CR, macronutrients and gene expression

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Hi All,

The pdf-available below paper is not yet in Medline. The data appear to

indicate

that CR is the predominant feature determining gene expression, irrespective of

the

macronutrient ratio. Would CR level out the changes in genes responding to

macronutrient levels that are seen ad lib?

The expression of genes for producing specific fats appear to be a possible

exception to the above suggestion that macronutrients matter minimally.

Ingrid Dahlman, a Linder, bet Arvidsson Nordström, Ingalena

Andersson,

Johan Lidén, Camilla Verdich, Thorkild IA Sørensen, Arner Nugenob

Changes in adipose tissue gene expression with energy-restricted diets in obese

women

Am J Clin Nutr 2005 81: 1275-1285.

ABSTRACT

.... Design: Forty obese women were randomly assigned to a moderate-fat,

moderate-carbohydrate diet or a low-fat, high-carbohydrate hypoenergetic (–600

kcal/d) diet for 10 wk. Subcutaneous adipose tissue samples were obtained before

and

after the diet period. High-quality RNA samples were obtained from 23 women at

both

time points, and these samples were hybridized to microarrays containing the

8500

most extensively described human genes. The results were confirmed by separate

messenger RNA measurements.

Results: Both diets resulted in weight losses of 7.5% of baseline body weight. A

total of 52 genes were significantly up-regulated and 44 were down-regulated as

a

result of the intervention, and no diet-specific effect was observed. No major

effect on lipid-specific transcription factors or genes regulating signal

transduction, lipolysis, or synthesis of acylglycerols was observed. Most

changes

were modest (<25% of baseline), but all genes regulating the formation of

polyunsaturated fatty acids from acetyl-CoA and malonyl-CoA were markedly

down-regulated (35–60% decrease).

Conclusions: Macronutrients have a secondary role in changes in adipocyte gene

expression after energy-restricted diets. The most striking alteration after

energy

restriction is a coordinated reduction in the expression of genes regulating the

production of polyunsaturated fatty acids.

INTRODUCTION

.... From a clinical point of view, human studies are of interest because the

regulation of function of human and rodent fat cells differs in many aspects

(6).

....

SUBJECTS AND METHODS

Subjects

.... The target macronutrient compositions of the 2 diets were as follows: for

the

low-fat diet, 20–25% of total energy from fat, 15% from protein, and 60–65% from

carbohydrate, and for the moderate-fat diet, 40–45% of total energy from fat,

15%

from protein, and 40–45% from carbohydrate. The randomization was done at the

coordinating center of the NUGENOB project in Copenhagen.

.... RESULTS

Clinical findings

All women completed the dietary intervention. Of the 40 obese women randomly

assigned to a diet group, we performed microarray experiments on 10 women in the

low-fat, high-carbohydrate group and 13 women in the moderate-fat,

moderate-carbohydrate group (see the " Microarray " section in Subjects and

Methods).

The clinical data on this subset of individuals are reported. BMI, body weight,

fat

cell size, age, and fasting plasma concentrations of insulin and glucose at

entry

and the changes in these variables during the intervention did not differ

significantly between the groups (Table 2). The decrease in body weight was 7.5%

in

both groups. There was no significant difference in self-reported, baseline

dietary

intake between the 2 groups of 10 and 13 women. The total amount of energy from

fat

during the interventions was within the targeted 40–45% in the moderate-fat

group

(42±3%) and close to the targeted 20–25% in the low-fat group (28±4%; P = 0.07

for

the change in fat as a percentage of energy from baseline). During the

intervention,

carbohydrate, protein, and fiber intakes were higher in the low-fat group than

in

the moderate-fat group. Values were 51±4% and 40±3% of energy (P < 0.001), 21±1%

and

19±1% of energy (P < 0.001), and 19±4 and 13±2 g/d (P < 0.001), respectively.

The

reduction in energy intake did not differ significantly between the 2 groups

(556±183 and 503±161 kcal/d for the low-fat and moderate-fat diet groups,

respectively). The ratio of saturated to monounsaturated to polyunsaturated

fatty

acids was 2:2:1 in the habitual diet and in the 2 intervention diets.

Self-reported

dietary intake corresponded with 1-d measured intake in the 2nd, 5th, and 7th

weeks

of the study (values not shown).

TABLE 2 Baseline and 10-wk measurements in women participating in the microarray

study1

----------------------------------

Measure Moderate-fat diet group (n = 13) Low-fat diet group (n = 10)

----------------------------------

------Baseline Change Baseline Change

-----------------------------------

Body weight (kg) 103.2±16.0 –7.8±2.72 102.8±11.9 –7.6±2.42

BMI (kg/m2) 37.3±5.1 –2.8±0.92 37.5±4.7 –2.8±0.92

Fat cell volume (pL) 836±141 –134±892 845±134 –136±992

Plasma insulin (mU/L) 11.7±7.3 –1.4±3.9 10.2±3.2 –1.2±4.0

Plasma glucose (mmol/L) 5.4±0.5 –0.2±0.33 5.3±0.3 –0.1±0.23

-------------------------------

1 All values are ±SD. There were no significant differences in baseline or

diet-induced changes in values between groups (two-sided unpaired t tests).

