abstract: Circulating interleukin (IL)-18 is elevated in obesity, but paradoxically causes hypophagia. We hypothesized that IL-18 may attenuate high-fat diet (HFD)-induced insulin resistance by activating AMP-activated protein kinase (AMPK). We studied mice with a global deletion of the α-isoform of the IL-18 receptor (IL-18R^−/−^) fed a standard chow or HFD. We next performed gain-of-function experiments in skeletal muscle, in vitro, ex vivo, and in vivo. We show that IL-18 is implicated in metabolic homeostasis, inflammation, and insulin resistance via mechanisms involving the activation of AMPK in skeletal muscle. IL-18R^−/−^ mice display increased weight gain, ectopic lipid deposition, inflammation, and reduced AMPK signaling in skeletal muscle. Treating myotubes or skeletal muscle strips with IL-18 activated AMPK and increased fat oxidation. Moreover, in vivo electroporation of IL-18 into skeletal muscle activated AMPK and concomitantly inhibited HFD-induced weight gain. In summary, IL-18 enhances AMPK signaling and lipid oxidation in skeletal muscle implicating IL-18 in metabolic homeostasis.
author: Birgitte Lindegaard; Vance B. Matthews; Claus Brandt; Pernille Hojman; Tamara L. Allen; Emma Estevez; Matthew J. Watt; Clinton R. Bruce; Ole H. Mortensen; Susanne Syberg; Caroline Rudnicka; Julie Abildgaard; Henriette Pilegaard; Juan Hidalgo; Susanne Ditlevsen; Thomas J. Alsted; Andreas N. Madsen; Bente K. Pedersen; Mark A. FebbraioCorresponding author: Mark A. Febbraio, <firstname.lastname@example.com>, or Bente K. Pedersen, <email@example.com>.
date: 2013-09
references:
title: Interleukin-18 Activates Skeletal Muscle AMPK and Reduces Weight Gain and Insulin Resistance in Mice

The cytokine interleukin (IL)-18 was identified ∼15 years ago as a cofactor that, together with IL-12, stimulates production of interferon-γ (1). This ∼18-kDa cytokine, which has structural similarities to the IL-1 cytokine family, is widely expressed in many mammalian cells and tissues, including liver, adipose tissue, skeletal muscle, pancreas, brain, and endothelium (2). IL-18 is best known for its role in inflammation, whereby proinflammatory stimuli such as lipopolysaccharide, Fas ligand, and tumor necrosis factor-α lead to caspase-1–mediated cleavage of pro-IL-18 into mature IL-18. IL-18 then can signal via a heterodimer of the transmembrane IL-18 receptors (α and β) and via a Toll-like receptor signaling cascade, ultimately leading to the activation of nuclear factor-κB and subsequent regulation of gene transcription (3). Although this is the most characterized signaling pathway for this cytokine, it is worth noting that IL-18 also has been implicated in mitogen-activated protein kinase, phosphatidylinositol 3-kinase, and signal transducer and activator of transcription (STAT) 3 signaling (4), which are all implicated in energy metabolism.

It is now well-accepted that obesity results in a state of low-grade chronic inflammation (5); therefore, it is not surprising that circulating IL-18 levels are elevated in human obesity (6) and in patients with type 2 diabetes (7). Somewhat paradoxically, the works of two separate groups have reported that mice with a global deletion of IL-18 become obese and insulin-resistant, whereas exogenous administration of recombinant IL-18 rescued this phenotype (8,9). These previous studies ascribed the mechanism of action of IL-18 in modulating nutrient homeostasis to be exclusively via neuronal control of food intake (8,9). However, in the earlier study (8), the authors demonstrated that the IL-18^−/−^ mice displayed decreased peripheral insulin sensitivity and that IL-18 signals via STAT3 in the liver, increasing the possibility that IL-18 may play a role in peripheral energy metabolism because STAT3 plays a major role in maintaining metabolic homeostasis in the liver (10). In addition, we (11,12) and others (13,14) have shown that cytokines that result in activation of STAT3 via transmembrane receptor signaling can activate the AMP-activated kinase (AMPK) signaling pathway to enhance fat oxidation in skeletal muscle, thereby attenuating high-fat diet (HFD)-induced insulin resistance. Together, these previous studies increase the possibility that IL-18 may attenuate HFD-induced insulin resistance via affecting metabolic processes, such as activation of AMPK, in skeletal muscle. This is important from a therapeutic viewpoint because drugs that effectively modulate food intake via targeting the central nervous system have, to date, proven unsuccessful because of side effects associated with activation of the lateral hypothalamus (15). In the current study, we tested the hypothesis that IL-18 signaling can modulate nutrient homeostasis via mechanisms associated with peripheral energy metabolism. We show that IL-18 activates AMPK and increases lipid oxidation in skeletal muscle, implicating IL-18 in diet-induced obesity and insulin resistance.

