Characterizing Cardiac Molecular Mechanisms of ... - ACS Publications

Article pubs.acs.org/jpr

Characterizing Cardiac Molecular Mechanisms of Mammalian Hibernation via Quantitative Proteogenomics Katie L. Vermillion,† Pratik Jagtap,‡,§ James E. Johnson,∥ Timothy J. Griffin,‡,§ and Matthew T. Andrews*,† †

Department of Biology, University of Minnesota Duluth, 1035 Kirby Drive, Duluth, Minnesota 55812, United States Center for Mass Spectrometry and Proteomics, University of Minnesota, 1479 Gortner Avenue, St. Paul, Minnesota 55108, United States § Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 321 Church St SE, Minneapolis, Minnesota 55455, United States ∥ Minnesota Supercomputing Institute, 512 Walter Library 117 Pleasant Street SE, Minneapolis, Minnesota 55455, United States ‡

S Supporting Information *

ABSTRACT: This study uses advanced proteogenomic approaches in a nonmodel organism to elucidate cardioprotective mechanisms used during mammalian hibernation. Mammalian hibernation is characterized by drastic reductions in body temperature, heart rate, metabolism, and oxygen consumption. These changes pose significant challenges to the physiology of hibernators, especially for the heart, which maintains function throughout the extreme conditions, resembling ischemia and reperfusion. To identify novel cardioadaptive strategies, we merged large-scale RNA-seq data with large-scale iTRAQ-based proteomic data in heart tissue from 13-lined ground squirrels (Ictidomys tridecemlineatus) throughout the circannual cycle. Protein identification and data analysis were run through Galaxy-P, a new multiomic data analysis platform enabling effective integration of RNA-seq and MS/MS proteomic data. Galaxy-P uses flexible, modular workflows that combine customized sequence database searching and iTRAQ quantification to identify novel ground squirrelspecific protein sequences and provide insight into molecular mechanisms of hibernation. This study allowed for the quantification of 2007 identified cardiac proteins, including over 350 peptide sequences derived from previously uncharacterized protein products. Identification of these peptides allows for improved genomic annotation of this nonmodel organism, as well as identification of potential splice variants, mutations, and genome reorganizations that provides insights into novel cardioprotective mechanisms used during hibernation. KEYWORDS: proteogenomics, Galaxy-P, hibernation, heart

■

INTRODUCTION

The extreme physiological changes experienced by hibernators make them a unique and medically relevant biological model to study many human ailments, including heart failure, stroke, obesity, and disuse atrophy. However, all mammalian hibernators are classified as nonmodel organisms, making basic science research related to them more difficult. With the recent sequencing of the 13-lined ground squirrel genome, and the advancement of RNA sequencing technologies, we have developed a better understanding of the changes in gene expression that occur throughout hibernation.6 However, highthroughput mass spectrometry (MS) proteomics data is increasingly being used to complement traditional structural genome annotation methods with expressed proteins providing experimental evidence that genes are being transcribed and

Mammalian hibernators undergo a remarkable phenotypic switch that involves profound changes in physiology, morphology, and behavior in response to unfavorable environmental conditions. During hibernation, 13-lined ground squirrels decrease their body temperature to near 0 °C and dramatically reduce their heart rate, but they arouse every 6−10 days, returning body temperature and heart rate to euthermic levels.1 This means that, while entering into and arousing from torpor, their body temperature passes through a critical level of 20 °C, a temperature region where nonhibernating mammals develop circulatory arrest, usually ventricular fibrillation (VF).2 The hibernator heart is resistant to VF,3 and although several mechanisms are thought to contribute to this resistance,4 including altered intracellular calcium handling,5 a complete molecular picture of this resistance or avoidance is lacking. © 2015 American Chemical Society

Received: July 10, 2015 Published: October 5, 2015 4792

DOI: 10.1021/acs.jproteome.5b00575 J. Proteome Res. 2015, 14, 4792−4804

Journal of Proteome Research

■

translated. This confluence of genomics, transcriptomics, and proteomics defines the emerging field of proteogenomics.7 Proteogenomics is proving to be an invaluable tool for improving annotation of model organisms and enabling new research directions in nonmodel organisms.8 Proteogenomics utilizes high throughput sequencing data (e.g., RNA-seq data) and/or gene predictions as the template to translate potential protein products in silico. Tandem mass spectrometry (MS/ MS) data, usually from the same starting sample that generated the RNA-seq data, is then matched to the database of possible proteins and used to confirm translation of transcripts. Proteogenomic mapping has been used to identify new genes, new translational start sites, new alternative splice variants, and new upstream open reading frames.9 Effective proteogenomic analysis offers bioinformatics challenges. Researchers must manage large-scale “multi-omic” data (e.g., RNA-seq, MS-proteomics) as well as integrate disparate tools for integrated analysis of this data.10 Matched MS/MS data to protein products, especially those that are putatively novel variants, must undergo scrutiny to ensure veracity of reported results.7 To help researchers meet these challenges, we have developed a data analysis platform called Galaxy-P.11 This platform extends the genome-centric Galaxy bioinformatics framework, deploying additional tools for analysis of MS-based proteomics data, and providing modular, reproducible, and shareable workflows that allow for integration of genomic, transcriptomic, proteomic, and metabolomic data.10 We have utilized the Galaxy-P platform for proteogenomic analysis to gain new insights into the molecular mechanisms driving cardioprotection offered by the unique biological phenotype of hibernation. This analysis provides new-system wide insights into the molecular mechanisms driving cardioprotection. These insights can be translated into treatments and therapies to prevent cardiovascular disease in humans. Mammalian hibernation also provides an ideal model system to study the role of differential gene expression in adaptive evolution. In the absence of hibernation-specific genes, it is believed that the hibernation phenotype results from the differential expression of existing genes and proteins found in all mammals.12 This study used iTRAQ labeling and proteogenomics combining RNA-seq and MS/MS data to identify and quantify over 2000 ground squirrel proteins using a customized ground squirrel database. These results offer a significant advancement from our past proteomics work on ground squirrels,13 which suffered from the lack of a reliable, ground squirrel-specific database of proteins. Differential expression analysis identified key signaling pathways that are enriched in Torpor (TOR) relative to August (AUG), highlighting their significance in the hibernation phenotype. Comparative analysis between the transcriptome and proteome also show that increased transcriptional expression correlates well with increased translational expression during the euthermic interbout arousal (IBA). Additionally, proteogenomic analysis integrating RNA-seq and MS/MS proteomic data identified over 350 novel peptide sequences. Many of these can be used to improve the genomic annotation of the 13-lined ground squirrel, whereas others may be significant in regulating the hibernation phenotype. Overall, this study offers new insights into mammalian hibernation and also serves as a framework and reference point for detailed proteogenomic analysis of the ground squirrel.

Article

EXPERIMENTAL SECTION

Animal Models

Thirteen-lined ground squirrels, Ictidomys tridecemlineatus, used in this study were live-trapped near Paynesville, Minnesota and housed in the AAALAC-accredited Animal Care Facility at the University of Minnesota Duluth School of Medicine. Squirrels are individually housed in plastic top-load rat cages with aspen shavings. From April through October, the squirrels were maintained under standard conditions of 18−21 °C, 12:12 h light:dark cycle, and fed rodent chow and water ad lib. During the hibernation season (November through March), animals were kept at an ambient temperature of 5 °C, 24 h dark cycle, and no food, but water was provided ad lib. All experimental animal procedures were approved by the University of Minnesota Institutional Animal Care and Use Committee. The following groups of animals were used for this study to reveal changes in protein expression that occur throughout the circannual cycle: April active (APR), August active (AUG), October active (OCT), January torpor (TOR), January IBA (JIBA), and March IBA (M-IBA). Three males and three females were collected at each time point. The animal state at each collection point was determined by rectal temperature and animal behavior. For all time points, animals were anesthetized with isofluorane and sacrificed by decapitation. The pericardium was removed from the heart, excised, and halved sagitally to include both atria and ventricle. Heart dissection was performed on ice and samples were rapidly frozen in liquid nitrogen and stored at −80 °C. All animals from April through October were active at the time of sacrifice with a body temperature of 35−37 °C and were observed as awake and active (open eyes and coordinated body movements). August (AUG) animals were used for relative protein expression to represent an active state of the animal when they are fattening in preparation for hibernation. October (OCT) animals have near maximal body mass and are preparing for hibernation by performing brief intermittent torpor bouts,14 at which time body temperature drops as low as 23 °C for up to 24 h. All animals collected at this time point were collected in the first 2 weeks of October and were active at the time of sacrifice. Torpor (TOR) and Interbout arousal (IBA) collection points reflect the extreme conditions experienced by hibernating ground squirrels in the wild. The sawdusting method was used to identify the squirrels in TOR or IBA.15 Briefly, a small amount of sawdust is placed on the animals back and monitored daily, arousal removes the sawdust from their back. TOR animals were collected after a minimum of 3 days in a torpor bout, showed no visible signs of arousal, and had a rectal temperature of 5−7 °C at the time of sacrifice. January IBA (J-IBA) and March IBA (M-IBA) animals aroused naturally and were awake, active, and showed coordinated body movements at the time of sacrifice. Rectal temperatures at the time of sacrifice were 35−37 °C. TOR and J-IBA animals were collected in January and February, when torpor bouts are longest. March interbout arousal animals were collected in late March (M-IBA) to determine if changes in protein expression occur throughout the hibernation season. Protein Extraction and iTRAQ Labeling

All samples were prepared as follows. Frozen tissues were ground to a fine powder under liquid N2 and were reconstituted with a ratio of 10 μL of extraction buffer [7 M urea, 2 M thiourea, 0.4 M triethylammonium bicarbonate 4793


Article


time, and FT first mass mode fixed at 111 m/z. Dynamic exclusion settings were repeat count of 1, maximum of 50 values, 12 s duration, and mass tolerance of −0.7 to 1.25 atomic mass unit.