2,3 Significantly different from baseline (one-sample, two-sided t test): 2P <

0.001, 3P < 0.05.

Clinical variables were also investigated in all 40 subjects. The initial

clinical

profile, dietary data, and the changes in BMI, body weight, body fat, fat cell

volume, and plasma concentrations of glucose and insulin in response to the

intervention were not significantly different between the whole group and the

subgroup of subjects involved in the microarray experiment (values not shown).

Thus,

the 2 diets were equally effective in the whole group as in the subjects

participating in the microarray experiments. ...

Gene expression by microarray

In total, 3746 genes were present on >23, ie, one-half, of the arrays according

to

MICROARRAY SUITE criteria. Genes specifically expressed in the immune system,

such

as CD4, CD8, immunoglobulins, and co-stimulatory molecules, whose expression

would

indicate contamination with blood vessel tissue, were not detected on the

arrays.

Ninety-six genes with a statistically significant change in gene expression in

either subgroup or the pooled sample were identified, excluding 6 genes that

gave

such weak specific signals on the arrays that they were scored as absent on

40–46

microarrays by the software. Thus, a significant change in expression was

observed

for 2.5% of the genes present in more than one-half of the analyzed samples.

Fold

change (expression after diet divided by before diet) among these genes varied

in

the up-regulated group between 1.10 and 1.46 and in the down-regulated group

between

0.35 and 0.90, respectively. These genes are depicted in Table 3 (52 genes

up-regulated) and Table 4 (44 genes down-regulated). The expected number of

false

positives was 2 genes out of 84 significant genes in total in the whole

population;

2 genes out of 29 significant genes in the moderate-fat, moderate-carbohydrate

subgroup; and 3 genes out of 10 significant genes in the low-fat,

high-carbohydrate

subgroup.