# RESEARCH DESIGN AND METHODS

## Animal experimental protocols.

For the diet intervention study, 12-week-old *Il18R^−/−^* (back-crossed 11 generations to C57BL/6J) and wild-type C57BL/6J mice were obtained from Charles River Laboratories (L'Arbresle, France). Subsequent to this initial cohort, we then performed experiments in *Il18R^−/−^* and wild-type mice obtained from heterozygous mating. Eight-week-old C57BL/6J mice (inbred; Herlev, Denmark) were used for a DNA electroporation study, and 13-week-old C57/BL6 mice (inbred; Melbourne, Australia) were used for ex vivo experiments. All experiments were approved by The Animal Experiments Inspectorate in Denmark or the Baker IDI Alfred Medical Research and Education Precinct Animal Ethics Committee (or both) in accordance with the National Health and Medical Research Council of Australia Guidelines on Animal Experimentation. Mice were maintained on a 12-h light, 12-h dark cycle on a standard rodent chow diet (27%, 13%, and 60% kcal from protein, fat, and carbohydrates, respectively) or HFD composed of 60% calories from fat (Research Diets 12492; 20%, 60%, and 20% kcal from protein, fat, and carbohydrates, respectively) for 18 weeks (diet-induced obesity study) or 4 weeks (in vivo electroporation study).

In vivo electroporation experiments were performed as previously described (16). Briefly, the regulatory plasmid pTet-On was obtained from Clontech (Palo Alto, CA). Because IL-18 lacks a typical signal sequence, the V-J2-C region of murine immunoglobin k-chain was cloned upstream of the mature IL-18 sequence. The tibialis anterior muscle of each mouse was directly injected with 20 µL plasmid solution (0.5 µg/µL) and electric pulsing was applied using 4-mm plate electrodes and an electric field of one high-voltage pulse (100 µs, 800 V/cm) and one low-voltage pulse (400 ms, 100 V/cm) (17). Induction of gene expression was obtained by administering drinking water consisting of distilled water containing 0.2 mg/mL doxycycline (doxycycline hyclate; Sigma-Aldrich) (18).

For ex vivo experiments, mice were fed a standard chow diet and drinking water, available ad libitum. To examine palmitate metabolism, mice were first anesthetized and soleus muscles were carefully dissected into longitudinal strips from tendon to tendon. Strips were removed and \[^14^C\]palmitate oxidation was analyzed as previously described (19). The dose of 100 ng/mL IL-18 was used and PBS served as placebo. The strips from the same animal were used for both IL-18 and PBS.

## Skeletal muscle cell culture.

To examine whether IL-18 affected phosphorylation (Thr^172^) of AMPK and acetyl CoA carboxylase-β (ACCβ; Ser^79^), fully fused L6 myotubes were treated with recombinant rat IL-18 (MBL, Woburn, MA). Cells were treated with IL-18 for 10, 30, and 60 min at doses of 1 and 10 ng/mL.

## Analysis of body composition.

We measured body composition analysis in IL18R^−/−^ by dual-energy X-ray absorptiometry using the Lunar PIXImus Mouse Densitometer (GE Medical Systems) and in IL-18 electroporated mice by using a Lunar Prodigy scanner with a small animal software application (GE Healthcare Systems). Animals were anesthetized by intraperitoneal injection of Hypnorm (0.4 mL/kg; Janssen, Saunderton, U.K.) and Dormicum (2 mg/kg; Roche, Basel, Switzerland), and laid flat on the scanning platform on their ventral side. To confirm dual-energy X-ray absorptiometry fat mass determination, mice were killed and two intra-abdominal fat pads (gonadal, retroperitoneal) and one subcutaneous fat pad (inguinal) were dissected and weighed.

