Energy Metabolic Re-Programming in the Hypertrophied and Early Stage Failing Heart: A Multi-systems Approach Ling Lai, Teresa C. Leone, Mark P. Keller, Ola J. Martin, Aimee T. Broman, Jessica Nigro, Kapil Kapoor, Timothy R. Koves, Robert Stevens, Olga R. Ilkayeva, Rick B. Vega, Alan D. Attie, Deborah M. Muoio and Daniel P. Kelly

Circ Heart Fail. published online September 18, 2014; Circulation: Heart Failure is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231 Copyright © 2014 American Heart Association, Inc. All rights reserved. Print ISSN: 1941-3289. Online ISSN: 1941-3297

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 Energy Metabolic Re-Programming in the Hypertrophied and Early Stage

Failing Heart: A Multi-systems Approach

Lai et al: Metabolic Remodeling in Heart Failure

Ling Lai, MD, PhD1; Teresa C. Leone, BS1; Mark P. Keller, PhD2; Ola J. Martin, PhD1;

Aimee T. Broman, MA3 ; Jessica Nigro, BS1; Kapil Kapoor, MD, PhD1;

Timothy R. Koves, PhD4; Robert Stevens, PhD4; Olga R. Ilkayeva, PhD4; Rick B. Vega, PhD1;

Alan D. Attie, PhD2; Deborah M. Muoio, PhD4; Daniel P. Kelly, MD1

1Diabetes and Obesity Research Center, Cardiovascular Pathobiologyologgy PrProgram,ograr m,m SSanford- Burnham Medical Research IInstitute,nstitute, Orlando, FL

2Department of Biochemistry,cheemistry, 3DepartmentDeD pparttmem ntn ooff BiBiostatisticsosstaatiistics anandnd MeMedicaledicaal InIInformatics,fof rmma University of Wisconsin-Madison,son, MaMadison,did soson, WWII

4Duke Molecular Physiologyhhyysiologgy InInstitute,stitutte,e DDepartmentsepe artmeentts ooff MeMedicine,edid cic ne, PharmacoloPharmacology,gy and Cancer Biology, Duke University,versityy, DuDurham,rhham, NC

Correspondence to Daniel P. Kelly, MD Sanford-Burnham Medical Research Institute at Lake Nona 6400 Sanger Road Orlando, FL 32827 Phone: 407-745-2136 Fax: 407-745-2033 Email: [email protected]

DOI: 10.1161/CIRCHEARTFAILURE.114.001469

Journal Subject Codes: Hypertension:[15] Hypertrophy, Myocardial biology:[107] Biochemistry and metabolism, Basic science research:[130] Animal models of human disease, Atherosclerosis:[142] Gene expression, Atherosclerosis:[146] Genomics

Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 Abstract Background—An unbiased systems approach was utilized to define energy metabolic events that occur during the pathologic cardiac remodeling en route to heart failure. Methods and Results—Combined myocardial transcriptomic and metabolomic profiling were conducted in a well-defined mouse model of heart failure that allows comparative assessment of compensated and decompensated (heart failure) forms of cardiac hypertrophy due to pressure overload. The pressure overload datasets were also compared with the myocardial transcriptome and metabolome for an adaptive (physiological) form of cardiac hypertrophy due to endurance exercise training. Comparative analysis of the datasets led to the following conclusions: 1) expression of most genes involved in mitochondrial energy transduction were not significantly changed in the hypertrophied or failing heart, with the notable exception of a progressive downregulation of transcripts encoding proteins and enzymes involvedvolved in mmyocyteyocy fatty acid transport and oxidation during the development of heart failure; 22)) ttissueisssue metabolitemetabbo profiles were more broadly rreregulatedegullateed ththanhan corresponding memetabolictabolic gegenenee rregulatoryegulatory chchanges,ann suggesting significant regulationn aatt the popost-transcriptionalostt-trraansccriptioonnal llelevel;vev l; 33)) mmemetabolomictaabooloomim c ssisignaturesgng aatuureess distinguished pathologic and physiologicaliologiical forms ooff caccardiacrdiac hhypertrophyyppertrophyy aandnd serveservedd as robust markers for the onset of heart failure;e; anandd 4)) tthehe pappatternttern of mmetaboliteetabolite dederangementsranggements iinn ththee fafailingiling heart suggests “bottlenecks” of carbon substrate flux into the Krebs cycle. Conclusions—Mitochondrial energy metabolic derangements that occur during the early development of pressure overload-induced heart failure involve both transcriptional and post- transcriptional events. A subset of the myocardial metabolomic profile robustly distinguished pathologic and physiologic cardiac remodeling. Key Words: transcriptomics, metabolomics, heart failure, energy metabolism, mitochondria

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 Evidence is emerging that derangements in cardiac energy metabolism and mitochondrial function contribute to the pathologic remodeling that leads to heart failure. During pathological hypertrophic growth due to chronic pressure overload, the heart undergoes a re-programming of its fuel preference to a “fetal” format, shifting towards glucose, away from reliance on fatty acids, the chief energy substrate for the normal adult mammalian heart.1-8 The capacity of cardiac mitochondria to generate ATP is also compromised during the development of heart failure.

Studies conducted in animal models and in humans have shown that myocardial high-energy phosphate stores (phosphocreatine levels), the main reservoir source of ATP, are reduced in the hypertrophied and failing heart.9-13 The potential role of fuel metabolictabolic and bbioenergeticiooe disturbances as causal in heart failure is also supported by the observationservav tiion thatthat humanhuh inborn errors in mitochondrialrial fuelfuel catabolismcatabolo isi m andand ATPATP productionprrooduction causecauuses cardiomyopathy.cardiomyopap t 14-16

There is considerablesiderable ininterestntet rer sts iinn dedelineatinglineating tthehee mmolecularolecular rregulatoryeggulata oro y eveventse involved in the metabolic re-programmingogrammingg ooff ththee hyhypertrophiedypep rtrophp ied anandd fafailingiling hheart.eart. To ddate,atee, sstudiestu have focused largely on candidate gene regulatory mechanisms in severe, late-stage disease.

Accordingly, such studies do not necessarily provide insight into the primary mechanisms that drive energy metabolic alterations that occur in the early stages of pathological cardiac remodeling. Characterization of the fuel and energy metabolic disturbances, and corresponding upstream regulatory events occurring during the early stages of heart failure is an important first step towards the identification of new therapeutic targets for the early-stage treatment of heart failure.

In this study, unbiased transcriptomic and targeted quantitative metabolomic profiling were conducted to identify metabolic gene regulatory events and corresponding changes in metabolite pools that occur during the development of heart failure due to pressure overload in

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 mice. The results were compared with that of an adaptive form of cardiac hypertrophy secondary to endurance exercise training. Surprisingly, we found that whereas transcriptional control of fatty acid oxidation (FAO) was altered, many genes involved in downstream mitochondrial energy transduction and ATP synthesis were not significantly regulated in the early stages of heart failure or in the exercise-trained heart. In striking contrast, myocardial metabolite profiles were distinct across each cardiac phenotype, providing robust signatures for the transition to heart failure, as well distinguishing pathologic versus physiological forms of cardiac hypertrophy.

Methods

An expanded Methodsoodds section,sectioon,n includingincluding allall proceduresprocec dud res usedused inn thisthih s report,report, is availableav in the

Data Supplement. Inn bbrief,rief, alalll anaanimalimala eexperimentsxpperiments anandnd eueuthanasiathana asa iaa pprotocolsrotocoolss wwereerer conducted in strict accordance withtth tthehe NNIHIH gguidelinesuidelines foforor huhumanemane ttreatmentreatmentn ooff ananimalsimals anandd aapproved by the

IACUC Committee at the Sanford-Burnham Medical Research Institute at Lake Nona. 8 week old female C57BL6/J mice in the following groups were utilized: compensated hypertrophy

(CH) vs sham controls; heart failure (HF) vs sham controls; physiological hypertrophy (PH) vs sedentary controls. CH was achieved by transverse aortic constriction (TAC). HF was achieved by TAC plus a small apical myocardial infarction. Mice were harvested 1 month following each procedure. PH was achieved by 8 weeks on a voluntary running wheel. At the end of all studies, hearts were excised and transcriptomic and metabolomics profiling was performed. The gene array data discussed in this publication have been deposited in NCBI’s

Gene Expression Omnibus and are accessible through GEO Series accession number GSE56348.

The following link has been created to allow review of record GSE56348 prior to publication:

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=mxavguwebfaptcp&acc=GSE56348

Validation by qRT-PCR was performed. Primer sets can be found in Supplemental Table 1.

Data were analyzed using nonparametric Mann-Whitney test (GraphPad Prism 6), two-tailed, unless otherwise noted. For pathway analysis, Fisher’s exact test was used to denote significance.

For all statistics, the level of significance was set at p < 0.05, and data are reported as mean ±

S.D. unless otherwise noted.

Results

In order to identify metabolic changes that occur in the relativelyy early stages ooff pressure overload-induced cardiac left ventricular (LV) remodeling, a mouseuse momodeldel of cchronich pressure overload was used. LLVV prespressuresuure ooverloadverload wawass ininducedndud cceed by pperformingere foorminng trtransverseansverses aortic constriction (TAC) oonn fefemalemalel CC57BL6/J57BBL6/J mmiceicce as ppreviouslyreviiously dedescribed.sccrir bed.d 17 In thisthis model, mice develop significant LLV hhypertrophyypertropphy wiwithth ppreservedreserved ssystolicysy tolic LV ffunctionunction at tthehe 44-week time point after the TAC procedure, referred to here as compensated hypertrophy (CH). As expected, the

CH group developed significant LV hypertrophy 4 weeks after TAC compared to sham-operated controls [(bi-ventricle weight/body weight, 6.55±1.58 (n=13) vs 4.00±0.22 (n=17), p < 0.001)] with preserved LV function (data not shown). In a second experimental group, TAC was combined with a small apical myocardial infarction achieved by placing a ligature in the distal portion of the left anterior descending coronary artery (LAD) resulting in global LV systolic and diastolic dilatation, and significantly reduced LV ejection fraction 4 weeks after the procedure

(Supplemental Figure 1 and Supplemental Table 2), termed the heart failure (HF) group.

Notably, this HF model results in global pathological remodeling [(bi-ventricle weight/body weight, 7.08±1.21(n=23) vs 4.10±0.32 (n=30), p < 0.001)] , without evidence of fibrosis outside

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 of the excised infarct region, consistent with early stage heart failure (data not shown). The apical myocardial infarction alone did not result in significant LV remodeling (Supplemental

Figure 1). Transcriptomic and quantitative targeted metabolomic profiling were conducted on ventricular samples harvested from CH and HF groups and corresponding sham-operated controls. The infarcted apical region was removed from the HF hearts and corresponding tissue region was removed from sham-operated control samples.

Transcriptomic profiling of pathological cardiac hypertrophy and heart failure

Gene expression profiling was conducted on samples from the fourour groups fofollowedlllowo by informatics analyses (see Supplemental Methods). Genes with a posteriorpostterioi r probabilityproba of greater than or equal to 0.955 werewwere consideredconnsis dered significantlysignificicantlt y differentialddidifferentiallyffferentiala lyy eexpressedxppressedd ((DE)DE ) relative to corresponding sham-operatedm-operar ted controlcontn ror l mice.mim ce. 10801080 genesgennes werewere DE inin thethe CH groupgrou (731 upregulated; 349 downregulated)wnregulated) anandd 16162727 ggenesenes wewerere rregulatedeggulated iinn ththee HF ggrouproupp (947 upregulated; 680 downregulated). Notably, the majority of genes that were DE in the CH group were also DE in the HF group (Figure 1A). In addition, genes that were DE in both CH and HF were almost always directionally concordant (Figure 1B). The directional expression of DE genes in CH and HF groups was highly positively correlated (R2 = 0.9, and p=2u10-16; Figure

1B). These results indicate that the majority of gene regulatory events in HF were initiated in

CH.

Ingenuity Pathway Analysis (IPA) analysis was conducted with the gene expression datasets to identify regulated pathways and biological processes in the CH and HF groups.

Separate analyses were performed for genes upregulated or downregulated in each group compared to corresponding controls. Multiple pathways involved in metabolism were

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 represented by downregulated genes in both CH and HF groups (Supplemental Tables 3 and 4).

In contrast, and consistent with the hypertrophic growth response, many pathways involved in inflammation, growth, and cellular assembly/organization/movement were identified by upregulated genes in both CH and HF groups (Supplemental Tables 3 and 4).

A significant proportion of downregulated genes were noted in pathways involved in fatty acid uptake, transport, and catabolism in the CH and HF groups, with a greater number in the latter group (Supplemental Tables 3 and 4). Specifically, genes involved in FAO (Acaa2,

Ecil, Acadvl, Hadh), fatty acid uptake (Slc27a1, Acsl5, Acsl6), and acyl trafficking (Cpt2) were significantly downregulated in the HF group (Supplemental Tablele 5)5).. The exextentteentn of this regulation among several key lipid utilization pathways is shownn in ggreaterreata er ddetailetaai in Figure 2.

