Comprehensive proteomics and functional annotation of mouse brown adipose tissue.

Knowledge about the mouse brown adipose tissue (BAT) proteome can provide a deeper understanding of the function of mammalian BAT. Herein, a comprehensive analysis of interscapular BAT from C57BL/6J female mice was conducted by 2DLC and high-resolution mass spectrometry to construct a comprehensive proteome dataset of mouse BAT proteins. A total of 4949 nonredundant proteins were identified, and 4495 were quantified using the iBAQ method. According to the iBAQ values, the BAT proteome was divided into high-, middle- and low-abundance proteins. The functions of the high-abundance proteins were mainly related to glucose and fatty acid oxidation to produce heat for thermoregulation, while the functions of the middle- and low-abundance proteins were mainly related to protein synthesis and apoptosis, respectively. Additionally, 497 proteins were predicted to have signal peptides using SignalP4 software, and 75 were confirmed in previous studies. This study, for the first time, comprehensively profiled and functionally annotated the BAT proteome. This study will be helpful for future studies focused on biomarker identification and BAT molecular mechanisms.

1. Introduction

Two types of adipocytes exist in mammals, white and brown adipocytes [1]. White adipocytes are the major constituents of white adipose tissue (WAT), which contains energy-dense triglycerides that store energy [2]. Brown adipocytes are the major constituents of brown adipose tissue (BAT), which is situated in the interscapular, cervical, mediastinal and retroperitoneal regions [3, 4]. BAT plays a prominent role in thermogenesis and energy expenditure [5]. In 1976, Ricquier et al. found a particular 32,000 Mr membranous protein in rat BAT mitochondria, which was upregulated upon cold exposure [6]. In 1978, the same 32,000 Mr protein was also found by Jean Himms-Hagen [7] and was later named uncoupling protein-1 (UCP-1). UCP-1, found in the inner mitochondrial membrane [8], uncouples the respiratory chain from oxidative phosphorylation, yielding a high oxidation rate and enabling the cellular use of metabolic energy to provide heat [9, 10], indicating its primary role in inducing nonshivering thermogenesis [10]. BAT is a secretory tissue producing batokines, batokines act locally to control the remodelling of BAT, however, and also acts on peripheral organs, such as the liver, skeletal muscle, pancreas, bone, the immune system and the CNS, affecting systemic glucose levels in a thermogenesis-dependent and -independent manner [11].Considering that BAT is closely related to systemic energy metabolism and fat accumulation-related diseases, such as obesity [12], hyperlipidemia, type 2 diabetes (T2D) and cardiovascular disease [13], increasing BAT activity or promoting fat browning may be potential ways to partially prevent or treat these diseases [14, 15].

In exploring the biological functions of BAT, proteomic techniques have proven to be useful [16, 17]. One of the earliest studies on the BAT proteome was led by Sanchez et al. In 2001, these researchers profiled the BAT proteome of C57BL/6 mice using two-dimensional electrophoresis technology. In this study, they separated 87 spots corresponding to 37 protein entries [18]. With the development of mass spectrometry, many efforts have been made to identify more BAT proteins using high-resolution mass spectra in recent years [19-21]. In 2009, Francesca Forner and colleagues compared the brown and white fat mitochondrial proteomes and analyzed the response of the mitochondrial proteome to cold-induced thermogenesis, identifying 2434 proteins in the BAT mitochondria [9]. Recently, Li et al. conducted a BAT proteome analysis using iTRAQ-coupled 2D LC–MS/MS, and 3048 proteins were identified in BAT. By comparing the BAT proteomes from subjects fed a high-fat diet (HFD) and a normal diet (ND), the authors identified 727 differentially expressed proteins [22].

With the development of proteomics technologies [23, 24], the number of identified proteins has quickly increased and even approached that of protein-coding genes in the complete human genome [25]. Compared with the depth of human tissue proteomic research, the mouse BAT proteome has been studied relatively less often. Therefore, in this study, a comprehensive proteomic analysis of BAT profiling was performed using 2D-RPLC and TripleTOF 5600. Qualitative and quantitative analyses were integrated to comprehensively profile the normal BAT proteome. The PANTHER classification system and Ingenuity Pathway Analysis (IPA) software were used to analyze BAT functions and potential biomarkers in BAT. The BAT proteome database developed in this study may facilitate qualitative bioinformatics analysis and provide a basis for future compositional and functional studies focused on BAT.

2. Materials and methods

2.1. Apparatuses

A TripleTOF 5600 mass spectrometer from AB Sciex (Framingham, MA, USA) and an ACQUITY UPLC system from Waters (Milford, MA, USA) were used.

2.2. Reagents

Deionized water from a MilliQ RG ultrapure water system (Millipore, Bedford, MA, USA) was used at all times. LCMS grade acetonitrile (ACN) and formic acid, ammonium bicarbonate, iodoacetamide, dithiothreitol, sequencing-grade modified trypsin, and the protease inhibitor phenylmethanesulfonyl fluoride (PMSF) were purchased from Sigma-Aldrich (St. Louis, MO, USA).

3. Brown adipose tissue collection

3.1. Animals

All animals were handled according to the Standards for Laboratory Animals (GB14925-2001) and the Guidelines on the Humane Treatment of Laboratory Animals (MOST 2006a) established by the People’s Republic of China. The two guidelines were conducted in adherence to the regulations of the Ethics Committee of Peking Union Medical College Hospital Institutional Animal Care and Use Committee (IACUC), and all animal procedures were approved by the Institutional Animal Care and Use Committee (approval number: SCXK Beijing- 2009–0004). All efforts were made to minimize suffering. Six- to eight-week-old female C57BL/6J mice were purchased from HFK Bioscience Laboratories (Beijing, China). According to previous studies diurnal and circadian rhythms related to gene expression patterns and tissue function [26-28]. Female mice were housed at room temperature in a 12:12-h light-dark cycle at 23±2°C, with free access to water and diet. The time of lights on is eight a.m., the time of lights off is eight p.m., and the time of collection tissue is nine a.m. Mice were fed standard chow (SC; 10% lipids) diet for 6–8 weeks. Food and water intake for 24 hours and body weight (once per week) were dynamically monitored any anesthesia method was not used, all mice were decapitated directly.

3.2. Preparation of BAT samples

According to previous studies [21, 29, 30] Both gender and age can affect the expression level of BAT protein, so all the mice used in this experiment were C57BL/6 female mice with 6 weeks.

Then washed with a cold saline solution. BATs were snap-frozen in liquid nitrogen and stored at -80 ℃ until analysis. The tissues were cut into small pieces and lysed in buffer solution containing 7 M urea, 2 M thiourea, 65 mM DTE, and 83 mM Tris (Sigma-Aldrich, St. Louis, MO, USA) and then homogenized (IKA R104, Janke& Kunkel KG. IKA-werk, Germany) on ice. The extracts were centrifuged at 20,000 g for 10 min at 4 °C, and the supernatant was then stored at -80 ℃. The protein contents of the adipose tissues were determined by the Bradford method with Bradford reagents (Thermo Fischer Scientific, USA).

