Obesity and its accompanying metabolic complications have risen to epidemic proportions over the past 30 years. Together, these diseases are creating economic and social burdens that are expected to increase. Although multifactorial in their etiology and polygenic, the development of diseases such as metabolic syndrome in humans (obesity, fatty liver, insulin resistance [IR] and dyslipidemia) is facilitated by access to cheap, calorically dense foods. Many animal models, particularly laboratory rats and mice, have been developed to identify the underlying mechanisms involved in risk factors associated with metabolic diseases, especially Type II diabetes (T2D) and IR. Genetic models and dietary manipulations have aided our understanding of these disorders; however, it is important to recognize that examples of reversible obesity and IR occur in nature. Importantly, these conditions in animals are not associated with the negative consequences observed in humans. These observations together suggest that metabolic controls are sufficiently flexible to avoid the highly deleterious, maladapted process in humans and serve as a survival mechanism in some animals. An example of this is obesity of almost unlimited proportions (i.e., >50% body fat) and insulin resistance present in hibernating bears. Both phenotypes are reversible naturally. Our studies using cultured bear fat cells collected during hibernation recapitulate the natural insulin resistance observed in vivo. In addition, proteins or peptides found in active season serum can reverse this insulin resistance of cells from hibernating bears, indicating that circulating factors also play an important role in the seasonal modulation of insulin sensitivity. Experimentally, we can reverse the insulin resistance temporarily during hibernation by simply feeding glucose for a few weeks; thus, an important control can be added to minimize influences of body fat content, season, and diet composition. Bears therefore offer a unique model in which whole animal and cell culture systems can be used in highly standardized, easily manipulated, and controlled experiments to explore the underlying basis for reversible IR. By revealing the mechanisms involved in this reversible physiology we can gain a broader understanding of metabolic controls and the regulation of energy balance. The Washington State University (WSU) Bear Research, Education and Conservation Center is the only facility of its kind in the world dedicated to basic nutrition and physiological research using captive bears. Based on this rationale, the main objective of the proposed studies is to reveal the underlying molecular changes occurring in bears during different states of insulin sensitivity by identifying the serum proteins/peptides associated with hibernation by comparing serum from active season and hibernating bears, as well as bears that have restoring insulin sensitivity during hibernation.

Protein Concentration, Lysis and Digestion Performed BCA assay (Pierce) to analyze serum concentration of all samples. Each bear serum sample was diluted to 7.5 ug/ul final concentration with PBS. 0.1% of PPS surfactant (Expedeon) in 50 mM ammonium bicarbonate was added to 50 ug of each diluted bear serum. 200 ng of human ApoA1 protein (Millipore) was added to each individual sample as a protein process control. Samples were briefly vortexed, reduced with DTT, alkylated with IAA, quenched with DTT, and digested with trypsin at a 1:50 enzyme to substrate ratio for 16 hours at 37°C. PPS was cleaved with the addition of 200 mM HCl. Samples were cleaned with MCX columns (Waters) and resuspended in 0.1% formic acid. A heavy labeled Peptide Retention Time Calibrant (PRTC) mixture (Thermo, cat # 88321) was added to each sample. Liquid Chromatography and Mass Spectrometry One ug of each sample with 50 femtomole of PRTC was loaded onto a 30 cm fused silica picofrit (New Objective) 75 μm column and 4 cm 150 μm fused silica Kasil1 (PQ Corporation) frit trap loaded with 3 μm Reprosil-Pur C18 (Dr. Maisch) reverse-phase resin analyzed with a Waters nanoACQUITY UPLC system. The PRTC mixture is used to assess quality of the column before and during analysis. Four of these quality control runs are analyzed prior to any sample analysis and then after every six sample runs another quality control run is analyzed. For the quality control analysis, buffer A was 0.1% formic acid in water and buffer B was 0.1% formic acid in acetonitrile. The 45-minute QC gradient consisted of a 2 to 35% B in 30 minutes, 35 to 60% B in 10 minutes, 60 to 95% B in 5 minutes, followed by a wash of 5 minutes and 15 minutes of column equilibration. A cycle of one 60,000 resolution full-scan mass spectrum (400-800 m/z) followed by a data-independent MS/MS spectra on loop count of 17 data-independent MS/MS spectra using an inclusion list at 30,000 resolution, AGC target of 1e6, 55 sec maximum injection time, 27% NCE with a 2 m/z isolation window. As the peptides were eluted from the column, they entered