These results serve ass a formform of vvalidationalidation forfor ththehe trtranscriptionalraanscriptionaal prpprofilingofilling dadatasetdatatasee given that many published studiesdiesdies hhaveavve shsshownown ththathat ggenesennes iinvolvednvn olved in ccardiacardiacc mmyocyteyocyte ffatfattyata tyty acid uptake and oxidation are downregulatedreegulated in tthehe hypertrophiedhyppertropphied andand failingfailingg heart.heart.8,18-228,18-22

The transcriptomic analyses identified amino acid metabolism as a second regulated metabolic pathway category in the CH and HF samples. Regulation in this category largely reflected downregulation of genes involved in amino acid degradation pathways (Supplemental

Tables 3 and 4). Specifically, expression of genes involved in the degradation of proline, alanine, tryptophan, and branched-chain amino acids (BCAA) were downregulated (Supplemental Table

6 and Figure 3). These results are of interest given that several of these pathways can serve as sources of anaplerotic input into the tricarboxylic acid (TCA) cycle.

A number of published gene expression profiling studies conducted largely in severe end- stage heart failure in several different models and species have demonstrated downregulated expression of genes involved in the electron transport complex (ETC) and oxidative

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 phosphorylation (OXPHOS).23-26 Surprisingly, we found very little regulation of genes involved in ETC, OXPHOS, or the tricarboxylic acid (TCA) cycle in either the CH (data not shown) or

HF groups (Supplemental Figure 2).

Metabolomic profiling of pathological cardiac hypertrophy and heart failure

The transcriptomic data did not identify many signatures that distinguished HF from CH, particularly in metabolic pathways. Accordingly, metabolomic profiling was conducted to determine whether metabolite levels could provide additional insight into the energy metabolic changes that occur during the progression from compensated cardiacdiac hypertrohypertrophyphy to heart failure. Samples from the CH and HF groups and correspondingg controlscontrolss wwereere ssubjected to targeted quantitativee memmetabolomicstabooloomim cs uusingsing mmassasa s spsspectrometryecctroometrt y tot ddefineefe inne lelevelsveels ooff mmetabolites in three main classes relevantelevana t tot sseveraleve erala mmitochondrialiti oochondrir ala ffueluel memetabolictaboliic papathways:thwaaysy : acaacylcarnitinesc

(FAO), organic acidsdds ((lactate/pyruvatelactate/pyruvate ooxidationxidation aandnd tthehe TTCACA ccycle),ycy le),) aandnd aaminominoo acids. In contrast to the profile of metabolic gene regulation, the metabolomics data revealed a distinctly different pattern in the HF group compared to the CH group (Supplemental Table 7). Indeed, there were relatively few differences in metabolite levels from the CH samples compared to the control samples. The most striking distinguishing metabolite signature in the HF group was exhibited in the acylcarnitine profile. Levels of many acylcarnitine species were increased in the

HF group compared to controls (Figure 4 and Supplemental Table 7). This was particularly notable for the long-chain (C14-C18) species. In addition, C2 carnitine levels (acetylcarnitine) were significantly increased in the HF samples. Consistent with increased levels of acylcarnitine esters, free carnitine levels (C0 carnitine) were modestly but significantly decreased in the HF group (Figure 4).

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 The organic acid profile also revealed interesting differences between the CH and HF

groups. First, lactate levels and the lactate/pyruvate ratio were increased in HF but not CH,

suggesting reduced flux of pyruvate into the TCA cycle with diversion to lactate (Figure 5A and

Supplemental Table 7). Second, levels of most TCA cycle intermediates (fumarate, malate, D- ketoglutarate) were decreased in HF (Figure 5B). One exception was succinate which was increased in the HF group (Figure 5B). The amino acid profile was less remarkable (Figure 5C and Supplemental Table 7). However, a few modest but significant differences were noted including increased levels of Asx (aspartic acid/asparagine) in the HF group (Figure 5C). In addition, with minor exceptions, changes in cardiac metabolites were not reflectedreflectet in circulating plasma metabolite levels (Supplemental Table 8). Notably, a smallall butbut significantsignifican increase in some branched-chainn aminoaamino acidsacidi s (Leu/Ile)(Leu/I/ le) waswaas observedobo seervved inin thetht e plasmaplp asma butbut notnot myocardiumm of the HF group.

The observedd eelevationlevation ooff lolong-chainngg-chain aacylcarnitinecyylcarnitiine sspeciespep cies ttogetheroggether wwithith a general reduction in TCA cycle intermediates in the HF group suggested a “bottleneck” of carbon flux within the FAO pathway. The reduced expression of a subset of genes involved in FAO observed in the transcriptomic profile in HF samples (Supplemental Table 5) is consistent with this hypothesis. To explore this further, quantitative PCR was used to determine the level of representative genes involved in the cellular uptake and mitochondrial E-oxidation of long-chain fatty acids, some of which were identified as downregulated in the gene expression array data.

The expression of genes encoding the fatty acid transporter (Slc27a1) and enzymes involved in mitochondrial fatty acid uptake and oxidation (Cpt2 and Acadvl) were all significantly downregulated only in the HF group (Figure 6A). In addition, expression of the gene encoding

Ehhadh, a peroxisomal E-oxidation enzyme, was also downregulated in the HF samples (Figure

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 6A). Consistent with reduced expression of genes involved in long-chain FAO, palmitoylcarnitine-driven state 3 respiration was significantly diminished in isolated cardiac muscle strips in the HF samples compared with control (Figure 6B). In contrast, respiration rates with pyruvate as a substrate were unchanged in the HF group, consistent with the conclusion that the deficit in palmitoylcarnitine-driven respiration in HF is a result of a decreased capacity for

FAO (Figure 6B).

To determine whether post-transcriptional changes in FAO enzyme levels contribute to the alteration in acylcarnitine levels, immunoblotting experiments were conducted on a subset of

FAO enzymes for which the corresponding transcript was downregulatedregulated [ve[veryryy llong-chain acyl-

CoA dehydrogenase (VLCAD)] or unchanged [medium-chain acyl-CoAcyl-CCoA ddehydrogenaseehydror

(MCAD) and long-chaincchhaia n acacyl-CoAyly -CoAA dedehydrogenasehydrogennasa e (LCAD)].(LCAD)]]. ConsistentConsistentn withwith thtthee transcriptional profiling results, levelsvells ofo VLCADVLCAD pproteinrootein wwereere rerreducedduced in thethe HFHF samplessamplees whereaswherer LCAD and

MCAD levels were unchangedunchangged (Supplemental(Supplp emental FigureFigug re 3A).3A).

Transcriptomic/metabolomic profiling of physiological cardiac hypertrophy

We next conducted cardiac transcriptomic and metabolomic profiling in an adaptive

(physiological) cardiac growth state for comparison with the pressure overload datasets. To this end, cardiac ventricular samples obtained from C57BL/6J female mice subjected to a nocturnal wheel running-based exercise training regimen for 8 weeks were compared to gender-, strain- and age-matched control samples. This intervention has been shown to result in physiologic hypertrophy (PH) of the LV,27 albeit to a lesser degree than that achieved with the pressure overload regimen. The training regimen resulted in an approximately 10% increase in bi- ventricle weight/body weight (Supplemental Table 9).

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 The gene expression profiling results for the PH samples (compared to sedentary controls) revealed minimal regulation, with only 6 DE genes, none of which were within metabolic pathways (Supplemental Table 10). In contrast, significant changes were observed in the metabolome of the PH samples. Interestingly, the pattern of metabolite changes observed in the PH group was strikingly distinct from that of the HF profile. In contrast to the increase in long-chain acylcarnitines observed in HF, the levels of many medium- and long-chain acylcarnitines were decreased in the PH samples (Figure 7A and Supplemental Table 7). In addition, there was no change in acetylcarnitine levels in the PH group in contrast to the observed elevation in HF (Figures 7A and 4, and Supplemental Table 7).7). DiDifferencesfffere e were also noted in the organic acid species. Whereas both HF and PH exhibitedbited reducedreduuced levelslle of a shared subset of organic acidsiidds (fumarate(fummarrate anandd malate),malate),) levelsleve ells off succinatesucu cinate aandnd llactateaca tate wwereerr reduced in PH but elevated in HF (FiguresFFiigurer s 7B7B,, 5A,5AA, 5BB andandn SupplementalSupplp emental TableTable 7).77). Lastly,Lasa tlly,y therether were modest but significant changesgges in a subsetsubset of aminoamino acidacid levelslevels includingincludingg alanine,alanine, leucine/isoleucine,leucin and

Asx (Supplemental Table 7). As described above for the HF samples, levels of proteins for a subset of unregulated FAO transcripts were unchanged (Supplemental Figure 3B).

Comparative analysis of transcriptomic and metabolomic profiles across the phenotypic groups

We next compared the collective results of our transcriptomic and metabolomic analyses. This comparative analysis is depicted in heat maps (Figure 8) displaying the level and direction of differential expression compared to the corresponding controls. This comparative analysis, which was restricted to the subset of genes involved in mitochondrial fuel and energy metabolism relevant to the metabolites measured in the metabolomic analyses, led to several major conclusions. First, as noted above, a subset of genes involved in cellular fatty acid

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 uptake/oxidation were downregulated in both the CH and to a greater extent the HF groups.

Second, in contrast to the lipid utilization genes, the majority of genes involved in downstream mitochondrial energy transduction pathways, including ETC and OXPHOS, were not significantly regulated in the hypertrophied and failing heart. This observation is further highlighted by comparing the metabolic gene expression profiles in the CH and HF samples with the corresponding gene expression data obtained previously28 from the hearts of mice deficient for the transcriptional coactivators, PGC-1D and PGC-1E master regulators of genes involved in mitochondrial energy metabolism29-32 (Figure 8A). Third, very few gene regulatory changes were observed in the hearts undergoing adaptive physiological cardiacardiac hyhypertrophyypep rtrop (PH group)

(Figure 8A).

In contrast too tthehhe tratranscriptomicnsscripptomim c reresults,sultts, tthehe memetabolomicetaboloomiic sissignaturesgnaturu es sservedervee as a robust correlate of the cardiaciacc phenotypephenon tyt pe (Figure(FiF guree 8B),8B),) distinguishingdisi tit nguishing thethe CH,CHH, HF,HF, andand PHP groups

(Figure 8B). This wasasas paparticularlyrticcularlyy eevidentvident uuponpop n ananalysisalysy iss ooff acacylcarnitineyly carnitine spspeciespecies,ecies many of which were unchanged in CH, increased in HF, and decreased in PH. The organic acid profile was also distinct among the metabolites as described above. Taken together, these results indicate that metabolite levels distinguished the cardiac hypertrophic phenotype and that post-transcriptional regulatory events contribute to the metabolic re-programming that occurs in HF and PH. In addition, many observed changes in gene expression do not correlate with changes in metabolite pool size changes or cardiac dysfunction. This latter point is underscored by the observed lack of alterations in metabolite pools in the hearts of the PGC-1DE-/- hearts which exhibit widespread downregulation in the expression of genes involved in mitochondrial metabolism (Figure 8A) yet exhibit normal cardiac function28 and minimal changes in the metabolome (Figure 8B).

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 Discussion

Herein we undertook an unbiased approach to identify changes in energy metabolic gene

expression and metabolite pools in several forms of cardiac hypertrophy and in the early stages

of heart failure in a mouse model. The results demonstrated the power of employing combined

unbiased molecular profiling to define signatures of complex physiological and disease states.

The transcriptional profiling results were remarkable for the lack of changes in

expression of genes involved in ETC and OXPHOS. These results were surprising given that a

number of previously published studies conducted in heart failure animal models and in humans

with heart failure have shown reduced expression of ETC and OXPHOSXPHOS gengenesess aand proteins.23-26

However, most of these studies focused on later stages of heart failure,ailurer , suggestingsuggestiin that many of the observed metabolicoollic gengenee reregulatorygulatoryy eeventsventn s rerreflectflece t irirreversiblereversr ibble mmitochondrialitochoondriala dysfunction and myocyte death. In thishhiis study,studdyy, focusedfoco used onon earliereearlier stagessts agess wwithith lelessss ssevereevvere ppathologicalata hoh log cardiac remodeling, we foundnnd a moremore selectiveselective patternpap ttern off alteredaltered metabolicmetabolic genegene regulationregulati including a significant subset of genes involved in lipid and amino acid metabolism. The observed reduction in expression of genes involved in mitochondrial FAO serve as a validation of our gene expression profiling, given that this pathway has been shown to be downregulated in many studies of pressure overload-induced cardiac hypertrophy and heart failure, in a variety of animal models and humans.8,18-22 We also found that expression of a subset of genes involved in amino

acid degradation pathways were downregulated in CH and HF. The relevance of this finding is

unclear but could relate to the balance between growth and catabolism. Specifically, cardiac

hypertrophic growth requires activation of anabolic pathways involved in protein synthesis,

which may result in feedback suppression of amino acid degradation pathways. This finding

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 could also have implications for fuel metabolism given that several of the downregulated amino acid catabolic pathways serve as an important source of anaplerotic carbon for the TCA cycle.

The metabolite profile served to robustly distinguish between the various cardiac phenotypes in this study. Many species in the acylcarnitine profile were elevated in the HF group, but were normal in CH. In addition, elevations in lactate and acetylcarnitine levels, along with a reduction in most organic acid TCA cycle intermediates were specific to the HF samples.