The six BAT protein samples were pooled into one mixed sample with equal amounts of protein. The mixed sample (100 μg in total) from one group was deoxidized with 20 mM DTT, alkylated with 50 mM IAA and digested with trypsin. After digestion, the peptides were desalted with HLB 3 cc extraction cartridges (Oasis, Waters, Ireland), cleaned with 500 μL of 0.1% formic acid and eluted with 500 μL of 100% ACN. The peptide eluate was vacuum-dried and stored at -80 °C. The peptides were processed through columns (micro Bio-spin, nanosep 30k omega; PALL, USA) according to the manufacturer’s instructions.

3.3. HPLC separation

The lyophilized peptide mixtures were redissolved in 0.1% formic acid and fractionated with a high-pH RPLC column from Waters (4.6 mm × 250 mm, Xbridge C18, 3 μm). The peptide mixture was loaded onto the column in buffer A2 (H2O, pH = 10). The elution gradient, 5%–30% buffer B2 (90% ACN, pH = 10; flow rate, 1 mL/min), was applied over 60 min. The eluted peptides were collected at one fraction per minute. Sixty dried fractions were resuspended in 0.1% formic acid and pooled into 20 samples by combining fractions 1, 21 and 41; 2, 22 and 42; and so on. A total of 20 fractions from one sample were analyzed by LC–MS/MS.

3.4. LC–MS/MS analysis

Each sample was analyzed by LC–MS/MS using a reverse-phase C18 self-packed capillary LC column (75 μm×100 mm, 3 μm; Packing: Reprosil-PUR, C18-AQ, 1.9 μm, 120 Å, Dr. Maisch). An elution gradient of 5–30% buffer B1 (ACN and 0.1% formic acid; buffer A1: 98% H2O, 2% ACN, and 0.1% formic acid; flow rate, 0.3 μL/min) was applied over 60 min for the analysis. A TripleTOF 5600 mass spectrometer was used to analyze the peptides eluted from the LC column, and a nanosource was used. Triple TOF 5600 were used to analyze the sample. The MS data were acquired with high sensitivity mode using the following parameters: 30 data-dependent MS/MS scans per every full scan; full scans was acquired at resolution 40,000 and MS/MS scans at 20,000; 35% normalized collision energy, charge state screening (including precursors with +2 to +4 charge state) and dynamic exclusion (exclusion duration 15 s); MS/MS scan range was 100–1800 m/z and scan time was 100 ms.

3.5. Data processing

The MS/MS spectra were searched against the SwissProt mouse database from the UniProt website (http://www.UniProt.org) using Mascot software version 2.3.02 (Matrix Science, UK). Trypsin was chosen to calculate the cleavage specificity with a maximum number of allowed missed cleavages of two. Carbamidomethylation (C) was set as a fixed modification. The searches were performed using a peptide and production tolerance of 0.05 Da. A scaffold was used to further filter the database search results using the decoy database method. The following filters were used in this study: 1% false positive rate at the protein level and 2 unique peptides for each protein. After filtering the results with the above filter, the peptide abundances in different reporter ion channels from the MS/MS scan were normalized. The protein abundance ratio was based on the unique peptide results.

3.6. Intensity-based absolute quantification (iBAQ) of proteins

Protein abundances were estimated using the iBAQ algorithm [31]. The iBAQ values were calculated by dividing the peptide intensities by the number of theoretically observable peptides from the protein (calculated by in silico protein digestion; all fully tryptic peptides between 6 and 30 amino acids were counted) [32]. The relative iBAQ intensities were computed by dividing the absolute iBAQ intensities from the sum of all absolute iBAQ intensities. The estimated protein abundances were calculated by multiplying the relative iBAQ intensities by the molecular weight of the protein.

3.7. Functional annotation

All proteins were assigned a gene symbol using the PANTHER database (http://www.pantherdb.org/). Protein classification was performed based on the functional annotations of the GO project for the cellular compartment, molecular function and biological process categories. When more than one assignment was available, all of the functional annotations were considered in the results. Moreover, all of the selected proteins with significant fold changes were used for pathway analysis using IPA software (Ingenuity Systems Mountain View, CA) for network analysis [33].

3.8. SignalP 4.1 Server protein analysis

All proteins were used for adipokine prediction. For this purpose, the SwissProt accession numbers were inserted into SignalP 4.1 (http://www.cbs.dtu.dk/services/SignalP/) [34]. This software predicts the presence and location of signal peptide cleavage sites in amino acid sequences and suggests proteins with signal peptides.

3.9. Post-translational modifications analysis

The MS raw data were processed using the Peaks Online (X build, 1.2.1010.68). High-quality de novo sequence tags were then used by PEAKS DB to search the mouse proteome database. A mass tolerance of 20 ppm and 0.05 Da was set for the precursor ions and fragment ions, respectively. carbamidomethylation was set as fixed modification, Trypsin allowing 3 missed cleavages was chosen as the enzyme. A decoy database was also searched to calculate the false-discovery rate (FDR) using the decoyfusion method.

4. Results and discussion

4.1. Proteome analysis workflow

To construct a comprehensive dataset for the mouse BAT proteome, proteins were extracted from interscapular BAT harvested from C57BL/6J female mice. The proteins were digested using the filter-aided sample preparation (FASP) method [35]. The pooled digested peptides were separated into 60 fractions by RPLC, mixed into 20 fractions, and then analyzed by nano-RPLC–MS/MS. An iBAQ quantitative analysis method was used to quantify the BAT proteins. The PANTHER classification system and IPA software were used to analyze BAT functions. Brown fat proteins were used for signal peptide prediction via SignalP 4.1 (S1 Fig).

4.2. A comprehensive profile of the BAT proteome

4.2.1. The qualitative identification of BAT proteins

In this study, BAT samples were used to establish a large database of BAT proteins. As a result, a total of 439,604 spectra, 46,590 unique peptides and 4,949 nonredundant proteins were identified (FDRs < 1% at the peptide and protein level and at least 2 unique peptides for each protein; detailed data are provided in (S1, S2 and S3 Tables). Previous studies reported that brown adipose tissue contained the non-adipocytes including stem cells, [36] immune cells, blood/neural tissue, [37] stromal-vascular (S-V) cells (non-adipocytes) [38], Therefore, our database included the proteins from these non-adipocytes. In addition, blood vessels are also around brown adipose tissue, thus the proteins from plasma were also included in our database. In addition, blood vessels are also around brown adipose tissue, thus the proteins from plasma were also included in our database. In total, 82.8% (4,097/4,949) of the protein groups were identified from the three technical replicates (S1A Fig).

Table 1 summarizes the current studies on the mouse BAT proteome [2, 9, 19–22, 39]. As shown in Fig 1A, compared to the large-scale data, Our study was overlapped 66.8% with Forner et al. [9] and 95% with Li et al. [22]. In addition, we compared the proteins we identified to human adipose tissue proteins, the result showed that 2088 proteins were overlapped with Muller et al. [40]

Table 1

Proteomic studies focused on the mouse WAT proteome.