## Insulin tolerance tests.

We performed insulin tolerance tests in 7.5-month-old male mice 4 h after removal of food. Blood samples were obtained by tail cut and were analyzed for glucose content using a glucometer (Accu-chek Compact plus) immediately before and at 15, 30, 45, 60, 90, and 120 min after an intraperitoneal injection of insulin (0.75 units/kg; Actrapid; Novo Nordisk).

## Indirect calorimetry.

Mice were placed in a 16-chamber indirect calorimetry system (TSE Systems, Bad Homburg, Germany) cages for 10 days; the first 5 days were considered the acclimation phase and data were analyzed only for the last final days. After 3 days, the mice were fasted for 24 h. The Vo~2~ (mL/h/kg), respiratory exchange ratio, and activity (beam breaks) were measured using the system. Mice had free access to food and water while in the chambers. Food intake was measured for the duration of data collection while mice underwent the indirect calorimetry measurements.

## Plasma parameters analysis.

We collected blood samples from the tail vein of the mice into EDTA tubes and they were immediately spun at 6,000*g* for 10 min at 4°C and plasma was removed. Plasma was stored at −80°C until analysis. Plasma insulin was determined by ELISA (Crystal Chem). Blood glucose was measured by glucometer (Accu-chek Compact Plus). Adiponectin concentration was measured by RIA kit. Leptin, monocyte chemoattractant protein 1, plasminogen activator inhibitor 1, IL-6, and tumor necrosis factor-α were measured by using a Lincoplex mouse serum adipokine panel (Linco).

## Insulin signaling tissue collection.

Animals were anesthetized with an injection of Hypnorm (0.4 mL/kg; Janssen, Saunderton, UK) and Dormicum (2 mg/kg; Roche, Basel, Switzerland), and the gastrocnemius muscle as well as the right lobe of the liver (after a ligature around the blood vessel) were removed and stored in liquid nitrogen until further processing. This was followed by injection of insulin (1.5 units/kg lean body mass) into the abdominal aorta and removal of the contralateral gastrocnemius and liver lobe occurred 2 min after injection. Samples were stored in liquid nitrogen until further processing.