Taken together, this metabolite accumulation pattern suggests a “bottleneck” in carbon flux

(fatty acids and glucose) into the TCA cycle, which could reduce capacity to generate the reducing equivalents needed to generate ATP contributing to contractilentractile dysfunction.dysfunnc During preparation of this manuscript, Sansbury et al reported the resultss off ggloballobal memetabolomictab profiling in a mouse mmodelodel ooff TATACC anandnd MIMI..33 InI ccontrastono trrast too ooururr rresults,esuultl s, thisthis studystudud reported a reduced level of acylcarnitinesylcarnin tiinen s in ttheheh TTACAC aandnd MIM model.model. TheThe basisbasa is forfor thisthih s differencedid ffff is unknown but could rrerelatelate ttoo coco-existing-existing seseverevere iischemicschemic ininsultsult oorr ththee dudurationration ooff the heart failure

(8 weeks in the study by Sansbury et al). Taken together, these findings suggest the interesting possibility that the myocardial metabolite profile reflects stage and etiology of the heart failure.

The metabolite profile was also regulated to a greater extent than the transcriptome in the exercise-trained heart. Interestingly, the regulated metabolite levels in the adaptive hypertrophy

(PH) samples were generally directionally discordant from the pattern observed in the HF group.

Specifically, in contrast to the failing heart, many acylcarnitine and organic acid species were concordantly decreased in the trained heart, suggesting matched carbon flux (absence of

“bottlenecking”). Consistent with this conclusion, acetylcarnitine and lactate levels were normal.

It should be noted that several previously published studies have shown a somewhat larger number of genes regulated in heart following exercise training.34-38 The basis for this difference

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 is not clear but could relate to training protocol, species, or degree of cardiac hypertrophy achieved.

Several limitations of this study should be noted. First, the studies were only conducted with female animals. Accordingly, we cannot conclude that the same results will be seen in male mice. Second, the HF model involved pressure overload together with a small apical infarct which results in a predictable global LV dilatation and reduced ejection fraction. This approach allowed us to compare animals with compensated (CH) versus decompensated (HF) LV hypertrophy at the same relatively early timepoint (4 weeks). It is possible that the molecular profiling results shown here will differ from other HF models. Futureutuure compacomparativeraatit v studies will be important to address this question.

The striking ddifferencesiffferencces oobservedbserved bebetweentweee n thtthee trtranscriptomicanscripttomicc aandndn mmetaboliteetabo profiles suggest that significantant ppost-translationalosst-t trranslatit onnall rregulatoryeggulatoro y eveventsvennts mmayaya bbee ooperativeperar tiivev iinn tthe metabolic re- programming that occurscccurs duduringringg tthehe eearlyarlyy sstagestageg s of hhearteart ffailure.ailure. EEvidencevidence is eemergingm that changes in the levels of mitochondrial metabolite pools, such as acetyl-CoA and succinyl-CoA can lead to modification of mitochondrial enzyme function through acetylation and succinylation, respectively.39-45 Indeed, a recent study has shown that heart failure in mice due to targeted disruption of Ndufs4 involves a significant component of mitochondrial protein hyperacetylation and alterations in metabolite pools.42 It will be of significant interest to determine whether such post-translational changes are relevant to the metabolic derangements that occur during the early stages of heart failure.

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 Acknowledgements

We are grateful to Lorenzo Thomas for assistance with manuscript preparation; Jian-Liang Li and Feng Qi from the Applied Bioinformatics Core at Sanford-Burnham Medical Research

Institute at Lake Nona (SBMRI-LN) for helpful discussion; Metabolomics Core at SBMRI-LN;

Carla Weinheimer and Attila Kovacs (Washington University School of Medicine) for guidance related to the mouse surgical model and echocardiographic analyses; and Orlando Rodriguez and

Lauren Ashley Gabriel for assistance with animal studies.

Sources of Funding

This work was supported by NIH grants R01 HL58493 (D.P.K.) andandd R01R01 HL101189HL1001 (D.P.K.,

A.D.A., and D.M.M.).M.).

DiDisclosuresscllosuures

None.

References

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 mitochondrial oxidative pathways in pressure overload-induced heart failure. Cardiovasc Res. 2010;85:376-384. 25. Barth AS, Kumordzie A, Frangakis C, Margulies KB, Cappola TP, Tomaselli GF. Reciprocal transcriptional regulation of metabolic and signaling pathways correlates with disease severity in heart failure. Circ Cardiovasc Genet. 2011;4:475-483. 26. Xu J, Nie HG, Zhang XD, Tian Y, Yu B. Down-regulated energy metabolism genes associated with mitochondria oxidative phosphorylation and fatty acid metabolism in viral cardiomyopathy mouse heart. Mol Biol Rep. 2011;38:4007-4013. 27. Allen DL, Harrison BC, Maass A, Bell ML, Byrnes WC, Leinwand LA. Cardiac and skeletal muscle adaptations to voluntary wheel running in the mouse. J Appl Physiol. 2001;90:1900-1908. 28. Martin OJ, Lai L, Soundarapandian MM, Leone TC, Zorzano A, Keller MP, Attie AD, Muoio DM, Kelly DP. A role for peroxisome proliferator-activated receptor gamma coactivator-1 in the control of mitochondrial dynamics during postnatal cardiac growth. Circ Res. 2014;114:626-636. 29. Lin J, Handschin C, Spiegelman BM. Metabolic control through the PGC-1 family of transcription coactivators. Cell Metab. 2005;1:361-370. 30. Finck BN, Kelly DP. PGC-1 coactivators: inducible regulatorslators of enenergyeergy metabolism in health and disease. J Clin Invest. 2006;116:615-622. 31. Handschin C,C, SSpiegelmanpiegele man BMBM.. PPeroxisomeeroxisomee proliferator-activatedproliferator-activated recepreceptorreceptto gamma coactivator 1 coactivators,coactivav tors, enenergyergy hhomeostasis,omo eostasa is, anandd memmetabolism.taabob liism. EnEndocrdocrc Rev. 2006;27:728-735.-735. 32. Scarpulla RC,C, VeVegagaa RRB,B,B KKellyele lyy DDP.P.P TTranscriptionalranscs riptp ional inintegrationtegrg atiion ooff mimitochondrialtoochh biogenesis. TrendsTrends EndocrinolEndocrinool MetabMetab. 2022012;23:459-466.122;223:454 9-9 4666. 33. Sansbury BE,E,E DDee MaMartinortino AMAM,, XiXiee Z, BBrooksrooks ACAC,, BrBrainardainard RRE,E, WWatsoWatsonatson LJ, Defilippis AP, Cumminsns TDTD, HaHHarbesonrbbeson MMA,A BBrittianriittiai n KRKR, PrPPrabhuabhuabbhuh SSD, D BBhatnagarhah tnt agar AA, Jones SP, Hill BG. Metabolomic analysis of pressure-overloaded and infarcted mouse hearts. Circ Heart Fail. 2014;7:634-642. 34. Galindo CL, Skinner MA, Errami M, Olson LD, Watson DA, Li J, McCormick JF, McIver LJ, Kumar NM, Pham TQ, Garner HR. Transcriptional profile of isoproterenol- induced cardiomyopathy and comparison to exercise-induced cardiac hypertrophy and human cardiac failure. BMC Physiol. 2009;9:23. 35. Iemitsu M, Maeda S, Miyauchi T, Matsuda M, Tanaka H. Gene expression profiling of exercise-induced cardiac hypertrophy in rats. Acta Physiol Scand. 2005;185:259-270. 36. Strom CC, Aplin M, Ploug T, Christoffersen TE, Langfort J, Viese M, Galbo H, Haunso S, Sheikh SP. Expression profiling reveals differences in metabolic gene expression between exercise-induced cardiac effects and maladaptive cardiac hypertrophy. FEBS J. 2005;272:2684-2695. 37. Diffee GM, Seversen EA, Stein TD, Johnson JA. Microarray expression analysis of effects of exercise training: increase in atrial MLC-1 in rat ventricles. Am J Physiol Heart Circ Physiol. 2003;284:H830-837. 38. Budiono BP, See Hoe LE, Peart JN, Sabapathy S, Ashton KJ, Haseler LJ, Headrick JP. Voluntary running in mice beneficially modulates myocardial ischemic tolerance, signaling kinases, and gene expression patterns. Am J Physiol Regul Integr Comp Physiol. 2012;302:R1091-1100.

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Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 Figure Legends

Figure 1. Myocardial transcriptional profiles for compensated (CH) and decompensated

(HF) pathologic cardiac hypertrophy exhibit significant concordance. (A) Venn diagram showing differentially expressed (DE) genes in CH (left, n=5) or HF groups (right, n=5) compared to corresponding sham-operated controls (n=5 per group). The overlapping region

(red) represents DE genes in both groups. (B) Scatter plot showing genes exhibiting DE t 0.950 in either CH (green circles), HF (blue circles), or both (red circles) groups compared to corresponding controls. The log10-transformed fold change of genes in CH is shown on the x- axis, and corresponding fold change for HF is shown on the y-axis,is,, plottedplotted onon a linearl scale.

Figure 2. Altered expressionxppressionn ooff gegenesnes ininvolvedvolvl ede inn ffattyattty aacidcid cacatabolictaabolic papathwaysthwawa in HF.

Pathway map showingng caccardiacrddiiac gegenesnes sisignificantlyigniificcantly (D(DEE t 0.950)0.9950) upregulatedupreegullatet d (p((pink)inin or downregulated (green)een) comparedcompared to controls.contrt olo s.s PathwaysPathwaya s involvedinvolvved in lipidlipid utilizationutilizatii are shown including FAO (A), mitochondrial L-carnitine shuttle (B), and triglyceride degradation (C) as identified by IPA (see Supplemental Methods). The ovals in the map represent enzyme complexes with components of each gene/enzyme displayed individually as denoted by the arrows. The intensity of the color within the symbols and ovals denotes the degree of regulation.

Figure 3. Altered expression of genes involved in amino acid degradation in HF. Pathway map showing myocardial genes significantly (DE t 0.950) upregulated (pink) or downregulated

(green) compared to controls in valine, leucine, and isoleucine degradation pathways identified by IPA.

20

Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 Figure 4. Accumulation of acylcarnitine esters in myocardial samples from the HF group.

Levels of free carnitine (C0) and acylcarnitine species isolated from the bi-ventricle of CH (n=6) and HF (n=6) hearts and corresponding sham-operated controls are indicated by the labels on the x-axis. Acyl chain lengths are denoted by the numbers. Acylcarnitine species that represent monohydroxylated (OH) or dicarboxylic acid (DC) species are also shown. Note that the OH and DC carnitine species are isobaric and, thus, were not separated in this analysis. Bars represent mean ± S.E. *p< 0.05 compared with corresponded sham-operated controls (n=6).

Figure 5. Altered myocardial organic acid and amino acid levelsvels in HF sasamples.mpm Metabolite profiles showing (A) lactate and pyruvate, (B) organic acid intermediatesmediiatese wwithinithin the TCA cycle and (C) amino acids determineddetermined inin samplessamples isolatedisisollatedd fromfroom thethe bi-ventriclebii-venntricclel ofof CH (n=6), HF (n=6) corresponding controlol hearts.hearts. BaBarsrss representrepe rer seentt meanmean ± S.E.S.S EE. *p<*p< 0.00.05055 ccomparedompared wiwwitht corresponding sham-operated control.rroll.

Figure 6. Reduced expression of genes involved in fatty acid uptake and oxidation in HF.

(A) Results of quantitative RT-PCR analysis of RNA extracted from hearts of CH (n=7), HF

(n=7) and corresponding sham-operated controls (n=5-7) for the transcripts as labeled. Bars represent mean ± S.E. normalized (=1.0) to each sham control. *p < 0.05 compared to corresponding sham-operated control. (B) Mean (± S.E.) mitochondrial respiration rates of permeabilized cardiac muscle fiber strips prepared from HF mice (n=11) and sham-operated controls (n=5-8) using palmitoylcarnitine/malate (top) or pyruvate/malate (bottom) as substrate.

Basal, state III (ADP-stimulated), and post-oligomycin rates are shown. Respiratory control ratio

(RC) = state III/state IV. *p < 0.05 compared to Sham (HF).

21

Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 Figure 7. Metabolomic profile of exercise-trained mouse heart. Levels of (A) free carnitine

(C0), acetylcarnitine (C2), and other acylcarnitine species, and (B) organic acids isolated from

the bi-ventricle of PH hearts (Run). Bars represent mean ± S.E. *p< 0.05 compared with age-,

gender- (female), and strain (C57BL6/J)-matched sedentary controls (n=6 per group).

