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Title Hepatic proteome analysis reveals altered mitochondrial metabolism and suppressed acyl- CoA synthetase-1 in colon-26 tumor-induced cachexia.

Permalink https://escholarship.org/uc/item/5xb4k7mj

Journal Physiological genomics, 52(5)

ISSN 1094-8341

Authors Khamoui, Andy V Tokmina-Roszyk, Dorota Rossiter, Harry B et al.

Publication Date 2020-05-01

DOI 10.1152/physiolgenomics.00124.2019

Peer reviewed

eScholarship.org Powered by the California Digital Library University of California 1 Hepatic proteome analysis reveals altered mitochondrial metabolism and suppressed acyl-CoA

2 synthetase-1 in colon-26 tumor-induced cachexia

3

4 Andy V. Khamoui1,2*, Dorota Tokmina-Roszyk2,3, Harry B. Rossiter5,6, Gregg B. Fields2,3,4, Nishant P.

5 Visavadiya1

6

7 1Department of Exercise Science and Health Promotion, Florida Atlantic University, Boca Raton, FL, USA;

8 2Institute for Human Health & Disease Intervention, Florida Atlantic University, Boca Raton, FL, USA;

9 3Department of Chemistry & Biochemistry, Florida Atlantic University, Jupiter, FL, USA; 4Department of

10 Chemistry, The Scripps Research Institute, Jupiter, FL, USA; 5Division of Respiratory and Critical Care

11 Physiology and Medicine, Department of Medicine, The Lundquist Institute for Biomedical Innovation at

12 Harbor-UCLA Medical Center, Torrance, CA, USA; 6Faculty of Biological Sciences, University of Leeds, Leeds,

13 UK

14

15 Running Title: Hepatic proteome in cancer cachexia

16

17 *Correspondence

18 Andy V. Khamoui PhD

19 Department of Exercise Science and Health Promotion

20 Florida Atlantic University

21 777 Glades Rd, FH-11A, Rm 128-B

22 Boca Raton, FL 33431

23 Tel: (561) 297-4450 | Email: [email protected]

24

25

26

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28

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Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 29 Abstract

30 Cachexia is a life-threatening complication of cancer traditionally characterized by weight loss and muscle

31 dysfunction. Cachexia, however, is a systemic disease that also involves remodeling of non-muscle organs.

32 The liver exerts major control over systemic metabolism yet its role in cancer cachexia is not well-understood.

33 To advance the understanding of how the liver contributes to cancer cachexia, we used quantitative

34 proteomics and bioinformatics to identify hepatic pathways and cellular processes dysregulated in mice with

35 moderate and severe colon-26 tumor-induced cachexia. ~300 differentially expressed proteins identified

36 during the induction of moderate cachexia were also differentially regulated in the transition to severe

37 cachexia. KEGG pathways enrichment revealed representation by oxidative phosphorylation, indicating

38 altered hepatic mitochondrial function as a common feature across cachexia severity. Glycogen catabolism

39 was also observed in cachexic livers along with decreased pyruvate dehydrogenase protein X component

40 (Pdhx), increased lactate dehydrogenase A chain (Ldha), and increased lactate transporter Mct1. Together

41 this suggests altered lactate metabolism and transport in cachexic livers, which may contribute to energetically

42 inefficient inter-organ lactate cycling. Acyl-CoA synthetase-1 (ACSL1), known for activating long-chain fatty

43 acids, was decreased in moderate and severe cachexia based on LC-MS/MS analysis and immunoblotting.

44 ACSL1 showed strong linear relationships with percent body weight change and muscle fiber size (R2=0.73-

45 0.76, P<0.01). Mitochondrial coupling efficiency, which is compromised in cachexic livers to potentially

46 increase energy expenditure and weight loss, also showed a linear relationship with ACSL1. Findings suggest

47 altered mitochondrial and substrate metabolism of the liver in cancer cachexia, and possible hepatic targets for

48 intervention.

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56 Keywords: mitochondrial function, TMT, quantitative proteomics, bioinformatics, STRING

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Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 57 Introduction

58 Cancer cachexia is a life-threatening condition in which tumor-induced metabolic abnormalities lead to

59 severe weight loss, skeletal muscle atrophy, and intolerance to anti-cancer treatment (10, 11). Approximately

60 half of all cancer patients experience cachexia, with incidence rising to ~80% in advanced cancer patients (33).

61 Although cachexia is believed to be directly responsible for ~20% of cancer-related deaths, this debilitating

62 complication remains an under-appreciated issue in oncology (33). Nutritional support does not ameliorate

63 cachexia due to the metabolic abnormalities present, and approved treatments options are currently

64 unavailable, contributing to significant frustration among patients and their family members (10, 26).

65 While skeletal muscle remains the most widely studied organ in cancer cachexia, increasing attention

66 has been devoted to the mechanistic roles of other organs such as adipose, bone, brain, heart, and liver,

67 reflecting a greater appreciation for the systemic nature of the disease (25). The multi-organ involvement

68 highlights the significant complexities of tumor-induced cachexia, and unraveling the root cause remains a

69 challenge. Omics technologies offer a global, unbiased approach to identify regulators of the cachexic

70 phenotype (15, 31). In various omics platforms, mechanisms distinguishing cachexic from healthy muscle

71 have been identified in patient-derived tissues, pre-clinical models, and myotubes treated with tumor-

72 conditioned media (5, 14, 27-29). These investigations report that cachexic skeletal muscle displays enriched

73 ubiquitin-dependent protein degradation, loss of sarcomere structure, mitochondrial dysfunction, disrupted

74 energy metabolism, and energetic stress, offering an explanation for skeletal muscle atrophy and the symptom

75 of fatigue.

76 In contrast to skeletal muscle, the liver has been relatively unexplored in cancer cachexia despite

77 exerting major control over systemic metabolism. Several lines of limited evidence point to an influential role of

78 the liver in cachexia-associated muscle atrophy and weight loss. First, the liver participates in the systemic

79 acute phase response to disease and infection by synthesizing acute phase proteins. In order to support the

80 increased demand for acute phase protein synthesis, amino acids are mobilized and released from skeletal

81 muscle, contributing to tumor-induced muscle atrophy (6). It is also suggested that uncoupling of mitochondrial

82 oxidative phosphorylation (OXPHOS) in the liver in cancer cachexia contributes to inefficiency, heat

83 production, increased metabolic rate, and weight loss (9, 25). Indeed, a recent report found cachexic liver

84 mitochondria to have reduced respiratory control ratio and increased LEAK respiration, suggesting impaired

3

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 85 OXPHOS coupling efficiency (18). Greater hepatic heat production arising from mitochondrial uncoupling may

86 increase whole body resting energy expenditure and contribute to unintended weight loss. Lastly, cachexic

87 livers may demonstrate increased gluconeogenesis supported by amino acids mobilized from the catabolism of

88 skeletal muscle (17). When hepatic gluconeogenesis was normalized using a PPARα agonist, tumor-bearing

89 mice were protected against weight loss and muscle atrophy (17), implying that hallmark features of cancer

90 cachexia can be mitigated by targeting hepatic metabolism.

91 To identify hepatic pathways and cellular processes associated with cancer cachexia, as well as

92 candidate hepatic targets for therapeutic intervention, unbiased omics analysis of cachexic liver tissues are

93 warranted. Here we used tandem mass tag (TMT)-based quantitative proteomics to identify differentially

94 expressed hepatic proteins and dysregulated metabolic pathways during the induction and progression of

95 colon-26 tumor-induced cachexia, a well-established pre-clinical mouse model (7). Bioinformatics identified

96 functional enrichments associated with the different degrees of cachexia severity, while findings were validated

97 with immunoblotting and biochemical assays.

98

99 Methods

100 Animals and design

101 Ten-week-old Balb/c males (Envigo) were randomly assigned to receive either an injection of sterile

102 PBS or colon-26 (C26) tumor cells. A tumor growth period of three weeks is typically allowed to elapse before

103 tissue collection in order for hallmark features of cachexia to occur (i.e. weight loss, muscle atrophy). To

104 evaluate mechanisms of cachexia severity, tissue was collected from C26 mice between days 14-21 after

105 tumor cell injection, and classified according to weight loss in accordance with previous literature (6, 34). The

106 4 groups included: 1) Tumor-free, weight-stable mice that were PBS injected (PBS-WS, n=4), 2) C26 mice with

107 confirmed tumors that did not exhibit weight loss and were therefore weight-stable (C26-WS, n=6), 3) C26

108 mice with moderate cachexia (10% weight loss; C26-MOD, n=7), and 4) C26 mice with severe cachexia (≥20%

109 weight loss; C26-SEV, n=6). These classifications were adapted from previous pre-clinical investigations (6,

110 34). Weight loss for each mouse was determined by the percentage change between carcass weight (i.e.

111 tumor-free body weight) and body weight recorded on the day of cell injection. Mice were individually housed,

112 provided ad libitum water and food (5L0D, protein 29% fat 13%, carbohydrate 58%, PicoLab Laboratory

4

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 113 Rodent Diet), and maintained on a 12:12 hr light:dark cycle. C26 mice may or may not exhibit reduced food

114 intake depending on the source of the C26 cells and the phenotype it generates when transplanted in vivo (23).

115 The C26 cells in this study were obtained from a cell bank that others have used to demonstrate no significant

116 effect of C26 cancer cachexia on food intake (4). Approval was obtained by the Institutional Animal Care and

117 Use Committee (#A16-39) before any procedures were performed. Body and organ weights, muscle fiber size,

118 and mitochondrial respiration data from this cohort of C26 mice were reported previously (18) and were used

119 here to establish relationships with proteomic analyses generated by liquid chromatography-tandem mass

120 spectrometry (LC-MS/MS) and immunoblotting.