Year	Mouse strain/age/sex	Number of identifications	Fractionation method	MS instrument	Identification quality (FDR)	Reference
2001	8 week old C57Bl/6J females	37	2-DE	Q-TOF-MS	N/A	Sanchez et al. [18]
2004	C57BL/6 female mice with 6 weeks	20	2-DE	Q-TOF-MS	N/A	Schmid et al. [39]
2007	Wistar male rats	58	2-DE	UltraFlex II MALDI-TOF-TOF	N/A	Navet et al. [20]
2009	C57BL/6 mice (5 to 10 weeks old)	2434	SILAC	LTQ-Orbitrap	Protein level< 1%	Forner et al. [9]
2011	Male and female SLC Sprague-Dawley (SD) rats (5 weeks of age)	55	2-DE	MALDI-TOF-MS	N/A	Choi et al. [21]
2015	C57BL/6 female mice with 6 weeks	3048	iTRAQ	Triple TOF 5600	Protein level< 1%	Li et al. [22]
2016	Wild-caught thirteen-lined ground squirrels	778	iTRAQ	Velos Orbitrap	Protein level< 1%	Ballinger et al. [19]
2016	11 patients	2019	TMT	Orbitrap Fusion tribrid	Protein level< 1%	Muller etal. [40]
2019	C57BL/6 female mice with 6 weeks	4949	RPLC	Triple TOF5600	Protein level<1%	This study

Fig 1

AT proteome profile analysis.

B (A) A Venn diagram comparing BAT proteome large-scale studies. (B) The quantitative protein abundance ranges in BAT samples and the proteins that included a secreted signal peptide according to the iBAQ algorithm.

4.2.2. The quantitative identification of BAT proteins

A peak intensity-based semiquantification method (iBAQ) was applied to estimate the abundances of BAT proteins, with 4,495 proteins being quantified. S1C Fig shows the repeatability of the three quantitative experiments, and the correlation between the two protein quantitative intensity runs was approximately 0.98. To reduce the interference caused by technical errors, proteins with coefficients of variation (CVs) greater than 0.3 were excluded (S1B Fig); finally, 4393 proteins were used for further analysis. The iBAQ intensities and estimated protein abundances are provided in S4 Table The dynamic range of the relative abundances spanned five orders of magnitude (Fig 1B).

The top ten abundant proteins in the BAT proteome are listed in Table 2; these proteins account for 19.5% of the total BAT abundance. Fatty acid-binding proteins, such as FABP3, are essential for cold tolerance and efficient fatty acid oxidation in mouse BAT. FABP3 levels are a determinant of BAT fatty acid oxidation efficiency and represent a potential target for the modulation of energy dissipation [41]. The other proteins included enzymes in the mitochondria that are mainly related to catabolism. Specifically, aconitate hydratase, long-chain specific acyl-CoA dehydrogenase, 3-ketoacyl-CoA thiolase and alpha-enolase were identified as enzymes related to fatty acid oxidation [42, 43], tricarboxylic acid cycle [44] and glycolysis [45]. Electron transfer flavoprotein subunit alpha and cytochrome c oxidase subunit 5A have also been related to mitochondrial respiration [46].

Table 2

The top ten abundant proteins in the mouse BAT proteome.

Accession	Protein name	iBAQ value (a)	Percentage
P04117	Fatty acid-binding protein	4.97E+05	3.19%
P07724	Serum albumin	1.05E+05	3.16%
P01942	Hemoglobin subunit alpha	3.56E+05	2.35%
P02088	Cluster of Hemoglobin subunit beta-1	3.25E+05	2.25%
Q99KI0	Aconitate hydratase	4.31E+04	1.61%
P51174	Long-chain specific acyl-CoA dehydrogenase	7.02E+04	1.47%
Q8BWT1	3-ketoacyl-CoA thiolase	8.04E+04	1.47%
Q99LC5	Electron transfer flavoprotein subunit alpha	8.74E+04	1.34%
P17182	Alpha-enolase	6.43E+04	1.33%
P12787	Cytochrome c oxidase subunit 5A	1.83E+05	1.29%

4.3. PANTHER classification of the BAT proteome

To analyze the functions of BAT more comprehensively, we divided the BAT proteins into three groups according to their abundance, including high-abundance (abundance: top 90%, 809 proteins), middle-abundance (abundance: 90%-99%, 1,891 proteins) and low-abundance (abundance: 99%-100%, 1,692 proteins) proteins. The functional classification of the proteins identified in BAT was performed using the PANTHER classification system (http://www.pantherdb.org/genes/batchIdSearch.jsp).

From a cellular component perspective (Fig 2A), the GO analysis results showed that more than 50% of BAT proteins were annotated as cell parts and macromolecular complexes, with fewer proteins related to the membrane (10.1%). GO analysis suggested that brown fat proteins were mostly intracellular. Furthermore, among the high-abundance proteins, the proportion of extracellular proteins was higher than that in the middle- and low-abundance proteins.

Fig 2

GO analysis of the BAT proteome.

(A) Cellular component. (B) Molecular function. (C) Biological process.

In terms of molecular function (Fig 2B), the functions of the total proteins included mainly catalytic activity (43.2%) and structural molecule activity (37.6%); and few proteins exhibited transporter activity (5.5%) and signal transduction activity (2.1%). The function of the high-abundance proteins was enriched for catalytic activity (48.8%) and structural molecule activity (30.7%); in contrast, among low- and medium-abundance proteins, 51.1% were related to catalytic activity and 4.4% were related to structural molecule activity. Additionally, a small portion of high-abundance proteins exerted antioxidant activity functions (1.9%). Low-abundance proteins exhibited a higher receptor activity but a lower structural molecule activity than the middle- and high-abundance proteins.

From a biological process point of view (Fig 2C), the metabolic process (25.8%), cellular component organization or biogenesis (10.3%) and cellular process (31.3%) categories were highly correlated with brown fat proteins. High-abundance proteins were shown to be mainly involved in metabolic processes (29.0%), especially in the generation of precursor metabolites and energy. In contrast, low- and middle-abundance proteins were shown to mainly participate in cellular processes (32.5%), including intracellular signal transduction, cell proliferation and localization processes, such as transport.

4.4. Canonical pathway and functional analysis of the BAT proteome

IPA software was used to investigate the functions of the BAT proteome. As shown in Fig 3, BAT functions were mainly related to the regulation of glucose and lipid metabolism, protein synthesis and apoptosis (detailed information is provided in S4 and S5 Tables).

Fig 3

IPA analysis of the BAT proteome profile.

(A) The top canonical pathways of BAT proteins. (B) Major functions of BAT proteins.

The regulation of glucose and lipoid metabolism mainly involves pathways associated with mitochondrial dysfunction, oxidative phosphorylation, sirtuin signaling and fatty acid β-oxidation [47]. A previous study suggested that BAT could directly improve glucose homeostasis [48-50] and predominantly metabolize lipids for thermogenesis [51]. Protein synthesis focuses on the synthesis of proteins required for organism homeostasis and involves the EIF2 signaling pathway, regulation of eIF4 and the p70S6K signaling pathway and tRNA charging [52]. The apoptosis function was highlighted by apoptosis-related pathways and 3-phosphoinositide degradation, suggesting that apoptosis indeed occurs in BAT under unstimulated conditions; interestingly, cold exposure could inhibit this apoptosis [53].