Figure 8. Comparative analysis of transcriptomic and metabolomic profiles representing

mitochondrial energy metabolic pathways for CH, HF, and PH hearts. (A) A heat map

containing gene expression array datasets representing the level off expression ofo genes in fatty

acid utilization, TCA cycle, and ETC/OXPHOS pathways definedd bbyy IPIPAA foforr CCHCH, HF, PH, and

-/- adult PGC-1DE heartsarts (PGC(PGC DKO;DKO; n=5n=5 perper group).groupp)). EachEach rowror w representsreprresents thethe loglogg2-transformed

fold change comparedeedd wwithiti h cocorrespondingorrespoondding cocontrols.ontrols. ThThee papathwaysthwaw ys aarere rrankedana ked acaccordingcc to the

posterior probabilitieses in HF,HF, toptopp beingbeingg thethe mostmost significant.siggnificant. DownregulationDownregug lation inin blueb and

upregulation in red. (B) Heat map containing the log2-transformed ratio of the mean relative

levels of acylcarnitines, organic acids, and amino acids among CH, HF, PH, and adult PGC-1DE-

/- hearts (n=6) compared to corresponding controls. The intensity of color indicates the

magnitude of the fold change.

22

Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 Figure 1

A.

B.

0.88

0.4 R2=0.9009 p=2 u10-16 0

-0.4 DE Genes in HF (fold DE change) -0.4 0.40 0.8 DE Genes in CH (fold change)

Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 Figure 2

A. Fatty Acid E-Oxidation

2,3,4-saturated fatty acid Coenzyme A AMP

Long-chain fatty-acid-CoA ligase diphosphate reduced electron-transfer ATP flavoprotein 2,3,4-saturated fatty acyl CoA Acyl-CoA dehydrogenase Dodecenoyl-CoA D-isomerase oxidized electron-transfer cis-3-enoyl-CoA fatty acyl(n-2) CoA flavoprotein trans-2-enoyl-CoA

acetyl-CoA

3-ketoacyl-CoA Enoyl-CoAl-CooA thiolase hydratasehdratasehyydratase coenzyme A NANAD+D+

3-oxoacyl-CoA (3(3S)-3-hydroxyacyl-CoA3SS)-33-hyydroxyaacyc l-CoA (3R)-3-hydroxyacyl-CoA(3R)-3-hyy H+ HydroxyacylHyHHydroxyacyl-CoAdroxyacyl-CoACoC A NADH dehydrogenese B. Mitochondrial C. Triglyceride Degradation L-carnitine Shuttle

Long-chain fatty-acid-CoA ligase Carboxylic ester hydrolase

long-chain acyl-CoA L-carnitine H2O H2O 2-monoacylglycerol 1-monoacylglycerol triacylglycerol diacylglycerol Fatty Acid E-oxidation I H2O H+ H+ carboxylate fatty acid Triacylglycerol Acetylcarnitine lipase transferase

Coenzyme A Acylglycerol fatty lipase acid O-acyl-L-carnitine glycerol

Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 Figure 3

Valine, Leucine and Isoleucine Degradation

L-leucine L-E-leucine L-valine L-isoleucine

(R)-4-methyl-3- S-3-methyl-2- oxopentanoate oxopentanoate 4-methyl-2- Branched-chain oxopentanoate 2-oxoisopentanoate D-keto acid dehydrogenase complex

3-methylbutanoyl-CoA Isobutyryl- 2-methylbutanoyl- CoA CoA branched chain Acyl-CoA fatty acid ddehydrogenase

3-hydroxyisovaleryl- Isovaleryl-CoA CoA dehydrogenaserogene asa e 3-methylbut-2-enoyl-CoAooyyl-CCoA methylacrylyl-CoAmethyly acrylyl-CooA Trans-2-methyl-TTranns-2-mem thhyly - But-2-enoyl-CoABBut-22-ene oyyl-CooA Enoyl-CoA Methylcrotonoyl-rotono oyl- hydratase CoA carboxylaseboxyllase (S)-3-hydroxy-2-(SS)-33-hyh drroxy-2- methylbutyryl-CoAmethylbutyryyl-CoA 3-methylglutaconyl-CoA (S)-3-hydroxyisobutyryl-CoA(S)-3-hydroxyisobuutyyryyl-CoC A 3-hydroxyacyl-CoA dehydrogenase (S)-3-hydroxy-3-methylglutaryl-CoA (S)-3-hydroxyisobutyrate 2-methylacetoacetyl-CoA acetoacetyl-CoA Acetyl-CoA c-acyltransferase

acetyl-CoA propionyl-CoA

Propionyl-CoA citrate cycle carboxylase methylmalonate

(S)-methyl-malonyl-CoA succinyl-CoA (R)-methyl-malonyl-CoA

Aldehyde dehydrogenase

Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 Figure 4

1200 250 * 0.9 Sham (CH) * 0.8 CH 1000 200 0.7 * Sham (HF) 800 * 0.6 150 HF 600 0.5 100 0.4 * * 400 0.3 * * 200 50 0.2 * pmol/mg tissue tissue pmol/mg 0.1 0 0 0.0 C0

3.0 2.5 14 * 2.0 * * * 12 1.5 10 * * 1.0 * * 8 pmol/mg tissue 0.5 * * 6 0.0 4 * * *

pmol/mg tissue tissue pmol/mg * 2 * * * * * * * * * 0

Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 Figure 5

A. B. 10000 400 * 30 500 14 600 * 160 1200 350 12 140 8000 * 25 400 500 1000 300 120 10 * * 250 20 400 100 * 800 6000 300 8 * 200 15 300 80 600 4000 200 6 150 10 200 60 400 100 4 40 2000 100 50 5 2 100 20 200 pmol/mg tissue 0 0 0 0 0 0 0 0 Lactate Lactate/ Pyruvate Citrate D-KG Succinate Fumarate Malate Pyruvate TCA Intermediates C. 10000 2500 Sham (CH) * CH 2000 8000 * Sham (HF) * * HF 6000 1500 4000 1000 2000 500500 * pmol/mg tissue 0 0 Glx GlyGlly Al Alaa Se Serer LLe Leu/Ileu//Ile AsxAAsx 500

400 *

300 * * 200 * * * * *

pmol/mg tissue 100 0 Pro Val Met His Phe Tyr Orn Cit Arg

Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 Figure 6

A. Slc27a1 Cpt1b Cpt2 1.2 1.2 1.2 1.0 1.0 1.0 0.8 0.8 0.8 * 0.6 * 0.6 0.6 0.4 0.4 0.4 0.2 0.2 0.2 Normalized AU 0.0 0.0 0.0 Acadvl Ehhadh 1.4 1.4 Sham (CH) CH 1.2 1.2 Sham (HF) 1.0 1.0 HF 0.8 0.8 * 0.6 0.6 * 0.4 0.4

Normalized AU 0.2 0.2020.0 2 0.0 0.00.0

B. PalmitoylcarnitinePalmitoylcarnitine 50 Sham (HF) HF 40 30 * 20 10 (nmol/min/mg dw) (nmol/min/mg 2 0 Baseline ADPState (state III Oligomycin RC VO (ADP)III)

Pyruvate 80

60

40

20 (nmol/min/mg dw) (nmol/min/mg 2 0 Baseline State III Oligomycin RC VO Downloaded from http://circheartfailure.ahajournals.org/(ADP) at Loyola University of Chicago on November 3, 2014 Figure 7 A. 1200 160 0.45 Sedentary Run 140 0.40 1000 0.35 120 800 0.30 100 0.25 600 80 * 0.20 * 60 0.15 400 * * 40 0.10 * * pmol/mg tissue tissue pmol/mg 200 * 20 0.05 * 0 0 0.00

3.5 3.0 2.5 2.0 1.5 1.0 pmol/mg tissue tissue pmol/mg * * 0.5 * * * * * * * * * * * * 0.0

B. 7000 350 25 500 10 600 140 1000 6000 300 120 20 400 8 500 800 5000 * 250 * 400 100 4000 200 15 300 6 * 80 600 300 * * 3000 150 10 200 4 60 400 200 2000 100 40

pmol/mg tissue pmol/mg 5 100 2 200 1000 50 100 20 0 0 0 0 0 0 0 0 Lactate Lactate/ Pyruvate Citrate D-KG Succinate Fumarate Malate Pyruvate TCA Intermediates

Downloaded from http://circheartfailure.ahajournals.org/ at Loyola University of Chicago on November 3, 2014 A. B. B. A. Electron Transport Chain/ TCA Oxidative Phosphorylation Cycle Fatty Acid Metabolism HHF CH Downloaded from HC HF PH CH http://circheartfailure.ahajournals.org/ DKO PGC

Organic Very Long Short Amino Acid Acid Chain LongLoL ng CChainhain Medium Chain Chain C20-OH/C18-DC/C22:6 C12:1-OH/C10:1-DC C12:2-OH/C10:2-DC C16:1-OH/C14:1-DC C16:2-OH/C14:2-DC C16:3-OH/C14:3-DC C14:1-OH/C12:1-DC C14:2-OH/C12:2-DC C14:3-OH/C12:3-DC C16: C16:C C16:C16 C C14: C20:1-OH/C18:1-DC C20:2-OH/C18:2-DC C20:3-OH/C18:3-DC C18:1-OH/C16:1-DC C18:2-OH/C16:2-DC C18:3-OH/C16:3-DC 16 14: C8:1-OH/C6:1-DC atLoyola Universityof Chicago onNovember 3,2014 C12-OH/C10-DC C16-OH/C14-DC C14-OH/C12-DC C16 C1 C1C14 C18-OH/C16-DC 1 2 3 1 2 C10-OH/C8-DC C4-DC/Ci4-DC - - - - - C6-DC/C8-OH OH/C OH/C14:2 OH/COH OH/CO OH/C - - OH/COH/ OH/C Succinate F Carnitine Pyruvate C8:1-DC /C umarate 14:11 3 14 14:3 12:112 12:212 C10 Lactate 23 Citrate Leu/Ile Malate C5-OH C4-OH C4/Ci4 C5-DC C3-DC C7-DC 1 121 C10:1 C10:2 C10:3 C12:1 C12:2 C14:1 C14:2 C14:3 C14: C14: C14:C14 C18:1C18:C18 C18:2C18:C18 C18:3C18: C16:1C16: C16:2C16: C16:C16:3 C22:1 C22:2 C22:3 C22:4 C22:5 C20:1 C20:2 C20:3 C20:4 D D 4 3 C8:1 C5:1 16:1 16:161 C10 C12 C14 C14 C20 C18 C16 C16 C22 -KG Asx Phe ------Orn Met Arg Pro Glx Ser Gly Tyr His L/P Ala Val DC DC DC 44: 6: DC DC DC DC DC DC Cit C5 C3 C2 C8 C6 : C C 4 1 2 3 1 2 3 1 2 3 PH DKO PGC Figure 8

SUPPLEMENTAL MATERIAL

SUPPLEMENTAL METHODS

Animal Studies

All animal experiments and euthanasia protocols were conducted in strict accordance with the

NIH guidelines for humane treatment of animals and approved by the IACUC Committee at the

Sanford-Burnham Medical Research Institute at Lake Nona. Inducible adult PGC-1-/- mouse

model (PGC-1-/-) in C57BL/6Jxsv129 hybrid background has been previously described.1

PGC-1 double deficiency was induced by intraperitoneal injection of 50 mg/kg/day of

tamoxifen (Sigma) dissolved in sunflower seed oil (Sigma) for 2 days, at the age of 2 months,

and was compared directly with their sex-matched littermates injected with vehicle (PGC-1-/-).

Mice were harvested 1 month after injection of tamoxifen or vehicle as described below.

Mouse surgery

The transverse aortic constriction (TAC) and heart failure [combination of TAC and small apical

myocardial infarction (MI)] surgeries were performed on 8-week-old female C57BL/6J mice.

TAC, a surgical insult to induce compensated left ventricular hypertrophy (CH group), has been

described.2 Briefly, mice were anesthetized with ketamine [87mg/kg] / xylazine [13mg/kg].

Following aortic dissection through intercostal muscles, a 7.0 silk suture was tied around a blunt

26-28 gauge needle placed over the transverse aorta. The needle was removed to introduce a

tight constriction. Combining TAC and MI surgery is a method to induce decompensated heart

failure (HF group). Immediately following the TAC surgery, described above, a left

thoracotomy was performed, and the left ventricle and left main coronary artery system exposed.

The left anterior descending branch (LAD) of the left coronary artery was ligated with a 9-0 nylon suture approximately ¼ distance from the apex to create a small distal MI. Four weeks post-surgery, food was removed at 0900 for 4 hours, the bi-ventricle was excised as stated below for each endpoint. Prior to analysis of all samples, the infarcted apical region was removed from the hearts of the HF mice (and the corresponding cardiac tissue region was removed from sham- operated controls). 2-3 hearts per sample were pulverized, pooled, and split for transcriptomic and metabolomic analysis.

Echocardiography

Echocardiography was performed non-invasively using a Vevo 2100 microultrasound System

(VisualSonics Inc, Toronto, Ontario, Canada) as described.3,4 Briefly, adult mice were

anesthetized with an intraperitoneal injection (0.05 mg/g) of 2% avertin (to maintain heart rate

close to 600 beats per minute or above in order to evaluate left ventricular systolic function)

which was followed by continuous inhalation of 1-1.5% gaseous isoflurane administered through

a customized nose cone for detailed cardiac morphometric analysis. Complete 2D and Doppler

ultrasound examinations were performed using multiple views. The following cardiac function

parameters were collected: heart rate (HR), end diastolic volume (EDV), end systolic volume

(ESV), ejection fraction (EF), and velocity time integral (VTI) ratio, a ratio of blood flow

velocity at the TAC constriction band site to that at the left ventricular outflow tract, used to

confirm significant aortic constriction.