## RNA extraction and real-time quantitative PCR.

Mouse tissue was isolated, frozen in liquid nitrogen or in dry ice and absolute alcohol, and stored at −80°C until extraction. Total RNA was isolated from adipose tissue with TriZol (Life Technology), as described by the manufacturer; 500 ng RNA was reverse-transcribed to cDNA with the use of random hexamers (Taqman reverse-transcription reagents; Applied Biosystems). Real-time PCR was performed on an ABI PRISM 7900 sequence detector or 7500 fast sequence detector (Applied Biosystems). Each assay included (in triplicate) the following: a cDNA standard curve of five serial dilution points (range, 1–0.01); a no-template control; a no-reverse-transcriptase control; and 7.5 ng (0.375 ng for 18S rRNA) of each sample cDNA. For *18S rRNA, SREEBP1c*, fatty acid synthase (*FAS*), *HADB*, phosphoenolpyruvate carboxykinase (*PEPCK*), glucose-6 phosphate dehydrogenase, and carnitine palmitoyl transferase 1 (*CPT1*), the amplification mixtures were prepared with 2X Taqman Universal PCR master mix. All assay reagents were from Applied Biosystems. Primers and Taqman probes were designed for sterol regulatory–element binding protein-1c and *FAS* using a mouse-specific database (ensemble.com) and Primer Express (Applied Biosystems) and primers for *CPT1* and *HADB* were designed using the free program primer3. The sequences to amplify a fragment of sterol regulatory–element binding protein-1c was as follows: FP: 5′ GACCACGGAGCCATGGAT; 3′and RP: 5′ GGCCCGGGAAGTCACTGT; 3′ and TaqMan probe: 5′ ACATTTGAAGACATGCTCCAGCTCATCAACA; 3′, a fragment of *FAS* FP: 5′ ATCCTGGAACGAGAACACGATCT 3′; RP: 5′ GGACTTGGGGGCTGTCGTGTCA; 3′ and TaqMan probe: 5′ CACGCTGCGGAAACTTCAGGAAATGT; 3′, a fragment of *HAD* FP: GTGGAGAAGACCCTGAGCTA; RP: GCAAATCGGTCTTGTCTAGT; a fragment of *PEPCK* FP: GGCGGAGCATATGCT and RP: CCACAGGCACTAGGGAAGGC, a fragment of glucose-6 phosphate dehydrogenase FP: TCAACCTCGTCTTCAAGTGGATT; RP: GCTGTAGTAGTCGGTGTCCAGGA; CPTI FP: GTCGCTTCTTCAAGGTCTGG; and RP: AAGAAAGCAGCACGTTCCAT. Oligos for *SREBP1* and *FAS* were obtained from TaqCopenhagen (Copenhagen, Denmark) for *CPT1*, oligos for *HAD* were obtained from DNA technology (Aarhus, Denmark), and oligos for *PEPCK* and glucose-6 phosphate dehydrogenase were obtained from Geneworks (South Australia, Australia). The *18S rRNA* content was determined using a predeveloped assay reagent (Applied Biosystems). The relative concentrations of measured mRNAs were determined by plotting the threshold cycle versus the log of the serial dilution points, and the relative expression of the gene of interest was determined after normalization to *18S*, which was unaffected by genotype and diet.

## AMPK activity and lipid measurements.

AMPK activity was assayed in frozen tibialis anterior muscle homogenized in lysis buffer as described previously (12). Briefly, muscle lysate containing 200 μg protein was immunoprecipitated with antibody specific to the α2 or α1 catalytic subunit of AMPK and protein A/G agarose beads. Beads were washed five times, and the activity of the immobilized enzyme was assayed based on the phosphorylation of substrate for AMP-activated protein kinase peptide (0.2 mmol/L) by 0.2 mmol/L ATP (containing 2 μCi \[γ-^32^P\] ATP) in the presence and absence of 0.2 mmol/L AMP. Label incorporation into the substrate for AMP-activated protein kinase peptide was measured on a Racbeta 1214 scintillation counter. For measurement of intramuscular triglycerides, freeze-dried muscle samples were dissected free of visible connective tissue and blood. Lipid was extracted by a Folch extraction, the triacylglycerol was saponified in an ethanol/KOH solution at 60°C, and glycerol content was determined fluorometrically as described previously (12).

## Protein analysis.

Tissue lysates (40 μg) were solubilized in Laemmli sample buffer and boiled for 5 min, resolved by SDS-PAGE on 10% polyacrylamide gels, transferred (semi-dry) to nitrocellulose membrane, blocked with 5% BSA, and immunoblotted with primary antibodies (2.5% BSA) overnight. After incubation in horseradish peroxidase-conjugated secondary antibody (2.5% BSA; Amersham Bioscience), the immunoreactive proteins were detected with enhanced chemiluminescence (Amersham Bioscience) and quantified by densitometry (ChemiDoc XRS). Membranes were stripped, washed, and reprobed for total protein content or housekeeping protein when appropriate. Total AKT was run on a separate gel and was not stripped. Samples from mice fed a chow or HFD were run on separate gels. The antibodies used for detection of total AKT and β-actin and phosphorylation of JNK1/2, AKT, AMPKα, and ACCβ were purchased from Cell Signaling. The α-tubulin antibody was obtained from Sigma Aldrich.

## Statistical analysis.

All analyses were performed using SAS 9.1. We performed comparison between two groups using unpaired Student *t* test and one-way ANOVA followed the Tukey post hoc test. Time-series data were analyzed with PROC MIXED. *P* \< 0.05 was considered significant. When appropriate, values were logarithmically transformed to ensure normality and equal variance.

