Quantitative Proteomics Using 15 N SILAC Mouse

In biomedical research the use of mammalian tissues is crucial to increase our understanding of complex human diseases. Mass spectrometry-based proteomic approach has become the most powerful tool of studying large-scale protein expression profiles in mammalian tissues. To perform global proteome analysis quantification of mammalian tissues, we generated N SILAC mice to obtain tissue-matched labeled peptide libraries for mass spectrometry-based quantitative proteomic analysis. We developed a new labeling protocol to circumvent adverse effects of introducing N labeled diet to mice, and showed that the new labeling scheme has no significant effect on the fertility and reproduction of C57/BL6 mice. Using labeled tissues from these mice, we compared the reproducibility of mass spectrometry-based quantification with or without N labeled internal standards among biological replicates of young and old brains. We found that labeled-based quantification is less susceptible to variations from instrument conditions and produces more consistent quantifications among biological replicates than label-free quantification. Lastly, we showed that over 60% of peptides from the human brain are quantifiable with internal standards from N labeled mouse brain and therefore present a promising alternative of quantifying human tissues that do not have existing cell lines available for SILAC labeling. Corresponding author: Emily I. Chen, Ph.D. ; emily.chen@stonybrook.edu ; Phone (631)-444-3134, Fax (631)-444-9749 ; Stony Brook University, BST 8-125, Stony Brook, NY 11794-8651 RUNNING TITLE: The utility of N SILAC mouse tissues for quantitative proteomic studies


Introduction
Liquid chromatography (LC) coupled to electrospray mass spectrometry (MS) is well established in shotgun proteomics to rapidly identify and quantify large numbers of proteins. Quantification of identified proteins from different samples in LC-MS/MS is performed using certain physical attributes of peptides as surrogate measurements. One approach to perform global quantitative proteome comparisons by LC-MS/MS is to include a labeled version of peptides in the samples. Stable-isotope labeled amino acids or isobaric chemical mass tags can be introduced to cells through the growth media (SILAC) [1][2][3] or covalently linked to proteolyzed peptides (ICAT, iTRAQ, or TMT) [4][5][6][7][8][9] respectively to generate internal standards for LC-MS/ MS based quantification. Alternatively, label-free LC-MS/ MS based protein quantification can be achieved by measuring mass spectral peak intensities of peptide ions or the total number of spectra identified for a protein (spectral counting) [10][11][12].
Although label-based protein quantification methods are superior for quantifying biological relevant changes, label-free protein quantification methods have been widely adapted in many laboratories due to lower starting costs and improved bioinformatics software for peptide peak quantification [13][14][15][16][17][18][19]. With the recent advances in mass spectrometers and informatics, highresolution LC-MS/MS technology has become the most powerful tool to identify and quantify thousands of proteins from complex tissues. However, unlike cells in culture, mammalian tissues consist of heterogeneous cell types and are known to exhibit more variations than tissue culture cells. Proteomic analyses of biological replicates provide a handle on experimental variation and allow statistical analyses to be used to identify differences of protein expression levels in samples. The more replicates, the more robust the statistics, but analyzing replicates is expensive and time consuming, and often it is difficult to generate sufficient starting material for evaluating biological and technical replications. In this scenario, quantification methods that are less susceptible to the sampling bias produced by the mass spectrometer will allow us to differentiate true biological differences from small differences inherent amongst biological replicates. In this paper, we report that quantitative analysis using tissue-matched internal standards from 15 N SILAC mice identified more proteins that are significantly altered between the young and old brain tissues from 3 biological replicates than quantitative analysis of the same tissues using the spectral counting method. To extend the utility of these labeled reference proteins, we examined the possibility of quantifying human proteins using labeled mouse proteins from 15 N SILAC mice. As a result we found that a large amount of peptides from the human brain could be quantified when combined with analysis of labeled peptides from 15 N SILAC mouse brain.

Statistical Analysis
Standard statistical methods were performed to analyze the MS data sets as described and referenced in the manuscript. Pearson Correlation Analysis and Coefficient of Variation analysis were used to survey correlations of overall protein profiles between and within groups.
LIMMA was employed to assess statistical significant changes of protein expression in different samples based on linear models [21]. To match human and mouse peptides, the following corrections were applied to the dataset. First, we allowed the match of identical sequences with interchangeable amino acids such as I/L and K/Q. Then, we allowed the match of peptide sequences wherein two amino acids are switched (e.g. AD and DA) whereas the rest is identical.

Minimizing Adverse Effects Of 15 N Metabolic Labeling In Mice Through Adaptation
The feasibility of labeling rats and mice metabolically with a diet containing 13 C 6 lysine or 15 N labeled amino acids [20,22,23] has been reported. The labeled protein source provides free 15

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females on the regular mouse chow (Fig. 1A).
Consequently, lower incorporation (B). Incorporation of 15 N amino acids in ATP synthase beta subunit in the heart, lung, and ovary from 15 N labeled mice with or without adaptation as shown by the mass spectrum of one of its peptides.
(C). The adaptation scheme of the new metabolic labeling.