Exercise training

Mice were exercise-trained following a modified version of the voluntary running-wheel

protocol described previously.5 Two-month-old female C57BL/6J mice were housed for two months in individual cages equipped with 4.5” diameter Run-Around exercise wheels (Super Pet,

Elk Grove village, IL). The daily running distances were recorded and monitored via a Bell F12 cyclocomputer (Easton-Bell Sports, Van Nuys, CA). Mice that did not run at least 5 miles per night were removed from the study. Twenty-four hours after removing the exercise wheels, food was removed at 0900 for 4 hours, the bi-ventricle was excised, rinsed in PBS, and freeze- clamped in liquid nitrogen. 2-3 hearts per sample were pulverized, pooled, and split for transcriptomic and metabolomic analysis.

RNA analysis and RT-PCR

Total RNA was isolated from mouse bi-ventricle using the RNAzol method (Tel-Test).

Quantitative real-time RT-PCR was performed as previously described.6 In brief, total RNA was

isolated and reverse transcribed with AffinityScript QPCR cDNA Synthesis Kit (Ailgent

Technologies). PCR reactions were performed in triplicate in a 96-well format using a MX3005P

Real-Time PCR System (Stratagene). The mouse-specific primer sets (SYBR green) used to

detect specific gene expression can be found in Supplemental Table I. 36B4 primer set was

included in a separate well (in triplicate) and used to normalize the gene expression data.

Immunoblotting studies

Western blotting was performed with bi-ventricle protein lysates as previously described.7 The following antibodies were used: Rabbit polyclonal MCAD antibody;8 Rabbit polyclonal LCAD

and VLCAD antibodies (provided by Arnold W. Strauss, University of Cincinnati College of

Medicine) as previously described;9,10 Ribosomal protein s6 (S6RP) (Cell Signaling Technology

No. 2217S). Detection was performed by measuring the chemiluminescent signal as assayed by

SuperSignal Dura (Pierce).

Mitochondrial respiration

Mitochondrial respiration rates were determined on saponin-permeabilized left ventricular muscle strips with palmitoylcarnitine (20M)/malate (5mM) and pyruvate (10mM)/malate

(5mM) as substrate as previously described.11 In brief, 3 month-old female C57BL/6J mice (one

month post-surgery) were euthanized by CO2 inhalation. The muscle fibers from left ventricular

myocardium were separated and then transferred to a buffer (2.77 mM K2Ca-EGTA, 7.23 mM

K2EGTA, 6.56 mM MgCl2, 20 mM imidazole, 53.3 mM K-MES, 20 mM taurine, 5.3 mM ATP,

15 mM PCr, 3 mM KH2PO4, 0.5 mM DTT [pH 7.1]) supplemented with 50 g/ml saponin and

permeabilized for 30 min at 4°C with gentle stirring. Fibers were then washed twice for 10 min

each (2.77 mM K2Ca-EGTA, 7.23 mM K2EGTA, 1.38 mM MgCl2, 20 mM imidazole, 100 mM

K-MES, 20 mM taurine, 3 mM KH2PO4, 0.5 mM DTT, 2 mg/ml BSA [pH 7.1]). Mitochondrial

respiration was performed at 25°C using an optical probe (Oxygen FOXY Probe, Ocean Optics).

Following measurement of basal respiration, maximal (ADP-stimulated) respiration was

determined by exposing the mitochondria to 1 mM ADP. Uncoupled respiration was evaluated

following addition of oligomycin (1 μg/mL). The solubility of oxygen in the respiration buffer at

25°C was taken as 230 nmol O2 per milliliter. Respiration rates were expressed as “nmol O2 min−1 mg dry weight−1”.

Gene Microarray and Pathway Analysis

To conduct transcriptomimcs analysis in mouse heart, immediately following deep anesthesia by

intraperitoneal injection of pentobarbital (100mg/kg body weight), bi-ventricle was excised, rinsed in PBS, and freeze-clamped in liquid nitrogen. Total RNA was isolated from powdered bi- ventricle using the RNAzol method (Tel-Test). The Analytical Genomics Core at Sanford-

Burnham Medical Research Institute performed gene microarray studies by using Affymetrix

GeneChip Mouse Gene 1.0 ST array. The Affymetrix probe level data was processed using

Robust Multi-Array Analysis (RMA) to obtain normalized summary scores of expression for each probe set on each array.12 EBarrays was used to identify genes differentially expressed in

any two conditions,13,14 including CH vs sham, HF vs sham, Run (PH) vs Sedentary. R was used

for both RMA and EBarrays analysis. The list of genes with posterior probabilities greater than

or equal to 0.95 (expected FDR less than 5%) was considered a regulated gene. Linear regression

analysis (Igor Pro, Wavemetrics) was used to assess the correlation between the fold change in

gene expression between CH and HF models. All genes in the canonical pathways of Fatty Acid

Metabolism, Tricarboxylic Acid (TCA) Cycle, and Oxidative Phosphorylation defined by

Ingenuity Pathway Analysis (IPA; version #11631407) were used to create the heat map. The heat map for transcriptomics was generated by using the log2-transformed fold change of average expression in experimental group vs control, including CH vs sham, HF vs sham, Run

(PH) vs Sedentary, and PGC-1-/- vs. PGC-1-/- control. The gene array data discussed in this

publication have been deposited in NCBI’s Gene Expression Omnibus and are accessible

through GEO Series accession number GSE56348.

For pathway analysis, either upregulated or downregulated genes were uploaded into

Ingenuity Pathway Analysis (IPA) software. The core analysis was used to interpret data, and the

pathways were reviewed using the canonical pathways defined by IPA (Version #17199142).

The p value was calculated using Fisher’s exact test. The pathways with a p value less than 0.05

were considered a regulated pathway. Canonical pathways were grouped into the function category defined by IPA, which is ranked by the total number of pathways regulated in the category. To map the genes regulated in canonical pathways defined by IPA, the genes with posterior probabilities greater than or equal to 0.95 were uploaded into IPA, and the genes in the biopathway as well as the directionality of regulation were reviewed.

Metabolomics

To conduct metabolomics analysis in mouse heart, immediately following deep anesthesia by intraperitoneal injection of pentobarbital (100mg/kg body weight), bi-ventricle was excised, rinsed in PBS, and freeze-clamped in liquid nitrogen. Specimens of powdered bi-ventricle tissue were diluted 20-fold (mass:volume) in 50% acetonitrile supplemented with 0.3% formate

(acylcarnitines, amino acids, and organic acids). Samples were homogenized in a TissueLyser II

(Qiagen). Tissue extracts were derivatized and analyzed as previously described.15 Levels of amino acids, acylcarnitines, and organic acids were determined using stable isotope dilution techniques. Amino acid and acylcarnitine measurements prepared from tissue homogenate and plasma were made using flow injection tandem mass spectrometry using sample preparation methods described previously.16,17 The data were acquired using a Waters AcquityTM UPLC system equipped with a TQ (triple quadrupole) detector and a data system controlled by

MassLynx 4.1 operating system (Waters, Milford, MA). Organic acids were quantified using methods described previously,18 employing Trace Ultra GC coupled to a Trace DSQ MS operating under Excalibur 1.4 (Thermo Fisher Scientific, Austin, TX). The heat map for metabolomics was generated using the log2-transformed fold change of average value of metabolites in experimental group vs control, including CH vs sham, HF vs sham, Run (PH) vs

Sedentary, and PGC-1-/- vs. PGC-1-/- control. Supplemental Table I. Mouse primer sequences for qRT-PCR analysis. Primers Forward Reverse Slc27a1 ATCTACGGGTTGACGGTGGTA GGTAGCGGCAGATTTCACCTA Cpt1b ATGTCTACCTCCGAAGCAGGA GCTGCTTGCACATTTGTGTTC Cpt2 GCACAGAAGCCTCTCTTGAATG TCTGTTTATCCTGAGCGAGCA Acadvl ATCTCTGCCCAGCGACTTT TTCTGGCTTGTCCAGAACTG Ehhadh GCCATAGTGATCTGTGGAGCA CCACCACTGGCTTCTGGTATC 36B4 TGGAAGTCCAACTACTTCCTCAA ATCTGCTGCATCTGCTTGGAG Supplemental Table II. Echocardiographic analysis of HF mice. Parameters Sham (HF) HF p-value N 5 23-24 HR (bpm) 615.0 ± 14.5 608.0 ± 36.84 0.44 EF (%) 69.51 ± 3.63 38.42 ± 12.78* <0.001 EDV (ul) 27.14 ± 6.08 59.69 ± 16.07* <0.001 ESV (ul) 8.35 ± 2.62 38.32 ± 17.10* <0.001 VTI ratio 0.63 ± 0.12 5.89 ± 1.71* <0.001 *p < 0.05 compared with sham (HF). HR, Heart rate; EF, Ejection fraction; EDV, End diastolic volume; ESV, End systolic volume; VTI ratio, Velocity time integral ratio. Supplemental Table III. Pathways containing upregulated or downregulated genes in CH myocardium. # of genes # of genes in Pathway/Function Categories Pathways p-Value regulated pathway Downregulated Lipid Metabolism LPS/IL-1 Mediated Inhibition of RXR Function 12 215 <0.001 PXR/RXR Activation 6 60 <0.001 Glutaryl-CoA Degradation 3 11 <0.001 AMPK Signaling 7 134 0.006 Noradrenaline and Adrenaline Degradation 3 29 0.012 Fatty Acid β-oxidation I 3 30 0.013 Ubiquinol-10 Biosynthesis (Eukaryotic) 2 12 0.014 Glutathione Redox Reactions I 2 16 0.026

AA Metabolism Valine Degradation I 4 18 <0.001 Isoleucine Degradation I 3 14 0.002 Branched-chain α-keto acid Dehydrogenase Complex 2 4 0.002 Tryptophan Degradation X (Mammalian, via Tryptamine) 2 17 0.030 Putrescine Degradation III 2 16 0.030 Dopamine-DARPP32 Feedback in cAMP Signaling 6 155 0.046

Cell Signaling G-Protein Coupled Receptor Signaling 11 253 0.004 Cellular Effects of Sildenafil (Viagra) 7 123 0.005 cAMP-mediated signaling 9 211 0.010 Aryl Hydrocarbon Receptor Signaling 6 139 0.023

Carbohydrate Metabolism Glutathione-mediated Detoxification 6 23 <0.001 tRNA Splicing 3 34 0.020

Lipid and AA Metabolism Tryptophan Degradation III (Eukaryotic) 3 20 0.004 Phenylalanine Degradation IV (Mammalian, via Side Chain) 2 13 0.020

Growth and Development HIF1α Signaling 5 101 0.026

Other Xenobiotic Metabolism Signaling 12 264 <0.001 Myo-inositol Biosynthesis 2 4 0.002 Serotonin Degradation 3 47 0.031 D-myo-inositol (1,4,5)-trisphosphate Degradation 2 18 0.037 Dopamine Degradation 2 20 0.042

Upregulated Immune/Inflammatory Response IL-8 Signaling 13 183 0.014 HMGB1 Signaling 8 92 0.019 iNOS Signaling 5 45 0.021 Regulation of IL-2 Expression in Activated and Anergic T Lymphocytes 7 82 0.021 Interferon Signaling 4 29 0.021 MIF-mediated Glucocorticoid Regulation 4 33 0.027 Chemokine Signaling 6 66 0.033 TREM1 Signaling 5 54 0.037 Toll-like Receptor Signaling 5 53 0.037 Prostanoid Biosynthesis 2 9 0.042 CXCR4 Signaling 10 152 0.046 CCR3 Signaling in Eosinophils 8 114 0.048

Growth and Development TGF-β Signaling 9 84 0.003 Role of NFAT in Cardiac Hypertrophy 14 182 0.004 VEGF Signaling 9 94 0.005 Cardiac Hypertrophy Signaling 16 219 0.005 Colorectal Cancer Metastasis Signaling 16 237 0.010 Glucocorticoid Receptor Signaling 16 264 0.022 Cardiomyocyte Differentiation via BMP Receptors 3 19 0.023 Melanocyte Development and Pigmentation Signaling 7 83 0.031 Neuregulin Signaling 7 93 0.032 Pancreatic Adenocarcinoma Signaling 8 111 0.042

Cell Signaling RhoA Signaling 11 114 0.003 Gap Junction Signaling 12 152 0.009 Sphingosine-1-phosphate Signaling 9 109 0.014 Synaptic Long Term Depression 10 142 0.026 Role of Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid Arthritis 17 300 0.030 α-Adrenergic Signaling 7 87 0.032 G Beta Gamma Signaling 7 97 0.038 D-myo-inositol-5-phosphate Metabolism 9 132 0.043

Cellular Assembly and Organization Remodeling of Epithelial Adherens Junctions 8 65 0.002 Epithelial Adherens Junction Signaling 12 142 0.005 Actin Nucleation by ARP-WASP Complex 6 64 0.016 Integrin Signaling 13 202 0.026 Axonal Guidance Signaling 23 440 0.034 Semaphorin Signaling in Neurons 5 51 0.040 Germ Cell-Sertoli Cell Junction Signaling 10 154 0.049