# RESULTS

## IL-18 receptor–deficient mice are prone to weight gain that is not associated with hyperphagia.

To evaluate the role of IL-18 signaling in the etiology of body weight homeostasis, we first performed loss-of-function experiments by phenotyping mice with a global deletion of the α-isoform of the IL-18 receptor (IL-18R^−/−^). Consistent with a previous study (8), IL-18R^−/−^ mice become heavier than their wild-type counterparts (control \[CON\]) at ∼6 months of age when fed a regular chow diet (Fig. 1A).

The increase in body mass was attributable to an increase in adiposity because we observed differences in percent fat (Fig. 1B) but not in percent fat-free mass (data not shown). This increase in adiposity was attributable to increases in visceral, but not subcutaneous, fat mass because both epididymal and retroperitoneal, but not inguinal, fat pad masses were higher in IL-18R^−/−^ mice relative to CON (Fig. 1C). Although both IL-18R^−/−^ and CON mice had markedly increased body mass and fat pad mass when fed an HFD for 16 weeks, the differences observed when comparing genotypes on a regular chow diet were not evident when animals were fed an HFD (Fig. 1A–D). As discussed, previous studies have demonstrated that IL-18^−/−^ mice are prone to weight gain because of hyperphagia (8,9). Although the earlier study reported weight gain and insulin resistance in IL-18R^−/−^ mice, they did not make reference to altered feeding behavior in these animals (8). To determine whether the increase in adiposity observed in IL-18R^−/−^ mice was attributable to an increase in food intake or a decrease in energy expenditure (or both), we next performed whole-body indirect calorimetry experiments. We observed no difference in food intake (Fig. 1E), whole-body oxygen consumption (Fig. 1F), or activity (data not shown) measured over 24 h when comparing IL-18R^−/−^ mice with CON irrespective of diet. As expected, consumption of the HFD decreased whole-body RER, indicative of an increase in whole-body fat oxidation. When mice were provided with HFD ad libitum, average RER (over a 72-h period) was not different when comparing IL-18R^−/−^ mice with CON irrespective of diet (Fig. 1H). Of note, however, when mice were fasted and refed at the cessation of the 72-h period, IL-18R^−/−^ mice displayed a significantly higher RER in both the fasted and refed conditions, relative to CON (Fig. 1H).

## IL-18R^−/−^ mice are insulin-resistant.

We next examined whether IL-18R^−/−^ mice were insulin-resistant. At 3 month of age, and before the IL-18R^−/−^ mice became obese on a chow diet, there were no differences in insulin resistance as measured by an intraperitoneal tolerance test (ITT) (data not shown). However, with age and irrespective of diet, IL-18R^−/−^ mice displayed whole-body insulin resistance as measured by both fasting hyperinsulinemia (Fig. 2A) and impaired glucose clearance during an ITT (Fig. 2B and [Supplementary Fig. 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1095/-/DC1)). We next examined insulin signaling in both muscle and liver by analyzing the phosphorylation of Akt (Ser^473^) before and 2 min after a bolus dose of insulin. Although insulin increased pAkt in the skeletal muscle (Fig. 2C) and liver (Fig. 2E) of CON animals fed a chow diet, this effect was markedly blunted in IL-18R^−/−^ mice (Fig. 2C and E). No differences in pAkt in either skeletal muscle (Fig. 2D) or liver (Fig. 2F) were observed when comparing IL-18R^−/−^ with CON mice when fed an HFD.