the mass spectrometer via an electrospray ionization interface with a 3 kV spray voltage. In each mass spectrometry run, the sample was loaded onto the trapping column and washed with a mixture of 98% buffer A (water in 0.1% formic acid) and 2% buffer B (acetonitrile in 0.1% formic acid). After trapping, the sample was loaded onto the analytical column and separated over a 90 minute linear gradient from 2%-35% buffer B, followed by a 20 minute gradient of 65%-95% buffer B and a final 10 minute equilibration with 2% buffer B. For DIA with on-column gas-phase fractionation, a pooled sample for each serum state (active, hibernation, and reversal) was analyzed with six DIA LC-MS/MS runs collectively covering 400-1000 m/z. Each DIA run acquired comprehensive MS/MS data on all precursors in a 100 m/z range. Precursor MS spectra consisted of a wide spectrum (400-1600 m/z at 60,000 resolution) and a narrower spectrum matching the 100 m/z range (390-510, 490-610, etc at 60,000 resolution, AGC target 3e6, maximum injection time of 100 ms) acquired every 25 MS/MS scans. The MS/MS scans used an overlapping 4 m/z wide isolation window, at 30,000 resolution, AGC target 1e6, maximum injection time of 55 ms and 27% NCE acquired every 25 MS/MS scans. For DIA quantitative acquisition of individual samples, comprehensive MS/MS data on all precursors between 400 and 1000 m/z was acquired with a 25 x 24 m/z isolation width. Precursor MS scan resolution was 30,000, AGC target 3e6, maximum injection time 100 ms and 27% NCE. The MS/MS scan resolution was 30,000, AGC target 1e6, maximum injection time 55 ms and 27% NCE using an "overlapping window" multiplexing approach in which alternating cycles of MS/MS scans are offset by 12 m/z relative to one another. Proteomics Data Analysis Thermo RAW files were converted to mzML format using Proteowizard (version 3.0.18110) using vendor peak picking and demultiplexing. Chromatogram spectral libraries were created using default settings (10 ppm tolerances, trypsin digestion, HCD b- and y-ions) of EncyclopeDIA (version 0.9.5) using a Prosit predicted spectra library based on (WSU generated) bear FASTA background. Prosit library settings were 1 missed cleavage, 33% NCE, charge states of 2 and 3, m/z range of 396.4 to 1002.7, and a default charge state of 3. Quantitative spectral libraries were created by mapping spectral to the chromatogram spectral library using EncyclopeDIA requiring a minimum of 3 quantitative ions and filtering peptides at a 1% FDR using Percolator 3.01. The quantitative spectral library was imported into Skyline (daily version 20.1.1.146) with the bear FASTA as the background proteome to map peptides to proteins. A csv file of peptide level total area fragments (TAFs) for each replicate was exported from Skyline. Statistical analysis Data preprocessing prior to the statistical analysis involves the transformation of the data to the log2 scale, visual inspection of data adherence to parametric assumptions, and removal of the extreme values on the low end of the distribution. These extreme values are treated as missing data for downstream imputation. Globally, median location normalization is applied to the data such that sample abundances share a common center value. Estimation of missingness associated with MCAR/MAR is performed using group-means imputation to replace the missing values with the group mean of all known abundances of the feature. The underestimation of the variance which leads to biased estimates is minimized by limiting the imputation to a single value per feature group. Surrogate variable (SV) analysis is performed to capture the heterogeneity across the data set caused by unknown confounding effects (Leek and Storey 2007). Using the ‘sva’ module available through the R/Bioconductor framework, 4 SVs are estimated using default settings. All of the SVs are included as covariates in the downstream linear model to adjust for effects of unwanted variability. To infer the protein groups, a bipartite graph of peptide-protein interactions is constructed to generate protein groupings through the parsimony reduction of the graph. Peptide abundances at the nodes are summed to estimate the protein-level abundance. Error rates of the quantitation are not estimated. Protein group abundance is modeled using a linear model with hibernation state as factor covariate and surrogate variables as numerical covariate. Empirical Bayes method as part of the LIMMA package was used to estimate the parameters in R (Smyth 2004). Between group comparisons were made using the P-values of the moderated t-test adjusted by the Benjamini-Hochberg procedure which controls the false discovery rate (FDR). Differentially abundant proteins were determined using the cutoff of FDR < 0.05.

JK01 and JK04-JK30 are the 28 bear serum samples which characterize 3 conditions - 11 samples are serum from active bears, 10 samples are serum from hibernating bears and 7 samples are serum from bears rescued from hibernation with glucose (reversal).