 4 h. Precipitated proteins were pelleted by a 10 minute centrifugation at 3,000 rpm, and resuspended in iTRAQ labeling buffer (8 M urea, 2% Triton X100, 0.1% SDS and 25 mM triethyl ammonium bicarbonate (TEAB) pH 8.5). Protein concentration was determined using the detergent-compatible BCA protein assay (Pierce, Rockford, IL).

Each sample (100 μg protein) was then reduced (4 mM Tris(2-carboxyethyl) phosphine (TCEP), 20°C, 1 h) and cysteines blocked (8 mM methyl methanethiosulfonate (MMTS), 20°C 10 minutes). Samples were then diluted with 50 mM TEAB (pH 8.5) such that the final urea concentration was below 1 M, digested with trypsin (1:20) overnight at 37°C (Promega, Madison, WI; 2.5 μg added at 0 and 1 h) and lyophilized using a Savant AES2010 speed vacuum system.

Three separate iTRAQ labeling experiments were carried out such that each sample corresponding to a nutrient limitation and growth rate was labeled once. In each experiment one of the iTRAQ tags was used to label a pooled sample comprising equal amounts of each sample analyzed within the experiment (see Additional data file 1 (Figure S28) for the iTRAQ labeling scheme). Each lyophilized sample was resuspended in 100 μl labeling buffer (0.25 M TEAB, 75% ethanol), added to one unit of the corresponding iTRAQ reagent and incubated for 1 h at 20°C. Residual reagent was quenched by adding 100 μl water and incubating for a further 15 minutes at 20°C. The samples belonging to the iTRAQ comparisons were then pooled (pooled standard) and lyophilized.

LC-MS/MS analysis

Peptides were separated and analyzed using an Ultimate Plus nano-LC system (Dionex) coupled to a QSTAR XL quadrupole TOF hybrid mass spectrometer (Applied Biosystems, Foster City, CA). Samples (60 μl) were loaded onto an Acclaim PA C16 pre-column (5 mm × 300 μm internal diameter, Dionex) at 20 μl/minute and washed with 0.1% formic acid (FA; also at 20 μl/minute) for 25 minutes to desalt the samples. Peptides were then eluted onto a PepMap C18 analytical column (15 cm × 75 μm internal diameter, Dionex) at 150 nl/minute and separated using a 165 minute gradient of 5-32% ACN (0.1% FA). The QSTAR XL was operated in information-dependent acquisition (IDA) mode, in which a 1 second TOF-MS scan from 400-1,600 m/z was performed, followed by 3 second product ion scans from 100-1,580 m/z on the two most intense doubly (2+) or triply (3+) charged ions.

Determination of relative changes in translational control efficiencies

To encompass all mechanisms involved in translational control and to quantify its global effect, we define the translational control efficiency of each mRNAi (Trlc Effi) as the effective translation of each transcript into protein (encompassing synthesis and degradation processes; net P/R ratio (protein/mRNA) as follows:

Trlc Eff= ([Proteini]/([mRNAi]) (see also Additional data file 7).

From protein-transcriptome correlation studies we can define the ratio of relative changes in protein versus transcript levels (ratio[(/)p/(/) tr]) as:


Ratio[(/)p/(/)tr]= ([Proteini]2/[Proteini]1)/([mRNAi]2/[mRNAi]1)(1)


Thus, as an example, when applied to relative changes between two growth rates, for example, 0.2 versus 0.1/h:


Ratio[(/)p/(/)tr]= [(Protein0.2/Protein0.1)/(Transcript0.2/Transcript0.1)]= ([Proteini] 0.2/[Proteini] 0.1)/([mRNAi] 0.2/[mRNAi] 0.1)


From here, as Equation (1) can be rearranged as:


Ratio[(/)p/(/)tr]= ([Proteini]2/([mRNAi]2)/([Proteini]1/[mRNAi]1)(2)


it follows that the Ratio[(/)p/(/)tr]is numerically equal to the ratio of translational control efficiencies i, from condition 1 to condition 2:


Ratio[(/)p/(/)tr]= (Trlc Eff)2/(Trlc Eff)= (Ratio Trlc Eff)i(3)


See also Additional data file 7.

Relative changes in translational control efficiencies are obtained from microarray and proteomic studies. These compare relative changes in gene expression of the same individual transcript (or protein) between two different growth rates. In these one-to-one comparisons, systematic errors due to, for example, different labeling or hybridization efficiencies are minimized. Moreover, we have sought to reduce all sources of systematic error and a summary of the strategies applied is included below. Despite all these precautions, translational control efficiencies will always be dependent on the accuracy of the techniques used to determine relative changes in gene expression. To evaluate these data, one must take into account the CV obtained for each independent technique used. These are provided above.

Transcriptional studies

Biomass was harvested and total RNA extracted as previously described [14]. For growth-rate dependence studies, the microarray experimental design consisted of four nutrient-limiting conditions grown at three growth rates. These 12 conditions were analyzed in quadruplicate using Affymetrix Yeast Genome S98 GeneChip oligonucleotide arrays (Affymetrix, Santa Clara, CA). For the rapamycin study, samples were analyzed in duplicate using YG_S98 arrays (Affymetrix). Arrays that passed outlier data-quality assessment using dChip software [121] were normalized with RMAExpress [122]. For each probe set the coefficient of variation (CV) was calculated for each condition (%CV = (standard deviation/mean) × 100). The mean CV (variance in transcriptional studies) was calculated, being in the range between 2.4-3.9% in all cases. The data were submitted in MIAME-compliant format to the ArrayExpress public repository [123] under accession numbers E-MEXP-115 (growth-rate studies) and E-MAXD-4 (rapamycin studies).

Statistical analyses (PCA, t-tests, ANOVA/ANCOVA and false-discovery rate estimation) to identify significantly regulated genes were performed with Matlab (MathWorks, Natick, MA) [124], Q-value[40], GeneSpring (Agilent Technologies, Santa Clara, CA) [125] and maxdView software (available from [126]); see Additional data file 4 for details. Transcriptome results were validated by comparison of the patterns of gene expression of genes from equivalent chemostat experiments using an independent macroarray technique [19] and, in addition, triplicate analyses of eight genes were performed by QRT-PCR.

Minimization of systematic error

We sought to minimize sources of systematic error, first by careful experimental design (see above) and the application of the following strategies: use of steady-state chemostat cultures ensuring carefully controlled environmental conditions at each constant growth rate[14-17]; avoidance of prolonged cultivation studies (that is, keeping to below 60 generations) to eliminate risks of strain variability and/or mutational effects; fast sampling and growth-arrest methods avoiding environmental disturbances for proper transcriptome, proteome and metabolome analyses; for each omic analysis, all samples were processed by the same specialist researcher; careful analytical strategies and normalization methods (see Additional data file 4).