TABLE 3 Genes up-regulated by diet1

----------------------------------

Locus link Gene All women (n = 23) Moderate-fat diet group (n = 13) Low-fat diet

group (n = 10) Absent

--------------------------------------------------------------------------------

CIDEA Cell-death-inducing DFFA-like effector A 1.46±0.492 1.54±0.53 1.40±0.47 1

CBR3 Carbonyl reductase 3 1.32±0.402 1.31±0.49 1.34±0.272 15

EPB41L4B Erythrocyte membrane protein band 4,1 like 4B 1.31±0.262 1.37±0.272

1.23±0.23 0

SRPX Sushi-repeat-containing protein, X chromosome 1.30±0.312 1.32±0.332

1.27±0.30 0

MT1X Metallothionein 1 1.29±0.402 1.36±0.42 1.22±0.38 0

RNASE4 Ribonuclease, RNase A family, 4 1.26±0.332 1.18±0.23 1.36±0.40 0

WISP2 WNT1 inducible signaling pathway protein 2 1.26±0.382 1.21±0.35 1.32±0.43

1

AEBP1 AE-binding protein 1 1.26±0.302 1.28±0.262 1.23±0.362 1

ESR1 Estrogen receptor 1 1.26±0.302 1.24±0.32 1.28±0.28 2

FXYD1 FXYD-domain-containing ion transport regulator 1 1.26±0.292 1.25±0.36

1.27±0.172 2

UGDH UDP-glucose dehydrogenase 1.25±0.442 1.17±0.31 1.37±0.55 0

SOD3 Superoxide dismutase 3, extracellular 1.25±0.282 1.23±0.32 1.28±0.23 0

DEPP Decidual protein induced by progesterone 1.24±0.312 1.27±0.232 1.21±0.40 0

EIF4B Eukaryotic translation initiation factor 4B 1.24±0.312 1.17±0.25 1.34±0.34

0

KHDRBS3 KH-domain-containing, RNA-binding, signal transduction associated 3

1.23±0.202 1.28±0.222 1.16±0.17 6

EIF4EBP2 Eukaryotic translation initiation factor 4E-binding protein 2

1.22±0.172

1.28±0.162 1.18±0.19 0

MATN2 Matrilin 2 1.22±0.232 1.20±0.182 1.25±0.29 0

SERPING1 Serine (or cysteine) proteinase inhibitor, clade G, member 1 1.20±0.222

1.21±0.26 1.19±0.16 0

HS.109798.0 Clone HS.109798.0 1.20±0.102 1.16±0.11 1.24±0.20 0

RFC1 Replication factor C (activator 1) 1, 145 kDa 1.19±0.222 1.26±0.232

1.11±0.17 2

MGST2 Microsomal glutathione S-transferase 2 1.19±0.242 1.21±0.29 1.15±0.18 2

RBPMS RNA-binding protein gene with multiple splicing 1.19±0.192 1.20±0.18

1.16±0.21

0

PTPRA Protein tyrosine phosphatase, receptor type, A 1.18±0.30 1.34±0.262

1.02±0.27

1

ATP5G2 ATP synthase, H+ transporting, mitochondrial F0 complex, subunit c

1.17±0.272

1.21±0.34 1.11±0.11 0

FLJ14249 Hypothetical protein similar to HS1-binding protein 3 1.17±0.232

1.17±0.25

1.17±0.22 0

CDKN1C Cyclin-dependent kinase inhibitor 1C (p57, Kip2) 1.17±0.192 1.13±0.15

1.20±0.24 0

DKC1 Dyskeratosis congenita 1, dyskerin 1.16±0.202 1.12±0.19 1.22±0.19 0

PDK2 Pyruvate dehydrogenase kinase, isoenzyme 2 1.16±0.182 1.18±0.23 1.14±0.07 3

IGFBP3 Insulin-like growth factor–binding protein 3 1.16±0.212 1.20±0.152

1.11±0.28

0

ZNF289 Zinc finger protein 289, ID1 regulated 1.16±0.242 1.22±0.262 1.08±0.192 0

ENPP2 Ectonucleotide pyrophosphatase–phosphodiesterase 2 (autotaxin) 1.15±0.222

1.15±0.20 1.14±0.25 0

RARS Arginyl-tRNA synthetase 1.14±0.51 1.09±0.68 1.24±0.18 12

RRAS Related RAS viral (r-ras) oncogene homolog 1.14±0.132 1.19±0.152 1.09±0.08

0

RPL10A Ribosomal protein L10a 1.14±0.20 1.17±0.162 1.09±0.25 0

WBP4 Domain-binding protein 4 (formin-binding protein 21) 1.13±0.24 1.03±0.27

1.28±0.122 0

SART1 Squamous cell carcinoma antigen recognized by T cells 1.13±0.30 1.10±0.38

1.18±0.142 2

PABPC4 Poly(A)-binding protein, cytoplasmic 4 1.14±0.122 1.16±0.102 1.11±0.16 0

GPX3 Glutathione peroxidase 3 (plasma) 1.13±0.172 1.15±0.21 1.10±0.11 0

SOX18 SRY (sex-determining region Y)-box 18 1.12±0.22 1.08±0.27 1.18±0.112 0

ADD3 Adducin 3 (gamma) 1.13±0.162 1.14±0.132 1.11±0.19 0

RPS6 Ribosomal protein S6 1.12±0.162 1.14±0.18 1.10±0.13 0

RRAS2 Related RAS viral (r-ras) oncogene homolog 2 1.11±0.18 1.18±0.172

1.00±0.11 0

LOC56920 Semaphorin sem2 1.11±0.19 1.22±0.152 0.99±0.17 0

EIF3S3 Eukaryotic translation initiation factor 3, subunit 3 1.11±0.152

1.11±0.16

1.10±0.15 0

USP1 Ubiquitin-specific protease 1 1.11±0.152 1.14±0.17 1.05±0.10 0

PLSCR4 Phospholipid scramblase 4 1.10±0.21 1.19±0.172 1.00±0.22 0

OS4 Conserved gene amplified in osteosarcoma 1.10±0.122 1.10±0.082 1.10±0.16 0

RPL15 Ribosomal protein L15 1.10±0.142 1.07±0.15 1.12±0.12 0

OAZIN Ornithine decarboxylase antizyme inhibitor 1.10±0.122 1.12±0.12 1.06±0.12

0

HMGN2 High-mobility group nucleosomal-binding domain 2 1.04±0.13 0.98±0.13

1.12±0.072 0

SNRPN Small nuclear ribonucleoprotein polypeptide N 1.01±0.16 0.94±0.18

1.10±0.082 0

------------------------------------

1 All values are ±SD. Expected false-positives: all women, 2; moderate-fat

group, 2;

low-fat group, 3. Values after compared with before are expressed as ratios and

were

compared by using the SAM method (significance analysis of microarrays; 12).

Absent,

number of samples having no specific, significant detectable signal array

according

to MICROARRAY SUITE (Affymetrix Inc, Santa Clara, CA) criteria.