121

122 C26 tumor cell culture and injection

123 C26 cells were cultured in a humidified incubator with 5% CO2 using media that contained RPMI 1640

124 with 1% penicillin/streptomycin (vol/vol) and 10% FBS (vol/vol) (CLS Cell Lines Service, Eppelheim, Germany).

125 Culture media was replaced every two to three days. At sub-confluency, cells were harvested by incubation

126 with trypsin (0.05%, Gibco) and pelleted by centrifugation. The supernatant was discarded and the pellet

127 resuspended in sterile PBS. Viable cells were identified and counted in a hemocytometer by trypan blue

128 staining and light microscopy. Mice assigned to C26 groups were injected subcutaneously in the upper back

129 with a cell suspension containing 1 x 106 cells. Mice assigned to the control group were administered an

130 equivalent volume of sterile PBS (8, 21).

131

132 Tissue collection

133 Mice were euthanized by overdose with ketamine/xylazine cocktail injected i.p. (300/30 mg/kg).

134 Euthanasia was performed during a four-hour time window from 10:00 am to 2:00 pm to ensure consistency in

135 the timing of tissue collection. In order to attain 10% and 20% weight loss in moderate and severe cachexia,

136 respectively, body weight was routinely monitored following tumor cell injection. Based upon our previous

137 experience, we expected a final tumor mass at necropsy of ~2g with severe cachexia (20% target weight loss),

138 and a tumor mass of ~1g with moderate cachexia (10% target weight loss). These parameters were factored

139 into routine weight monitoring because measurements of body weight made before euthanasia would be

140 confounded by tumor weight. For the typical Balb/c adult male weighing 25g, a tumor mass of 1g would

5

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 141 represent ~3-4% of body weight. During routine monitoring, a mouse showing ~7% weight loss would be

142 euthanized in anticipation of tumor mass accounting for ~3-4% of body weight. This enabled the target weight

143 loss of 10% for the moderate cachexia group (C26-MOD), with tissue being collected on day 14 (n=4), 15

144 (n=1), 17 (n=1), and 21 (n=1). For mice with confirmed, palpable tumors but no weight loss (C26-WS), tissue

145 was collected on day 14 (n=1), 20 (n=2), and 21 (n=3). For all mice with severe cachexia (C26-SEV, n=6),

146 tissue was collected on day 21 post-injection. Mice were not food deprived overnight or immediately prior to

147 tissue collection. Collected tissue samples were weighed, sectioned, snap frozen and stored at -80°C.

148

149 High-resolution respirometry

150 Mitochondrial respiration was measured in situ in fresh liver tissue as we previously described and

151 reported (18). Briefly, duplicate sections of liver tissue weighing ~6 mg each were gently separated in BIOPS

152 solution under a dissecting microscope. Mechanically separated samples were placed into the respirometer

153 chambers (Oxygraph-2k, Oroboros Instruments) and analyzed by injection of substrates, uncouplers, and

154 inhibitors to determine mass-specific oxygen flux (pmol·s-1·mg-1). Several indicators of mitochondrial coupling

155 were derived from high-resolution respirometry experiments including the respiratory control ratio (RCR), an

156 index of OXPHOS coupling efficiency, which was calculated by dividing complex I supported OXPHOS by

157 complex I supported LEAK respiration. In addition, LEAK respiration was normalized to both maximal

158 OXPHOS and electron transfer system (ETS) capacities, which were obtained from injection of complex I and

159 II linked substrates and 5 mM ADP (OXPHOS) or 0.5 µM CCCP (ETS).

160

161 Sample preparation and TMT labeling

162 Harvested liver tissue was transferred to lysis buffer (8 M Urea, 1 mM phenylmethylsulfonyl fluoride, 1x

163 protease inhibitor cocktail) and sonicated (SONICS VCX750; amplitude 35%; pulse on 1 sec; pulse off 3 sec;

164 total processing time 3 min) to release cell contents. Protein quantitation was performed using the Pierce

165 quantitative colorimetric assay kit at λ = 480 nm according to the manufacturer’s instructions (cat# 23275,

166 ThermoFisher Scientific, USA). 250 ug of total protein per sample were then reduced with 200 mM tris(2-

167 carboxyethyl)phosphine at 56°C for 1 h followed by alkylation with 20 mM iodoacetamide at room temperature

168 in the dark for 1 h. An equal amount of protein was used for each tryptic digestion (1:50 ratio of

6

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 169 trypsin:substrate) at 37 °C overnight after which time the reaction was quenched with 0.1% formic acid.

170 Samples were then lyophilized and subsequently dissolved in 100 mM triethylammonium bicarbonate. Tryptic

171 peptides, 50 ug per sample, were labeled with TMT reagents for 1 h at room temperature after which the

172 reaction was quenched with 5% hydroxylamine. Samples from each group were combined with equal amounts

173 of TMT labeled internal standard, which was comprised of a mixture of protein extracts from all 23 samples.

174 The sample labeling scheme is shown in Supplementary Table S1. Each pooled sample was then fractionated

175 using RP-HPLC (Waters 2695 HPLC System equipped with Thermo Betasil C18 column, 80Å, 10 mm x 250

176 mm, 5 μm) with the separation gradient set to 8-32% B in 60 min (flow rate = 1mL/min, 20°C; A: 0.1% formic

177 acid in water; B: 0.1% formic acid in acetonitrile) to yield 6 fractions.

178

179 LC-MS/MS and data analysis

180 Fractionated peptides were further separated using Nanoflow UPLC: Ultimate 3000 nano UHPLC

181 system (ThermoFisher Scientific, USA) equipped with a trapping column (PepMap C18, 100 Å, 110 μm x 2 cm,

182 5μm) and an analytical column (PepMap C18, 100 Å, 75 μm x 50 cm, 2 μm). LC linear gradient was set to 2-

183 8% B in 3 min, 8 to 20% B in 50 min, 20-40% B in 26 min, and 40-90% B in 4 min (A: 0.1% formic acid in

184 water; B: 0.1% formic acid in 80% acetonitrile) with flow rate = 250 nL/min at 20°C. Peptides were then

185 analyzed with Q Exactive HF mass spectrometer (ThermoFisher Scientific, USA). Precursor ion range was set

186 to 300.0-1650.0 with 6x104 resolution at 200 m/z and MS/MS product range starting from 100 m/z. Data was

187 acquired in data dependent acquisition mode with up to 20 most intense peptide ions from preview scan

188 selected for MS/MS. From each group 6 raw MS files were searched against mouse protein database using

189 Maxquant (1.5.6.5). Search parameters included carbamidomethylation of cysteine (fixed) and oxidation of

190 methionine (variable) in protein modification; enzyme specificity was set to trypsin with maximum of 2 missed

191 cleavages and mass tolerance of 10 ppm for precursor ion and 0.6 Da for MS/MS. Proteins were selected for

192 further analysis based on ≥1 unique peptides identified. The false discovery rate (FDR) was set to <1%.

193

194 Tissue homogenate

195 For all other in vitro experiments, liver tissue homogenate was prepared as we recently described (18).

196 Briefly, 250 mg liver tissue was excised and washed with ice-cold mitochondrial isolation buffer

7

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 197 (215 mM mannitol, 75 mM sucrose, 0.1% BSA, 20 mM HEPES, 1 mM EGTA, and pH adjusted to 7.2 with

198 KOH). Subsequently, tissue was homogenized in 1 mL of mitochondrial isolation buffer using a manual Potter-

199 Elvehjem tissue grinder. The tissue homogenate was frozen immediately at -80 °C for later biochemical

200 assays and western blotting. Protein concentration was determined by BCA kit (Pierce, Rockford, Il, USA).

201

202 Biochemical assays

203 Hepatic galactose (cat# ab83382, Abcam), free fatty acid (cat# ab65341, Abcam), triglyceride (cat#

204 ab65336, Abcam) and glycogen (cat# K646, Biovision) content, as well as α-glucosidase (cat# ab174093,

205 Abcam) and acyl-CoA synthetase (cat# K184, Biovision) enzyme activity was assayed using commercially

206 available kits according to the manufacturers’ protocols. The α-glucosidase enzyme activity was measured

207 spectrophotometrically at λ = 410 nm. For the remaining assays, fluorescence signals generated from

208 samples were measured at λexcitation = 530 nm and λemission = 590 nm filters by a Biotek Synergy HTX

209 spectrofluorometer (Winooski, VT). All parameters were normalized against total protein concentrations.