For high-abundance proteins, the canonical pathways included mitochondrial dysfunction, oxidative phosphorylation, sirtuin signaling, and fatty acid β-oxidation. These metabolic regulatory pathways are mainly performed by oxidizing fatty acids to produce heat [54]. Oxidative phosphorylation releases energy from oxidative sugars, lipids and amino acids. In this process, electrons or protons in the respiratory chain are transferred from ADP phosphorylates to ATP. Fatty acid β-oxidation occurs in the mitochondria. During this process, fatty acids are activated to form acyl-CoA, which then enters the mitochondria and becomes oxidized to produce energy. Carnitine acyl transferase I affects the entry of lipid-CoA into the mitochondria and serves as the rate-limiting enzyme during this oxidation process [55]. More importantly, UCP-1 plays a significant role in this oxidation process. UCP-1 increases the inner mitochondrial membrane conductance for H+ to dissipate the mitochondrial H+ gradient and converts the energy from substrate oxidation into heat [56]. Therefore, the main role of the high-abundance proteins is also reflected in heat generation and energy supply.

For the middle-abundance proteins, the main related pathways were the regulation of eIF4 and p70S6K signaling, the EIF2 signaling pathway and tRNA charging. p70 ribosomal S6 kinase (p70 S6K) has been identified as a potentially important element in the p70S6K signaling pathway for controlling proteolysis in brown adipocytes. In 1992 and 1996, Waldbillig et al. [57] and Moazed et al. [58] proved that insulin increases the phosphorylation of p70S6K, which stimulates protein synthesis in brown adipocytes and inhibits proteolysis, respectively. Moazed et al. found that insulin and its receptor could lead to the activation of PI3-kinase, followed by the phosphoinositide activation of a kinase cascade and p70S6K phosphorylation, which increases proteolysis in brown adipocytes [59]. Increased protein synthesis and mitochondrial biogenesis are expected to promote brown adipocyte proliferation and differentiation, ultimately leading to enhanced BAT growth and development [60].

Low-abundance proteins have been found to be primarily involved in apoptotic signaling pathways, such as 3-phosphoinositide degradation and IL-8 signaling. A previous study performed by Hellman and Hellerström in 1961 suggested that brown fat is associated with apoptosis [61]. The authors showed that apoptosis was an ongoing process in BAT even under normal conditions. Because brown fat is closely related to heat production, reducing brown fat cell apoptosis has become an important aspect of brown fat research. Briscini et al. found that the prolonged exposure of obese animals to cold could prevent brown fat cell apoptosis [62] Lindquist et al. showed that low temperature (4 °C) rapidly increased the phosphorylation of the mitogen-activated protein kinases (p42/p44) Erk1 and Erk2 and protected cells in the tissue from apoptosis. The return of mice exposed to cold temperatures to warm temperatures led to a decrease in ErK1 and ErK2 stimulation and an increase in DNA fragmentation and the apoptosis rate [63]. Norepinephrine, which inhibits apoptosis in brown adipocytes, could stimulate the Erk cascade through both a1- and b-adrenergic receptors mediated by Ca2+ and cAMP via the Erk kinase MEK. Norepinephrine also stimulates the expression and secretion of basic fibroblast growth factor from cells, which further promotes cell survival via the MEK-dependent activation of Erk1/2 [63]. The inhibition of apoptosis and retention of more cells promotes an increase in adipocyte cell numbers in the tissue.

4.5. Canonical pathway and functional analysis of the BAT mitochondrial proteome

We used GO analysis to annotate the cellular components of all the proteins, and 214 mitochondria proteins were found. These BAT mitochondrial proteins were subdivided into three groups according to their abundance, 72 high-abundance, 90 middle-abundance, 39 low-abundance proteins.

The functions of BAT proteins were mainly related to the regulation of glucose and lipid metabolism, protein synthesis and apoptosis. The mitochondrial proteins were mainly involved in pathways of valine degradation I, isoleucine degradation I, ketogenesis, sirtuin signaling pathway and tRNA charging, which were related to oxidative phosphorylation and amino acid metabolism (Fig 4A).

Fig 4

IPA analysis of the BAT mitochondrial proteome profile and PTM proteins.

(A) The pathways comparisons of BAT proteins and mitochondrial proteins. (B) the pathways of comparisons of high-, middle-, low- abundance proteins of mitochondrial proteins of BAT. (C) The pathway of BAT PTM proteins.

The high-abundance proteins were mainly involved in amino acid degradation, the middle-abundance proteins were mainly related to amino acid biosynthesis and degradation, and the low-abundance proteins were found to be involved in apoptotic signaling pathways such as death receptor Signaling and thioredoxin Pathway (Fig 4B).

4.6. The post-translational modifications of BAT proteins

The MS/MS spectra were processed using Peaks Online (X build, 1.2.1010.68) to identify the post-translational modifications (PTM) of BAT proteome. Total 562 post-translational modifications proteins, 1665 peptides and 4464 MS/MS spectra were found in our data, including 142 acetylation, 117 phosphorylation and 179 methylation etc. (Table 3 and S6 Table). We also quantitatively analyzed the PTM proteins by label-free approach. The PTM proteins abundance accounted for 0.37% of total abundance. Among them methylation showed the highest abundance (0.07%).

Table 3

The post-translational modifications of BAT proteins.

Name	Protein number	peptide number	#PSM	proportion
Acetylation (K)	142	338	1070	0.07%
Amidation	217	302	547	0.04%
Methylation(KR)	179	343	901	0.09%
Phosphorylation (STY)	117	168	903	0.03%
Carboxylation (DKW)	54	83	180	0.06%
Fluorination	45	54	76	0.00%
Formylation (Protein N-term)	16	20	47	0.00%
Formylation	177	357	740	0.07%
sum				0.37%

To reveal the function of PTM proteins, they were input IPA for function annotation. The canonical pathways of PTM proteins were involved in mitochondrial dysfunction, epithelial adherens junction signaling and oxidative phosphorylation, which was consistent with BAT function (S3 Fig). Among acetylation proteins, sirtuin 3(SIRT3) was an important protein in BAT function, which is the master deacetylase of deacetylation in the mitochondria. Previous study showed SIRT3 absence in mice resulted in impaired BAT lipid use, whole body thermoregulation, and respiration in BAT mitochondria without affecting UCP1 expression. Further analysis indicated SIRT3 controlled brown fat thermogenesis by deacetylation regulation of pathways upstream of UCP1 [64].