(TEAB), pH 8.5, 20% acetonitrile, and 4 mM tris (2carboxyethyl)phosphine (TCEP)] per milligram of tissue on ice. The samples were vortexed briefly and then sonicated at 30% amplitude for 7 s with a Branson Digital Sonifier 250. For each sample, 150 μL was transferred to a PCT tube with a 150 μL cap for the Barocycler NEP2320 (Pressure Biosciences, Inc., South Easton, MA) and cycled between 35 kpsi for 30 s and 0 psi for 15 s for 40 cycles at 37 °C. The PCT tube was uncapped, and 200 mM methylmethanethiosulfonate (MMTS) was added to a final concentration of 8 mM MMTS, recapped, inverted several times, and incubated for 15 min at room temperature. The samples were transferred to a new 1.5 mL microfuge Eppendorf Protein LoBind tube. Two aliquots for each sample were taken for protein concentration determination by Bradford assay. A 100 μg aliquot of each sample was transferred to a new 1.5 mL microfuge tube and brought to the same volume with protein extraction buffer plus 8 mM MMTS. All samples were diluted 4-fold with ultrapure water, and trypsin (Promega, Madison, WI) was added in a 35:1 total protein/trypsin ratio. Samples were incubated overnight for 16 h at 37 °C, frozen at −80 °C for 0.5 h, and dried in a vacuum centrifuge. Each sample was then cleaned with a 4 mL Extract Clean C18 SPE cartridge from Grace-Davidson (Deerfield, IL). Eluates were vacuum-dried and resuspended in dissolution buffer (0.5 M triethylammonium bicarbonate, pH 8.5) to a final 2 μg/μL concentration. For each iTRAQ 8-plex, 40 μg of each sample was labeled with iTRAQ reagent per the manufacturer’s protocol (Sciex, Foster City, CA) (see Supporting Information and Methods for labeling strategy). After labeling, the samples were multiplexed together and vacuum-dried. The multiplexed sample was cleaned with a 4 mL Extract Clean C18 SPE cartridge, and the eluate was dried in a vacuum centrifuge.

Bioinformatic Analysis

A customized ground squirrel database was generated by using the 14,332 distinct transcripts identified by RNA-seq data generated in the Andrews lab6,17 to identify 19,895 translated proteins using the Trinity program (TransDecoder), combined with the 27,739 NCBI predicted proteins (Accession number 43179) from the annotated ground squirrel genome, and 115 protein sequences from the contaminant protein database to create a protein database with a combined 46,651 protein sequences. The contaminant protein database is a list of proteins commonly found in proteomics experiments that are present either by accident or through unavoidable contamination of protein samples. The MS/MS spectra were searched against this customized ground squirrel database using Paragon Algorithm (V. 4.5.0.0) search engines in ProteinPilot (V. 4.5, Sciex, Foster City, CA) via the Galaxy-P platform (University of Minnesota). The precursor mass window was set to subppm, and MS/MS settings were set at Orbi/Orbi. Eight-plex iTRAQ peptide labeling and cysteine MMTS alkylation were set as fixed modifications. The search effort was thorough, and the ID focus was set for biological modifications. All peptides were identified with 95% confidence scores as specified by the Paragon Algorithm and less than a 1% false discovery rate (FDR) based on target-decoy database searches. We have accounted for multiple hypothesis testing for quantitative analysis of these samples. In particular, ratio channels with two similar conditions (technical replicates) were assigned as decoy, and ratio channels with unlike conditions (e.g., sample from APR vs sample from AUG) were assigned as target. We used the Sciex ProteinPilot Descriptive Statistics Template (PDST) outputs to estimate the threshold P-values to be used for differential analysis. Specifically, P-value thresholds in the decoy channel pair were calculated after considering ascending Pvalues for the total number of quantifiable proteins (Benjamini−Hochberg FDR method). On the basis of PDST template calculations, a P-value of 0.05 for a quantitative FDR of 1% (for replicates 1 and 2) and 2% (for replicate 3) was used for all three replicates. Relative quantification of proteins was determined by ProteinPilot in a normalized log10-based relative iTRAQ ratio format with the AUG time point as the reference denominator. Differentially expressed proteins were submitted to DAVID for functional annotation and pathway analysis of the affected signaling pathways. Differentially expressed proteins are listed for APR, OCT, TOR, J-IBA, and M-IBA in the Supplemental Tables along with DAVID-generated KEGG pathways (p-value < 0.05) and DAVID-generated functional annotation clusters (FACs) (Enrichment score > 2.0). Proteins were also separated into upregulated and downregulated proteins, and similar DAVID analysis was performed and is included in the Supplemental Tables. Galaxy-P was used to identify novel peptide sequences by first filtering out all peptides identified from the known NCBI genome. The remaining peptides identified from RNA-seq data were searched against the NCBI 13-lined ground squirrel nonredundant database using a BLAST-P workflow within Galaxy-P. Peptides identified by BLAST-P were further filtered to account for the percentage of identical amino acids, the number of gaps in the query sequence, and the length of the

Peptide Liquid Chromatography Fractionation and Mass Spectrometry

The iTRAQ-labeled samples were resuspended in Buffer A (20 mM ammonium formate, pH 10, in 98:2 water/acetonitrile) and fractionated offline by high pH C18 reversed-phase (RP) chromatography.16 A Shimadzu Promenance HPLC (Shimadzu, Columbia, MD) was used with a C18 XBridge column, 150 mm × 2.1 mm internal diameter, 5 μm particle size (Waters Corporation, Milford, MA). The flow rate was 200 μL/min with a gradient from 2 to 35% Buffer B over 60 min followed by 35−60% over 5 min. Buffer B was 20 mM ammonium formate, pH 10, in 10:90 water/acetonitrile. Fractions were collected every 2 min and UV absorbances were monitored at 215 and 280 nm. Peptide-containing fractions were divided into two equal numbered groups, “early” and “late”. The first “early” fraction was concatenated with the first “late” fraction, and so on. Concatenated samples were dried in a vacuum centrifuge, resuspended in load solvent (98:2:0.01, water/acetonitrile/ formic acid), and 1−1.5 μg aliquots were run on a Velos Orbitrap mass spectrometer (Thermo Fisher Scientific, Incorporated, Waltham, MA). Spray voltage was 2 kV, and the heated capillary was maintained at 260 °C. The orbital trap was set to acquire survey mass spectra (300−1800 m/z) with a resolution of 30,000 at 400 m/z with automatic gain control (AGC) 1 × 10E6, 500 ms min injection time, and lock mass at 445.1200 m/z (polysiloxane). The six most intense ions from the full scan were selected for fragmentation by higher-energy collisional dissociation with normalized collision energy of 40%, activation time of 20 ms, and detector settings of 7500 resolution, AGC 1 × 10E5 ions, 500 ms maximum injection 4794


Article

Journal of Proteome Research query peptide relative to the input. Resulting mismatched peptides were analyzed for the quality of peptide spectral match (PSM), and protein sequences were aligned to ground squirrel and human NCBI proteins to determine how the identified peptide sequences differed from predicted or known sequences. The Galaxy-P workflows used in this study were modified from those originally described by Jagtap et al.11b The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD002457.