210

211 Western blotting

212 For western blot analysis, 30 μg of protein homogenate was mixed with 4X Laemmli sample buffer

213 (cat# 161-0747, Bio-Rad) containing 355 mM 2-mercaptoethanol and separated on SDS-polyacrylamide gel

214 electrophoresis, using 4–20% Criterion™ TGX™ Precast gels (cat# 5671095, Bio-Rad). The gel proteins were

215 transferred onto an Immun-Blot PVDF membrane and then blocked with 5% nonfat dry milk in TBST buffer

216 (Tris-buffered saline, 0.1% Tween-20) for 1 hour at room temperature. The PVDF membrane was incubated

217 with the following primary antibodies overnight on a shaker at 4 °C in 5% milk: mitochondrial OXPHOS

218 complexes (I to V) mouse mAb cocktail (1:2000, cat#ab110413, Abcam), 4-hydroxynonenal rabbit polyAb (4-

219 HNE, 1:1000 dilution, cat# ab46545, Abcam), long-chain acyl-CoA synthetase 1 (ACSL1, 1:2000 dilution, cat#

220 4047, Cell Signaling) and GAPDH rabbit mAb (1:5000 dilution, cat# 5174, Cell Signaling). GAPDH was taken

221 as an internal protein loading control. After 3 times TBST wash, the membranes were incubated with

222 appropriate horseradish peroxidase (HRP)-conjugated horse anti-mouse IgG (cat#7076, Cell Signaling) or goat

223 anti-rabbit IgG (cat# 7074, Cell Signaling) secondary antibodies for an hour at room temperature. Further,

224 membranes were washed 3 times in TBST and then incubated for 5 minutes in SuperSignal™ West Pico 8

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 225 PLUS Chemiluminescent Substrate solution (cat# PI34580, Thermo Fisher) for immunoreactive protein

226 reaction. Subsequently, ChemiDocTM XRS+ imager with Image Lab™ software (Bio-rad) detected the reactive

227 bands and ImageJ software (NIH) measured individual protein band density.

228

229 Bioinformatics and statistical analysis

230 Proteins differentially expressed between two compared groups (p-value < 0.05) and with 1.5 or 1/1.5

231 fold change were deemed significant. Venn diagrams were generated from protein lists using software by

232 Ghent University Bioinformatics and Systems Biology (http://bioinformatics.psb.ugent.be/webtools/Venn/).

233 Heat maps were prepared using Morpheus (https://software.broadinstitute.org/morpheus). Bioinformatics

234 analysis of proteomics datasets were performed using STRING version 11 (30), including enrichments of

235 KEGG and Reactome pathways and UniProt keywords. Significantly enriched pathways and keywords were

236 identified by a FDR q-value < 0.05. Pairwise comparisons for C26-WS vs. C26-MOD, C26-WS vs. C26-SEV,

237 and C26-MOD vs. C26-SEV were prioritized for bioinformatics. For each pairwise comparison, differentially

238 expressed proteins that increased (up-regulated) and decreased (down-regulated) were analyzed separately in

239 the STRING database (20, 24). Hepatic glycogen, galactose, free fatty acids, triglycerides, enzyme activity,

240 and protein expression from immunoblotting experiments were analyzed by one-way ANOVA. In the event of a

241 significant F-test, posthoc analysis was conducted using Tukey’s HSD. Significance was accepted at p<0.05.

242

243 Results

244 Phenotype of mice with moderate and severe colon-26 cancer cachexia

245 Tumor mass increased with cachexia severity (Table 1). Mean body weight loss was 10±1% and

246 22±2% in C26-MOD and C26-SEV, respectively (Table 1). Hindlimb muscle mass was ~20-30% lower in C26-

247 MOD and C26-SEV compared to the WS groups (Table 1). Consistent with whole muscle atrophy, mean fiber

248 area of the gastrocnemius was ~45-55% lower in C26-MOD and C26-SEV compared to WS groups (Table 1).

249 Epididymal fat mass was lower in C26-MOD relative to PBS-WS and C26-WS (Table 1). Epididymal fat was

250 fully depleted in C26-SEV and not measured (Table 1). The spleen was enlarged in all C26 groups by ~35-

251 75% compared to PBS-WS (Table 1), indicating an inflammatory response to the C26 tumor. The liver was

252 atrophied in C26-MOD and C26-SEV compared to the WS groups (Table 1).

9

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 253 The liver proteome in colon-26 tumor-induced cachexia

254 A total of 2,510 proteins were identified in liver protein extracts by LC-MS/MS, with ~50% common to all

255 four groups, 3% exclusive to moderate cachexia, 8% unique to severe cachexia, and ~4% common to both

256 moderate and severe cachexia (Fig. 1a, Table S2). When using PBS-WS mice as the control, 487 proteins

257 were differentially expressed in C26-WS (up-/down-regulated: 283/204), 532 in C26-MOD (247/285), and 523

258 in C26-SEV (250/273) (Fig. 1b, Table S3). When using C26-WS (cancer non-cachexia) as the control, 771

259 proteins were differentially expressed in C26-MOD (up-/down-regulated: 355/416), and 769 in C26-SEV

260 (341/428) (Fig. 1b,c, Table S4). Thus, selection of control impacts the detection of differentially expressed

261 proteins in C26 cancer cachexia. When compared to C26-MOD, 557 proteins were differentially expressed in

262 C26-SEV (up-/down-regulated: 260/297) (Fig. 1b,d, Table S4), and these proteins may be involved in the

263 transition from moderate to severe cachexia.

264 A Venn diagram generated from the differentially expressed proteins in C26-WS vs. C26-MOD, and

265 C26-MOD vs. C26-SEV, revealed an overlap region of 306 proteins (Fig. 1e, Table S5). These 306 proteins

266 are differentially regulated at the induction of moderate cachexia, and in the transition to severe cachexia.

267 Thus, these proteins may represent a hepatic proteomic signature needed to maintain the cachexic state.

268 Functional annotation by KEGG pathways enrichment showed metabolic pathways, Alzheimer’s disease,

269 Parkinson’s disease, Huntington’s disease, lysosome, oxidative phosphorylation, and non-alcoholic fatty liver

270 disease among the top 10 pathways (Fig. 1e). Dysregulation of mitochondrial function, particularly in its

271 metabolic activities such as OXPHOS, are common to Alzheimer’s, Huntington’s, Parkinson’s, and non-

272 alcoholic fatty liver diseases, suggesting altered liver mitochondrial OXPHOS to also feature in cachexia

273 regardless of severity. Further, 251 differentially expressed proteins were unique only in severe cachexia (Fig.

274 1e and Table S5), and these proteins (of the total 557 differentially expressed in severe vs. moderate) may be

275 influential in supporting the transition from moderate to a severe presentation of the disease.

276

277 Altered mitochondrial function and substrate metabolism in moderate cachexia

278 Differentially expressed proteins were analyzed separately in STRING. KEGG pathways enrichment of

279 proteins down-regulated in moderate cachexia included metabolic pathways, Alzheimer’s disease,

280 Huntington’s disease, Parkinson’s disease, oxidative phosphorylation, thermogenesis, and non-alcoholic fatty

10

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 281 liver disease among the top 10 pathways (Fig. 2a). These enriched pathways suggest a depression in liver

282 mitochondrial OXPHOS to be associated with early cachexia. In addition, ~20% of genes involved in the TCA

283 cycle were identified (relative to background gene count) in the down-regulated list, the largest among the

284 represented pathways (Fig. 2a). Similarly, UniProt keywords enrichment analysis lists ‘mitochondrion’,

285 ‘mitochondrion inner membrane’, ‘oxidoreductase’, ‘respiratory chain’, and ‘electron transport’ in the top 15

286 represented keywords from the down-regulated protein list (Table S6). The reactome pathways enrichment

287 analysis also identified terms related to energy metabolism and mitochondrial function among the top 10

288 pathways (Table S6).

289 KEGG pathways enrichment of the up-regulated protein list in moderate cachexia show metabolic

290 pathways, complement and coagulation cascades, carbon metabolism, biosynthesis of amino acids, protein

291 processing in the endoplasmic reticulum, proteasome, amino sugar and nucleotide sugar metabolism,

292 lysosome, and glycolysis/gluconeogenesis among the top 10 represented pathways (Fig. 2a). Galactose

293 metabolism equated to ~20% of the observed/background gene count (Fig. 2a). Thus, metabolism of

294 carbohydrates, proteins/amino acids, and nucleic acids are prominent in the livers of moderately cachexic

295 mice. ‘Mitochondrion’, ‘oxidoreductase’, and ‘electron transport’ are represented in the top 20 keywords,

296 indicating that some proteins associated with mitochondrial metabolism are increased (Table S6). ‘Acute

297 phase’ appears as a top 10 keyword from the up-regulated protein data set (Table S5), consistent with the

298 complement and coagulation cascades in the KEGG analysis. After ‘thioester bond’, ‘acute phase’ is the most

299 represented enrichment by percentage in the up-regulated protein list with an observed/background gene

300 count of ~30% (Table S6). The reactome pathways further supports a role of the acute phase response in

301 moderate cachexia, with innate immune system and immune system in the top 5 (Table S6).

302

303 Transition to severe cachexia is also associated with altered energy metabolism

304 KEGG analysis of down-regulated proteins in severe cachexia showed metabolic pathways,

305 proteasome, Parkinson’s disease, Huntington’s disease, Alzheimer’s disease, oxidative phosphorylation, non-

306 alcoholic fatty liver disease, lysosome, drug metabolism, and thermogenesis among the top 10 enriched

307 pathways (Figs. 2b, 3a), pointing to a suppression of mitochondrial OXPHOS similar to moderate cachexia.

308 This suppression is consistent with the UniProt analysis where ‘respiratory chain’, ‘mitochondrion’ and ‘electron

11

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 309 transport’ appear in the top 20 keywords (Table S7), and with the immunoblots of selected respiratory chain

310 subunits (Fig. 3b). The proteasome accounted for the greatest percentage of genes represented in the KEGG

311 analysis at ~30% observed relative to the background gene count (Fig. 2b). This aligns with the UniProt

312 keywords analysis which indicated ‘proteasome’ to be the third ranked pathway by FDR, and second most

313 observed/background gene count at 25% (Table S7). In the transition to severe cachexia, galactose

314 metabolism was enriched in the down-regulated protein list, with a 16% observed/background gene count.