4.7. BAT secretion functions

The ability of BAT to protect against chronic metabolic disease has traditionally been attributed to its capacity to utilize glucose and lipids for thermogenesis [65]. Moreover, BAT also has a secretory role that could contribute to the systemic consequences of its activity. Herein, using SignalP 4.1 software, we predicted 497 proteins with signal peptides, which are potential secretory proteins. Among them, 95, 214, and 188 proteins belonged to the high-, middle-, and low-abundance categories, respectively. The iBAQ method was used to analyze the 497 predicted secreted factors (S7 Table); the dynamic range of the relative abundance spanned five orders of magnitude and accounted for 6.6% of the total BAT abundance. Of these proteins, 72 have been identified as brown fat secretory proteins [66] and 5 have been documented as brown adipokines [65]. For example, a relationship between IL-6 and BAT metabolism has been documented. In rats, IL-6 overexpression was shown to increase thermogenic gene expression and elevate UCP-1 protein levels in BAT, which was mediated by phosphorylation of signal transducer and activator of transcription 3 (pSTAT3) [67, 68]. This result suggests that IL-6 is indeed required to maintain the profound metabolic effects of BAT transplantations and that BAT-derived IL-6 could be a key factor acting as an autocrine or paracrine agent [69].

4.8. The discovery of BAT proteome biomarkers

Using IPA analysis, 281 proteins (129 high-abundance proteins, 105 middle-abundance proteins, and 48 low-abundance proteins) were found to be related to diseases, and 30 were associated with metabolic disease (S5 Table).

Some of these proteins have been previously reported, such as visfatin. Visfatin was originally isolated as a presumptive cytokine and named a pre-B cell colony-enhancing factor (PBEF) [70]. In 2005, Fukuhara et al. reidentified PBEF as a “new visceral fat-derived hormone” (visfatin) [71]. Visfatin is an essential NMN/NAD biosynthetic enzyme and is considered a novel adipokine with both intracellular and extracellular enzymatic functions [72]. It stimulates insulin secretion and increases insulin sensitivity and glucose uptake in muscle cells and adipocytes. In 2016, Pisani et al. found that visfatin was highly and preferentially expressed and secreted by human brown and white adipocytes; thus, the authors suggested that visfatin could be considered a rodent brown adipocyte biomarker. However, because available data showed visfatin as a beneficial BAT-derived cytokine for the treatment of obesity and T2D, the search for other potential brown adipocyte-secreted molecules that act in concert with or without visfatin remains urgent [73].

According to previous studies the protein level of brown fat might be changed in different environments such as cold exposure, thermoneutrality, or HFD feeding. [74-76] Therefore, the BAT protein biomarker analysis under above conditions might be important to illustrate the function of brown fat, which should be investigated in the future.

5. Conclusion

In this study, we report a large dataset for the mouse BAT proteome based on 2DLC/MS/MS data. The proteins in the comprehensive BAT proteome showed various biological functions and characteristics. This investigation will pave the way for further comparative proteomics approaches focused on the molecular mechanisms involved in BAT disorders.

Because blood contaminant proteins (5.52% abundance) were identified, future studies should aim to remove high-abundance serum proteins. Additionally, more sophisticated instruments (e.g., Orbitrap Fusion Lumos Tribrid mass spectrometer), different quantitative methods (e.g., SILAC labeling) and new techniques (e.g., data-independent acquisition technique) could provide a more precise quantification and higher throughput for BAT studies than those used herein. Furthermore, with the development of genomics, transcriptomics, proteomics and metabolomics, the combination of multiple omics analysis could lead to a better platform for medical biomarker detection, which will allow for the identification of more proteins.

List of proteins in the BAT proteome.

(XLSX)

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List of peptides in the BAT proteome.

(XLSX)

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Qualitative results for the BAT proteome from the three technical replicates.

(XLSX)

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The iBAQ intensities and estimated protein abundances for the quantitated BAT proteins.

(XLSX)

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BAT protein pathways, functions and associated predicted biomarkers.

(XLS)

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(XLSX)

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iBAQ intensity and estimated protein abundance of potential secretory proteins.

(XLSX)

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The workflow of this study.

(DOCX)

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Qualitative and quantitative analyses of triplicate LC/MS/MS runs.

(DOCX)

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The pathway analysis of BAT proteins and BAT PTM proteins.

(DOCX)

Click here for additional data file.

24 Oct 2019

PONE-D-19-18696

A comprehensive map and functional annotation of mouse brown adipose tissue

PLOS ONE

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Reviewer #1: In the entitled “A comprehensive map and functional annotation of mouse brown adipose tissue”, the authors profiled and functionally annotated a large number of proteins in brown adipose tissue using the iBAQ method. The authors further classified them into high-, middle-, and low-abundance proteins by iBAQ values. Additionally, the relationship between various biological functions and their divided levels were discussed. Newly identified BAT proteome could be helpful for future studies to identify markers for obesity treatment or prevention. Thus, their study may be of interest to those in the field. However, there are several important comments that should be addressed by the authors for publication.

Major:

1. Although the current study suggested possible candidates for obesity markers, the authors should provide more information about those proteins. Under cold exposure, thermoneutrality, or HFD feeding, how is the level of the proteins changed?

2. Is there any sex or age difference? According to previous report (Choi, D.K., et al., Cell Physiol Biochem, 2011. 28(5): p. 933-48.), gender-dimorphic protein modulation in BAT may provide conclusive results showing higher expression of numerous proteins involved in thermogenesis and fat oxidation as well as lower expression of proteins contributing to fat synthesis in female rats than in male rats.

3. It is interesting that high, middle, and low abundance proteins are involved in different signaling pathways. However, for signaling transduction, not only the level of proteins but also the post-translational modification of the proteins is important. It will be interesting and important to examine post-translational modifications of the representative signaling proteins in brown adipocytes under different environment.

Reviewer #2: The current study complements and expands upon other related proteomic analyzes of brown adipose tissue. The experiment was conducted in a technically appropriate manner, and the analytics and downstream analyses are appropriate. Some of the techniques are novel. The discussion provides some interesting insight into expression/function. Overall the study is sound, and provides a platform for new useful data that can be utilized in other hypothesis driven experiments focuses on brown adipose tissue function.

The manuscript is well written and easy to follow.

Minor recommendations: Please provide in the methods section the time of day that the tissue was collected in relation to the photoperiod that the mice were maintained on. There are clear differences in gene expression patterns and tissue function (e.g. glucose metabolism) according to the time of day as it related to diurnal and circadian rhythms (e.g. Van der Veen 2012; Zhang et al 2014; Zvonic et al 2006).

van der Veen DR, Shao J, Chapman S, Leevy WM, Duffield GE. A diurnal rhythm in glucose uptake in brown adipose tissue revealed by in vivo PET-FDG imaging. Obesity (Silver Spring). 2012 Jul;20(7):1527-9. doi: 10.1038/oby.2012.78. Epub 2012 Mar 26.

Zvonic S, Ptitsyn AA, Conrad SA et al. Characterization of peripheral

circadian clocks in adipose tissues. Diabetes 2006;55:962–970.

Zhang, N. F. Lahens, H. I. Ballance, M. E. Hughes, and J.

B. Hogenesch, “A circadian gene expression atlas in mammals:

implications for biology and medicine,” Proceedings of the

National Academy of Sciences of the United States of America,

vol. 111, no. 45, pp. 16219–16224, 2014.