Cellular Movement Hepatic Fibrosis / Hepatic Stellate Cell Activation 21 137 <0.001 Macropinocytosis Signaling 7 73 0.012 Virus Entry via Endocytic Pathways 8 88 0.013 Inhibition of Matrix Metalloproteases 4 37 0.047

Lipid Metabolism PPARα/RXRα Activation 11 173 0.041

Other Endothelin-1 Signaling 14 167 0.003 eNOS Signaling 10 127 0.014 Role of Osteoblasts, Osteoclasts and Chondrocytes in Rheumatoid Arthritis 14 219 0.027 Apoptosis Signaling 7 89 0.032 Systemic Lupus Erythematosus Signaling 11 190 0.038 UDP-N-acetyl-D-galactosamine Biosynthesis II 2 9 0.042 The p value was calculated by Fisher's exact test. p<0.05 was considered statistically significant. Supplemental Table IV. Pathways containing upregulated or downregulated genes in HF myocardium. # of genes # of genes in Pathway/Function Category Pathways p-Value regulated pathway Downregulated Lipid Metabolism Fatty Acid β-oxidation I 8 30 <0.001 LPS/IL-1 Mediated Inhibition of RXR Function 20 215 <0.001 AMPK Signaling 14 134 <0.001 Glutaryl-CoA Degradation 4 11 <0.001 Oxidative Ethanol Degradation III 4 28 0.001 Mitochondrial L-carnitine Shuttle Pathway 4 18 0.002 Ethanol Degradation II 5 28 0.002 Ethanol Degradation IV 4 18 0.002 Noradrenaline and Adrenaline Degradation 5 29 0.002 Fatty Acid β-oxidation III (Unsaturated, Odd Number) 2 3 0.003 Stearate Biosynthesis I (Animals) 5 34 0.005 Fatty Acid α-oxidation 3 14 0.009 PXR/RXR Activation 6 60 0.011 Glutathione Redox Reactions I 3 16 0.013 Triacylglycerol Degradation 3 23 0.042

AA Metabolism Tryptophan Degradation X (Mammalian, via Tryptamine) 4 17 0.002 Putrescine Degradation III 4 16 0.002 Leucine Degradation I 3 9 0.003 TR/RXR Activation 8 85 0.006 Branched-chain α-keto acid Dehydrogenase Complex 2 4 0.007 Valine Degradation I 3 18 0.021

Immune/Inflammatory Response IL-22 Signaling 4 24 0.008 Role of JAK2 in Hormone-like Cytokine Signaling 4 34 0.022 IL-9 Signaling 4 35 0.027 IL-3 Signaling 6 71 0.029 IL-2 Signaling 5 55 0.033

Carbohydrate Metabolism Glutathione-mediated Detoxification 5 23 0.001 Type II Diabetes Mellitus Signaling 10 127 0.005 Glycolysis I 3 22 0.037 Gluconeogenesis I 3 23 0.042

Cell Signaling Aryl Hydrocarbon Receptor Signaling 11 139 0.004 RAR Activation 13 172 0.004 G-Protein Coupled Receptor Signaling 14 253 0.043

Growth and Development Growth Hormone Signaling 7 69 0.007 FGF Signaling 7 86 0.023 Thrombopoietin Signaling 5 58 0.035

Lipid and AA Metabolism Proline Degradation 2 2 0.001 Tryptophan Degradation III (Eukaryotic) 4 20 0.003

Carbohydrate and AA Metabolism Alanine Degradation III 2 2 0.001 Alanine Biosynthesis II 2 2 0.001

Lipid and Carbohydrate Metabolism Acyl-CoA Hydrolysis 3 11 0.005

Cellular Assembly and Orgnization GDNF Family Ligand-Receptor Interactions 6 69 0.028

Other Xenobiotic Metabolism Signaling 20 264 <0.001 Dopamine Degradation 4 20 0.003 NRF2-mediated Oxidative Stress Response 13 182 0.005 Myo-inositol Biosynthesis 2 4 0.007 Histamine Degradation 3 13 0.009 Serotonin Degradation 5 47 0.011

Upregulated Immune/Inflammatory Response IL-8 Signaling 23 183 <0.001 TREM1 Signaling 11 54 <0.001 Acute Phase Response Signaling 20 163 <0.001 Leukocyte Extravasation Signaling 22 193 <0.001 Dendritic Cell Maturation 18 172 0.001 Macropinocytosis Signaling 11 73 0.001 Agranulocyte Adhesion and Diapedesis 19 166 0.001 Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses 13 95 0.001 Granulocyte Adhesion and Diapedesis 16 155 0.005 Fcγ Receptor-mediated Phagocytosis in Macrophages and Monocytes 11 94 0.007 Prostanoid Biosynthesis 3 9 0.009 Altered T Cell and B Cell Signaling in Rheumatoid Arthritis 9 84 0.012 T Helper Cell Differentiation 8 68 0.012 HMGB1 Signaling 10 92 0.018 Natural Killer Cell Signaling 10 95 0.021 IL-17 Signaling 8 68 0.021 NF-κB Activation by Viruses 8 76 0.029 IL-6 Signaling 11 116 0.030 Antiproliferative Role of TOB in T Cell Signaling 4 26 0.037 NF-κB Signaling 13 161 0.041 Role of IL-17F in Allergic Inflammatory Airway Diseases 5 42 0.044

Growth and Development ILK Signaling 24 178 <0.001 Colorectal Cancer Metastasis Signaling 27 237 <0.001 VEGF Signaling 14 94 <0.001 Factors Promoting Cardiogenesis in Vertebrates 13 88 <0.001 Ovarian Cancer Signaling 16 134 0.001 Pancreatic Adenocarcinoma Signaling 14 111 0.001 Wnt/β-catenin Signaling 18 168 0.002 TGF-β Signaling 11 84 0.003 Neuregulin Signaling 11 93 0.003 HGF Signaling 12 101 0.004 Human Embryonic Stem Cell Pluripotency 14 142 0.006 Role of NFAT in Cardiac Hypertrophy 17 182 0.006 Melanocyte Development and Pigmentation Signaling 10 83 0.008 IGF-1 Signaling 11 98 0.009 Bladder Cancer Signaling 10 88 0.011 HIF1α Signaling 11 101 0.012 HER-2 Signaling in Breast Cancer 9 77 0.012 Chronic Myeloid Leukemia Signaling 10 98 0.018 Cardiac Hypertrophy Signaling 18 219 0.024 Molecular Mechanisms of Cancer 25 347 0.028 VEGF Family Ligand-Receptor Interactions 8 74 0.031 Salvage Pathways of Pyrimidine Ribonucleotides 8 80 0.049

Role of Macrophages, Fibroblasts and Endothelial Cells in Cell Signaling Rheumatoid Arthritis 35 300 <0.001 RhoA Signaling 13 114 0.005 Signaling by Rho Family GTPases 21 239 0.006 Aryl Hydrocarbon Receptor Signaling 14 139 0.006 Sphingosine-1-phosphate Signaling 12 109 0.006 Caveolar-mediated Endocytosis Signaling 9 78 0.009 Rac Signaling 11 112 0.014 Gα12/13 Signaling 12 118 0.015 p38 MAPK Signaling 11 111 0.023 RhoGDI Signaling 15 184 0.028 PI3K/AKT Signaling 11 131 0.038 Gap Junction Signaling 13 152 0.039 Docosahexaenoic Acid (DHA) Signaling 5 39 0.039 Phospholipase C Signaling 17 232 0.048

Cellular Assembly and Organization Epithelial Adherens Junction Signaling 20 142 <0.001 Axonal Guidance Signaling 40 440 <0.001 Clathrin-mediated Endocytosis Signaling 21 180 <0.001 Actin Cytoskeleton Signaling 21 222 0.002 Remodeling of Epithelial Adherens Junctions 9 65 0.005 Integrin Signaling 18 202 0.010 Paxillin Signaling 11 108 0.012 Ephrin Receptor Signaling 16 188 0.013 Germ Cell-Sertoli Cell Junction Signaling 14 154 0.020 Semaphorin Signaling in Neurons 6 51 0.046

Cellular Movement Hepatic Fibrosis / Hepatic Stellate Cell Activation 34 137 <0.001 Inhibition of Matrix Metalloproteases 8 37 <0.001 Atherosclerosis Signaling 14 117 0.001 Glioma Invasiveness Signaling 9 57 0.002 Role of Tissue Factor in Cancer 13 110 0.003 Virus Entry via Endocytic Pathways 11 88 0.004 Regulation of the Epithelial-Mesenchymal Transition Pathway 17 185 0.010 Oncostatin M Signaling 5 33 0.025

Lipid Metabolism LXR/RXR Activation 11 114 0.024 Fatty Acid α-oxidation 3 14 0.026 Eicosanoid Signaling 7 60 0.033 Hepatic Cholestasis 12 135 0.033 PPARα/RXRα Activation 14 173 0.041

AA Metabolism Proline Biosynthesis I 2 4 0.015 Tryptophan Degradation X (Mammalian, via Tryptamine) 3 17 0.046 Putrescine Degradation III 3 16 0.046