315 This was the third highest percentage among all enriched pathways (Fig. 2b). Other glycan degradation was

316 also an enriched KEGG pathway with observed/background gene count of 12% (Fig. 2b), supportive of

317 ongoing carbohydrate metabolism in severe cachexia.

318 KEGG analysis of the up-regulated proteins yielded only 12 enriched pathways, with metabolic

319 pathways, RNA transport, spliceosome, ribosome, and peroxisome represented in the top 5 (Fig. 2b). The

320 UniProt keywords enrichment lists ‘ribonucleoprotein’, ‘RNA binding’, ‘mRNA splicing’, ‘mRNA processing’,

321 ‘ribosomal protein’, ‘spliceosome’, and ‘initiation factor’ all represented in the top 20 (Table S7). Reactome

322 pathways also showed predominant representation by translation and related events (Table S7). Together this

323 points to increased processing of mRNA and protein synthesis. As in moderate cachexia, ‘acute phase’

324 remains an enriched keyword in severe cachexia (Table S8), implying an acute phase inflammatory response

325 that persists across cachexia severity.

326

327 Hepatic carbohydrate metabolism in colon-26 tumor-induced cachexia

328 Bioinformatics revealed galactose metabolism to be significantly enriched in moderate cachexia, and in

329 the transition to severe cachexia (Fig. 2a,b). Galk1, Galt, and Gale, three major proteins of the Leloir pathway

330 responsible for interconversion of galactose and glucose, were increased in moderate cachexia and decreased

331 from moderate to severe cachexia (Fig 4a). Their abundance, however, remained elevated in the severe state

332 relative to non-cachexic controls (C26-WS) (Fig. 4a), suggesting that enzymes regulating galactose

333 metabolism in moderate cachexia tends to persist into the severe state. Enrichment of these enzymes could

334 allow for the catabolism of galactose and subsequent entry into the glycolytic pathway. Interestingly, galactose

335 content in liver homogenates was not affected by cachexia (Fig. 4b) despite the increased expression of

336 several key enzymes. Lysosomal α-glucosidase, which regulates glycogen catabolism in lysosomes, showed

12

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 337 especially robust increase based on fold-change (>7 vs. C26-WS) without altered activity (Figs. 4a,c).

338 Consistent with glycogen breakdown, glycogen content in liver tissue homogenates are in fact decreased in

339 cachexia (Fig. 4d), implying active glycogenolysis. Presumptive conversion of glycogen into glucose 1-

340 phoshate and glucose 6-phosphate would suggest increased activity of glycogenolysis and glycolysis. Indeed,

341 glycolysis is represented in the KEGG analysis of up-regulated proteins (Fig. 2a).

342 Given the impairment of mitochondrial OXPHOS and catabolism of carbohydrates, as well as previous

343 suggestions of altered lactate metabolism in cancer cachexia, we explored the proteomics data sets for

344 differentially expressed proteins involved in lactate metabolism and transport. We found decreased pyruvate

345 dehydrogenase protein X component (Pdhx) and increased abundance of lactate dehydrogenase A chain

346 (Ldha) according to LC-MS/MS (Table S4, Fig. 4e), changes which are expected to favor lactate formation.

347 We also found that the lactate transporter monocarboxylate transporter 1 (Mct1) was expressed in livers of

348 mice with both moderate and severe cachexia (Table S2, Fig. 4e). Mct1 was increased in C26-SEV compared

349 to C26-MOD (fold-change 2.62, P<0.05) (Table S4), suggesting that Mct1 expression increases with cachexia

350 severity. Collectively, these data indicate greater abundance of proteins that favor lactate formation as well as

351 a key lactate transporter that could facilitate futile cycling of lactate between the livers of cachexic mice and the

352 tumor.

353

354 Hepatic lipid metabolism in colon-26 tumor-induced cachexia

355 Previous reports of abnormal lipid metabolism in cancer cachexia agreed with our bioinformatics

356 analysis, which showed enrichment of fatty acid metabolism (Fig. 3a, Table S8). We therefore further

357 examined how fatty acid transport and oxidation were affected by cachexia. No differences were detected by

358 LC/MS-MS in members of the fatty acid transport protein (FATP) family or fatty acid translocase/CD36 (Table

359 S4), suggesting that fatty acid transport across the hepatic cell membrane was not affected by cachexia. No

360 differences were detected in carnitine palmitoyltransferase I (Cpt1a) (Table S4), which is responsible for fatty

361 acid transport across the mitochondrial outer membrane. There was a decrease, however, in

362 carnitine/acylcarnitine carrier protein (Table S4), which regulates fatty acid transport across the inner

363 membrane into the matrix. Together this suggests a possible impairment of fatty acid transport into the

364 mitochondrial matrix.

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Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 365 We next generated a heatmap showing the abundance of proteins associated with fatty acid oxidation

366 in C26 livers (Fig. 5a). Of these proteins, 20 were differentially expressed in moderate and/or severe cachexia

367 (Fig. 5a). These differentially expressed proteins showed significant relationships with hallmark features of

368 cancer cachexia including body weight change and muscle fiber size (Fig. 5b). They also showed significant

369 relationships with indices of mitochondrial coupling including the respiratory control ratio, an index of OXPHOS

370 coupling efficiency, and LEAK respiration (surrogate of proton leak) normalized to maximal OXPHOS and

371 electron transfer system (ETS) capacities (Fig. 5b). Among the 20 differentially expressed proteins, 6 were

372 decreased in both moderate and severe cachexia (Fig. 5a). These 6 proteins included alcohol dehydrogenase

373 1 (Adh1), 3-ketoacyl-CoA thiolases A (Acaa1a) and B (Acaa1b), peroxisomal L-bifunctional enzyme (Ehhadh),

374 long chain enoyl-CoA hydratase/long-chain 3-hydroxyacyl-CoA dehydrogenase (Hadha), and long chain acyl-

375 CoA synthetase 1 (ACSL1). With the exception of Adh1, these proteins are essential components of

376 peroxisomal and mitochondrial beta-oxidation (Figs. 5c,d). Collectively, this profile suggests dysregulation of

377 the beta-oxidation machinery and an impaired ability of the cachexic liver to oxidize lipids.

378 An impairment of hepatic lipid oxidation would be expected to cause an accumulation of lipids in the

379 liver, which in turn may contribute to uncoupling of OXPHOS. To confirm the presence of hepatic steatosis, we

380 assayed free fatty acids and triglycerides in liver tissue homogenates. Both were decreased in severe

381 cachexia (Fig. 5e,f), and these events were preceded by increased acyl-CoA synthetase enzyme activity (Fig.

382 5g). Together this implies that lipid oxidation remains intact in the cachexic liver and that lipid accumulation

383 and lipotoxicity is not a significant contributor to uncoupling of OXPHOS. To explore the possibility that

384 oxidative stress to lipids contributes to uncoupling of OXPHOS in the cachexic liver, we assayed 4-HNE

385 formation in liver tissue homogenates by immunoblotting (Fig. 6). 4-HNE formation from 15-250 kDa in the full

386 blot was not different between groups (P>0.05) (Fig. 6b). However, there was significantly greater 4-HNE

387 formation in the ~37-45 kDa bands in severe cachexia compared to non-cachexic control (Fig. 6c), suggesting

388 the presence of some lipid peroxidation and cellular oxidative damage. Therefore, tumor-induced oxidative

389 stress may contribute to uncoupling of OXPHOS in the cachexic liver.

390

391 Loss of acyl-CoA synthetase-1 (ACSL1) in colon-26 tumor-induced cachexia

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Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 392 ACSL1 is a key fatty acyl-CoA synthetase responsible for activating imported long-chain fatty acids by

393 esterification with coenzyme A, thereby enabling further processing (e.g. by oxidation). Our proteomics

394 analysis indicated decreased hepatic ACSL1 in moderate and severe cachexia (Fig. 5a). To validate this

395 finding, we probed for ACSL1 in liver tissue homogenates by follow-up immunoblotting experiments. Indeed,

396 ACSL1 expression showed a clear stepwise decrease as cachexia worsened (Figs. 7a,b). ACSL1 had strong

397 linear relationships with percent body weight change (r=0.874, P<0.01) and myofiber size (r=0.856, P<0.01),

398 the hallmark features of cancer cachexia (Fig. 7c). These correlations suggest greater hepatic ACSL1 in

399 weight-stable mice with larger muscles, and lower ACSL1 in weight-losing mice with smaller muscles. ACSL1

400 also related linearly with the respiratory control ratio (r=0.618, P<0.01), an index of OXPHOS coupling

401 efficiency, and inversely with LEAK respiration normalized to maximal electron transfer system capacity (r=-

402 0.505, P<0.05) (Fig. 7c). Together these correlations suggest that weight-stable mice with larger myofibers

403 and higher hepatic Acsl1 also had lower proton leak consistent with tighter OXPHOS coupling and

404 energetically efficient mitochondria that dissipate less heat.

405

406 Discussion

407 Mass spectrometry-based proteomics have been used to profile cachexic skeletal muscle, but we are

408 unaware of its application in cachexic liver. Here we report proteome wide changes in livers from mice with

409 moderate and severe cancer cachexia. Of the roughly 2,500 hepatic proteins detected by mass spectrometry,

410 20-30% (~600-800) were identified as being differentially expressed in moderate and severe cachexia. This

411 high number of differentially expressed proteins highlights the potential role of liver metabolism in cancer

412 cachexia. By comparison, in the mdx-4cv mouse model of muscular dystrophy, only ~100 of the roughly 2,000

413 hepatic proteins detected (~5%) were found to be differentially regulated (24). The relatively lower proportion

414 of differentially expressed proteins in muscular dystrophy could be explained by the disease being mainly a

415 pathology of skeletal muscle, with only indirect effects on the liver. The greater percentage of hepatic proteins

416 identified as differentially expressed in our C26 mice implies the liver, in addition to skeletal muscle, to be a

417 significant contributor to cancer cachexia onset and progression, and an important site for targeted therapies.