2 Significant change in gene expression.

TABLE 4 Genes down-regulated by diet1

-----------------------------------

Locus link Gene All women (n = 23) Moderate-fat diet group (n = 13) Low-fat diet

group (n = 10) Absent

-----------------------------------

XLKD1 Extracellular link domain containing 1 0.90±0.34 0.80±0.112 1.02±0.46 1

HSPA9B Heat shock 70-kDa protein 9B (mortalin-2) 0.90±0.102 0.89±0.11 0.91±0.09

0

LDHA Lactate dehydrogenase A 0.89±0.122 0.89±0.13 0.88±0.11 0

SYBL1 Synaptobrevin-like 1 0.88±0.142 0.87±0.14 0.89±0.14 0

IDH1 Isocitrate dehydrogenase 1 (NADP+), soluble 0.87±0.132 0.90±0.13 0.82±0.11

0

SLC31A2 Solute carrier family 31 (copper transporters), member 2 0.86±0.152

0.85±0.17 0.86±0.12 0

GLB1 Galactosidase, ß1 0.85±0.142 0.83±0.17 0.88±0.11 0

GPX1 Glutathione peroxidase 1 0.85±0.162 0.82±0.142 0.88±0.19 0

ATP2C1 ATPase, Ca2+ transporting, type 2C, member 1 0.85±0.192 0.81±0.16

0.89±0.22 0

BZRP Benzodiazapine receptor (peripheral) 0.84±0.182 0.84±0.19 0.84±0.19 0

NEDD9 Neural precursor cell expressed, developmentally down-regulated 9

0.84±0.182

0.87±0.17 0.78±0.18 0

MMD Monocyte-to-macrophage differentiation-associated 0.84±0.142 0.83±0.162

0.84±0.11 0

RGN Regucalcin (senescence marker protein-30) 0.83±0.172 0.82±0.17 0.84±0.17 0

LYPLA1 Lysophospholipase I 0.83±0.202 0.82±0.19 0.84±0.22 0

OXCT 3-Oxoacid CoA transferase 0.83±0.172 0.78±0.162 0.89±0.17 2

DBI epam-binding inhibitor (GABA receptor modulator, acyl-coenzyme A binding

protein) 0.83±0.202 0.82±0.22 0.83±0.19 0

FAT FAT tumor suppressor homolog 1 (Drosophila) 0.83±0.162 0.83±0.122 0.82±0.21

0

TEM1 Tumor endothelial marker 1 precursor 0.82±0.202 0.81±0.24 0.84±0.14 0

H1F2 H1 histone family, member 2 0.82±0.202 0.79±0.23 0.85±0.17 0

TF Transferrin 0.81±0.232 0.80±0.22 0.82±0.27 1

GDF10 Growth differentiation factor 10 0.81±0.182 0.79±0.18 0.83±0.18 10

STK17A Serine-threonine kinase 17a (apoptosis-inducing) 0.81±0.182 0.79±0.23

0.84±0.09 1

TRIM16 Tripartite motif-containing 16 0.81±0.212 0.79±0.23 0.82±0.19 1

FMOD Fibromodulin 0.81±0.252 0.81±0.28 0.79±0.22 0

FGF13 Fibroblast growth factor 13 0.80±0.212 0.81±0.23 0.78±0.20 0

C20ORF24 Chromosome 20 open reading frame 24 0.80±0.142 0.81±0.16 0.77±0.11 0

ABCC6 ATP-binding cassette, subfamily C (CFTR/MRP), member 6 0.78±0.162

0.76±0.152

0.80±0.18 0

SC4MOL Sterol-C4-methyl oxidase-like 0.78±0.192 0.72±0.21 0.85±0.15 1

CDO1 Cysteine dioxygenase, type I 0.76±0.282 0.75±0.36 0.77±0.13 0

P1K3R3 Phosphoinositide-3-kinase, regulatory subunit, polypeptide 3, p55

0.76±0.222

0.79±0.21 0.73±0.24 0

TNMD Tenomodulin protein 0.75±0.272 0.76±0.30 0.73±0.24 0

ME1 Malic enzyme 1, NADP+-dependent, cytosolic 0.75±0.222 0.72±0.27 0.80±0.15 0

MME Membrane metallo-endopeptidase (neutral endopeptidase) 0.74±0.31 0.68±0.22

0.83±0.382 1

GYS1 Glycogen synthase 1 (muscle) 0.74±0.212 0.72±0.25 0.76±0.16 0

CRHBP Corticotropin-releasing hormone–binding protein 0.72±0.202 0.74±0.21

0.71±0.18

1

CDH13 Cadherin 13, H-cadherin (heart) 0.70±0.382 0.74±0.48 0.62±0.17 4

DHCR24 24-Dehydrocholesterol reductase 0.67±0.212 0.66±0.142 0.67±0.27 0

FASN Fatty acid synthase 0.64±0.262 0.58±0.28 0.74±0.23 0

ALDOC Aldolase C, fructose-bisphosphate 0.64±0.212 0.62±0.202 0.66±0.22 0

COL11A1 Collagen, type XI, 1 0.58±0.552 0.64±0.65 0.52±0.38 25

FADS1 Fatty acid desaturase 1 0.57±0.152 0.54±0.102 0.60±0.19 0

B4GALT6 ß,1,4-Galactosyltransferase 6 0.56±0.432 0.50±0.51 0.65±0.30 3

SCD Stearoyl-CoA desaturase (9-desaturase) 0.55±0.272 0.51±0.232 0.60±0.33 0

FADS2 Fatty acid desaturase 2 0.40±0.362 0.35±0.312 0.49±0.43 9

------------------------------------

1 All values are ±SD. Expected false-positives; all women, 2; moderate-fat

group, 2;

low-fat group, 3. Values after compared with before are expressed as ratios and

were

compared by using the SAM method (significance analysis of microarrays; 12).

Absent,

number of samples having no specific significant detectable signal array

according

to MICROARRAY SUITE (Affymatrix Inc, Santa Clara, CA) criteria.