■

RESULTS AND DISCUSSION

Quantitative Proteomic Analysis of Ground Squirrel Hibernation

The goal of this study was to identify proteomic changes that occur in the heart of the hibernating 13-lined ground squirrel to provide insight into the mechanisms allowing the hibernator’s heart to continuously function throughout extreme physiological and metabolic changes. A thorough quantitative analysis of cardiac protein expression was performed using an iTRAQlabeling experiment highlighting six time points throughout the circannual cycle (Figure 1A, B). Three sets of 8-plex iTRAQlabeling experiments were performed with each experiment including two technical replicates (Table S1). For each set of iTRAQ experiments, the MS/MS spectra were imported into Galaxy-P and searched against a customized ground squirrel protein database generated via gene modeling of the NCBI 13lined ground squirrel genome merged with RNA-seq data generated in the Andrews lab.6,17 The combined database resulted in increased protein, peptide, and spectral identifications relative to either database alone (Table S2) and is the first ground squirrel-specific database to be used for proteomic analysis. This thorough search effort resulted in the identification of a total of 127,051 PSMs from 289,919 spectra and 2007 proteins identified at a 1% global protein FDR (Figure 1C). Relative quantification of protein expression was determined in ProteinPilot in a normalized log10-based relative iTRAQ ratio format with the AUG time point as the reference denominator. Proteins were considered differentially expressed relative to AUG if they had at least two unique peptides, an experimentwide FDR of no more than 2% based on target-decoy analysis using technical replicates, and a p-value ≤ 0.05 in two or three of the iTRAQ experiments. This resulted in the identification of a total of 250 differentially expressed proteins with several proteins being differentially expressed at more than one time point. The IBA time points had the largest number of differentially expressed proteins with the January IBA (J-IBA) having 153 proteins and the March IBA (M-IBA) having 123 proteins showing differential expression relative to AUG (Figure 1D). This was surprising because both the AUG time point and the IBA time points are euthermic time points, however, the IBA time points are surrounded by extended periods of hypothermic torpor and may require a different set of proteins that allow for the rapid increase in oxygen consumption, heart rate, and body temperature associated with the IBA. The J-IBA collection point also had the highest number of proteins (23) that were exclusively differentially expressed at that time point. Interestingly of these 23 proteins only 3 are upregulated: CAND1, CRYAB, and HSP90B1 (see Table S9 for abbreviations). CAND1 inhibits assembly of multisubunit E3 ubiquitin ligases, whereas CRYAB and

Figure 1. Summary of iTRAQ-labeling experiments on 13-lined ground squirrel heart tissue. (A) Tracing of core body temperature (Tb, black line) from a single animal measured by a surgically implanted transmitter along with the controlled ambient temperature (blue line) over the course of the hibernation season. Arrows indicate representative body temperatures and time points of animal collection: OCT (October), TOR (Torpor), J-IBA (January IBA), M-IBA (March IBA), APR (April), and AUG (August). (B) Heart tissue from one male and one female are collected at six time points throughout the circannual cycle and pooled. Protein lysates are trypsin digested and labeled with unique iTRAQ labels, including two technical replicates. Samples are combined prior to LC/MS/MS. (C) Identification summary table listing total spectra, proteins, and novel proteoforms identified in each of three iTRAQ-labeling experiments as well as the combined totals. (D) Differentially expressed proteins for each time point are listed relative to AUG, including the total number of differentially expressed proteins, number of unique proteins (found to be differentially expressed only at that time point), and up- and downregulated proteins. 4795


Article

Journal of Proteome Research HSP90B1 are both molecular chaperones involved in stabilizing and folding of proteins, all of which would correlate with increased protein production during the euthermic IBAs and their proper folding and assembly. It is thought that IBAs are required to allow for critical protein synthesis and biomolecule replenishment and repair1 with some studies suggesting that protein synthesis in hibernating mammals is hyperactivated by 150−200% during the IBAs and may be compensatory for the lack of protein synthesis during torpor bouts.18 The smallest number of differentially expressed proteins is found in OCT relative to AUG, which is not surprising given that some squirrels are experiencing brief intermittent test bouts as early as late July or early August (Figure 1A), and therefore protein expression associated with preparation for hibernation would be similar. Similar numbers of proteins are upregulated and downregulated in the OCT, TOR, and J-IBA time points (Figure 1D). This highlights a delicate balance between the pathways that are necessary to maintain contractile function in the heart while still reducing basal metabolic rates throughout the hibernation season. OCT is the only time point where a larger number of proteins are downregulated than are upregulated, which correlates with the preparatory phase for hibernation and may be important for triggering torpor (Figure 1D). Late in the hibernation season, a more drastic difference is observed in up- and downregulated proteins at M-IBA and APR points as animals are preparing to arouse from hibernation and return to euthermic conditions. DAVID pathway analysis highlights similar pathways that are upregulated between MIBA and APR, including glycolysis/gluconeogenesis, and Huntington’s and Parkinson’s disease pathways (Table S3− 7), which are largely related to mitochondrial function, and correlate with increased basal metabolic rates associated with the return to euthermic conditions. The increased protein production in M-IBA may be necessary to trigger the cessation of hibernation. To begin to identify signaling pathways and proteins that are important for regulating function at each of the time points, differentially expressed proteins were submitted to DAVID for pathway analysis and functional annotation clustering (FAC). Proteins lists were submitted to DAVID as all differentially expressed proteins for each time point as well as lists of up and downregulated proteins (Tables S2−6). Protein Expression in APR Reveals Shifts in Fuel Substrates

In comparing protein expression between APR and AUG, the most enriched FACs were for the mitochondria (Table S3). Mitochondrial proteins showing significant upregulation in APR include VDAC1 and VDAC2, important for the exchange of metabolites into the mitochondria; TUFM and HSPD1, which are involved in protein synthesis and folding in the mitochondria; SOD2 and PRDX1, important antioxidants in the mitochondria; HK1, GAPDH, ENO2, LDHA, and PDHB, important glycolytic enzymes that are linked to TCA cycle enzymes CS and GOT1; and the β-oxidation enzymes DECR1 and HADHB (Figure 2A). Additionally, a large subset of proteins involved in cellular respiration were upregulated in APR relative to AUG, including NDUFS1, NDUFB5, CYCS, SDHA, CISD1, and ATP5H. This extensive list of proteins that are upregulated in APR highlight the increased metabolic demand that is placed on the hearts of these animals upon exit from the hibernation season and return to increased metabolic rates. The increased mitochondrial metabolism observed in APR also reflects a reliance on both glucose and fatty acids as

Figure 2. Models of cardiomyocyte function based on differential protein expression. (A) Protein expression in APR relative to AUG shows increased mitochondrial function and regulation of muscle contraction. Proteins showing significantly increased expression are depicted in red, and proteins showing significantly decreased expression are depicted in blue. (B) Protein expression in OCT relative to AUG reveals increased hypoxia tolerance and hypertrophy at the time of decreased metabolism. (C) Protein expression in hibernation relative to AUG highlights fatty acid and ketone metabolism, altered calcium handling, and contractile function in the heart.

fuel substrates, as is observed further in the DAVID pathway analysis showing upregulated glycolysis and downregulated fatty acid metabolism relative to AUG. The FACS generated by DAVID analysis also highlights muscle contractile fibers and heart development as being upregulated in APR relative to AUG (Table S3). These proteins include the structural muscle proteins MYH6, 4796


Article

Journal of Proteome Research MYOM1, and TTN; regulatory muscle proteins MYBPC3, MYOZ2, TNNI3, and XIRP1; muscle anchoring proteins ACTN2, FLNC, and VCL; and proteins important for force transmission, including JUP and PKP2 (Figure 2A). Upregulated DAVID pathways related to these proteins include arrhythmogenic right ventricular cardiomyopathy, hypertrophic cardiomyopathy, and dilated cardiomyopathy. These pathways all contain similar proteins, and although it is documented that the heart becomes hypertrophic during hibernation,6,18,19 it is interesting that these pathways and proteins are also upregulated in APR relative to AUG. These may reflect the changes that occur in order to return the heart to the prehibernation state, and restructuring the proteins necessary to maintain function at 300−400 bpm continuously throughout the euthermic period of the year. Fewer proteins are significantly downregulated in APR than are upregulated, and these proteins highlight several pathways associated with decreased fatty acid metabolism, and decreased valine, leucine, and isoleucine degradation (Table S3). Proteins showing decreased expression include ACSS1, which is important for maintaining body temperature during fasting and is no longer required after the hibernation season; and CPT1B, which catalyzes the rate-controlling step of long-chain fatty acid β-oxidation (Figure 2A). Additionally, there are several proteins involved in the degradation of branched chain amino acids that show decreased expression in APR, including MUT, IVD, ALDH2, ACAD8, and PCCA. Decreased expression of these proteins gives evidence to support the heart’s reliance on glycolysis instead of fatty acids and proteins for energy during highly energetic euthermic time points such as APR. To summarize APR protein expression relative to AUG, we observed increased glycolysis and cellular respiration while proteins involved in amino acid catabolism are decreased, reflecting a shift away from fatty acid and protein metabolism during hibernation to glycolytic metabolism at euthermic temperatures. Additionally, several structural and regulatory muscle proteins are significantly increased in APR relative to AUG. This likely reflects the increased force transmission and contractile force associated with maintaining a heart rate of 300−400 bpm compared to 3−10 bpm in hibernation.