418 To derive biological interpretations of our proteomics datasets centered on cachexia severity, we used

419 standard bioinformatics software to analyze differentially expressed protein lists sorted by increased and

15

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 420 decreased abundance. Bioinformatics revealed mitochondrial OXPHOS, TCA cycle, and/or thermogenesis to

421 be represented during the induction of moderate cachexia (Figs. 2, Tables S2-3), and in the transition to

422 severe disease (Figs. 3, Tables S4-5), suggesting a role for hepatic mitochondrial function in the onset and

423 progression of cancer cachexia. We previously reported loss of in situ OXPHOS capacity in livers from the

424 same cohort of cachexic C26 mice (18), in line with OXPHOS being represented in down-regulated protein lists

425 (Figs. 2-3). Interestingly, thermogenesis was also represented in down-regulated protein lists in moderate

426 cachexia (Figs. 2a, Table S2), and in the transition to severe cachexia (Fig. 3a). If cachexic livers dissipate

427 energy and produce heat from mitochondrial uncoupling, an increase rather than a decrease in thermogenic

428 pathways might be expected. In phenotyping experiments, cachexic liver mitochondria show reduced P:O (i.e.

429 lower ATP produced per molecule of oxygen consumed) (9), which suggests compromised coupling efficiency

430 and energy wasting. Further, we previously observed loss of coupling control in livers from the same cohort of

431 cachexic C26 mice in this study, reflecting inefficiency of OXPHOS due in part to increased LEAK respiration

432 and Ant2, an inner membrane protein with uncoupling function (18). The current investigation also provided

433 evidence of oxidative stress as reflected by greater 4-HNE formation (Fig. 6), which may lead to uncoupling as

434 an ameliorative response. Together this data implies uncoupling of liver mitochondria in cachexic mice. It is

435 possible that the enrichment of thermogenesis in down-regulated datasets reflects a compensatory effort to

436 minimize uncoupling.

437 A noteworthy finding in the proteomics analysis was the identification of differentially expressed

438 proteins in cachexic livers that would impact lactate metabolism and transport. Cachexic livers displayed

439 decreased pyruvate dehydrogenase protein X component (Pdhx), increased lactate dehydrogenase A chain

440 (Ldha), and increased lactate transporter Mct1 (Table S4, Fig. 4). Given that these cachexic C26 livers also

441 demonstrated catabolism of glycogen and impaired OXPHOS, increased lactate formation and transport

442 involving extrahepatic tissues might be an anticipated outcome. Such inter-organ lactate cycling has been

443 previously proposed and discussed by many in recent frameworks defining potential mechanisms of cancer

444 cachexia (1-3, 12, 13, 25, 32). Often described is a Cori cycle in which significant use of glucose by the tumor

445 generates lactate, which is then exported and shuttled to the liver. Imported lactate is then used by the liver as

446 a gluconeogenic substrate, which further supplies glucose to the tumor. This cycling of lactate is associated

447 with energetic inefficiency because metabolism of glucose into lactate by the tumor generates less ATP (i.e. 2)

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Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 448 compared to the energy cost to convert lactate into glucose (i.e. 6 ATP) within the liver. Thus, while the

449 premise of altered lactate metabolism involving the liver and tumor has been previously suggested, we are

450 unaware of extensive experimental data to support these assertions specifically in cancer cachexia. Further

451 clarification would strengthen the existence of energetically inefficient inter-organ transport between the liver

452 and tumor, such as blood lactate concentration, the expression Mct1 and related family members as well as

453 glucose, pyruvate, and lactate content in both the liver and cachexia inducing C26 tumors.

454 A novel finding in the present work was the loss of ACSL1 in cachexic livers, which to our knowledge

455 has not been previously reported. As cachexia worsened, ACSL1 decreased in tandem. This outcome was

456 observed in both LC-MS/MS and immunoblotting experiments (Figs. 5,7). ACSL1 is well-known for activating

457 long chain fatty acids to form acyl-CoAs prior to further processing (e.g. oxidation) (22). ACSL1 has also been

458 shown to positively influence mitochondrial function and coupling in several cell and disease models. In mice

459 with heart failure, cardiac-specific overexpression of ACSL1 maintained mitochondrial oxidative energy

460 metabolism (16). Further, overexpression of ACSL1 in Schwann cells reduced oxidative stress, normalized

461 mitochondrial function, improved mitochondrial coupling efficiency, and reduced proton leak across the inner

462 membrane (19). These findings have intriguing implications for cancer cachexia. Cachexic C26 livers showed

463 evidence of oxidative stress as indicated by 4-HNE formation (Fig. 6). Further, cachexic C26 liver exhibits

464 uncoupling due in part to reduced OXPHOS and increased LEAK respiration (18). Uncoupling of OXPHOS

465 may contribute to cachexia-associated weight loss by dissipation of energy as heat and increase in whole body

466 energy expenditure (Fig. 7d). The possibility exists that targeted overexpression of ACSL1 in the cachexic liver

467 may be sufficient to improve mitochondrial function and coupling by increasing OXPHOS and/or reducing

468 proton leak, thereby mitigating heat production and normalizing energy expenditure. ACSL1 may therefore be

469 a candidate hepatic target for therapeutic intervention. Follow-up experiments in which ACSL1 is directly

470 manipulated in vivo are warranted to better understand whether ACSL1 has the potential to slow cancer

471 cachexia.

472 We note that food records for this investigation were incomplete, therefore we were unable to provide

473 food intake data for this cohort of mice. This information is important to understand whether reduced energy

474 intake may have contributed to cachexia-associated body weight loss, muscle atrophy, and changes to the

475 hepatic proteome. In a previous investigation that used C26 cells from the same cell bank, food intake in

17

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 476 cachexic C26 mice was not different from controls (4). This finding raises the possibility that our C26 mice

477 would also not exhibit significant anorexia, although this remains to be verified.

478 We conclude that the liver is an important site for targeted therapeutic strategies in cancer cachexia.

479 Altered hepatic mitochondrial function is a common feature of both moderate and severe cancer cachexia.

480 Proteome profiling suggests altered lactate metabolism and transport in cachexic livers, which raises the

481 possibility of energetically inefficient lactate shuttling between the liver and tumor that elevates whole body

482 energy expenditure and causes weight loss. Tumor-induced oxidative damage may contribute to uncoupling of

483 cachexic liver mitochondria, which could also increase energy expenditure and weight loss. As cancer

484 cachexia severity worsens, ACSL1 levels in the liver decline. In mice with the highest levels of ACSL1, body

485 weight was greater, muscles were larger, and mitochondria better coupled. We propose that ACSL1 is a

486 candidate target to consider for future experiments that aim to unravel mechanisms and therapies for cancer

487 cachexia.

488

489 Acknowledgements

490 We extend our sincere thanks to Ms. Peggy Donnelly and Ms. Denise Merrill for administrative support.

491

492 Conflict of Interest

493 The authors declare no conflict of interest.

494

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597 https://figshare.com/s/d591441d6e108e95c900

598 https://doi.org/10.6084/m9.figshare.11803266

599 Supplementary Table S2

600 https://figshare.com/s/12e1a8c8a969a2f62bb9

601 https://doi.org/10.6084/m9.figshare.11340920

602 Supplementary Table S3

603 https://figshare.com/s/60495c0f448a4b7d0210

604 https://doi.org/10.6084/m9.figshare.11340944

605 Supplementary Table S4

606 https://figshare.com/s/e025d24b3419cddf2f78

607 https://doi.org/10.6084/m9.figshare.11340950

608 Supplementary Table S5

609 https://figshare.com/s/78f8a8fcc9ab4da11bed

610 https://doi.org/10.6084/m9.figshare.11340953

611 Supplementary Table S6

612 https://figshare.com/s/9026f034fee9f1f5fa5f

613 https://doi.org/10.6084/m9.figshare.11340956

614 Supplementary Table S7

615 https://figshare.com/s/dfbba7ddce2f5aa240d4

616 https://doi.org/10.6084/m9.figshare.11340959

617 Supplementary Table S8

618 https://figshare.com/s/912870d3873c7bbbfba2

619 https://doi.org/10.6084/m9.figshare.11340962

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Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 624 Figure Legends

625 Figure 1. Moderate and severe cancer cachexia share ~300 differentially expressed proteins in liver

626 (a) Venn diagram illustrating the total number of proteins identified in each group, and the number of shared

627 proteins between groups. A total of 2,510 proteins were identified by liquid chromatography-tandem mass

628 spectrometry. Livers were assayed from PBS Weight-Stable (PBS-WS, n=4), C26 Weight-Stable (C26-WS,

629 n=6), C26 Moderate (C26-MOD, n=7), and C26 Severe (C26-SEV, n=6). (b) Number of differentially

630 expressed proteins that are up- or down-regulated for each paired comparison. (c-d) Heat maps generated

631 from differentially expressed proteins during the induction of moderate cachexia (C26-WS vs. C26-MOD), and

632 the transition from moderate to severe presentation of the disease (C26-MOD vs. C26-SEV). (e) Venn

633 diagram generated by using differentially expressed proteins detected during the induction of moderate

634 cachexia (C26-WS vs. C26-MOD), and the transition from moderate to severe presentation of the disease

635 (C26-MOD vs. C26-SEV). The 306 shared proteins in the overlap region may be necessary to maintain the

636 cachexic state, and are hypothesized to represent a proteomic signature of cachexia. KEGG pathways

637 enrichment of these differentially expressed proteins was conducted with STRING. The top 15 KEGG

638 pathways are shown.