## IL-18R^−/−^ mice store excess lipid in skeletal muscle and have inflamed livers and skeletal muscle.

Excess adiposity often is associated with ectopic lipid storage in metabolic tissues such as liver and skeletal muscle, which can mediate insulin resistance either directly or via the upregulation of serine threonine kinases such as c-jun terminal kinase (JNK) and inhibitor of κB kinase (5). Accordingly, we next measured intramyocellular and intrahepatic lipid content and the phosphorylation of JNK (Thr^183^/Tyr^185^) and inhibitor of κB kinase-αβ (Ser^180^/ Ser^181^) in these tissues. Irrespective of diet, triacylglycerol content was higher in the skeletal muscles of IL-18R^−/−^ relative to CON mice (Fig. 3A). This was associated with elevated JNK (Fig. 3B) but not with inhibitor of κB-kinase ([Supplementary Fig. 2](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1095/-/DC1)) phosphorylation. Conversely, we did not observe any differences in intrahepatic triacylglycerol concentration when comparing IL-18R^−/−^ with CON mice irrespective of diet (Fig. 3C). Notwithstanding, phosphorylation of JNK (Fig. 3D) and the mRNA expression of key fatty acid synthesis transcription factors/enzyme sterol regulatory–element binding protein-1c and *FAS* (Fig. 3E and F) were elevated in the liver when comparing IL-18R^−/−^ with CON mice when animals were fed a chow and an HFD. However, no differences were observed in the mRNA expression of key gluconeogenic enzymes *PEPCK* or glucose-6 phosphate dehydrogenase when comparing IL-18R^−/−^ with CON mice irrespective of diet ([Supplementary Fig. 3](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1095/-/DC1)). Given that neither hepatosteatosis nor the expression of key enzymes involved in regulating hepatic glucose production was different when comparing IL-18R^−/−^ with CON mice, it is unlikely that changes in liver insulin sensitivity were responsible for the reduced whole-body insulin sensitivity observed in IL-18R^−/−^, although this possibility cannot be entirely ruled out.

## IL-18R^−/−^ mice have reduced AMPK signaling in metabolic tissues.

Because IL-18R^−/−^ mice are prone to weight gain on a normal diet and ectopic lipid storage independent of feeding, we next examined whether pathways associated with lipid oxidation were impaired. One major pathway that regulates fatty acid oxidation is AMPK. AMPK phosphorylates ACCβ, resulting in inhibition of ACC activity, which in turn leads to a decrease in malonyl CoA content, relieving inhibition of CPT1 and increasing fatty acid oxidation. No significant differences between phenotypes were observed when measuring the phosphorylation of AMPK (Thr^172^) (data not shown). However, the phosphorylation of ACCβ (Ser^218^), a downstream marker of AMPK activity, was reduced in the skeletal muscle, liver, and adipose tissue of IL-18R^−/−^ when fed a chow diet (Fig. 4A–C). In addition, this effect was maintained in skeletal muscle (Fig. 4A) and liver (Fig. 4B), but not adipose tissue (Fig. 4C) when mice were fed the HFD.

## Exogenous IL-18 treatment increases AMPK signaling and fat oxidation in skeletal muscle in vitro and ex vivo.

Because we observed that IL-18R^−/−^ mice become obese, store more lipid in skeletal muscle, and have defective ACCβ phosphorylation in this organ, we next performed in vitro and ex vivo experiments in muscle cells and whole muscle strips to confirm the role of IL-18 on AMPK signaling and fat oxidation in this important metabolic tissue. In initial experiments, we demonstrated that as little as 1.0 ng/mL recombinant IL-18 protein was sufficient to phosphorylate both AMPK (Thr^172^) (Fig. 5A) and ACCβ (Ser^218^) (Fig. 5B) in L6 myotubes. We next performed experiments in isolated intact soleus muscle as previously reported (16,20,21). Treating these muscles with 100 ng/mL recombinant IL-18 was sufficient to increase both palmitate oxidation (Fig. 5C) and AMPK phosphorylation (Thr^172^) (Fig. 5D).

## Ectopic expression of IL-18 in a single tibialis anterior muscle is sufficient to protect against excess adipose tissue storage in mice fed an HFD.

To determine whether the results obtained in muscle cell and ex vivo muscle strips also were prevalent in vivo, we used the in vivo electroporation technique to overexpress *IL-18* cDNA in the tibialis anterior muscles of C57BL/6J mice that were placed on an HFD diet for 4 weeks. Using this technique, we previously have observed a transfection efficiency of ∼60%, as measured by the electroporation of a GFP construct as a control (21). IL-18 protein expression in the tibialis anterior was increased 30- to 40-fold above basal when compared with mice when the tibialis anterior was electroporated with an empty vector (sham) (Fig. 6A). No difference in body weight was observed when comparing IL-18 with sham electroporated mice (Fig. 6B). In accordance with this observation, adiposity was reduced when comparing IL-18 with sham electroporated mice at 4 weeks (Fig. 6C and D and [Supplementary Fig. 4](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1095/-/DC1)). However, this reduction in adiposity was insufficient to result in increased whole-body insulin sensitivity or glucose tolerance as measured by ITT and glucose tolerance test ([Supplementary Fig. 5](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1095/-/DC1)).