2 Significant change in gene expression.

The pattern of response to the 2 diets was almost identical. Thus, expression of

the

same genes was up- or down-regulated in response to both the moderate- and the

low-fat diet except for 3 genes. For the latter, however, no significant

difference

in fold change between diet groups was observed. In particular, the

quantitatively

most up-regulated (ratio >1.25) or down-regulated (ratio <0.75) genes were

essentially the same for the 2 diets. Therefore, all subsequent analyses were

done

on data from both groups combined.

Among the genes appearing to be up- or down-regulated by the energy restriction,

none encoded lipolytic enzymes or enzymes involved in acylglycerol formation,

lipid-specific gene transcription, or signal transduction systems related to

lipolysis or lipogenesis except for the p55 regulatory subunit of

phosphoinositide-3-kinase (PIK3R3). The ratio of PIK3R3 expression after

compared

with before was 0.76. PIK3R3 is involved in insulin signaling (16). The

expression

of 4 genes known to be of importance in the regulation of body fat stores

according

to studies in knock-out mice were significantly altered. Both estrogen

receptor-1

and metallothionein-1 null mice develop obesity (17-19); these genes were

up-regulated in response to energy restriction (ratios of 1.26 and 1.29,

respectively). Loss of stearoyl-CoA desaturase-1 (SCD1) or cell-death-inducing

DFFA-like effector A (CIDEA) protects mice against obesity (20, 21); these genes

were down- and up-regulated, respectively, in response to energy restriction

(ratios

of 0.55 and 1.46, respectively). Transferrin, which was recently implicated in

obesity in a mouse model (22), was down-regulated by energy restriction (ratio

of

0.80).

Individual mRNA measurements

To confirm the microarray results, we performed individual mRNA measurements by

using RT-qPCR for 7 of the genes that showed a marked change in expression after

dieting. We studied samples from the subjects who were included (RNA1) and those

who

were not included (RNA2) in the array separately and combined (Table 5). In all

groups, the results were the same. The results for the RNA2 samples were

confirmed

with repeated cDNA syntheses with almost identical results (values not shown).

We

were able to confirm the change in mRNA expression for all except one gene,

ß-1,4-galactosyltransferase 6 (B4GALT6). Therefore, we measured B4GALT6 in only

one

RNA cohort. The inability to confirm the change in expression for B4GALT6 was

not

surprising because the SAM analysis assumed a few false-positive significant

changes

in gene expression. A careful examination of the microarray data showed that, in

most cases, the signal values for B4GALT6 were low. This was not the case for

the

other genes in Table 5. Thus, the microarray data with B4GALT6 might be

uncertain.

TABLE 5 Individual messenger RNA measurements

---------------------------------------

Locus link1 Accession no. RNA 12 (n = 21) P3 RNA 2 (n = 15) P Total (n = 36) P

-----------------------------------------

FADS 1 NM_013402 0.68±0.434 0.0003 0.64±0.31 0.0012 0.66±0.38 <0.0001

FADS 2 NM_004265 0.73±0.43 0.0044 0.66±0.70 0.0044 0.70±0.55 0.0003

FASN NM_004104 0.88±0.40 0.036 0.94±1.50 0.0036 0.90±0.99 0.0032

ALDOC NM_005165 0.81±0.29 0.0032 0.60±0.22 <0.0001 0.72±0.28 <0.0001

SCD5 NM_005063 0.47±0.41 0.0004 0.44±0.35 0.0007 0.45±0.38 <0.0001

CIDEA NM_001279 1.77±0.73 0.0004 1.32±0.56 0.021 1.58±0.69 <0.0001

B4GALT6 NM_004775 1.56±1.64 0.27 — — — —

-------------------------------------------

1 FADS 1, fatty acid desaturase 1; FADS 2, fatty acid desaturase 2; FASN, fatty

acid

synthase; ALDOC, fructose-bisphosphate aldolase C; SCD, stearoyl-CoA desaturase;

CIDEA, cell-death-inducing DFFA-like effector A; B4GALT6,

ß-1,4-galactosyltransferase 6.

2 Messenger RNA was quantified relative to the reference gene GAPDH. Shown are

ratios of these measurements after compared with before diet.

3 Paired t test (one-sided).

4 ±SD (all such values).

5 Messenger RNA from 3 samples was nondetectable and therefore these samples

were

not included in the analysis.

Pathway, biological function, and cluster analysis

All 23 individuals were analyzed together because no diet-specific effect on

gene

expression was observed in the results obtained from the SAM analysis. A total

of

330 genes met the MAPPFINDER criteria, and of those, 81 were not linked to a

map.

Thus, only 249 genes could be used to calculate the results. The z score was

based

on analysis of 6113 of the Human Genome Focus array genes annotated in the

MAPPFINDER database. Pathways or Gene Ontology biological function terms with z

scores >3 are depicted in Table 6. The most affected terms were heme

biosynthesis

and porphyrin biosynthesis. The same 4 genes met the criteria in both terms:

ALAD,

ALAS1, HMBS, and UROS. However, none of these genes showed up as being

significantly

changed in their expression in the SAM analysis. Expression of these 4 genes was

quantified by using RT-qPCR in all 23 individuals used in the pathway analysis.