639

640 Figure 2. KEGG pathways enrichment of differentially expressed proteins in moderate cachexia

641 (a) KEGG pathways enrichment of down-regulated and up-regulated protein datasets in moderate cachexia.

642 Enriched KEGG pathways were identified from differentially expressed hepatic proteins in C26 Moderate

643 cachexia (C26-MOD, n=7) relative to C26 Weight-Stable (C26-WS, n=6) using the STRING database. (b)

644 KEGG pathways enrichment of down-regulated and up-regulated protein datasets in the transition from

645 moderate to severe cachexia. Enriched KEGG pathways were identified from differentially expressed hepatic

646 proteins in C26 Severe (C26-SEV, n=6) relative to C26 Moderate (C26-MOD, n=7) using the STRING

647 database. Values adjacent to individual bars are the percentage of identified genes from the imported protein

648 list relative to the background gene count for that pathway, and the false discovery rate q-value.

649

650

651

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Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 652 Figure 3. KEGG pathways enrichment of differentially expressed proteins in severe cachexia

653 (a) KEGG pathways enrichment of down-regulated and up-regulated protein datasets in severe cachexia.

654 Enriched pathways were identified from differentially expressed hepatic proteins in C26 mice with severe

655 cachexia (C26-SEV, n=6) relative to C26 Weight-Stable (C26-WS, n=6) using the STRING database. Values

656 adjacent to individual bars are the percentage of identified genes from the imported protein list relative to the

657 background gene count for that pathway, and the false discovery rate q-value. (b) Top panel is showing

658 immunoblots for complex I-V subunits of the mitochondrial respiratory chain probed in liver tissue

659 homogenates. Bottom figure is showing expression of the complex I-V subunits normalized to GAPDH. Data

660 presented as mean ± SE. Tissues assayed from PBS Weight-Stable (n=4), C26 Weight-Stable (n=6), C26

661 Moderate (n=7), and C26 Severe (n=6). Differences determined by one-way ANOVA. p<0.05 (*), p<0.01 (**),

662 p<0.001 (***).

663

664 Figure 4. Hepatic carbohydrate metabolism in colon-26 tumor-induced cachexia.

665 (a) Fold-changes for differentially expressed proteins involved in galactose metabolism. Arrows represent

666 increased or decreased abundance and values indicate the magnitude of the fold-change. Shown are proteins

667 that were differentially expressed in moderate cachexia (C26-WS vs. C26-MOD), severe cachexia (C26-WS

668 vs. C26-SEV), or the transition from moderate to severe cachexia (C26-MOD vs. C26-SEV). (b-d) Galactose,

669 α-glucosidase enzyme activity, and glycogen content in liver homogenates were measured by fluorometry and

670 spectrophotometry. Tissues assayed from C26 Weight-Stable (n=6), C26 Moderate (n=7), and C26 Severe

671 (n=6). Differences determined by one-way ANOVA. p<0.05 (*). (e) Summary of findings involving

672 carbohydrate metabolism in the cachexic liver. LC-MS/MS indicated decreased pyruvate dehydrogenase

673 protein X component (Pdhx), increased lactate dehydrogenase A chain (Ldha), and increased lactate

674 transporter Mct1. Cachexic C26 livers also demonstrated catabolism of glycogen and impaired OXPHOS,

675 therefore increased lactate formation and transport involving extrahepatic tissues (e.g. the tumor) are

676 expected. Such inter-organ lactate cycling is associated with energetic inefficiency because metabolism of

677 glucose into lactate by the tumor generates less ATP (i.e. 2) compared to the energy cost to convert lactate

678 into glucose (i.e. 6 ATP) within the liver.

679

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Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 680 Figure 5. Hepatic lipid metabolism in colon-26 tumor-induced cachexia.

681 (a) Heatmap of hepatic proteins involved in lipid oxidation from C26 weight-stable mice (C26-WS, n=6), C26

682 mice with moderate cachexia (C26-MOD, n=7), and C26 mice with severe cachexia (C26-SEV, n=6). (b) Of

683 the hepatic proteins shown in the heatmap, 20 were differentially expressed in moderate cachexia (C26-WS

684 vs. C26-MOD), severe cachexia (C26-WS vs. C26-SEV), or the transition from moderate to severe cachexia

685 (C26-MOD vs. C26-SEV). These 20 differentially expressed proteins were correlated with hallmark features of

686 cancer cachexia and indicators of mitochondrial coupling, as shown in the table color-coded by maximum (red)

687 and minimum (blue) Pearson-r correlation coefficients. The hallmark features of cachexia were percent body

688 weight change, and muscle fiber cross-sectional area (CSA) determined by hematoxylin staining and standard

689 morphometric techniques. Indicators of mitochondrial coupling were derived from high-resolution respirometry

690 experiments including the respiratory control ratio (RCR), and LEAK respiration normalized to maximal

691 oxidative phosphorylation (OXPHOS) and electron transfer system (ETS) capacities. Weight change, myofiber

692 size, and respiration data were previously published (18). p<0.05 (*), p<0.01 (**), p=0.05-0.09 (^) (c-d)

693 Proteins regulating mitochondrial and peroxisomal beta-oxidation of fatty acids are differentially expressed in

694 moderate (C26-WS vs. C26-MOD) and severe cachexia (C26-WS vs. C26-SEV). (e-g) Total free fatty acids,

695 triglycerides, and acyl-CoA synthetase enzyme activity in liver homogenates from C26-WS, C26-MOD, and

696 C26-SEV measured by fluorometry. Differences determined by one-way ANOVA. p<0.05 (*), p<0.01 (**).

697

698 Figure 6. Hepatic lipid peroxidation and oxidative stress in colon-26 tumor-induced cachexia.

699 (a) Immunoblots showing 4-HNE formation in the full blot from 15-250 kDa probed in liver tissue homogenates.

700 (b) 4-HNE formation in the full blot determined by expression of bands from 15-250 kDa normalized to GAPDH.

701 (c) 4-HNE formation of bands from ~37-45 kDa normalized to GAPDH. Data presented as mean ± SE.

702 Tissues assayed from PBS Weight-Stable (n=4), C26 Weight-Stable (n=6), C26 Moderate (n=7), and C26

703 Severe (n=6). Differences determined by one-way ANOVA. p<0.05 (*).

704

705 Figure 7. Loss of hepatic ACSL1 in colon-26 tumor-induced cachexia.

706 (a) Immunoblots showing Acyl-CoA synthetase-1 (ACSL1) expression in liver tissue homogenates. (b) ACSL1

707 expression normalized to GAPDH. Data presented as mean ± SE. Tissues assayed from PBS Weight-Stable

24

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. 708 (n=4), C26 Weight-Stable (n=6), C26 Moderate (n=7), and C26 Severe (n=6). Differences determined by one-

709 way ANOVA. p<0.05 (*), p<0.01 (**), p<0.001 (***). (c) Associations of ACSL1 with percent body weight

710 change, myofiber size, respiratory control ratio (RCR), and LEAK respiration in the complex I supported state

711 normalized to maximal electron transfer system capacity (ETS). Weight change, myofiber size, and respiration

712 data were previously published (18) and used here for correlative purposes. (d) Proposed mechanisms linking

713 hepatic metabolism to cancer cachexia. In response to tumor-secreted factors, the cachexic liver is subject to

714 oxidative stress as indicated by greater 4-HNE formation, which could contribute to uncoupling of mitochondrial

715 oxidative phosphorylation (OXPHOS) and lead to an increase in energy expenditure and weight loss. The

716 cachexic liver also shows decreased levels of ACSL1. ACSL1 has been shown to protect against oxidative

717 stress and positively influence mitochondrial function and coupling in several cell and disease models. Loss of

718 hepatic ACSL1 may therefore alter metabolism of the liver and contribute to cachexia-associated

719 abnormalities.