## Ectopic expression of IL-18 in a single tibialis anterior muscle increases AMPK signaling and markers of lipid oxidation in this organ.

We next measured AMPK signaling and markers of lipid oxidation in IL-18 and sham electroporated muscles. Both AMPK activity (Fig. 7A) and ACCβ phosphorylation (Ser^218^) (Fig. 7B) were markedly elevated in IL-18 compared with sham electroporated muscles. In addition, we also observed increased mRNA abundance of β-hydroxyacyl-CoA-dehydrogenase, a key enzyme involved in mitochondrial function (Fig. 7C) and *CPT1* (Fig. 7D), the enzyme that controls the transfer of long-chain fatty acyl CoA into mitochondria and enhances rates of fatty acid oxidation in the IL-18 electroporated mice relative to sham mice. It is now well-known that skeletal muscle can act as an endocrine organ, producing "myokines" to result in tissue cross-talk (22). To examine whether IL-18 could act as a myokine when overexpressed in skeletal muscle, we examined circulating levels of IL-18 and markers of insulin sensitivity, fat oxidation, and inflammation in other tissues such as the liver and adipose. Despite the increase in intramuscular IL-18 expression with electroporation, plasma IL-18 was not elevated in the IL-18 electroporated mice relative to sham ([Supplementary Fig. 5](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1095/-/DC1)). Therefore, it was not surprising that pAkt, pAMPK, pACC, and pJNK were not altered in the liver or adipose tissue of IL-18 electroporated mice relative to sham mice ([Supplementary Figs. 5 and 6](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1095/-/DC1)). Together, these data provide evidence that IL-18 can activate AMPK in skeletal muscle in vivo.

# DISCUSSION

IL-18 signaling has been implicated in the etiology of metabolic homeostasis and insulin resistance; however, studies in IL-18–deficient mice have suggested that IL-18 is required to prevent hyperphagia (8,9). In the current study, rather than use a model of genetic deletion of IL-18, we initially studied mice that harbor a global deletion of the functional IL-18R. We show that IL-18R^−/−^ mice are prone to weight gain on a chow diet and develop insulin resistance, a phenotype that is associated with ectopic skeletal muscle lipid expression, inflammation, and reduced AMPK signaling.

The IL-18R^−/−^ mice displayed significantly increased body weight and increased fat pad mass after ∼26 weeks of age. Although others have shown that IL-18–deficient mice display hyperphagia (8,9), we did not observe this in the IL-18R^−/−^ mice. We were careful to monitor food intake in their normal habitat and during metabolic caging, so we are confident that the food intake between the IL-18R^−/−^ and CON mice were comparable. Given the identical oxygen consumption between the IL-18R^−/−^ and CON mice, why would the IL-18R^−/−^ mice gain more weight if both energy input and expenditure were comparable? It should be noted that the difference in body weight when comparing the IL-18R^−/−^ and CON mice was ∼10% by the end of the study (Fig. 1A), whereas the overall difference in percent body fat was ∼4% (Fig. 1B). This would equate to an approximate increase of 0.15 g fat or 0.70 kJ energy per day. The daily energy expenditure of a mouse has been estimated to be 42 kJ per day (23). Consequently, \<2% difference in daily energy expenditure would be sufficient to result in the increased body weight but was unlikely to be detected with available techniques, such as metabolic caging. Similar problems have been encountered in other mouse models of obesity (23,24), and work from our group also has recently observed such an anomaly (25). Notwithstanding this apparent anomaly, it appears, based on the data we have reported, that the IL-18R^−/−^ mice gain weight and store excess lipid in skeletal muscle, which results in whole-body and skeletal muscle insulin resistance.