A

significant difference in gene expression was obtained for HMBS only [fold

change

after compared with before low-energy diet: 0.91±0.24 arbitrary units (AU), P =

0.036]. For the other 3 genes, the fold changes were as follows: ALAD, 1.10±0.63

AU;

ALAS1, 1.06±0.26 AU; and UROS, 1.07±0.17 AU. Because only the change in HMBS

expression was significant in the individual RT-qPCR measurements, and no fold

change was >10%, we did not analyze the heme or porphyrin biosynthesis pathways

further.

TABLE 6 MAPPFINDER analysis of pathways and biological function terms1

-------------------------------------------

GenMAPP pathways or GO term Changed2 Measured3 Percentage changed z Score4

Source

-------------------------------------------

Heme biosynthesis 4 7 57 6.7 GO

Heme biosynthesis 4 7 57 6.7 GenMAPP

Porphyrin biosynthesis 4 8 50 6.2 GO

Fatty acid metabolism 14 79 18 5.7 GO

Carboxylic acid metabolism 2 18 118 15 5.7 GO

Organic acid metabolism 2 18 118 15 5.7 GO

Metabolism 18 121 15 5.5 GO

Fatty acid degradation 6 20 30 5.5 GenMAPP

Heme metabolism 4 10 40 5.4 GO

TCA intermediate metabolism 4 10 40 5.4 GO

Porphyrin metabolism 4 11 36 5.1 GO

Glycogen biosynthesis 3 7 43 4.9 GO

Glucan biosynthesis 3 7 43 4.9 GO

Polysaccharide biosynthesis 3 8 38 4.5 GO

Cytolysis 4 14 29 4.4 GO

Oxygen and reactive oxygen species metabolism 8 46 17 4.2 GO

Pigment biosynthesis 4 16 25 4.0 GO

Cell death 3 10 30 3.9 GO

Coenzymes and prosthetic group biosynthesis 6 33 18 3.8 GO

Peroxidase reaction 4 17 24 3.8 GO

O-Linked glycosylation 3 11 27 3.7 GO

Fatty acid synthesis 3 11 27 3.7 GenMAPP

Fatty acid ß-oxidation 3 11 27 3.7 GO

Energy pathways 2 4 18 22 3.6 GO

Pigment metabolism 4 18 22 3.6 GO

Cell adhesion 15 138 11 3.6 GO

Cell motility 11 90 12 3.5 GO

Ubiquitin cycle 5 28 18 3.4 GO

Sterol metabolism 6 39 15 3.3 GO

Fatty acid oxidation 3 13 23 3.2 GO

Protein-ligand–dependent protein catabolism 9 73 12 3.2 GO

Ubiquitin-dependent protein catabolism 9 73 12 3.2 GO

Glycogen metabolism 5 30 17 3.2 GenMAPP

Carboxylic acid biosynthesis 5 30 17 3.2 GO

Organic acid biosynthesis 5 30 17 3.2 GO

Oxidation of organic compounds 10 86 12 3.2 GO

Ribonucleoside triphosphate metabolism 4 22 18 3.1 GO

Purine ribonucleoside triphosphate metabolism 4 22 18 3.1 GO

Superoxide metabolism 2 7 29 3.1 GO

Aldehyde metabolism 2 7 29 3.1 GO

ATP metabolism 3 14 21 3.1 GO

------------------------------------------

1 MAPPFINDER, GenMAPP, and Gene Ontology (GO) are described in references 13-15.

TCA, tricarboxylic acid.

2 Number of genes fulfilling the criteria for change, ie, significant gene

expression present in 26 samples. Paired t test (two-sided) for change in

expression

of individual gene, P 0.05. Mean signal fold change >10% from baseline.

3 Total number of genes in the GenMAPP pathway or GO category.

4 MAPPFINDER ranks pathways by the z score, which is based on the percentage of

genes in each pathway or set that meets a user-defined criterion for change in

expression.

Next in rank were the Gene Ontology biological function terms fatty acid

metabolism,

carboxylic acid metabolism 2, organic acid metabolism 2, and metabolism. A

detailed

analysis of these terms showed that their rank in each case was above all caused

by

genes in the fatty acid degradation and fatty acid synthesis subgroups meeting

the

predefined criterion for change in expression. The effect on fatty acid turnover

was

further supported by the observation that fatty acid degradation and synthesis

were

the highest ranked GenMAPP pathways, next to the heme biosynthesis pathway.

Regarding fatty acid degradation, 6 genes formed a pathway from triacylglycerol

breakdown by lipoprotein lipase to production of acyl-CoA. These were long-chain

fatty acid CoA ligase 1 and 2, carnitine palmitoyl transferase 1, carnitine-acyl

carnitine translocase, and long-chain acyl-CoA dehydrogenase (13–26% change from

baseline values). However, this pathway is uncertain because none of the genes

were

among those showing a significant change in the SAM analysis presented in Tables

3

and 4. Two more genes from Tables 3 and 4 could be added to the pathway, namely,

acyl-CoA binding protein (ratio 0.83) and malic enzyme 1 (ratio 0.75), which

play

important roles in acyl-CoA transport between the mitochondria and the cytoplasm

(20).