despite at least 10-fold decreases in respiratory rates. Additionally, hemoglobin oxygen affinity is typically higher in hibernating species and even higher at colder temperatures (reviewed in ref 20). The increase in hemoglobin observed in OCT may be partially responsible for this hypoxia resistance and allow for the maintenance of arterial oxygen pressures observed in ground squirrels during hibernation. Increased ALB and APOA1 protein expression has been observed at this time point previously and thought to be important for handling increased lipids in the blood in preparation for the shift to fatty acid metabolism−characteristic of the hibernation phenotype.21 APOA1 was also isolated as a prostacyclin-stabilizing factor, and therefore may have anticlotting effects as well. This would be beneficial to the hibernator as blood becomes more viscous at the cooler temperatures of hibernation. OCT is the only time point that shows a greater number of proteins being downregulated than upregulated (Table S4). Protein pathways that were significantly downregulated were fatty acid metabolism and valine, leucine, and isoleucine degradation pathways. However, each of these pathways only had three representative proteins in each pathway. DAVID FACs also highlighted the downregulation of mitochondrial function in the form of decreased fatty acid, ketone, and amino acid catabolism. Representative proteins showing decreased protein expression include ACSL1, which converts free-long chain fatty acids into fatty-acyl CoA esters; the beta-oxidation enzymes ACADVL, ECHS1, HADHA, and HADHB; the TCA cycle enzyme ACO2; and the electron transport chain enzymes NDUFA4, ETFA, and SOD2 (Figure 2B). Additionally, the ketone and amino acid catabolism enzymes BDH, ALDH4A1, HIBADH, and HIBCH all show significantly decreased expression. The reduction in fatty acid and protein catabolism observed in OCT likely reflects reduced metabolism and preservation of lipid and protein reserves for the upcoming hibernation season. The other proteins that are downregulated in OCT are sarcomeric proteins, including MYH7; the muscle contractile proteins MYBPC3, RYR2, SPTBN1, and SPTAN1; and the muscle structural proteins SLC4A1, FLNC, NEBL, and CTNNA1 (Figure 2B, Table S4). MYH7 shows the most dramatic reduction in protein expression, whereas MYH6 shows dramatically increased protein expression. MYH6 (or MYH-alpha) and MYH7 (or MYH-beta) isoforms are crucial determinants of contractile performance and differ in contraction speed, duration of force, and maximum force produced.22 Shifts in these cardiac isoforms of myosin are thought to be important for maintaining cardiac performance at low temperatures, at which time increased contractile force and stroke volume is required to counteract loss of contractile protein function and increased blood viscosity at low temperatures. Hypertrophy of the heart,and increased MYH6 have been reported;18,19 however, this is the first report of quantitative protein expression changes by MS/MS analysis. This data correlates with the shift in myosin isoforms occurring prior to the hibernation season observed in Nelson and Rourke19 and likely reflects a major preparatory step and necessary time to allow for such dramatic remodeling in muscle structural proteins that could not occur at the reduced temperatures of hibernation. The increased levels of MYH6 and decreased levels of MYH7 are maintained throughout hibernation. In summary, several blood proteins are increased in OCT in preparation for shifting metabolic requirements and substrates, which is evident in decreased amino acid

Preparation for Hibernation Is Evident in OCT Protein Expression

In comparing protein expression from OCT to AUG, there are only a few upregulated proteins that indicate specific pathways that are enhanced. This is likely due to similarities in proteins between the two hibernation preparatory time points of AUG and OCT, and the overall reduction in protein synthesis that occurs as more time is spent in brief intermittent torpor bouts. The only DAVID pathway that was upregulated in OCT was nitrogen metabolism (Table S4) with CA1 and CA2 being the only proteins encompassing that classification (Figure 2B). CA1 and CA2 are carbonic anhydrases responsible for catalyzing the rapid conversion of carbon dioxide and water in the blood to bicarbonate and protons. These enzymes are important for maintaining the acid−base balance in the blood and other tissues. Their upregulation coincides with the increased protein expression of several other blood proteins, including ALB, HBA2, HBB, and APOA1. The hemoglobins are essential for oxygen transport, whereas ALB and APOA1 are important for transporting cholesterols, steroids, and fatty acids in plasma and serum. Ground squirrels exhibit a higher hypoxia tolerance during hibernation than during euthermy 4797


Article


intracellular adhesive junctions that anchor intermediate filaments and the actin cytoskeleton, provide mechanical attachment between cells, and support the structural and functional integrity of the tissues (reviewed in ref 27). These proteins are likely important and upregulated at these time points to combat increased stroke volume, peripheral resistance, and diastolic pressure associated with hibernation.28 Interestingly, cardiomyopathy pathways and those related to muscle contraction are also downregulated in hibernation. The proteins that are significantly downregulated are more related to calcium-handling, the structural and regulatory proteins related to MYH7, or beta myosin heavy chain (Figure 2C). MYH7 shows a 50% relative reduction in protein expression in OCT and further reduced to 25% of the protein expression relative to AUG. MYH6 increases and confers greater contractile force, whereas MYH7 decreases, which has been previously reported. Along with MYH7 being downregulated, the regulatory light chains MYL2 and MYL3, which are associated with MYH7, are also downregulated. These are also Type I muscle proteins that are very resistant to fatigue, contract slowly, and produce a low amount of power when contracted. These are less likely to maintain contractile function in the cold and under increased stroke volume and peripheral resistance. The other proteins that are downregulated are calcium-handling proteins and include TNNC1, TNNT2, and RYR2. TNNT2 binds to tropomyosin and confers calciumsensitivity to regulate muscle contraction. TNNC1 is also a component of troponin, and in the presence of calcium, abolishes the inhibitory action of troponin I, allowing the interaction of actin/myosin, the hydrolysis of ATP, and the generation of tension. RYR2 is also downregulated and controls the release of Ca2+ from the sarcoplasmic reticulum, triggering muscle contraction. Decreased expression of these calciumhandling proteins may coincide with the decrease in MYH7 and may reflect a reduced response to calcium signaling during hibernation. When comparing all of the pathways that are upregulated in TOR, J-IBA, and M-IBA, there are not any pathways that are upregulated in TOR that are not also upregulated in J-IBA and M-IBA (Table S5−7). This highlights the similarity in proteins and their expression pattern during the hibernation season despite dramatic phenotypic differences between TOR and IBA. There are a few pathways that are upregulated in M-IBA that are not found in TOR and J-IBA, including those which were discussed earlier and share more similarities to APR than to the hibernation season. During the hibernation season, transport of fatty acids into the heart results in altered PPAR signaling and increased expression and activity of fatty acid and ketone metabolism to maintain ATP synthesis and cellular function throughout the hibernation season. Shifts in cardiac muscle proteins reflect decreased calcium sensitivity and increased structural and mechanical support, which may be necessary to maintain contractile function while increasing stroke volume and diastolic pressure.

metabolism and fatty acid metabolism, important for maintaining lipid and protein reserves for the upcoming hibernation season. Additionally a shift in cardiac myosin isoforms from MYH-beta (MYH7) to MYH-alpha (MYH6) is already becoming evident. This is likely important for maintaining contractile function throughout the hibernation season. Protein Expression Throughout the Hibernation Season