Reviewer #3:

Overview:

Interscapular BAT from BL6/J female mice was used for proteomics analysis using mass spec and 1620 new proteins were reported.  High abundance proteins were mainly related to glucose and fatty acid oxidation, while middle/low abundance proteins were mostly for protein synthesis and apoptosis. 497 proteins were predicted to have signal peptides.  In total the data set is likely useful but in the current presentation lacks detail, clarity, and interpretation that would provide novelty.

Major Concerns:

- title should include protein or proteomics so as to better match the content of the article

- female mice are included in the study, is there reason to anticipate sex differences?

- Ref 11 may be misinterpreted in the Introduction – BAT secretes factors that act in an autocrine/paracrine manner on BAT itself to carry out these functions?? More updated papers on BATokines should be cited, since recent work has investigated BAT secreted factors and their roles in energy metabolism.

- How does the current mass spec data set compare to the set produced in Ref 22 by Li et al? (and any other similar mass spec BAT proteomics studies?) Are there technical considerations (and what do they mean – clarification on Table 1 would help), tissue collection protocols, mouse strain/age/sex that may create different protein data sets?  Fig 2 is helpful but does not include these parameters, and the 3 studies outlined have relatively little overlap with 1316 proteins in common.  Have any studies been done in human WAT or BAT (or beige, the most common brown adipocyte in humans)?

- 6 BAT samples were collected (was overlying WAT carefully removed??) and pooled – how does this affect statistical rigor to have no biological replicates?

- Were some proteins potentially from non-adipocytes? (ie: BAT contains stem cells, immune cells, blood/neural tissue, etc. aside from pre-adipocytes and adipocytes).  In the top 10 proteins, serum albumin, hemoglobins, and potentially other proteins are part of contaminating blood likely...

- Since numerous mitochondrial proteomic studies in BAT have been undertaken, which of these proteins are thought to be mitochondrial (ie: in mitochondrial function and/or mitochondrially encoded)?

- Is PANTHER protein classification and annotation with GO terms enough for a proteomics data set?  How about pathway analysis in terms of protein interactions and protein networks?

Minor Concerns:

- Fig 1 is a bit vague and not very useful – adding specific details would help the reader orient to the study and analysis of data

- Fig. 3 -4 can not be read – please increase size of Figures and text.  I can’t see any words at all even when I zoom in, so I can not even comment on these data.

**********

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13 Feb 2020

1. Although the current study suggested possible candidates for obesity markers, the authors should provide more information about those proteins. Under cold exposure, thermoneutrality, or HFD feeding, how is the level of the proteins changed?

Answer:

Thanks for the reviewer’s good advice. We agree your suggestions that it is important to analyze the BAT proteins under different environment, which is useful to illustrate the function of BAT.

This study is a pilot work and the main purpose is to profile the mouse brown adipose tissue proteome. And we also try to present the potential BAT function annotation and possible candidates for obesity markers by previous references analysis.

In the future, we will try to analyze the function and the change of these biomarkers under differential environment. We have added above statements in the manuscript (page 16 line 369-372).

And we revised the manuscript as follows:

According to previous studies the protein level of brown fat might be changed in different environments such as cold exposure, thermoneutrality, or HFD feeding.(Peres Valgas da Silva, Hernandez-Saavedra et al. 2019) (Small, Gong et al. 2018) (Kuipers, Held et al. 2019) Therefore, the BAT protein biomarker analysis under above conditions might be important to illustrate the function of brown fat, which should be investigated in the future.

2. Is there any sex or age difference? According to previous report (Choi, D.K., et al., Cell Physiol Biochem, 2011. 28(5): p. 933-48.), gender-dimorphic protein modulation in BAT may provide conclusive results showing higher expression of numerous proteins involved in thermogenesis and fat oxidation as well as lower expression of proteins contributing to fat synthesis in female rats than in male rats.

Answer:

Thank you for your suggestions. According to previous studies both sex and age could affect the BAT.

Studies of adult rats and mice indicate that females have a higher threshold for BAT activation (i.e. activate BAT sooner upon cooling than males). In human female BAT has a higher thermogenic capacity than male (Choi, Oh et al. 2011, Harshaw, Culligan et al. 2014)

For age, previous studies reported that both the activity and mass of BAT gradually decreases with the increase of age (Schosserer, Grillari et al. 2018). We have added above references in the manuscript (Line 102-103 on page 5).

According to previous studies(Choi, Oh et al. 2011, Harshaw, Culligan et al. 2014) (Schosserer, Grillari et al. 2018) Both gender and age can affect the expression level of BAT protein, so all the mice used in this experiment were C57BL/6 female mice with 6 weeks

3. It is interesting that high, middle, and low abundance proteins are involved in different signaling pathways. However, for signaling transduction, not only the level of proteins but also the post-translational modification of the proteins is important. It will be interesting and important to examine post-translational modifications of the representative signaling proteins in brown adipocytes under different environment.

Answer:

Thanks for the reviewer’s good advice on protein post-translational modifications, therefore, we added “post-translational modifications” section in revised manuscript as followings.

The MS/MS spectra were processed using Peaks Online (X build, 1.2.1010.68) to identify the post-translational modifications (PTM) of BAT proteome. Total 562 post-translational modifications proteins, 1665 peptides and 4464 MS/MS spectra were found in our data, including 142 acetylation, 117 phosphorylation and 179 methylation etc. (S6 Table). We also quantitatively analyzed the PTM proteins by label-free approach. The PTM proteins abundance accounted for 0.37% of total abundance. Among them methylation showed the highest abundance (0.07%).

To reveal the function of PTM proteins, they were input IPA for function annotation. The canonical pathways of PTM proteins were involved in mitochondrial dysfunction, epithelial adherens junction signaling and oxidative phosphorylation, which was consistent with BAT function (Fig S3C). Among acetylation proteins, sirtuin 3(SIRT3) was an important protein in BAT function, which is the master deacetylase of deacetylation in the mitochondria. Previous study showed SIRT3 absence in mice resulted in impaired BAT lipid use, whole body thermoregulation, and respiration in BAT mitochondria without affecting UCP1 expression. Further analysis indicated SIRT3 controlled brown fat thermogenesis by deacetylation regulation of pathways upstream of UCP1(Sebaa, Johnson et al. 2019).

This study is a pilot work and the main purpose is to profile the mouse brown adipose tissue proteome, therefore we provided a PTM profile of BAT as you suggested. The representative signaling proteins in brown adipocytes under different environment were an important issue for BAT function analysis, which will be presented in future work.

we revised our study as follows: (Line 320-335 on page 14).

The MS/MS spectra were processed using Peaks Online (X build, 1.2.1010.68) to identify the post-translational modifications (PTM) of BAT proteome. Total 562 post-translational modifications proteins, 1665 peptides and 4464 MS/MS spectra were found in our data, including 142 acetylation, 117 phosphorylation and 179 methylation etc. (S6 Table). We also quantitatively analyzed the PTM proteins by label-free approach. The PTM proteins abundance accounted for 0.37% of total abundance. Among them methylation showed the highest abundance (0.07%).