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efficiency of 15 N amino acids was found in most tissues derived from these underweight pups (Fig. 1B, left panel). .
To increase the labeling efficiency of C57BL mice, we (B). Quantile normalization of ratios ( 15 N SILAC labeled) or spectra counts (label-free) quantifications of young and old brain tissue lysates. Three biological replicates were analyzed by each quantification method.
(C). Pearson correlation between young and old brain tissue lysates by 15 N SILAC labeled or label-free quantifications.

(D). Comparisons of triplicate values (log2) from proteins commonly identified in 15 N SILAC labeled and label-free quantifications.
(E). Volcano plot of ratios and p-value of 15 N SILAC labeled and label-free quantifications. Fold changes were derived from ratios or spectra counts of proteins from the young brain tissue lysate divided by the old brain tissue lysate. Weighed average of ratios or spectra counts were calculated to derive the log2(fold change) as shown in the x-axis. The y-axis represents the -log(adjusted p-value). Proteins fall above p< 0.05 (above 1.3 on the volcano plot) and above/below 2 fold changes (between 1 to -1 in the volcano plot) are identified as proteins with significant changes (red).
transformed SC have similar distributions among samples, indicating sufficient normalizations of both datasets.
Next, we performed a series of statistical analyses to study inter and intra-group variations of protein lists from these two methodologies. Using the Pearson Correlation Analysis, 15 N SILAC tissue quantifications showed higher correlations within biological replicates of the same age group and lower correlations among the biological replicates between two age groups, suggesting differential protein expression patterns in the brains of old and young mice (Fig. 2C).
In contrast, quantitative comparisons based on the spectral counting method showed similar correlations within biological replicates and between the two age groups (uniformly > 0.9). Collectively, our data reveals that SC quantification is less sensitive in detecting subtle changes, such those that occur during aging, than 15 N SILAC mouse quantification.
We also examined the within-group variations of these two methodologies. young, 13.6% old) (Fig. 2D). We speculated that the greater run-to-run variations of the SC approach could mask biologically relevant but subtle changes in protein expression, and therefore further interrogated the significance of both datasets using LIMMA statistics.
LIMMA statistics are widely used to analyze complex experiments such as microarray and large-scale proteomic analysis, which involve comparisons among many targets simultaneously [21]. The empirical Bayes moderated t test was applied to obtain estimated error rates of individual proteins in relation to other proteins in brain tissues derived from the young or old mice.
Also, the Benjamini-Hochberg method was used to correct the raw p-values for multiple testing (adjusted pvalues) [21]. Finally, volcano plots based on adjusted pvalues and fold change of protein quantification from both methods were used to demonstrate statistically significant changes associated with aging (Fig. 2E).
Using the criteria of greater than 2 fold changes and we found 3025 (60%) quantifiable human peptides from a total of 5040 human peptides identified. Data from two technical replications were merged and analyzed for overall ratio distribution. The ratio distribution of quantifiable peptides was unimodal and >75% of peptides were within a 3-fold ratio between human and mouse proteins (Fig. 3A). There was also a good agreement with ratios derived from human and mouse peptides between two technical replicates since more than 70% of proteins were within 25% CV (Fig. 3B & Suppl. Table 1). is found down regulated in the brains of aged mice (5 fold less) and plays a crucial role in several of these highly enriched age-regulated pathways listed above.
PKC is highly expressed in neuronal tissues and has been implicated in a broad spectrum of neuronal functions [25]. Many proteins related to synaptic transmission have been shown to be PKC targets. For instance, activation of PKC in neurons is frequently associated with the modulation of ion channels [26], desensitization of receptors [27], and enhancement of neurotransmitter release [28]. The growth associated protein GAP-43 (F1, B50) [29,30] and dephosphin [28] are presynaptic substrates of PKC. GAP-43 has been speculated to play a role in a wide range of neuronal (A). 15 N labeled mouse brain lysates were spiked into unlabeled human brain tissue lysates. Protein ratios derived from the human mouse paired peptides from two replications are plotted against the number of proteins (frequency) within the binned ratio.

(B). Coefficient variation (CV) analysis of number of peptides within the binned CV.
Freely Available Online functions including neurotransmitter release, neuronal development and regeneration, synaptogenesis, synaptic plasticity and memory formation, etc. Therefore, the PKC pathway may modulate the efficacy of synaptic transmission and provide a basis for memory formation.
Consequently, a decrease of PKC protein in the brain may lead to reduced PKC signaling resulting in synaptic/ neuronal loss as well as progressive loss of cognitive functions. In stark contrast to reports from similar proteomic studies that concluded minimal proteome changes during aging [31], our comparison identified age-dependent changes in the brain that provides a molecular basis of functional decline during aging.

Conclusion:
In summary, we report that our new 15