In evaluating protein expression in TOR, J-IBA, and M-IBA, many common pathways were found to be upregulated at three time points relative to AUG (Table S5−7). These pathways highlight some of the proteins required to maintain contractile function during hibernation. One of the pathways that is upregulated is the PPAR signaling pathway. Ligand-induced activation of PPARs controls the expression of many genes involved in energy homeostasis and lipid metabolism.23 All three PPARs are activated by binding fatty acids with a general preference for long-chain polyunsaturated fats.24 PPAR-alpha is mainly found in cardiac tissue where it activates the transcription of genes stimulating fatty acid transport and oxidation, ketogenesis, and gluconeogenesis.25 As ground squirrels enter into hibernation, a shift toward increased fatty acid and ketone metabolism is well-established, and increased unsaturated depot fats allow for ground squirrels to reach lower temperatures and still utilize fats for metabolic energy requirements. Increased protein expression associated with PPAR signaling includes the lipid and cholesterol transport proteins APOA1, APOA2, FABP3, FABP4, CPT1A, and CPT1B, and the acyl-CoA synthetase ACSL1. ACSL1 converts free long-chain fatty acids to fatty-acyl CoA esters, which are ligands of PPARs (Figure 2C). The increased transport and uptake of fatty acids during hibernation would be responsible for the activation of PPARs and increased transcription of genes required to catabolize fatty acids and ketones for energy. The majority of the remaining pathways that are enriched in TOR, J-IBA, and M-IBA share similar proteins and are utilized to metabolize fatty acids and proteins. These pathways include fatty acid metabolism, the TCA cycle, propanoate, butanoate, and tryptophan metabolism, and valine, leucine, and isoleucine degradation (Table S5−7). The upregulated proteins related to these pathways are similar at all three time points and include the fatty acid oxidation enzymes HADHA, HADHB, ACACB, and ACAA2; the ketone metabolism enzymes ACAT1 and OXCT1; and the peroxisomal antioxidant CAT (Figure 2C). These enzymes differ from those upregulated in APR with the exception of HADHA and HADHB, which are required for the last three steps of β-oxidation of any long-chain fatty acids that would be utilized at both times of the year. One of the pathways of interest of these is butanoate (or butyrate) metabolism, which has been found to increase resistance to metabolic and physical stress, including increased cold resistance.26 The other pathways that are consistently upregulated in the hibernation time points are those related to cardiomyopathies, including Arrhythmogenic right ventricular, dilated, and hypertrophic cardiomyopathies (Table S5−7). Again, many of the enriched proteins in these pathways are shared between pathways and the TOR, J-IBA, and M-IBA time points. These proteins include MYH6, the Ca2+ handling proteins ATP2A2, SLC8A1, and ATP1A1; and a subset of proteins related to the intercalated discs in cardiomyocytes, including JUP, DSP, PKP2, and DES. Desmosomes and adherens junctions are

Proteome-Transcriptome Comparison

We recently published an extensive cardiac transcriptome analysis of the circannual cycle that provided insight into mechanisms that are used by ground squirrels to maintain contractile function in the heart throughout hibernation.6 Despite the fact that mRNA concentrations are often used as a read-out for the concentrations and activities of corresponding proteins, current literature demonstrates a substantial role for 4798


Article

Journal of Proteome Research regulatory processes occurring after mRNA transcription, including differential splicing and post-translational modification (PTM) of proteins.29 An interesting part of the current study is that the same exact tissue samples were used for this quantitative proteomic analysis as were used for the transcriptome analysis, which is one of the first studies of this kind in the hibernation field. This allows us to make direct comparisons between the two to determine which genes, proteins, and pathways are regulated by transcriptional or posttranscriptional mechanisms. One thing that must be considered when making these comparisons is that the depth-of-coverage that is attainable by RNA-seq allows for the identification of a much larger set of genes than possible by current proteomic technologies. The transcriptome analysis identified 14,332 distinct transcripts and 8285 protein-coding genes, and the proteomic analysis in this paper was able to identify 2007 proteins. Because of these limitations, we proceeded with the proteome-transcriptome comparison using differentially expressed protein data sets. The transcriptome analyzed four time points: APR, OCT, TOR, and IBA (January). Therefore, for comparative analysis with the proteome, relative protein expression was set to APR, and differentially expressed protein lists for OCT, TOR, and IBA were generated using the same criteria for differential expression as the previous section. When comparing OCT to APR protein expression, we found 131 differentially expressed proteins that directly matched a transcript from our previous study. Of these 131 differentially expressed proteins, 55 transcripts encoding these proteins (42%) showed a similar expression pattern either being up- or downregulated (Figure 3A). However, only 25 transcripts of the 131 (19%) were also differentially expressed in the transcriptome analysis (p ≤ 0.05). Additionally, only 16 of those 25 (64%) showed the same expression pattern as the corresponding proteins, both being up or downregulated. For both TOR and IBA, there are a greater number of transcripts and proteins that have matching expression patterns when compared to OCT. When comparing TOR to APR, we found 122 differentially expressed proteins with matching transcripts. Of these 122 differentially expressed proteins, 66 transcripts (54%) showed a similar trend in expression (Figure 3A). Only 27 of the 122 (22%) matching transcripts showed differential gene expression (p ≤ 0.05), but 20 of those 27 (74%) matched the expression pattern of their corresponding protein. When comparing IBA to APR, we found 154 differentially expressed proteins with matching transcripts. Of these 154 differentially expressed proteins, 85 transcripts (55%) showed a similar expression pattern being either up- or downregulated (Figure 3A). Only 31 transcripts of the 154 (20%) were differentially expressed at the transcript level (p ≤ 0.05), and only 17 (54%) of those showed similar differential expression patterns between the proteome and the transcriptome. In all three time point comparisons, there is 55% or less correlation between protein and transcript expression. The fact that approximately half of the transcripts matched the protein expression pattern demonstrates that post-transcriptional regulation is occurring for many of these proteins and highlights the importance of performing proteomic studies as a better read-out of biological activity. Additionally, the fact that around 20% showed similar trends in differential expression, and even a smaller fraction of those showed the same pattern in differential expression, further shows the importance of quantitative proteomic analysis to provide insights into the

Figure 3. Proteome-Transcriptome Comparison. (A) Three graphs depicting the transcript comparison to differentially expressed proteins at each time point relative to APR. Protein/transcript expression patterns show the percentage of expression patterns that match as solid green, and those that do not match as striped. Differential expression shows the percentage of transcripts that also show differential expression and are not differentially expressed. Similar differential expression patterns shows the percentage of differentially expressed transcripts that show the same expression pattern as the differentially expressed protein. (B) Average relative protein levels divided by the average relative transcript levels to generate dot plots showing the correlation between protein and transcript expression relative to APR. OCT shows the highest correlation between protein and transcript expression.

mechanism hibernators use to maintain contractile function and can be applied to other systems as well. This type of comparison also allows for a more meaningful comparison than many other studies because the same tissues from the same animals were used in both analyses. The transcriptome analysis highlighted several pathways that were differentially regulated, which gave insight into adaptive strategies for dealing with the extremes of hibernation, including metabolic and muscle maintenance strategies. The depth of the transcriptome allowed us to find several transcription factors and genes that are expressed at low levels, most of which we were not able to be found in the proteome. However, doing the comparative proteome-transcriptome analysis allowed us to find several proteins that seem to be similarly expressed, which suggests that they undergo little post-transcriptional regulation. Some of these proteins include the glycolytic enzymes HK1, ENO1, ALDOC, and LDHB; the fatty acid binding proteins FABP3 and FABP4; the heat-shock proteins HSPD1 and HSPA9; the transport proteins SLC8A1, APOA1 and SORBS1; the oxidative stress proteins PRDX6 and CAT; and several muscle structural and regulatory proteins, including MYH7, TNNI3, TPM1, TNNC1, DMD, CKM, CKB, and CKMT2. However, when analyzing proteins showing similar expression between the transcriptome and proteome, very few pathways were found to be consistent between the time points, indicating that the majority of proteins are post4799


Article


Figure 4. Novel peptide sequence analysis. (A) Venn diagram depicting the number of BLAST-P identified novel peptide sequences from each set of proteomic samples, including peptides that were found in more than one data set. There were 162 novel peptide sequences identified in all three replicates. (B) Novel peptide sequences were classified by category and show that the majority of novel peptide sequences arise from a poorly annotated ground squirrel genome. (C) Peptides identified in BLAST searches as novel peptide sequences are evaluated for spectral quality within the Galaxy-P platform. The PSME program allows the user to detect specific ions and neutral losses at a set mass tolerance and visualize the quality of Peptide-Spectral match. It also shows the b and y ions identified and all potential fragmentation masses. (D) Peptides with good spectral quality are aligned with transcriptome contigs. Coordinates are used to map the transcripts and peptides to the genome using IGV viewer and the 13-lined ground squirrel genome. Visualization of the genome allows us to determine if novel peptides arise from a poorly annotated genome, splice variants, mutations, or genome reorganization.

transcriptionally modified in some way; therefore, transcriptome data should be used as a starting point for biological significance but is not always the best read-out for biological activity. Several other studies that have looked at the comparison between transcript and protein levels in yeast, zebrafish, and mice30 have also found a modest correlation between these levels, which further strengthens the need for indepth proteomic studies. To look at the entire set of differentially expressed proteins compared to their transcripts, we plotted the ratio of relative protein expression to relative transcript expression for each time point (Figure 3B). These plots show that of the three time

points analyzed the OCT protein to transcript expression is more closely correlated than those of TOR and IBA with OCT data points clustering closer to one. TOR and IBA both show similar scattered patterns, indicating greater dissimilarity between the relative proteome expression and relative transcript expression than seen in OCT, but that these two sets of data show similar variation. Additionally, from this data you can see that, in the OCT/APR comparison, a larger number of transcripts were upregulated than their corresponding proteins, resulting in a larger number of data points below one (Figure 3B). 4800