To reveal the function of PTM proteins, they were input IPA for function annotation. The canonical pathways of PTM proteins were involved in mitochondrial dysfunction, epithelial adherens junction signaling and oxidative phosphorylation, which was consistent with BAT function (Fig S3C). Among acetylation proteins, sirtuin 3(SIRT3) was an important protein in BAT function, which is the master deacetylase of deacetylation in the mitochondria. Previous study showed SIRT3 absence in mice resulted in impaired BAT lipid use, whole body thermoregulation, and respiration in BAT mitochondria without affecting UCP1 expression. Further analysis indicated SIRT3 controlled brown fat thermogenesis by deacetylation regulation of pathways upstream of UCP1. (Sebaa, Johnson et al. 2019).

Name Protein number peptide number #PSM proportion

Acetylation (K) 142 338 1070 0.07%

Amidation 217 302 547 0.04%

Methylation(KR) 179 343 901 0.09%

Phosphorylation (STY) 117 168 903 0.03%

Carboxylation (DKW) 54 83 180 0.06%

Fluorination 45 54 76 0.001%

Formylation (Protein N-term) 16 20 47 0.003%

Formylation 177 357 740 0.07%

sum 0.37%

Reviewer #2:

Minor recommendations: Please provide in the methods section the time of day that the tissue was collected in relation to the photoperiod that the mice were maintained on. There are clear differences in gene expression patterns and tissue function (e.g. glucose metabolism) according to the time of day as it related to diurnal and circadian rhythms (e.g. Van der Veen 2012; Zhang et al 2014; Zvonic et al 2006).

Answer:

Answer:

Thanks for your suggestions. We agree your comments that diurnal and circadian rhythms related to gene expression patterns and tissue function(Zvonic, Ptitsyn et al. 2006)

We have added above information in the manuscript: (Line 95-100 on page 5).

“According to previous studies diurnal and circadian rhythms related to gene expression patterns and tissue function(Zvonic, Ptitsyn et al. 2006, van der Veen, Shao et al. 2012, Zhang, Lahens et al. 2014). Female mice were housed at room temperature in a 12:12-h light-dark cycle at 23±2°C ………And the interscapular BATs were excised directly in 9 a.m by tweezers and scissors from the lateral scapular area of the mice back”

Reviewer #3:

Major Concerns:

- title should include protein or proteomics so as to better match the content of the article

Answer:

Thanks for your suggestions, the title had been revised as below:

“Comprehensive proteomic dataset and functional annotation of mouse brown adipose tissue“

- female mice are included in the study, is there reason to anticipate sex differences?

Answer:

Thank you for your suggestions. According to previous studies sex could affect the BAT. Studies of adult rats and mice indicate that females have a higher threshold for BAT activation (i.e. activate BAT sooner upon cooling than males). In human female BAT has a higher thermogenic capacity than male (Choi, Oh et al. 2011, Harshaw, Culligan et al. 2014)

We have added above information in the manuscript (Line 102-103 on page 5).

“According to previous studies(Choi, Oh et al. 2011, Harshaw, Culligan et al. 2014) (Schosserer, Grillari et al. 2018) Both gender and age can affect the expression level of BAT protein, so all the mice used in this experiment were C57BL/6 female mice with 6 weeks.”

- Ref 11 may be misinterpreted in the Introduction – BAT secretes factors that act in an autocrine/paracrine manner on BAT itself to carry out these functions?? More updated papers on BATokines should be cited, since recent work has investigated BAT secreted factors and their roles in energy metabolism.

Answer: Thank you for your comments. According to your suggestion, we have revised the manuscript as followings (Line 48-51 on page 3)

BAT is a secretory tissue, which can produce BATokines. BATokines act locally to control the remodeling of BAT, and they also act on peripheral organs, including the liver, skeletal muscle, pancreas, bone, the immune system and the CNS, thus affect systemic glucose levels in a thermogenesis-dependent and -independent manner.(Klepac, Georgiadi et al. 2019)

1. How does the current mass spec data set compare to the set produced in Ref 22 by Li et al? (and any other similar mass spec BAT proteomics studies?)

Answer: Thanks for your comments.

The protein accession numbers of previous report and our data were transferred to gene entry names using Uniprot website( https://www.uniprot.org/), then we make a qualitative comparison at the gene level.

2. Are there technical considerations (and what do they mean – clarification on Table 1 would help), tissue collection protocols, mouse strain/age/sex that may create different protein data sets? Fig 2 is helpful but does not include these parameters, and the 3 studies outlined have relatively little overlap with 1316 proteins in common.

Answer: Thank you for your suggestions.

According to previous studies, the protein identification will be different because of different tissue collection protocols, mouse strain/age/sex (Choi, Oh et al. 2011, Harshaw, Culligan et al. 2014) (Schosserer, Grillari et al. 2018) (Zvonic, Ptitsyn et al. 2006, van der Veen, Shao et al. 2012, Zhang, Lahens et al. 2014) . Besides above factors, many technical factors also influence the results, including protein digestion method, peptide separation method, mass spectrometry and database search software. Therefore, above factors lead to the relatively low overlap between different studies.

To clearly illustrate the results, according to your suggestions, we have added above factor in Table 1.

Have any studies been done in human WAT or BAT (or beige, the most common brown adipocyte in humans)?

Answer: Thank you for your suggestions

In 2016, Muller et al. present comprehensive proteome analysis of human brown and white adipose tissues. 2519 adipocyte proteins were identified (Muller, Balaz et al. 2016) and 2088 were found in our study.

we added the comparison with human BAT in the manuscript (Line 201-202 on page 8).

“In addition, we compared the proteins we identified to human adipose tissue proteins, the result showed that 2088 proteins were overlapped with Muller et al.(Muller, Balaz et al. 2016)

Year Mouse

strain/age/sex Number of identifications Fractionation method MS instrument Identification quality (FDR) Reference

1 2001 8 week old C57Bl/6J

females 37 2-DE Q-TOF-MS N/A Sanchez et al.(Sanchez, Chiappe et al. 2001)

2 2004 C57BL/6 female mice with 6 weeks 20 2-DE Q-TOF-MS N/A Schmid et al.(Schmid, Converset et al. 2004)

3 2007 Wistar male rats 58 2-DE UltraFlex II MALDI-TOF-TOF N/A Navet et al.(Navet, Mathy et al. 2007)

4 2009 C57BL/6 mice (5 to 10 weeks old) 2434 SILAC LTQ-Orbitrap Protein level< 1% Forner et al.(Forner, Kumar et al. 2009)

5 2011 Male and female SLC Sprague-Dawley (SD) rats (5 weeks

of age) 55 2-DE MALDI-TOF-MS N/A Choi et al.(Choi, Oh et al. 2011)

6 2015 C57BL/6 female mice with 6 weeks 3048 iTRAQ Triple TOF 5600 Protein level< 1% Li et al.(Li, Zhao et al. 2015)

7 2016 Wild-caught thirteen-lined ground squirrels 778 iTRAQ Velos

Orbitrap Protein level< 1% Ballinger et al.(Ballinger, Hess et al. 2016)

8 2016 11 patients 2019 TMT Orbitrap Fusion tribrid Protein level< 1% Muller etal.(Muller, Balaz et al. 2016)

9 2019 C57BL/6 female mice with 6 weeks 4949 RPLC Triple TOF5600 Protein level<1% This study

- 6 BAT samples were collected (was overlying WAT carefully removed??) and pooled – how does this affect statistical rigor to have no biological replicates?