Fatty acid synthesis according to GenMAPP contained several genes fulfilling the

SAM

criterion. Therefore, fatty acid synthesis was investigated in detail, as shown

in

Figure 1. All relevant genes involved in the final steps of synthesis of

polyunsaturated fatty acids were markedly down-regulated after dieting. The

pathway

started with fatty acid synthase (FASN; ratio of 0.64), which catalyzes the

formation of unsaturated fatty acids from malonyl-CoA and acetyl-CoA (23). The

next

step in this pathway is desaturation to monounsaturated fatty acids, which is

catalyzed by stearoyl-CoA desaturase-1 (ratio of 0.55) (24). The final step is

desaturation to polyunsaturated fatty acids, which is catalyzed by fatty acid

desaturase 1 (FADS1) and 2 (FADS2) (ratios of 0.57 and 0.40, respectively) (25).

It

is noteworthy that these genes were 4 of the 7 most down-regulated genes in the

microarray, all of which showed a significant change. GENE WEAVER produced no

obvious clusters, probably because of the small sample size and the rather small

number of genes undergoing a change in expression.

DISCUSSION

.... The expression of a limited number (~2.5%) of the genes in adipose tissue

was

changed in response to the energy restriction; the changes in expression varied

from

50% up-regulation to 60% down-regulation. Only a few genes showed a change in

expression that deviated >25% from the baseline value. This moderate effect on

gene

expression was expected because the energy restriction was mild (600 kcal

deficit/d), weight loss was moderate (7.5% of initial weight), and the treatment

period rather long (10 wk), which would allow for compensatory effects. It is

thus

possible that more marked effects could have been obtained with short-term

treatment

using very low calorie diets. This notion is supported by a recent microarray

study

of the effect of short-term, very-low-calorie diets on gene expression in

subcutaneous adipose tissue of obese subjects (26). The changes in expression

were

markedly different from our findings. There was, above all, a variation in

inflammatory genes.

The dietary data suggest that, in general, the subjects followed the food

instructions closely. Self-reported dietary intake corresponds well with

repeated

one-day food recordings. Thus, major differences in dietary intake of lipids and

carbohydrates were observed after the 2 types of intervention despite a similar

total energy intake. Nevertheless, weight loss and decrease in fat cell volume

induced by the intervention were on the same order of magnitude for both diets,

which suggests a superior role of energy restriction for loss of lipids in fat

cells

in the studied women. Concerning the role of macronutrients, there was no

important

difference in dietary-induced changes in gene expression when the moderate-fat,

moderate-carbohydrate and low-fat, high-carbohydrate diets were compared. This

indicates that adipose gene expression is also influenced by the energy deficit

and

not the macronutrient composition of the food, at least not the fat and

carbohydrate

contents. Our results agree with those of Viguerie et al (8), who, for 38

investigated genes, observed no significant differences in response between

low-fat,

high-carbohydrate and moderate-fat, moderate-carbohydrate diets. It should be

emphasized that the observed changes in mRNA expression in our study in general

were

modest and that a relatively small cohort was investigated. Therefore, type 2

statistical errors may have masked small differences in results between the

diets.

However, it is likely that such differences only relate to the magnitude of

changes

in gene expression and not to different expression patterns. Only 3 genes on the

arrays were regulated differently by the 2 diets.

Only 2 of the genes regulated by energy restriction in our study overlapped with

the

38 genes investigated by Viguerie et al (8): FASN and CIDEA, which were not

regulated by diet in their study. This discrepancy is not surprising because the

genetic and cultural background of the investigated subjects differed between

the

studies.

An unexpected finding was the lack of effect of energy restriction on common

genes

involved in the regulation of lipolysis or acylglycerol synthesis or signaling

pathways, such as G-protein, MAP kinase, insulin signal transduction

transcription

factors, and lipid-specific transcription factors. The expression of PIK3R3 was

25%

decreased. Although this gene is important for insulin signal transduction, it

is

unlikely that a small change in just one step of a complex signaling pathway has

an

important regulatory effect (16). As discussed, several coordinate changes in

gene

expression of a metabolic or signaling pathway should occur before the pathway

is

considered to have an important regulatory function (4, 5). The genes involved

in

the synthesis of polyunsaturated fatty acids fulfilled this criterion. Thus, the

expression of all genes involved in the transformation of acetyl-CoA and

malonyl-CoA

to polyunsaturated fatty acids was decreased during energy restriction. These

genes

(fatty acid synthase, stearoyl-CoA desaturase 1, and fatty acid desaturase 1 and

2)

were 4 of the 7 most down-regulated genes on the microarray (35–60% reduction

from

baseline). A third isoform of fatty acid desaturase is cloned in man (23). The

functional role of this gene is not known and it was not included in the

Affymetrix

chip. The pathway of inhibited production of fatty acids could also be further

expanded by including the somewhat uncertain findings with decreased expression

of

genes involved in fatty acid degradation (GenMAPP findings in Table 6 and the

additional findings with acyl-CoA binding protein and malic enzyme), because

these

events will lead to decreased formation of acetyl-CoA.