Article

Journal of Proteome Research Proteogenomic Analysis of a Nonmodel Organism

Genome investigations have identified a large number of gene mutations, including missense SNVs that are currently used for risk analysis of cancer, such as BRCA1 and BRCA2.31 However, it is important to remember that proteins, and not genes, have diverse biological functions and are the working units within the cell. Therefore, it is the expression pattern of these proteins and their variants that should be considered in characterizing altered biological states of hibernation or disease.31 Characterization of alternative splice variants (ASVs), single amino acids variants (SAVs), mutations, and post-translational modifications may play an important role in understanding the hibernation phenotype as well as many disease states in biology. Proteins that contain SAVs due to germline or somatic mutations may have normally provided cell protective properties, or in the case of the hibernator, may have optimal functioning at euthermic temperatures. Protein amino acid substitutions have the potential to be transformed into promoters, which could alter genetic stability as well as the potential to affect epigenetic regulation or metabolic control. Careful analysis of these amino acid variants could provide insight into how cardiac function is maintained at temperatures and conditions that are lethal to nonhibernators, as well as how they are able to withstand such extreme physiological changes associated with entering into and exiting from an IBA. Two interesting examples of SAVs that were identified are CAND1 and CAND2. CAND1 is significantly upregulated in J-IBA and is a key assembly factor of SCF E3 ubiquitin ligase complexes that promotes the exchange of the substrate-recognition F-box subunit in SCF complexes and can inhibit E3 ubiquitin ligase complex formation in certain neddylation states.32 Neddylation is the process by which the ubiquitin-like protein NEDD8 is conjugated to its target proteins, a process analogous to ubiquitination. The SAV is found within a conserved Armadillo/β-catenin-like repeat domain that is important for mediating protein−protein interactions. CAND2 plays a similar role in regulating assembly of SCF E3 ubiquitin ligase complexes, and this protein is expressed specifically in muscle tissues.33 CAND2 is significantly downregulated in TOR, J-IBA, and M-IBA (i.e., all of the hibernation time points), suggesting that these two proteins may serve distinct functions in hibernation. The SAV in CAND2 is found within a HEAT repeat domain, which is involved in intracellular transport. Further investigation into the function of these two proteins and their SAVs would be required to determine if they are important for the hibernation phenotype. Another interesting novel proteoform SAV occurs in HSPA9, which plays an important role in the suppression of apoptosis during glucose-deprived stress response, as in significantly upregulated in TOR, J-IBA, and M-IBA. The SAV occurs within the nucleotide-binding domain.34 An example of a peptide corresponding to a potential novel proteoform that has a large number of amino acids that differ from the predicted sequence is RYR2. RYR2 is a calcium channel that mediates the release of calcium from the sarcoplasmic reticulum into the cytoplasm and plays a key role in triggering cardiac muscle contraction. The NCBI predicted sequence matches closely with the transcript-derived protein except in the region where the peptide sequence corresponding to the novel proteoform is found. In this region, there is no homology to the predicted sequence and/or no sequence is predicted in that region, but the sequence surrounding this region matches closely. This sequence is not found within a conserved homology domain, and may be a result of error in contig assembly, but could also

An additional goal of this study was use a proteogenomic approach aimed at identifying peptides that may belong to novel proteoforms that may be specific to hibernators. Proteogenomics can be particularly useful in working with nonmodel organisms, such as the 13-lined ground squirrel, where only computationally predicted gene models are used for genomic annotation, which are prone to error.7 This analysis resulted in the identification of 363 peptides that belong to novel proteoforms from Heart 1, 265 from Heart 2, and 258 from Heart 3, for a total of 445 distinct, novel peptide sequences identified derived from 169 new proteoforms of proteins (Figure 1C). Of these peptides corresponding to novel proteoforms, several were found in more than 1 iTRAQ experiment with 162 being identified in all three iTRAQ experiments (Figure 4A). This highlights the reproducibility of the Galaxy-P workflows, especially the postprocessing steps used to ensure data quality, as well as the similarity between animal samples being analyzed. Prior to evaluating spectral matches using Galaxy-P quality assessment tools, all peptides that were identified with 95% confidence were evaluated for their correspondence to the NCBI-predicted ground squirrel proteins. Of the 445 novel peptides that were classified as novel peptides if they were different from the predicted protein sequence, 353 were identified with 95% or greater confidence (Figure 4B). This analysis determined that 68 of the peptides were not predicted by NCBI from the 13-lined ground squirrel genome. This is likely due to poor genomic annotation of this nonmodel organism, and this data can provide the necessary information to improve the genomic annotation. Thirty-eight of the peptides were due to translational start sites before the predicted ground squirrel start site, whereas 43 peptides were found after the predicted ground squirrel stop sites. This is largely due to improper annotation from the genome, and the confident identification of these peptides shows that this region of the mRNA is translated to give proteins. An additional 40 peptides were found in regions of the protein that were not predicted by NCBI. This indicates the possibility of alternative splicing from what was predicted from the genome, novel protein coding regions, or potential RNA editing. Further analysis of these peptides (Figure 4C) can provide annotation information on different protein isoforms that exist for these particular proteins. Of the 353 peptides that were analyzed, 189 of those peptides can be categorized by improper annotation of the genome and supports the fact that a large number of errors are typically associated with automated annotation of the genome.7 This also highlights the usefulness of this work that combines transcriptome and proteome data to improve on the genomic annotation of this nonmodel organism (Figure 4D). In addition to this improved genomic annotation, the other 164 peptides were found to differ from the predicted peptide sequence at the amino acid level (Figure 4B). The majority of them differed by a single amino acid, which indicates that a mutation has occurred in this peptide since that annotation of the ground squirrel genome or that the genomic sequencing is incorrect for this amino acid. Another 14 peptides differed from the predicted peptide by 2−5 amino acids, highlighting further mutations. Thirteen peptides differed by 6−9 amino acids, and 24 peptides differed by 10 or more amino acids. This may indicate genomic reorganization, different protein isoforms, or poor genomic annotation, and requires further analysis to determine the specific cause of each difference. 4801


Journal of Proteome Research be important for RYR2 function in the ground squirrel. Several examples exist of mutations in cardiac RYR2 that affect arrhythmogenic right ventricular cardiomyopathy and polymorphic ventricular tachycardia.35 These are just some representative examples of the information that we obtain from doing such extensive proteogenomic analysis on a nonmodel organism. In addition to analyzing each of these proteoforms that contain amino acid variations, and determining how this might affect their function, the iTRAQ quantitation allows us to determine the relative quantity of each of these peptides and their corresponding proteins throughout the circannual cycle. Differential expression of these novel protein isoforms may be essential for the hibernation phenotype and may provide novel therapeutic targets for heart disease. Further analysis into these amino acid variations is currently under investigation. In conclusion, this study is the first large-scale quantitative proteogenomic characterization of hibernation in the ground squirrel, and because we have also recently performed largescale quantitative transcriptomic analysis of hibernation, we were able to compare the transcriptomic and proteomic abundance levels to reveal regulatory mechanisms at play during different phases of hibernation and pathways potentially regulated via post-transcriptional events. The proteogenomic approach used in this study revealed numerous peptide sequences not predicted using current gene models, providing a reference point for improving genome annotation and pointing to possible novel proteoforms with a functional role in hibernation. Altogether, these findings provide fertile ground for the generation of hypotheses that can be tested in future studies of mammalian hibernation with the potential for new discoveries relevant to human health.

■

■

ACKNOWLEDGMENTS

■

REFERENCES

We thank LeeAnn Higgins and Todd Markowski for their role in proteomic data acquisition. This work was funded by NSF Grant 1147079 for the Galaxy-P team, NIH Grant 1RC2HL101625-01, USARMC contract W81XWH-11-0409 to M.T.A., and the University of Minnesota McKnight Presidential Endowment. We also acknowledge the Center for Mass Spectrometry and Proteomics and the Minnesota Supercomputing Institute for support.