Other Role of Osteoblasts, Osteoclasts and Chondrocytes in Rheumatoid Arthritis 27 219 <0.001 eNOS Signaling 14 127 0.004 Amyotrophic Lateral Sclerosis Signaling 11 97 0.009 Intrinsic Prothrombin Activation Pathway 5 32 0.009 Dopamine Degradation 4 20 0.014 Endothelin-1 Signaling 15 167 0.020 Inhibition of Angiogenesis by TSP1 5 33 0.022 Tetrahydrofolate Salvage from 5,10-methenyltetrahydrofolate 2 5 0.024 Coagulation System 5 35 0.028 Superoxide Radicals Degradation 2 6 0.035 The p value was calculated by Fisher's exact test. p<0.05 was considered statistically significant. Supplemental Table V. Differentially expressed genes involved in fatty acid metabolism in CH and HF model. CH HF Symbol Gene Name FC DE FC DE Aldh1a1 aldehyde dehydrogenase family 1, subfamily A1 1.470 1.000 1.587 1.000 Gcdh glutaryl-Coenzyme A dehydrogenase -1.385 0.999 -1.361 1.000 Slc27a1 solute carrier family 27 (fatty acid transporter), member 1 -1.443 0.999 -1.490 1.000 Eci1 enoyl-Coenzyme A delta isomerase 1 -1.289 0.557 -1.357 1.000 Acaa2 acetyl-Coenzyme A acyltransferase 2 -1.335 0.545 -1.404 1.000 Adhfe1 alcohol dehydrogenase, iron containing, 1 -1.274 0.104 -1.427 1.000 Aldh4a1 aldehyde dehydrogenase 4 family, member A1 -1.256 0.535 -1.335 1.000 Aldh2 aldehyde dehydrogenase 2, mitochondrial -1.307 0.985 -1.418 1.000 Ech1 enoyl coenzyme A hydratase 1, peroxisomal -1.366 0.674 -1.458 1.000 Ehhadh enoyl-Coenzyme A, hydratase/3-hydroxyacyl Coenzyme A dehydrogenase -1.486 0.984 -1.908 1.000 Aldh9a1 aldehyde dehydrogenase 9, subfamily A1 -1.205 0.050 -1.361 1.000 Aldh1a2 aldehyde dehydrogenase family 1, subfamily A2 1.292 0.209 1.319 0.999 Acadvl acyl-Coenzyme A dehydrogenase, very long chain -1.232 0.106 -1.267 0.998 Cpt2 carnitine palmitoyltransferase 2 -1.297 0.827 -1.321 0.998 Cpt1a carnitine palmitoyltransferase 1a, liver -1.202 0.328 -1.348 0.998 Acsl5 acyl-CoA synthetase long-chain family member 5 1.238 0.319 1.257 0.996 Hadh hydroxyacyl-Coenzyme A dehydrogenase -1.220 0.040 -1.264 0.994 Acsl6 acyl-CoA synthetase long-chain family member 6 -1.195 0.536 -1.242 0.953 Hsd17b10 hydroxysteroid (17-beta) dehydrogenase 10 -1.309 1.000 -1.235 0.937 FC, fold change; DE, posterior probability of differential expression; genes with DE ≥ 0.950 (bolded) are significantly regulated. Supplemental Table VI. Differentially expressed genes involved in amino acid degradation pathways in CH and HF. AA Degradation CH HF Symbol Gene Name Pathway FC DE FC DE Acaa2 acetyl-Coenzyme A acyltransferase 2 Ile -1.335 0.545 -1.404 1.000 Aldh2 aldehyde dehydrogenase 2, mitochondrial Trp, Phe -1.307 0.985 -1.418 1.000 Aldh4a1 aldehyde dehydrogenase 4 family, member A1 Pro, Trp -1.256 0.535 -1.335 1.000 Aldh9a1 aldehyde dehydrogenase 9, subfamily A1 Trp, Phe -1.205 0.050 -1.361 1.000 Bckdhb branched chain ketoacid dehydrogenase E1, beta polypeptide Val, Leu, Ile -1.357 0.988 -1.458 1.000 Ech1 enoyl coenzyme A hydratase 1, peroxisomal Val, Leu, Ile -1.366 0.674 -1.458 1.000 Ehhadh enoyl-Coenzyme A, hydratase/3-hydroxyacyl Coenzyme A dehydrogenase Trp, Val, Leu, Ile -1.486 0.984 -1.908 1.000 Gcdh glutaryl-Coenzyme A dehydrogenase Trp -1.385 0.999 -1.361 1.000 Gpt glutamic pyruvic transaminase, soluble Ala -1.247 0.818 -1.408 1.000 Gpt2 glutamic pyruvate transaminase (alanine aminotransferase) 2 Ala -1.495 0.999 -1.592 1.000 Maob monoamine oxidase B Trp, Phe -1.484 0.994 -1.631 1.000 Mccc1 methylcrotonoyl-Coenzyme A carboxylase 1 (alpha) Leu -1.255 0.705 -1.309 1.000 Mccc2 methylcrotonoyl-Coenzyme A carboxylase 2 (beta) Leu -1.285 0.871 -1.300 1.000 Prodh proline dehydrogenase Pro -1.250 0.397 -1.351 1.000 Aldh1a1 aldehyde dehydrogenase 1 family, member A1 Val, Trp 1.470 1.000 1.587 1.000 Bckdha branched chain ketoacid dehydrogenase E1, alpha polypeptide Val, Leu, Ile -1.309 0.970 -1.311 0.999 Ivd isovaleryl coenzyme A dehydrogenase Leu -1.230 0.021 -1.319 0.999 Aldh1a2 aldehyde dehydrogenase 1 family, member A2 Val, Trp 1.292 0.209 1.319 0.999 Abat 4-aminobutyrate aminotransferase Val 1.317 0.937 1.329 0.999 Acadvl acyl-Coenzyme A dehydrogenase, very long chain Val, Leu, Ile -1.232 0.106 -1.267 0.998 Hadh hydroxyacyl-Coenzyme A dehydrogenase Ile, Trp -1.220 0.040 -1.264 0.994 H2-Ke6 H2-K region expressed gene 6 Trp -1.114 0.011 -1.220 0.990 Maoa monoamine oxidase A Trp, Phe 1.222 0.872 1.276 0.976 Pcca propionyl-Coenzyme A carboxylase, alpha polypeptide Val, Ile -1.133 0.014 -1.209 0.975 Hsd17b10 hydroxysteroid (17-beta) dehydrogenase 10 Trp, Ile -1.309 1.000 -1.235 0.937 Bcat2 branched chain aminotransferase 2, mitochondrial Val, Ile -1.302 1.000 -1.225 0.838 Kmo kynurenine 3-monooxygenase (kynurenine 3-hydroxylase) Trp 1.248 0.957 1.139 0.032 FC, fold change; DE, posterior probability of differential expression; genes with DE≥ 0.950 (bolded) are significantly regulated. Supplemental Table VII. Metabolite measurements from CH, HF, and PH hearts. Metabolites Sham-CH CH Sham-HF HF Sed PH (Run) (n=6) (n=6) (n=6) (n=6) (n=6) (n=6) Acylcarnitine C0 1076.580±97.603 957.148±118.173* 985.941±77.057 727.739±91.744* 1116.819±77.572 1075.371±96.877 C2 116.975±9.806 130.096±33.841 150.543±18.575 219.455±32.981* 136.486±22.294 118.301±24.004 C3 5.005±0.775 3.761±0.498* 6.552±0.418 5.008±0.826* 5.736±0.560 4.513±0.611* C4/Ci4 1.562±0.507 1.291±0.278 1.904±0.351 2.522±0.868 1.167±0.353 1.278±0.757 C5:1 1.985±0.207 1.979±0.230 1.794±0.317 1.982±0.281 1.372±0.177 1.102±0.171* C5 0.810±0.170 1.007±0.163 0.826±0.144 1.101±0.420 0.595±0.124 0.656±0.191 C4-OH 2.699±0.459 2.751±1.020 3.648±0.557 7.705±3.270* 2.419±0.875 1.236±0.588* C6 0.303±0.070 0.314±0.087 0.372±0.070 0.558±0.160* 0.216±0.050 0.156±0.060 C5-OH 2.403±0.378 2.920±0.371* 2.181±0.276 2.295±0.537 1.758±0.144 2.173±0.472 C3-DC 3.267±0.275 2.912±0.310 3.024±0.276 2.542±0.216* 2.511±0.187 2.154±0.318 C4-DC/Ci4-DC 10.255±0.991 5.652±1.590* 8.968±0.560 4.194±0.142* 6.699±0.617 5.939±0.689 C8:1 0.425±0.105 0.378±0.055 0.330±0.065 0.344±0.078 0.175±0.040 0.131±0.038 C8 0.188±0.032 0.163±0.026 0.213±0.064 0.232±0.071 0.105±0.017 0.103±0.036 C5-DC 0.144±0.080 0.142±0.014 0.227±0.082 0.125±0.020* 0.096±0.041 0.102±0.013 C8:1-OH/C6:1-DC 0.089±0.040 0.117±0.020 0.085±0.034 0.127±0.048 0.096±0.022 0.071±0.027 C6-DC/C8-OH 0.191±0.052 0.212±0.064 0.173±0.028 0.316±0.086* 0.252±0.067 0.150±0.031* C10:3 0.285±0.070 0.276±0.040 0.238±0.032 0.254±0.041 0.063±0.020 0.051±0.011 C10:2 0.046±0.009 0.054±0.015 0.054±0.030 0.044±0.016 0.030±0.023 0.019±0.007 C10:1 0.137±0.033 0.113±0.032 0.145±0.035 0.136±0.043 0.096±0.029 0.069±0.023 C10 0.229±0.038 0.178±0.073 0.262±0.096 0.313±0.106 0.138±0.033 0.117±0.048 C7-DC 0.037±0.018 0.027±0.008 0.039±0.017 0.040±0.024 0.051±0.018 0.035±0.019 C8:1-DC 0.027±0.016 0.042±0.009 0.032±0.012 0.040±0.011 0.044±0.014 0.024±0.009* C10-OH/C8-DC 0.148±0.033 0.130±0.049 0.134±0.029 0.251±0.096* 0.183±0.091 0.080±0.007* C12:2 0.043±0.023 0.038±0.015 0.028±0.013 0.031±0.010 0.033±0.013 0.023±0.005 C12:1 0.223±0.017 0.205±0.065 0.228±0.037 0.332±0.098 0.174±0.053 0.105±0.013* C12 0.272±0.058 0.242±0.073 0.503±0.192 0.692±0.257 0.337±0.132 0.183±0.067* C12:2-OH/C10:2-DC 0.168±0.071 0.116±0.017* 0.144±0.025 0.121±0.031 0.094±0.033 0.055±0.004* C12:1-OH/C10:1-DC 0.120±0.029 0.128±0.044 0.120±0.020 0.212±0.081* 0.107±0.021 0.069±0.014* C12-OH/C10-DC 0.113±0.048 0.105±0.036 0.151±0.023 0.301±0.121* 0.220±0.112 0.106±0.034* C14:3 0.029±0.010 0.017±0.010 0.029±0.007 0.024±0.013 0.031±0.011 0.019±0.006 C14:2 0.338±0.102 0.247±0.063 0.401±0.103 0.425±0.091 0.208±0.056 0.133±0.053* C14:1 0.588±0.125 0.583±0.276 1.025±0.209 1.407±0.400 0.782±0.257 0.442±0.155* C14 0.762±0.182 0.778±0.316 1.632±0.516 2.465±0.894 1.014±0.324 0.594±0.279 C14:3-OH/C12:3-DC 0.013±0.004 0.017±0.009 0.012±0.008 0.013±0.014 0.016±0.011 0.011±0.007 C14:2-OH/C12:2-DC 0.144±0.052 0.139±0.055 0.139±0.028 0.240±0.079* 0.130±0.058 0.074±0.024 C14:1-OH/C12:1-DC 0.229±0.036 0.273±0.110 0.338±0.065 0.670±0.230* 0.380±0.119 0.172±0.051* C14-OH/C12-DC 0.219±0.039 0.222±0.114 0.401±0.071 0.793±0.286* 0.532±0.230 0.193±0.054* C16:3 0.077±0.017 0.075±0.037 0.113±0.022 0.183±0.048* 0.074±0.032 0.038±0.012 C16:2 0.508±0.108 0.490±0.274 1.052±0.284 1.688±0.375* 0.455±0.186 0.213±0.092* C16:1 0.684±0.156 0.802±0.433 1.640±0.479 2.743±0.582* 1.117±0.476 0.584±0.258* C16 2.373±0.745 2.639±1.131 5.590±2.191 8.142±2.650 2.540±0.818 1.656±1.145 C16:3-OH/C14:3-DC 0.021±0.006 0.020±0.011 0.027±0.008 0.048±0.014* 0.022±0.011 0.015±0.003 C16:2-OH/C14:2-DC 0.190±0.048 0.210±0.080 0.302±0.065 0.512±0.142* 0.220±0.084 0.096±0.031* C16:1-OH/C14:1-DC 0.222±0.041 0.300±0.136 0.465±0.136 0.909±0.265* 0.441±0.195 0.189±0.068* C16-OH/C14-DC 0.426±0.091 0.552±0.312 0.977±0.333 1.960±0.494* 0.930±0.531 0.310±0.130* C18:3 0.487±0.077 0.629±0.358 1.067±0.301 1.572±0.231* 0.438±0.159 0.199±0.120* C18:2 2.768±0.822 3.757±2.426 7.187±2.752 12.196±2.736* 2.098±0.900 1.249±1.152 C18:1 2.484±0.721 3.232±1.970 6.400±2.280 10.956±2.800* 2.603±0.924 1.702±1.353 C18 1.052±0.239 1.000±0.463 2.233±1.014 3.055±1.134 0.841±0.218 0.597±0.356 C18:3-OH/C16:3-DC 0.093±0.023 0.104±0.043 0.140±0.030 0.258±0.078* 0.096±0.044 0.036±0.008* C18:2-OH/C16:2-DC 0.620±0.102 1.052±0.749 1.464±0.445 3.353±0.835* 0.761±0.402 0.276±0.127* C18:1-OH/C16:1-DC 0.556±0.140 0.898±0.641 1.343±0.417 3.015±0.627* 1.056±0.547 0.370±0.148* C18-OH/C16-DC 0.184±0.047 0.195±0.085 0.338±0.083 0.638±0.156* 0.254±0.108 0.120±0.065* C20:4 0.269±0.060 0.220±0.090 0.352±0.047 0.354±0.117 0.201±0.060 0.090±0.118 C20:3 0.094±0.020 0.102±0.047 0.156±0.030 0.233±0.071* 0.057±0.022 0.061±0.045 C20:2 0.093±0.020 0.099±0.063 0.217±0.055 0.469±0.078* 0.075±0.024 0.062±0.051 C20:1 0.151±0.027 0.137±0.083 0.331±0.134 0.634±0.139* 0.095±0.055 0.086±0.067 C20 0.066±0.018 0.082±0.036 0.132±0.029 0.211±0.050* 0.054±0.014 0.037±0.024 C20:3-OH/C18:3-DC 0.045±0.013 0.045±0.009 0.048±0.007 0.066±0.023 0.039±0.025 0.019±0.014 C20:2-OH/C18:2-DC 0.073±0.019 0.082±0.028 0.098±0.018 0.223±0.079* 0.070±0.044 0.062±0.017 C20:1-OH/C18:1-DC 0.038±0.014 0.045±0.030 0.078±0.037 0.166±0.063* 0.047±0.016 0.025±0.006* C20-OH/C18-DC/C22:6 0.077±0.019 0.061±0.027 0.109±0.023 0.082±0.042 0.028±0.018 0.012±0.014 C22:5 0.038±0.014 0.032±0.022 0.049±0.024 0.039±0.030 0.015±0.010 0.025±0.023 C22:4 0.016±0.009 0.022±0.013 0.029±0.011 0.016±0.011 0.018±0.009 0.011±0.012 C22:3 0.006±0.005 0.005±0.004 0.007±0.005 0.009±0.009 0.010±0.010 0.007±0.004 C22:2 0.014±0.006 0.010±0.006 0.010±0.004 0.016±0.008 0.008±0.003 0.008±0.007 C22:1 0.044±0.007 0.047±0.013 0.046±0.021 0.052±0.008 0.043±0.013 0.031±0.007 C22 0.024±0.008 0.030±0.015 0.022±0.009 0.031±0.019 0.023±0.010 0.012±0.004* Metabolites Sham-CH CH Sham-HF HF Sed PH (Run) (n=6) (n=6) (n=6) (n=6) (n=6) (n=6) Organic Acid lactate 3380.2±314.5 3789.5±1032.9 4459.8±383.9 6975.6±2021.1* 5955.1±786.4 4558.7±544.6* succinate 362.5±12.6 340.8±48.6 447.3±33.1 538.2±72.5* 472.4±31.4 319.1±31.0* malate 1007.5±92.9 858.4±110.4 1030.6±62.8 752.7±85.6* 726.6±125.6 462.2±39.7* citrate 422.5±26.3 386.1±40.3 375.3±28.3 312.8±55.9 371.6±48.8 343.9±33.2 pyruvate 20.7±2.3 22.1±3.5 23.5±2.8 22.1±3.4 20.0±2.4 20.1±3.1 fumarate 125.5±12.7 104.0±12.0* 133.4±9.6 97.4±10.7* 109.7±12.8 66.8±5.5* a-KG 10.2±1.0 10.0±1.7 10.6±2.1 7.8±1.0* 7.6±1.1 7.0±0.9 Lactate/Pyruvate 164.9±24.2 172.8±44.9 190.5±12.1 324.7±122.0* 300.2±36.2 227.8±16.4* Amino Acid Glx 7493.8±812.1 6603.5±639.1* 6621.1±708.5 6156.5±582.7 9632.9±995.6 9486.3±945.4 Gly 657.0±52.7 668.8±101.4 625.5±56.9 676.9±62.9 563.3±67.7 570.0±60.3 Ala 1917.2±196.4 2072.6±133.1 2061.4±124.5 2166.5±241.1 1650.9±119.2 1382.3±70.9* Ser 1301.8±133.2 1533.6±135.2* 1201.4±121.9 1714.8±73.7* 938.7±77.5 979.1±67.7 Leu/Ile 510.9±66.0 427.8±30.6* 463.1±49.7 510.5±56.7 327.5±16.5 293.2±18.7* Asx 1594.9±183.9 1612.3±184.6 1538.8±170.1 1994.8±287.2* 2154.7±356.0 1616.7±217.4* Pro 98.6±8.0 133.7±28.0* 103.6±7.2 146.8±15.3* 68.0±9.6 72.7±4.3 Val 429.3±37.8 348.3±47.3* 353.3±34.3 411.2±66.6 201.0±20.2 214.3±29.7 Met 79.4±4.6 92.2±9.4* 77.2±8.9 97.1±6.1* 77.5±8.7 80.2±11.7 His 331.0±50.4 211.8±16.1* 295.6±40.4 250.7±39.6 242.1±31.1 242.8±26.9 Phe 80.3±8.7 89.1±11.9 81.2±7.6 104.8±7.1* 57.7±5.6 58.6±5.2 Tyr 97.5±24.8 104.2±13.7 89.5±12.7 102.4±10.1 61.4±8.6 72.8±11.4 Orn 36.1±3.8 41.8±5.3 53.5±17.2 45.5±6.9 31.4±3.9 34.0±3.4 Cit 293.5±50.9 264.4±36.3 288.8±27.5 264.9±30.5 207.0±16.7 207.6±21.4 Arg 298.4±31.6 257.4±14.5* 306.2±20.9 287.5±23.5 235.7±10.0 266.8±15.5* *p < 0.05 compared to corresponding controls. Supplemental Table VIII. Metabolite measurements from CH and HF plasma. Metabolites Sham-CH CH Sham-HF HF (n=5) (n=6) (n=7) (n=6) Acylcarnitine P-C0 30.5±6.7 31.6±5.6 30.3±5.8 30.2±4.9 P-C2 17.4±4.7 19.2±3.5 16.3±4.0 14.4±2.3 P-C3 0.36±0.05 0.31±0.06 0.30±0.06 0.20±0.04* P-C4/Ci4 0.40±0.17 0.33±0.10 0.86±0.40 0.76±0.36 P-C5:1 0.30±0.06 0.31±0.07 0.36±0.09 0.30±0.05 P-C5 0.112±0.021 0.124±0.066 0.147±0.063 0.124±0.046 P-C4-OH 0.157±0.060 0.213±0.061 0.162±0.029 0.116±0.029* P-C6 0.113±0.031 0.104±0.033 0.105±0.045 0.097±0.024 P-C5-OH 0.084±0.019 0.089±0.017 0.081±0.022 0.058±0.011* P-C3-DC 0.030±0.014 0.022±0.008 0.019±0.008 0.011±0.006 P-C4-DC/Ci4-DC 0.026±0.018 0.044±0.009 0.029±0.030 0.020±0.018 P-C8:1 0.023±0.009 0.027±0.010 0.021±0.008 0.021±0.008 P-C8 0.209±0.177 0.205±0.091 0.142±0.096 0.133±0.076 P-C5-DC 0.013±0.018 0.018±0.022 0.007±0.018 0.002±0.006 P-C8:1-OH/C6:1-DC 0.217±0.090 0.174±0.123 0.129±0.043 0.065±0.061 P-C6-DC 0.039±0.006 0.023±0.013* 0.038±0.013 0.015±0.011* P-C10:3 0.035±0.010 0.037±0.014 0.031±0.018 0.024±0.009 P-C10:2 0.008±0.005 0.016±0.009 0.017±0.010 0.010±0.010 P-C10:1 0.027±0.016 0.032±0.009 0.026±0.006 0.031±0.014 P-C10 0.027±0.020 0.027±0.017 0.027±0.016 0.031±0.017 P-C7-DC 0.009±0.007 0.017±0.011 0.012±0.007 0.020±0.015 P-C8:1-DC 0.060±0.016 0.064±0.023 0.048±0.007 0.046±0.021 P-C10-OH/C8-DC 0.014±0.031 0.252±0.258 0.027±0.072 0.071±0.112 P-C12:1 0.042±0.026 0.038±0.030 0.037±0.010 0.049±0.014 P-C12 0.082±0.012 0.085±0.011 0.075±0.013 0.076±0.014 P-C12-OH/C10-DC 0.007±0.006 0.012±0.010 0.005±0.007 0.006±0.006 P-C14:2 0.046±0.040 0.035±0.022 0.037±0.037 0.034±0.026 P-C14:1 0.060±0.019 0.068±0.017 0.063±0.016 0.065±0.011 P-C14 0.053±0.012 0.063±0.021 0.066±0.017 0.060±0.012 P-C14:1-OH 0.016±0.010 0.025±0.006 0.017±0.004 0.020±0.004 P-C14-OH/C12-DC 0.009±0.004 0.013±0.005 0.010±0.003 0.010±0.001 P-C16:2 0.021±0.009 0.024±0.005 0.021±0.010 0.020±0.005 P-C16:1 0.052±0.008 0.057±0.019 0.050±0.013 0.054±0.008 P-C16 0.126±0.031 0.132±0.037 0.153±0.020 0.144±0.041 P-C16:1-OH/C14:1-DC 0.017±0.007 0.023±0.005 0.013±0.003 0.017±0.005 P-C16-OH/C14-DC 0.010±0.003 0.011±0.003 0.014±0.005 0.011±0.004 P-C18:2 0.081±0.012 0.088±0.019 0.075±0.011 0.079±0.017 P-C18:1 0.164±0.034 0.182±0.051 0.162±0.017 0.166±0.046 P-C18 0.034±0.007 0.036±0.011 0.043±0.010 0.038±0.010 P-C18:2-OH 0.010±0.004 0.016±0.005* 0.009±0.006 0.011±0.002 P-C18:1-OH/C16:1-DC 0.027±0.010 0.029±0.005 0.022±0.003 0.026±0.003* P-C18-OH/C16-DC 0.008±0.017 0.037±0.061 0.027±0.047 0.014±0.035 P-C20:4 0.029±0.009 0.028±0.007 0.031±0.007 0.032±0.006 P-C20 0.004±0.001 0.004±0.001 0.006±0.002 0.004±0.002 P-C22 0.003±0.002 0.004±0.001 0.005±0.001 0.003±0.002 Amino Acid P-Glx 50.3±9.7 50.1±7.6 53.9±5.7 54.3±6.5 P-Gly 267.8±61.4 236.4±47.0 220.2±52.8 232.2±28.4 P-Ala 396.8±85.8 352.8±91.4 332.1±29.5 349.7±57.6 P-Ser 114.3±14.4 105.7±14.0 99.6±18.2 104.9±18.5 P-Leu/Ile 117.0±21.0 111.3±23.6 108.3±17.9 157.4±21.4* P-Asx 127.9±69.4 157.8±46.9 131.0±70.4 92.3±39.3 P-Pro 59.6±11.3 56.7±11.0 49.5±4.9 55.6±7.9 P-Val 118.5±14.8 113.5±22.4 117.5±18.5 133.2±15.6 P-Met 47.8±6.3 50.7±6.4 40.0±3.6 45.2±4.7* P-His 17.7±6.6 18.7±2.5 19.2±4.1 23.4±3.9 P-Phe 52.4±4.3 55.4±8.9 50.2±5.2 64.6±5.4* P-Tyr 68.9±7.8 67.6±12.5 61.1±9.9 65.4±8.8 P-Orn 42.3±5.8 39.5±9.2 31.9±4.3 38.4±6.3 P-Cit 54.5±12.9 54.9±10.5 43.5±7.9 60.4±11.7* P-Arg 93.4±19.6 87.7±18.0 86.4±10.5 90.5±14.9 *p < 0.05 compared to sham control. Supplemental Table IX. Cardiac hypertrophy in PH model. Parameters Sedentary PH (Run) p-value N1515 Body weight (g) 20.84 ± 1.50 21.67 ± 1.14 0.09 Bi-ventricle weight (g) 0.0889 ± 0.0102 0.1019 ± 0.0057* <0.001 Bi-ventricle w/BW 4.26 ± 0.29 4.71 ± 0.33* <0.001 *p < 0.05 compared to sedentary control. w/BW, weight/body weight ratio; g, grams Supplemental Table X. Differentially expressed genes in PH myocardium. PH (Run) Probe ID Symbol Gene Name FC DE 10359867 Lrrc52 leucine rich repeat containing 52 -1.553 1.000 10476793 NA NA -1.351 0.994 10476791 NA NA -1.443 0.989 10472426 Xirp2 xin actin-binding repeat containing 2 1.539 0.988 10488108 Esf1 ESF1, nucleolar pre-rRNA processing protein, homolog (S. cerevisiae) 1.513 0.986 10452516 Ankrd12 ankyrin repeat domain 12 1.378 0.981 FC, fold change; DE, posterior probability of differential expression; genes with DE ≥ 0.950 (bolded) are significantly regulated. Supplemental Figure I