25

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. a b 900 Up-regulated 800 Down-regulated 700 600 500 400 300 200 100 0 c e C26-WS vs. C26-MOD

C26-MOD vs. C26-SEV

Proteins with fold change >1.5 or <1/1.5 C26-WS C26-MOD in moderate and severe cancer cachexia d Liver cachexic signature Pathways enriched across cachexia severity Metabolic pathways Alzheimer's disease Parkinson's disease Huntington's disease Lysosome Oxidative phosphorylation Ribosome RNA transport Proteasome Non-alcoholic fatty liver disease (NAFLD) Thermogenesis Galactose metabolism Tight junction Peroxisome Retrograde endocannabinoid signaling C26-MOD C26-SEV 024681012 -log10 (p-value) Figure 1

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. a C26-WS vs. C26-MOD Down-regulated proteins Up-regulated proteins Metabolic pathways 6%, 6.81E-19 Metabolic pathways 6%, 1.24E-20 Alzheimer's disease 14%, 2.09E-11 Complement and coagulation cascades 13%, 4.06E-05 Huntington's disease 13%, 2.09E-11 Carbon metabolism 10%, 4.32E-05 Parkinson's disease 15%, 1.32E-10 Biosynthesis of amino acids 13%, 4.32E-05 Oxidative phosphorylation 16%, 2.62E-10 Protein processing in endoplasmic reticulum 9%, 4.32E-05 Thermogenesis 10%, 5.38E-08 Proteasome 18%, 5.15E-05 Ribosome 13%, 3.83E-07 Amino sugar and nucleotide sugar metabolism 16%, 7.81E-05 Non-alcoholic fatty liver disease (NAFLD) 12%, 3.83E-07 Lysosome 9%, 0.00021 RNA transport 10%, 7.52E-06 Glycolysis / Gluconeogenesis 12%, 0.00039 Retrograde endocannabinoid signaling 9%, 0.00021 Cysteine and methionine metabolism 15%, 0.00039 Bacterial invasion of epithelial cells 12%, 0.00053 Galactose metabolism 19%, 0.00046 Tight junction 8%, 0.00062 Arginine biosynthesis 26%, 0.00051 Citrate cycle (TCA cycle) 19%, 0.001 Pyruvate metabolism 16%, 0.00093 Steroid hormone biosynthesis 11%, 0.001 Drug metabolism - other enzymes 9%, 0.0016 Metabolism of xenobiotics by cytochrome P450 12%, 0.001 Purine metabolism 6%, 0.0022 Spliceosome 8%, 0.001 Citrate cycle (TCA cycle) 16%, 0.0033 Peroxisome 11%, 0.001 Pentose phosphate pathway 16%, 0.0033 Carbon metabolism 8%, 0.002 Oxidative phosphorylation 7%, 0.0033 Cardiac muscle contraction 11%, 0.002 Ribosome 7%, 0.0033 Endocytosis 6%, 0.0024 Huntington's disease 6%, 0.0033 Drug metabolism - cytochrome P450 10%, 0.0053 Valine, leucine and isoleucine degradation 11%, 0.0036 Propanoate metabolism 16%, 0.0057 Parkinson's disease 7%, 0.0047 9%, 0.006 Chemical carcinogenesis Glyoxylate and dicarboxylate metabolism 14%, 0.0148 11%, 0.0096 Valine, leucine and isoleucine degradation Phagosome 5%, 0.0148 Tyrosine metabolism 13%, 0.0126 Alzheimer's disease 5%, 0.0148 Phenylalanine metabolism 18%, 0.0126 Staphylococcus aureus infection 10%, 0.0148 Sulfur metabolism 27%, 0.0183 Thiamine metabolism 21%, 0.017 Butanoate metabolism 15%, 0.022 ECM-receptor interaction 7%, 0.0177 Regulation of actin cytoskeleton 5%, 0.0235 Peroxisome 7%, 0.0202 Fatty acid degradation 10%, 0.029 Non-alcoholic fatty liver disease (NAFLD) 5%, 0.0202 Fatty acid metabolism 10%, 0.0304 Systemic lupus erythematosus 7%, 0.0289 Protein processing in endoplasmic reticulum 6%, 0.0358 Porphyrin and chlorophyll metabolism 10%, 0.029 Primary bile acid biosynthesis 19%, 0.0372 Glycine, serine and threonine metabolism 10%, 0.0297 Legionellosis 9%, 0.0428 2-Oxocarboxylic acid metabolism 16%, 0.0297 PPAR signaling pathway 7%, 0.0497 Spliceosome 5%, 0.0337 0 20406080100 Regulation of actin cytoskeleton 4%, 0.0406 Gene count Bacterial invasion of epithelial cells 7%, 0.0442 0 20406080100 b C26-MOD vs. C26-SEV Gene count Down-regulated proteins Up-regulated proteins Metabolic pathways 4%, 1.02E-11 Metabolic pathways 4%, 1.86E-09 Proteasome 29%, 5.45E-11 RNA transport 7%, 8.81E-05 Parkinson's disease 12%, 1.61E-08 Spliceosome 8%, 8.81E-05 Huntington's disease 9%, 1.02E-07 Alzheimer's disease 9%, 8.70E-07 Ribosome 8%, 0.00027 Oxidative phosphorylation 10%, 1.88E-06 Peroxisome 8%, 0.0041 Non-alcoholic fatty liver disease (NAFLD) 9%, 6.11E-06 Cardiac muscle contraction 8%, 0.0133 Lysosome 10%, 6.41E-06 Citrate cycle (TCA cycle) 13%, 0.0247 Drug metabolism - other enzymes 12%, 1.35E-05 Pentose and glucuronate interconversions 12%, 0.0247 Thermogenesis 7%, 1.46E-05 Drug metabolism - cytochrome P450 12%, 0.00013 Alzheimer's disease 5%, 0.0254 Ribosome 8%, 0.00027 Endocytosis 4%, 0.0267 Retrograde endocannabinoid signaling 7%, 0.00069 Aldosterone-regulated sodium reabsorption 11%, 0.0283 Metabolism of xenobiotics by cytochrome P450 11%, 0.00071 Huntington's disease 4%, 0.0378 Chemical carcinogenesis 9%, 0.00078 Spliceosome 7%, 0.0013 0 102030405060 Galactose metabolism 16%, 0.0015 Gene count Hepatocellular carcinoma 6%, 0.0016 Valine, leucine and isoleucine degradation 11%, 0.0017 Other glycan degradation 12%, 0.002 Peroxisome 8%, 0.0021 Retinol metabolism 8%, 0.0028 Protein processing in endoplasmic reticulum 6%, 0.0042 Regulation of actin cytoskeleton 5%, 0.006 Nicotinate and nicotinamide metabolism 11%, 0.0149 Glutathione metabolism 8%, 0.0151 Glycine, serine and threonine metabolism 10%, 0.0217 Bacterial invasion of epithelial cells 7%, 0.0308 Platinum drug resistance 7%, 0.0331 Salmonella infection 6%, 0.0355 Arginine and proline metabolism 8%, 0.0398 Carbon metabolism 5%, 0.0418 Folate biosynthesis 12%, 0.042 Proteoglycans in cancer 4%, 0.042 Butanoate metabolism 11%, 0.0448 Vitamin B6 metabolism 25%, 0.045 RNA transport 4%, 0.0457 Tight junction 4%, 0.0459 Glyoxylate and dicarboxylate metabolism 10%, 0.0482 mRNA surveillance pathway 5%, 0.0489 Endometrial cancer 7%, 0.0489 0 102030405060 Gene count Figure 2

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. a C26-WS vs. C26-SEV Down-regulated proteins Up-regulated proteins Metabolic pathways 6%, 1.16E-17 Metabolic pathways 5%, 5.21E-12 Huntington's disease 12%, 1.96E-09 Protein processing in endoplasmic reticulum 10%, 1.26E-06 Oxidative phosphorylation 15%, 4.39E-09 Drug metabolism - other enzymes 14%, 2.04E-06 Ribosome 15%, 4.39E-09 Complement and coagulation cascades 14%, 2.04E-06 Alzheimer's disease 13%, 4.39E-09 Carbon metabolism 10%, 2.83E-05 Parkinson's disease 14%, 6.98E-09 Sulfur metabolism 45%, 0.00011 Spliceosome 13%, 1.12E-07 Spliceosome 8%, 0.00031 Thermogenesis 9%, 3.10E-07 Propanoate metabolism 19%, 0.00049 RNA transport 10%, 2.00E-06 Biosynthesis of amino acids 11%, 0.00089 Non-alcoholic fatty liver disease (NAFLD) 11%, 2.17E-06 Ribosome 8%, 0.001 Metabolism of xenobiotics by cytochrome P450 17%, 4.40E-06 beta-Alanine metabolism 16%, 0.0045 Drug metabolism - cytochrome P450 16%, 5.29E-06 Bacterial invasion of epithelial cells 9%, 0.0045 Valine, leucine and isoleucine degradation 18%, 7.48E-06 Valine, leucine and isoleucine degradation 11%, 0.0054 Chemical carcinogenesis 13%, 1.21E-05 Porphyrin and chlorophyll metabolism 13%, 0.0082 Tryptophan metabolism 18%, 0.00011 Protein export 17%, 0.0113 Peroxisome 12%, 0.00019 Endocytosis 5%, 0.0113 Fatty acid degradation 16%, 0.0002 Systemic lupus erythematosus 8%, 0.0113 Retrograde endocannabinoid signaling 8%, 0.00067 Cysteine and methionine metabolism 11%, 0.0127 Steroid hormone biosynthesis 11%, 0.00084 Phagosome 5%, 0.0156 Drug metabolism - other enzymes 10%, 0.001 Staphylococcus aureus infection 10%, 0.0161 Retinol metabolism 10%, 0.0012 Regulation of actin cytoskeleton 5%, 0.0182 Butanoate metabolism 19%, 0.0036 Salmonella infection 8%, 0.0182 Proteasome 13%, 0.0043 Legionellosis 9%, 0.0239 Protein processing in endoplasmic reticulum 7%, 0.0049 Huntington's disease 5%, 0.0278 Propanoate metabolism 16%, 0.0056 Pantothenate and CoA biosynthesis 17%, 0.034 Fatty acid metabolism 12%, 0.007 Focal adhesion 5%, 0.034 Tyrosine metabolism 13%, 0.0132 Metabolism of xenobiotics by cytochrome P450 8%, 0.0345 Glycine, serine and threonine metabolism 13%, 0.0141 Glycine, serine and threonine metabolism 10%, 0.035 Ferroptosis 13%, 0.0141 Oxidative phosphorylation 5%, 0.0358 Glutathione metabolism 10%, 0.0142 Drug metabolism - cytochrome P450 7%, 0.0358 Biosynthesis of unsaturated fatty acids 15%, 0.0215 RNA transport 5%, 0.0358 Parkinson's disease Carbon metabolism 7%, 0.0215 5%, 0.0459 Fluid shear stress and atherosclerosis mRNA surveillance pathway 8%, 0.0215 5%, 0.0478 Linoleic acid metabolism 10%, 0.0241 0 20406080100 Platelet activation 7%, 0.0246 Gene count beta-Alanine metabolism 13%, 0.0325 Serotonergic synapse 6%, 0.0334 Primary bile acid biosynthesis 19%, 0.034 SNARE interactions in vesicular transport 12%, 0.034 Focal adhesion 5%, 0.0377 Tight junction 5%, 0.0378 Lysine degradation 9%, 0.041 PPAR signaling pathway 7%, 0.0453 0 20406080100 Gene count b PBS Weight-Stable C26 Weight-Stable C26 Moderate C26 Severe