Answer: Thank you for the reviewers’ suggestions.

Based on our observations and previous study reports(Vargas-Castillo, Fuentes-Romero et al. 2017), the BAT and WAT can be clearly distinguished by appearance, since BAT has a characteristic yellow-brown color due to its high content of mitochondria and the color of white adipose tissue is white ,thus we took out the BAT directly from the back of the mouse and removed the white fat by color.

In proteomic database report several samples were usually pooled into one sample to reduce the individual effect on the final results. Then a comprehensive analysis will be done on the pooled sample. This is a normal strategy for proteomic database analysis(Vargas-Castillo, Fuentes-Romero et al. 2017). In this study we also adapted above strategy.

- Were some proteins potentially from non-adipocytes? (ie: BAT cotains stem cells, immune cells, blood/neural tissue, etc. aside from pre-adipocytes and adipocytes). In the top 10 proteins, serum albumin, hemoglobins, and potentially other proteins are part of contaminating blood likely...

Answer: Thanks for your question. previous studies reported that brown adipose tissue contained the non-adipocytes including stem cells, (Villarroya, Cereijo et al. 2019)immune cells, blood/neural tissue,(Payab, Goodarzi et al. 2018)stromal-vascular (S-V) cells (non-adipocytes)(Song, Fukui et al. 2010), Therefore, our database included the proteins from these non-adipocytes. In addition, blood vessels are also around brown adipose tissue, thus the proteins from plasma were also included in our database.

We have added your suggestions in the manuscript: (Line 191-195 on page 8)

Previous studies reported that brown adipose tissue contained the non-adipocytes including stem cells, (Villarroya, Cereijo et al. 2019)immune cells, blood/neural tissue,(Payab, Goodarzi et al. 2018)stromal-vascular (S-V) cells (non-adipocytes)(Song, Fukui et al. 2010), Therefore, our database included the proteins from these non-adipocytes. In addition, blood vessels are also around brown adipose tissue, thus the proteins from plasma were also included in our database.

- Since numerous mitochondrial proteomic studies in BAT have been undertaken, which of these proteins are thought to be mitochondrial (ie: in mitochondrial function and/or mitochondrially encoded)?

Answer: Thank you for your suggestions, according to your suggestions, we add the related results about BAT mitochondrial proteins as follows (page 14 line307-319)

We used GO analysis to annotate the cellular components of all the proteins, and 214 mitochondria proteins were found. These BAT mitochondrial proteins were subdivided into three groups according to their abundance, 72 high-abundance, 90 middle-abundance, 39 low-abundance proteins.

The functions of BAT proteins were mainly related to the regulation of glucose and lipid metabolism, protein synthesis and apoptosis. The mitochondrial proteins were mainly involved in pathways of valine degradation I, isoleucine degradation I, ketogenesis, sirtuin signaling pathway and tRNA charging, which were related to oxidative phosphorylation and amino acid metabolism (Fig4A).

The high-abundance proteins were mainly involved in amino acid degradation, the middle-abundance proteins were mainly related to amino acid biosynthesis and degradation, and the low-abundance proteins were found to be involved in apoptotic signaling pathways such as death receptor Signaling and thioredoxin Pathway. (Fig4B)

- Is PANTHER protein classification and annotation with GO terms enough for a proteomics data set? How about pathway analysis in terms of protein interactions and protein networks?

Answer: Thank you for your suggestions

Gene Ontology (GO) project provides the annotation of data. The GO analysis including three parts: cellular component, molecular function, biological process. From these three aspects of annotation, we can learn about the distribution of proteins in cells, the functions and biological processes of our proteins.

But GO analysis only provides an annotation to give us an initial understanding of the proteins in the data, and more accurate analysis of the protein data is mainly through Ingenuity Pathway Analysis (IPA) software.

IPA is based on the database of various biomedical literature and integrated from third-party databases. In addition, IPA use a suite of algorithms and tools for inferring and scoring regulator networks upstream and downstream of gene-expression data based this database. Furthermore, it predicted the related pathway analysis and diseases and function analysis (Kramer, Green et al. 2014). Currently, IPA analysis is a commonly used proteomic analysis. (Wei, Meng et al. 2019). Therefore, we used both GO annotation and IPA analysis in our study.

Minor Concerns:

- Fig 1 is a bit vague and not very useful – adding specific details would help the reader orient to the study and analysis of data

Answer: Thanks for your suggestion, we adjusted the definition of Fig 1 and moved this figure to the S1 Fig.

- Fig. 3 -4 can not be read – please increase size of Figures and text. I can’t see any words at all even when I zoom in, so I can not even comment on these data.

Answer: Thanks for your suggestion, I am sorry about the figure that you cannot read it. We recalibrated the picture definition

Submitted filename: Response to Reviewers20200110.docx

Click here for additional data file.

11 Mar 2020

PONE-D-19-18696R1

Comprehensive proteomics and functional annotation of mouse brown adipose tissue

PLOS ONE

Dear Mr sun,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Minor corrections needed.  Will not need another round of review.

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Jonathan M Peterson, Ph.D.

Academic Editor

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Additional Editor Comments (if provided):

Minor corrections needed. Will not need another round of review.

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Reviewers' comments:

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Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: The authors well answered questions we asked, and they revised the manuscript according to the questions.

Reviewer #2: The requested information as stated in the methods section is still insufficient or incorrect. Please state the time of lights OFF and time of lights ON, and time of collection of tissue within the methods section. The authors citation of Van der Veen 2012 (citation 26) is incorrect, please use the correct citation: Van der Veen (2012) A diurnal rhythm in glucose uptake in brown adipose tissue revealed by in vivo PET-FDG imaging. van der Veen DR, Shao J, Chapman S, Leevy WM, Duffield GE. Obesity (Silver Spring). 2012 Jul;20(7):1527-9. doi: 10.1038/oby.2012.78. Epub 2012 Mar 26. PMID: 22447290.

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Reviewer #1: No

Reviewer #2: No

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31 Mar 2020

Thanks for the reviewer’s good advice.

We revised the manuscript as follows:

“Female mice were housed at room temperature in a 12:12-h light-dark cycle at 23±2°C, with free access to water and diet. The time of lights on is eight a.m., the time of lights off is eight p.m., and the time of collection tissue is nine a.m.”

In addition, we have modified reference 26. Thank you very much for your correction.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

8 Apr 2020

Comprehensive proteomics and functional annotation of mouse brown adipose tissue

PONE-D-19-18696R2

Dear Dr. sun,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

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Jonathan M Peterson, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

all comments addressed. There was only a minor correction needed from previous submission.

Reviewers' comments:

20 Apr 2020

PONE-D-19-18696R2

Comprehensive proteomics and functional annotation of mouse brown adipose tissue

Dear Dr. sun:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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PLOS ONE Editorial Office Staff

on behalf of

Dr Jonathan M Peterson

Academic Editor

PLOS ONE