(1) (a) Andrews, M. T. Advances in molecular biology of hibernation in mammals. BioEssays 2007, 29, 431−40. (b) Carey, H. V.; Andrews, M. T.; Martin, S. L. Mammalian hibernation: cellular and molecular responses to depressed metabolism and low temperature. Physiol. Rev. 2003, 83, 1153−81. (2) Johansson, B. W. Heart and circulation in hibernators; Oliver & Boyd Ltd and American Elsevier: New York, 1967; p 200−218. (3) Johansson, B. W. The hibernator heart–nature’s model of resistance to ventricular fibrillation. Cardiovasc. Res. 1996, 31, 826− 832. (4) Hampton, M.; Nelson, B. T.; Andrews, M. T. Circulation and metabolic rates in a natural hibernator: an integrative physiological model. Am. J. Physiol Regul Integr Comp Physiol 2010, 299, R1478−88. (5) Wang, S. Q.; Lakatta, E. G.; Cheng, H.; Zhou, Z. Q. Adaptive mechanisms of intracellular calcium homeostasis in mammalian hibernators. J. Exp Biol. 2002, 205, 2957−62. (6) Vermillion, K. L.; Anderson, K. J.; Hampton, M.; Andrews, M. T. Gene expression changes controlling distinct adaptations in the heart and skeletal muscle of a hibernating mammal. Physiol. Genomics 2015, 47, 58−74. (7) Nesvizhskii, A. I. Proteogenomics: concepts, applications and computational strategies. Nat. Methods 2014, 11, 1114−25. (8) (a) Renuse, S.; Chaerkady, R.; Pandey, A. Proteogenomics. Proteomics 2011, 11, 620−30. (b) Armengaud, J.; Trapp, J.; Pible, O.; Geffard, O.; Chaumot, A.; Hartmann, E. M. Non-model organisms, a species endangered by proteogenomics. J. Proteomics 2014, 105, 5. (9) (a) Choudhary, J. S.; Blackstock, W. P.; Creasy, D. M.; Cottrell, J. S. Interrogating the human genome using uninterpreted mass spectrometry data. Proteomics 2001, 1, 651−67. (b) Brosch, M.; Saunders, G. I.; Frankish, A.; Collins, M. O.; Yu, L.; Wright, J.; Verstraten, R.; Adams, D. J.; Harrow, J.; Choudhary, J. S.; Hubbard, T. Shotgun proteomics aids discovery of novel protein-coding genes, alternative splicing, and ″resurrected″ pseudogenes in the mouse genome. Genome Res. 2011, 21, 756−67. (c) Menon, R.; Zhang, Q.; Zhang, Y.; Fermin, D.; Bardeesy, N.; DePinho, R. A.; Lu, C.; Hanash, S. M.; Omenn, G. S.; States, D. J. Identification of novel alternative splice isoforms of circulating proteins in a mouse model of human pancreatic cancer. Cancer Res. 2009, 69, 300−9. (d) Khatun, J.; Yu, Y.; Wrobel, J. A.; Risk, B. A.; Gunawardena, H. P.; Secrest, A.; Spitzer, W. J.; Xie, L.; Wang, L.; Chen, X.; Giddings, M. C. Whole human genome proteogenomic mapping for ENCODE cell line data: identifying protein-coding regions. BMC Genomics 2013, 14, 141. (10) Boekel, J.; Chilton, J. M.; Cooke, I. R.; Horvatovich, P. L.; Jagtap, P. D.; Kall, L.; Lehtio, J.; Lukasse, P.; Moerland, P. D.; Griffin, T. J. Multi-omic data analysis using Galaxy. Nat. Biotechnol. 2015, 33, 137−9. (11) (a) Jagtap, P. D.; Blakely, A.; Murray, K.; Stewart, S.; Kooren, J.; Johnson, J. E.; Rhodus, N. L.; Rudney, J.; Griffin, T. J. Metaproteomic analysis using the Galaxy framework. Proteomics 2015, 1. (b) Jagtap, P. D.; Johnson, J. E.; Onsongo, G.; Sadler, F. W.; Murray, K.; Wang, Y.; Shenykman, G. M.; Bandhakavi, S.; Smith, L. M.; Griffin, T. J. Flexible and accessible workflows for improved proteogenomic analysis using the Galaxy framework. J. Proteome Res. 2014, 13, 5898−908. (c) Sheynkman, G. M.; Johnson, J. E.; Jagtap, P. D.; Shortreed, M. R.; Onsongo, G.; Frey, B. L.; Griffin, T. J.; Smith, L. M. Using Galaxy-

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.5b00575. Table S1: iTRAQ labeling strategy; and Table S2: database comparisons (PDF) Table S3: APR differentially expressed proteins (XLSX) Table S4: OCT differentially expressed proteins (XLSX) Table S5: TOR differentially expressed proteins (XLSX) Table S6: J-IBA differentially expressed proteins (XLSX) Table S7: M-IBA differentially expressed proteins (XLSX) Table S8: Protein Pilot Protein Reports (XLSX) Table S9: Protein names and abbreviations (XLSX) Accession Codes

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD002457.

■

Article

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: 218-726-7271. Notes

The authors declare no competing financial interest. 4802


Article

Journal of Proteome Research P to leverage RNA-Seq for the discovery of novel protein variations. BMC Genomics 2014, 15, 703. (12) Srere, H. K.; Wang, L. C.; Martin, S. L. Central role for differential gene expression in mammalian hibernation. Proc. Natl. Acad. Sci. U. S. A. 1992, 89, 7119−23. (13) Russeth, K. P.; Higgins, L.; Andrews, M. T. Identification of proteins from non-model organisms using mass spectrometry: application to a hibernating mammal. J. Proteome Res. 2006, 5, 829−39. (14) Russell, R. L.; O’Neill, P. H.; Epperson, L. E.; Martin, S. L. Extensive use of torpor in 13-lined ground squirrels in the fall prior to cold exposure. J. Comp. Physiol., B 2010, 180, 1165−72. (15) Pengelley, E. T. F.; Fisher, K. C. Onset and cessation of hibernation under constant temperature and light in the goldenmantled ground squirrel, Citellus lateralis. Nature 1957, 180, 1371− 1372. (16) Yang, F.; Shen, Y.; Camp, D. G., 2nd; Smith, R. D. High-pH reversed-phase chromatography with fraction concatenation for 2D proteomic analysis. Expert Rev. Proteomics 2012, 9, 129−34. (17) (a) Hampton, M.; Melvin, R. G.; Andrews, M. T. Transcriptomic Analysis of Brown Adipose Tissue across the Physiological Extremes of Natural Hibernation. PLoS One 2013, 8, e85157. (b) Hampton, M.; Melvin, R. G.; Kendall, A. H.; Kirkpatrick, B. R.; Peterson, N.; Andrews, M. T. Deep sequencing the transcriptome reveals seasonal adaptive mechanisms in a hibernating mammal. PLoS One 2011, 6, e27021. (c) Schwartz, C.; Hampton, M.; Andrews, M. T. Seasonal and regional differences in gene expression in the brain of a hibernating mammal. PLoS One 2013, 8, e58427. (18) Wickler, S. J.; Hoyt, D. F.; van Breukelen, F. Disuse atrophy in the hibernating golden-mantled ground squirrel, Spermophilus lateralis. Am. J. Physiol. 1991, 261, R1214−7. (19) Nelson, O. L.; Rourke, B. C. Increase in cardiac myosin heavychain (MyHC) alpha protein isoform in hibernating ground squirrels, with echocardiographic visualization of ventricular wall hypertrophy and prolonged contraction. J. Exp. Biol. 2013, 216, 4678−90. (20) Drew, K. L.; Harris, M. B.; LaManna, J. C.; Smith, M. A.; Zhu, X. W.; Ma, Y. L. Hypoxia tolerance in mammalian heterotherms. J. Exp. Biol. 2004, 207, 3155−62. (21) Grabek, K. R.; Karimpour-Fard, A.; Epperson, L. E.; Hindle, A.; Hunter, L. E.; Martin, S. L. Multistate proteomics analysis reveals novel strategies used by a hibernator to precondition the heart and conserve ATP for winter heterothermy. Physiol. Genomics 2011, 43, 1263−75. (22) (a) Sugiura, S.; Kobayakawa, N.; Fujita, H.; Yamashita, H.; Momomura, S.; Chaen, S.; Omata, M.; Sugi, H. Comparison of unitary displacements and forces between 2 cardiac myosin isoforms by the optical trap technique: molecular basis for cardiac adaptation. Circ. Res. 1998, 82, 1029−34. (b) Alpert, N. R.; Brosseau, C.; Federico, A.; Krenz, M.; Robbins, J.; Warshaw, D. M. Molecular mechanics of mouse cardiac myosin isoforms. Am. J. Physiol Heart Circ Physiol 2002, 283, H1446−54. (c) Galler, S.; Puchert, E.; Gohlsch, B.; Schmid, D.; Pette, D. Kinetic properties of cardiac myosin heavy chain isoforms in rat. Pfluegers Arch. 2002, 445, 218−23. (d) Malmqvist, U. P.; Aronshtam, A.; Lowey, S. Cardiac myosin isoforms from different species have unique enzymatic and mechanical properties. Biochemistry 2004, 43, 15058−65. (e) Narolska, N. A.; Eiras, S.; van Loon, R. B.; Boontje, N. M.; Zaremba, R.; Spiegelen Berg, S. R.; Stooker, W.; Huybregts, M. A.; Visser, F. C.; van der Velden, J.; Stienen, G. J. Myosin heavy chain composition and the economy of contraction in healthy and diseased human myocardium. J. Muscle Res. Cell Motil. 2005, 26, 39−48. (23) Mangelsdorf, D. J.; Thummel, C.; Beato, M.; Herrlich, P.; Schutz, G.; Umesono, K.; Blumberg, B.; Kastner, P.; Mark, M.; Chambon, P.; Evans, R. M. The nuclear receptor superfamily: the second decade. Cell 1995, 83, 835−9. (24) (a) Keller, H.; Dreyer, C.; Medin, J.; Mahfoudi, A.; Ozato, K.; Wahli, W. Fatty acids and retinoids control lipid metabolism through activation of peroxisome proliferator-activated receptor-retinoid X receptor heterodimers. Proc. Natl. Acad. Sci. U. S. A. 1993, 90, 2160−4. (b) Kliewer, S. A.; Sundseth, S. S.; Jones, S. A.; Brown, P. J.; Wisely, G.