The present data with pathway analysis strongly suggest an important role of

genes

involved in the production of polyunsaturated fatty acids in the regulation of

lipid

loss in human fat cells during energy-restricted dieting. According to the

results

of animal experiments, these fatty acids have a variety of effects in fat cells,

including gene transcription, metabolism, cellular membrane composition,

adipocyte

differentiation, and signal transduction (27-29). In this respect, it is

important

to note that human fat cells have a significant capacity to synthesize fatty

acids

from glucose, although the rate of synthesis seems to be much lower than that in

rodents (30). The exact role of polyunsaturated fatty acids in human adipocyte

function is not known, but we can speculate. The coordinated decrease in

expression

during energy restriction might be of importance for depletion of adipocyte

lipids.

The whole pathway of genes forming polyunsaturated fatty acids from acetyl- or

malonyl-CoA could also be a negative feedback regulator of obesity.

Polyunsaturated

fatty acids in high concentration might inhibit adipocyte differentiation or

lipid

accumulation. If so, decreased production could be a way to counteract adipocyte

depletion during weight loss. The knockout data with stearoyl-CoA desaturase-1

support the latter idea (20). It is unlikely that a decreased supply of fatty

acids

to acylglycerol synthesis is a major cause of the alterations in gene

expression.

Polyunsaturated fatty acids except linoleic acid contribute marginally to the

total

fatty acids in human adipose tissue, and the linoleic acid content seems largely

dependent on dietary supply (31).

Fat cells can synthesize several monounsaturated and polyunsaturated fatty acids

(27, 28, 31). At present, we do not know which unsaturated fatty acids play a

regulatory role during energy restriction. Unfortunately, the amount of adipose

tissue available was far too small for detailed studies on these lipids.

According to Gene Ontology, the pathway most affected by energy restriction was

heme-porphyrin biosynthesis. The 4 genes contributing to this result, ALAD,

ALAS1,

HMBS, and UROS, displayed small (<10%) differences in gene expression by

RT-qPCR,

and the results were significant for HMBS only. Furthermore, there was no

uniform

picture: one gene was down-regulated and 3 genes were up-regulated by the

low-energy

diet. Thus, there is not convincing support that these pathways are involved in

body

weight regulation. MAPPFINDER pathway or biological term analysis is above all

valuable for grouping large numbers of analyzed genes and ranking the relative

importance of these groups. It was previously shown that additional information

can

be gained if the pathway analysis includes genes that demonstrate minor,

nonsignificant changes in gene expression between groups (4). We therefore used

less

stringent criteria for change in gene expression and inclusion in pathways

analysis

than that applied in SAM when determining the 96 genes with a significant

difference

in gene expression on the microarrays. As it turned out, the criteria applied in

the

MAPPFINDER pathway analysis lead to the inclusion of genes where a change in

expression could not be confirmed by RT-qPCR and to unreliable results, eg,

heme-porphyrin biosynthesis. Thus, in our experiment, when evaluating the

MAPPFINDER

results, one needs to consider to what extent individual pathway genes are on

the

SAM list, which seems to select most of the genes affected by dieting.

As mentioned earlier, the expression of some genes known to be important for

body

weight regulation in rodents was altered by energy restriction, ie, estrogen

receptor 1, metallothionein 1, CIDEA, and transferrin. This finding highlights

the

critical importance of mouse models to the discovery of candidate regulatory

genes

for human obesity. Recently, obesity was shown to be associated with changes in

the

expression of several inflammatory genes of adipose tissue, which occur both in

adipocytes and in stromal cells of adipose tissue (32). Note, however, that we

found

no consistent change in expression of inflammatory genes after diet.

Another finding in the present study was that leptin gene expression was not

down-regulated according to the array. These results may well reflect the fact

that

low levels of transcripts are not always detected as being present in the RNA

preparation when using MAS 5.0 from Affymetrix. In separate RT-qPCR experiments

on

adipose tissue from the present cohort, however, we found that leptin mRNA was

significantly decreased by 20% after diet (33).

This study was conducted on abdominal subcutaneous adipose tissue from healthy

obese

women. We do not know at present how the findings relate to men, other fat

depots,

or obesity with comorbidity.

In conclusion, it appears that macronutrients are of no or little importance for

changes in gene expression in human adipose tissue of obese women after energy

restriction. A moderate energy restriction has no important effect on adipocyte

genes involved in the regulation of acylglycerol turnover. However, a marked

effect

was seen on the genes regulating the production of polyunsaturated fatty acids

and

on the genes shown to regulated obesity in experimental models.

Al Pater, PhD; email: old542000@...

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