ShamCH MI HF Supplemental Figure II

A.

TCA acetyl-CoA Citrate Aconitase Malate oxaloacetate synthase citrate isocitrate dehydrogenase Cis-Aconitate (s)-malate CoA

Fumarase (3S)-citryl-CoA Isocitrate oxalosuccinate dehydrogenase acetyl-CoA Succinate fumarate dehydrogenase Acetate

CO2 succinyl-CoA ThPP CO2 succinate 2-oxoglutarate Oxoglutarate dihydrolipoamide lipoamide dehydrogenase Succinyl-CoA Ligase

B. ETC/OXPHOS 4H+ 2 X 2H+ 2H+ 3H+

Intermembrane space Inner mitochondrial membrane Mitochondrial matrix NAD+ fumarate ubiquinonel ATP NADH succinate ubiquinol O H OADP 2 2 H2O H+ phosphate

diphosphate triphosphate Supplemental Figure III

A. Sham (HF) HF Sham (HF) HF VLCAD MCAD LCAD S6RP S6RP

VLCAD LCAD MCAD

0.4 p=0.016 1.0 1.0 p=0.10 p=0.67 Sham (HF) 0.8 0.8 0.3 * HF 0.6 0.6 0.2 0.4 0.4 0.1 0.2 0.2 Arbitrary Units 0.0 0.0 0.0 B. Sedentary Run VLCAD LCAD MCAD S6RP

VLCAD LCAD MCAD 1.2 p=0.310.4 p=0.57 1.0 p=0.68 Sedentary 1.0 0.8 0.3 Run 0.8 0.6 0.6 0.2 0.4 0.4 0.1 0.2 0.2 Arbitrary Units 0.0 0.0 0.0

SUPPLEMENTAL FIGURE LEGENDS

Figure I. Representative ventricular remodeling in compensated (CH) and decompensated

(HF) pressure overload cardiac hypertrophy models. (A) Representative M-mode echocardiographic images through the mid-LV from 12-week-old female C57BL6/J mice one month after being subjected to sham, transverse aortic constriction (CH), small apical myocardial infarction (MI), or combined transverse aortic constriction with small myocardial infarction

(HF).

Figure II. Minimal changes in expression of myocardial genes involved in tricarboxylic acid

(TCA) cycle, electron transport chain (ETC)/oxidative phosphorylation (OXPHOS) in HF.

Pathway map showing genes significantly (DE  0.950) upregulated (pink) or downregulated

(green) in HF compared to sham control in canonical pathways such as (A) TCA and (B)

ETC/OXPHOS identified by Ingenuity Pathway Analysis. Complexes I through V in the

ETC/OXPHOS pathway are denoted as I, II, III, IV, and V. The ovals in the map represent enzyme complexes.

Figure III. Determination of fatty acid -oxidation (FAO) protein levels in HF samples.

Representative Western blots assessing levels of protein extracted from hearts of (A) HF and corresponding sham-operated controls and (B) sedentary and wheel-exercised mice (Run) for the following proteins: acyl-Coenzyme A dehydrogenase, very long chain (VLCAD), acyl-

Coenzyme A dehydrogenase, long-chain (LCAD) and medium chain (MCAD). Ribosomal protein S6 (S6RP) was used as loading control. Quantification of the blot analysis is shown at the bottom of each panel. Bars represent mean±S.E. *p< 0.05 compared with corresponding control

(n=5-6 per group).

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