55 kDa CV subunit ATP5A 48 kDa CIII subunit UQCRC2 40 kDa CIV subunit MTCO1 30 kDa CII subunit SDHB

20 kDa CI subunit NDUFB8

37 kDa GAPDH

*** 1.6 * PBS Weight-Stable 1.4 *** C26 Weight-Stable C26 Moderate ** *** 1.2 C26 Severe *** 1.0 *** ** 0.8 ** 0.6 0.4

Relative Expression 0.2 0.0 CI subunit CII subunit CIII subunit CIV subunit CV subunit NDUFB8 SDHB UQCRC2 MTCO1 ATP5A

Figure 3

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. a Fold changes for differentially expressed proteins regulating galactose metabolism Black arrows: C26-WS vs. C26-MOD Red arrows: C26-MOD vs. C26-SEV β-Galactosidase (Glb1, ↓0.3699) Blue arrows: C26-WS vs. C26-SEV Galactose (Catabolism)

Lactose Galactokinase (GALK1, ↑1 .5513, ↓0.5548)

Galactose 1-Phosphate + UDP Glucose

α-D (+) glucose to β-D (+) glucose UDP-glucose (or Galactose) 4-epimerase Galactose 1-Phosphate (GALE, ↑3.7307, ↓0.448, ↑1.9448) (Mutarotase: Aldose 1-epimerase Uridylytransfarase (GALT, ↑1.512) (Galm, ↓0.4365, ↓0.3558, ↓0.1812)

Phospho- glucomutase-1 α-D (+) Hexo- Glucose (90% activity, Glucose 1-Phosphate + UDP galactose Glucose kinase 6-Phosphate Pgm-1, ↑ 1.584 & 10% activity, Pgm-2 ↑ 2.356) Lysosomal α-glucosidase (Gaa, ↑7.8382, ↓ 0.1718, ↑1.5714)

b 15 Glycogen Catabolism (in Lysosome)

10 e 5 Cachexic Liver Tumor 0 Galactose (nm/mg) ? Lactate Shuttle ? c 24 Glycogen 20 (increased catabolism) 16 Futile cycle 12 Pyruvate 8 dehydrogenase protein Glucosidase 4 ↑REE & (nm/min/mg) α- X component (Pdhx) 0 Weight loss Mitochondrial OXPHOS

d 40 * Lactate dehydrogenase a chain 30 (Ldha) 20 Monocarboxylate transporter 1 10 (Mct1)

Glycogen (μg/mg) 0

Figure 4

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. b a % Weight Fiber RCR LEAK/ LEAK/ Change CSA OXPHOS ETS

Acsl1 0.59 ** 0.49 * 0.28 -0.42 * -0.44 * Adh1 0.60 ** 0.17 0.20 -0.17 -0.26 Acaa1a 0.76 ** 0.63 ** 0.39 ^ -0.28 -0.29 Acaa1b 0.69 ** 0.36 -0.10 0.12 -0.05 Adh4 -0.06 -0.46 * -0.17 0.28 0.04 Acads 0.17 -0.32 -0.03 0.04 -0.16 Aldh1b1 -0.52 * -0.70 ** -0.51 * 0.46 * 0.39 ^ Echs1 -0.58 ** -0.38 ^ -0.41 ^ 0.13 0.43 * Ehhadh 0.68 ** 0.55 * 0.14 -0.32 -0.30 Aldh3a2 0.45 **0.46 0.50 * -0.50 * -0.50 * Acadm -0.29 -0.23 -0.26 0.15 0.19 Slc25a20 -0.01 -0.23 -0.56 * 0.23 0.20 Hadha 0.36 * 0.06 -0.25 -0.03 -0.07 Scarb2 -0.29 -0.51 * -0.02 0.15 -0.05 Slc27a5 -0.43 * -0.42 ^ -0.23 0.04 0.07 Eci1 -0.20 -0.26 0.19 0.09 0.09 Acsf2 -0.45 * -0.22 -0.62 ** 0.17 0.41 * Mut 0.06 -0.33 -0.28 0.53 ** 0.29 Acot1 0.22 0.24 0.53 ** -0.40 ^ -0.44 * Acad10 0.45 * 0.31 0.46 * -0.15 -0.35

* c d e 60 ** Mitochondrial β-oxidation Peroxisomal β-oxidation 50 Moderate Severe Moderate Severe 40 30 Acsl1 ↓ ↓ Acox1 ↑ ↔ 20

(pmol/μg) 10 Cpt1a ↔↔ Acox2 ↔↔

Free Fatty Acids 0 Slc25a20 ↓ ↔ Acox3 ↔ ↔ Cpt2 ↔ ↔ Acaa1a ↓ ↓ ** Acadm ↔ ↑ Acaa1b ↓ ↓ 80 ** f 60 Echs1 ↔ ↑ Scp2 ↔ ↔ 40 Hadha ↓ ↓ Hsd17b4 ↔ ↔

(nM/mg) 20

Acaa2 ↔ ↔ Ehhadh ↓ ↓ Triglycerides 0

** g 80 ** 60 40 20 CoA Synthetase

(pM/min/ug) 0 Acyl-

Figure 5

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. a PBS Weight-Stable C26 Weight-Stable C26 Moderate C26 Severe 4-HNE formation

250 kDa

100 kDa

50 kDa

~37-45 kDa

25 kDa

15 kDa

37 kDa GAPDH b 2.5 c 3.5 * 2.0 3.0 2.5 1.5 2.0 1.0 1.5 1.0 0.5 0.5 4-HNE (37-45 (37-45 4-HNE kDa)

4-HNE (15-250 (15-250 4-HNE kDa) 0.0 0.0

Figure 6

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. a

PBS Weight-Stable C26 Weight-Stable C26 Moderate C26 Severe

78 kDa ACSL1

37 kDa GAPDH

b *** c 2.0 2 2.0 2 *** R =0.763 R =0.733 1.6 * 1.4 *** 1.5 P<0.01 1.5 P<0.01 1.2 *** 1.0 1.0 1.0 ACSL1 ACSL1 0.8 *** 0.5 0.5 0.6 ACSL1 0.4 0.0 0.0 0.2 -40 -20 0 20 0 1000 2000 3000 0.0 % Body Weight Change Fiber CSA (μm2)

2.0 R2=0.382 2.0 R2=0.256 1.5 P<0.01 1.5 P<0.05

d Tumor 1.0 1.0 ACSL1 ACSL1 0.5 0.5 ? Secreted Factors ? 0.0 0.0 0246 0.0 0.2 0.4 0.6 Liver RCR LEAK/ETS 4-HNE Formation Oxidative Stress Lipid Peroxidation ? ACSL1 OXPHOS Uncoupling

↑REE & Weight loss

Figure 7

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020. Table 1. Phenotype of mice with moderate and severe colon-26 tumor-induced cachexia

PBS Weight-Stable C26 Weight-Stable C26 Moderate Cachexia C26 Severe Cachexia

Tumor weight (g) -- 0.4±0.1 ##, @@@ 1.1±0.1 @@@ 2.1±0.2

Body weight change (%) 1±1 ###, @@@ 8±1 ###, @@@ -10±1 @@@ -22±2

Quadriceps (mg) 166±10 #, @@ 164±7 #, @@ 132±9 121±4

Plantaris (mg) 13.1±0.7 ##, @ 12.4±0.3 #, @ 10.0±0.7 10.3±0.4

Gastrocnemius (mg) 112±2 ##, @@@ 110±3 ##, @@@ 90±3 84±4

Fiber CSA (µm2) 1,965±126 ###, @@@ 1,758±189 ###, @@@ 994±37 820±105

Epididymal fat (mg) 173±26 ### 183±12 ### 58±9 ND

Spleen (mg) 89±4 ^, ## 128±7 155±8 119±12

Liver (mg) 1,598±65 ##, @@ 1,607±41 ##, @@ 1,311±44 1,311±44

The phenotype for this cohort of mice has been reported previously (18) and are provided here for descriptive purposes. CSA, cross- sectional area of the gastrocnemius. ND, not detected. n=4-7/group. Data are mean ± SE. Differences determined by one-way ANOVA. P<0.05 (^) vs. C26 Weight-Stable. P<0.05 (#), P<0.01 (##), P<0.001 (###) vs. C26 Moderate Cachexia. P<0.05 (@), P<0.01 (@@), P<0.001 (@@@) vs. C26 Severe Cachexia.

Downloaded from journals.physiology.org/journal/physiolgenomics by Harry Rossiter (047.144.146.194) on March 14, 2020.