B.; Koble, C. S.; Devchand, P.; Wahli, W.; Willson, T. M.; Lenhard, J. M.; Lehmann, J. M. Fatty acids and eicosanoids regulate gene expression through direct interactions with peroxisome proliferatoractivated receptors alpha and gamma. Proc. Natl. Acad. Sci. U. S. A. 1997, 94, 4318−23. (25) Lefebvre, P.; Chinetti, G.; Fruchart, J. C.; Staels, B. Sorting out the roles of PPAR alpha in energy metabolism and vascular homeostasis. J. Clin. Invest. 2006, 116, 571−80. (26) Ferrante, R. J.; Kubilus, J. K.; Lee, J.; Ryu, H.; Beesen, A.; Zucker, B.; Smith, K.; Kowall, N. W.; Ratan, R. R.; Luthi-Carter, R.; Hersch, S. M. Histone deacetylase inhibition by sodium butyrate chemotherapy ameliorates the neurodegenerative phenotype in Huntington’s disease mice. J. Neurosci. 2003, 23, 9418−27. (27) Li, J.; Radice, G. L. A new perspective on intercalated disc organization: implications for heart disease. Dermatology research and practice 2010, 2010, 207835. (28) Xu, R.; Andres-Mateos, E.; Mejias, R.; MacDonald, E. M.; Leinwand, L. A.; Merriman, D. K.; Fink, R. H.; Cohn, R. D. Hibernating squirrel muscle activates the endurance exercise pathway despite prolonged immobilization. Exp. Neurol. 2013, 247, 392−401. (29) (a) de Sousa Abreu, R.; Penalva, L. O.; Marcotte, E. M.; Vogel, C. Global signatures of protein and mRNA expression levels. Mol. BioSyst. 2009, 5, 1512−26. (b) Lu, P.; Vogel, C.; Wang, R.; Yao, X.; Marcotte, E. M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat. Biotechnol. 2007, 25, 117−24. (30) (a) Palmblad, M.; Henkel, C. V.; Dirks, R. P.; Meijer, A. H.; Deelder, A. M.; Spaink, H. P. Parallel deep transcriptome and proteome analysis of zebrafish larvae. BMC Res. Notes 2013, 6, 428. (b) Ghazalpour, A.; Bennett, B.; Petyuk, V. A.; Orozco, L.; Hagopian, R.; Mungrue, I. N.; Farber, C. R.; Sinsheimer, J.; Kang, H. M.; Furlotte, N.; Park, C. C.; Wen, P. Z.; Brewer, H.; Weitz, K.; Camp, D. G., 2nd; Pan, C.; Yordanova, R.; Neuhaus, I.; Tilford, C.; Siemers, N.; Gargalovic, P.; Eskin, E.; Kirchgessner, T.; Smith, D. J.; Smith, R. D.; Lusis, A. J. Comparative analysis of proteome and transcriptome variation in mouse. PLoS Genet. 2011, 7, e1001393. (c) Foss, E. J.; Radulovic, D.; Shaffer, S. A.; Ruderfer, D. M.; Bedalov, A.; Goodlett, D. R.; Kruglyak, L. Genetic basis of proteome variation in yeast. Nat. Genet. 2007, 39, 1369−75. (31) Wood, L. D.; Parsons, D. W.; Jones, S.; Lin, J.; Sjoblom, T.; Leary, R. J.; Shen, D.; Boca, S. M.; Barber, T.; Ptak, J.; Silliman, N.; Szabo, S.; Dezso, Z.; Ustyanksky, V.; Nikolskaya, T.; Nikolsky, Y.; Karchin, R.; Wilson, P. A.; Kaminker, J. S.; Zhang, Z.; Croshaw, R.; Willis, J.; Dawson, D.; Shipitsin, M.; Willson, J. K.; Sukumar, S.; Polyak, K.; Park, B. H.; Pethiyagoda, C. L.; Pant, P. V.; Ballinger, D. G.; Sparks, A. B.; Hartigan, J.; Smith, D. R.; Suh, E.; Papadopoulos, N.; Buckhaults, P.; Markowitz, S. D.; Parmigiani, G.; Kinzler, K. W.; Velculescu, V. E.; Vogelstein, B. The genomic landscapes of human breast and colorectal cancers. Science 2007, 318, 1108−13. (32) (a) Goldenberg, S. J.; Cascio, T. C.; Shumway, S. D.; Garbutt, K. C.; Liu, J.; Xiong, Y.; Zheng, N. Structure of the Cand1-Cul1-Roc1 complex reveals regulatory mechanisms for the assembly of the multisubunit cullin-dependent ubiquitin ligases. Cell 2004, 119, 517− 28. (b) Pierce, N. W.; Lee, J. E.; Liu, X.; Sweredoski, M. J.; Graham, R. L.; Larimore, E. A.; Rome, M.; Zheng, N.; Clurman, B. E.; Hess, S.; Shan, S. O.; Deshaies, R. J. Cand1 promotes assembly of new SCF complexes through dynamic exchange of F box proteins. Cell 2013, 153, 206−15. (33) (a) Aoki, T.; Okada, N.; Ishida, M.; Yogosawa, S.; Makino, Y.; Tamura, T. A. TIP120B: a novel TIP120-family protein that is expressed specifically in muscle tissues. Biochem. Biophys. Res. Commun. 1999, 261, 911−6. (b) You, J.; Wang, M.; Aoki, T.; Tamura, T. A.; Pickart, C. M. Proteolytic targeting of transcriptional regulator TIP120B by a HECT domain E3 ligase. J. Biol. Chem. 2003, 278, 23369−75. (34) Marchler-Bauer, A.; Zheng, C.; Chitsaz, F.; Derbyshire, M. K.; Geer, L. Y.; Geer, R. C.; Gonzales, N. R.; Gwadz, M.; Hurwitz, D. I.; Lanczycki, C. J.; Lu, F.; Lu, S.; Marchler, G. H.; Song, J. S.; Thanki, N.; Yamashita, R. A.; Zhang, D.; Bryant, S. H. CDD: conserved domains 4803


Article

Journal of Proteome Research and protein three-dimensional structure. Nucleic Acids Res. 2013, 41, D348−52. (35) (a) Tiso, N.; Stephan, D. A.; Nava, A.; Bagattin, A.; Devaney, J. M.; Stanchi, F.; Larderet, G.; Brahmbhatt, B.; Brown, K.; Bauce, B.; Muriago, M.; Basso, C.; Thiene, G.; Danieli, G. A.; Rampazzo, A. Identification of mutations in the cardiac ryanodine receptor gene in families affected with arrhythmogenic right ventricular cardiomyopathy type 2 (ARVD2). Hum. Mol. Genet. 2001, 10, 189−94. (b) Laitinen, P. J.; Brown, K. M.; Piippo, K.; Swan, H.; Devaney, J. M.; Brahmbhatt, B.; Donarum, E. A.; Marino, M.; Tiso, N.; Viitasalo, M.; Toivonen, L.; Stephan, D. A.; Kontula, K. Mutations of the cardiac ryanodine receptor (RyR2) gene in familial polymorphic ventricular tachycardia. Circulation 2001, 103, 485−90. (c) Priori, S. G.; Napolitano, C.; Tiso, N.; Memmi, M.; Vignati, G.; Bloise, R.; Sorrentino, V.; Danieli, G. A. Mutations in the cardiac ryanodine receptor gene (hRyR2) underlie catecholaminergic polymorphic ventricular tachycardia. Circulation 2001, 103, 196−200. (d) Milting, H.; Lukas, N.; Klauke, B.; Korfer, R.; Perrot, A.; Osterziel, K. J.; Vogt, J.; Peters, S.; Thieleczek, R.; Varsanyi, M. Composite polymorphisms in the ryanodine receptor 2 gene associated with arrhythmogenic right ventricular cardiomyopathy. Cardiovasc. Res. 2006, 71, 496−505.

4804