It has been suggested that IL-18R also might be activated by ligands other than IL-18 (26,27), and this, potentially, could explain why hyperphagia previously was observed in IL-18–deficient mice (8,9) but not in IL-18R^−/−^ mice in the current study. Interestingly, when mice fed an HFD were fasted and refed, IL-18R^−/−^ mice displayed a significantly higher RER in the refed condition (Fig. 1H). This suggests that mice lacking the IL-18R cannot oxidize lipids and rely on utilization of carbohydrates as a preferred energy substrate during refeeding. This is supported by our gain-of-function data (IL-18 increased fat oxidation). Interestingly, Zorrilla et al. (9) observed that mice with IL-18 deficiency were not hyperphagic when fed an HFD and suggested that they differentially process carbohydrate-rich compared with lipid-rich diets or differentially use these macronutrients as fuel. Our study supports this hypothesis.

By performing whole-body insulin tolerance tests, we cannot ascertain whether the whole-body insulin resistance observed in the IL-18R^−/−^ mice relative to control mice was attributable to insulin resistance in skeletal muscle, liver, or a combination of the two. However, several lines of evidence suggest that the defect in the IL-18R^−/−^ mice was primarily in the skeletal muscle, and this is the rationale for choosing to study this organ in depth. First, it is well-acknowledged that ectopic lipid expression is a primary mechanism leading to insulin resistance (28). Whereas intramuscular triglycerides were elevated in the IL-18R^−/−^ mice relative to CON mice, no such increase was observed in the liver (Fig. 3). Second, the mRNA expression of key gluconeogenic enzymes PEPCK and glucose-6 phosphate dehydrogenase were not different in the liver of the IL-18R^−/−^ mice. Third, even though the IL-18R^−/−^ mice were insulin-resistant on an HFD relative to CON mice, there was no evidence of decreased pAkt (Fig. 2F) or of mRNA expression of *SREBP1* or *FAS* (Fig. 3E and F) in the liver. Together, these data suggest that skeletal muscle was the origin of the primary defect, although we acknowledge that the effects of IL-18R^−/−^ on the liver cannot be completely discounted because we did not directly measure insulin sensitivity in this organ.

Of note was our observation that pJNK was markedly elevated in the livers of the IL-18R^−/−^ mice relative to CON mice despite equivalent lipid content when animals were fed a chow diet (Fig. 3). Although this may seem counterintuitive, this observation is not novel. We previously have observed such a phenomenon in IL-6–deficient mice (25). A potential mechanism recently has been proposed by Flavell et al., who demonstrated that NLRP6 and NLRP3 inflammasomes and, importantly, IL-18 negatively regulates nonalcoholic fatty liver disease/nonalcoholic steatohepatitis progression as well as multiple aspects of metabolic syndrome via modulation of the gut microbiota not necessarily related to hepatosteatosis (29). Although speculative, the increased inflammation observed in the IL-18R^−/−^ mice in the presence of relatively normal lipid levels may be related to such a mechanism.

Based on our loss-of-function and gain-of-function models, IL-18 signaling is implicated in fatty acid oxidation rates in skeletal muscle as a result of activation of AMPK. As discussed, AMPK phosphorylates ACCβ, resulting in inhibition of ACC activity, which in turn leads to a decrease in malonyl CoA content, relieving inhibition of CPT1 and increasing fatty acid oxidation. The phosphorylation of ACCβ was reduced in the skeletal muscle, liver, and adipose tissue of IL-18R^−/−^ (Fig. 4). Moreover, when cultured skeletal muscle cells or isolated skeletal muscle strips were treated with IL-18, phosphorylation of AMPK or ACC (or both) was increased and, in the case of intact ex vivo–treated skeletal muscle, this increase was associated with enhanced fatty acid oxidation. Finally, when IL-18 was overexpressed in skeletal muscle in vivo, AMPK activity and ACC phosphorylation were increased, not only in the electoporated muscle (Fig. 7A and B). Taken together, the data provide evidence implicating IL-18 in the activation of AMPK.

It is now well-known that many cytokines, including leptin, adiponectin, ghrelin, IL-6, and ciliary neurotrophic factor can activate AMPK (30,31), but this is the first report indicating that IL-18 can act as an AMPK agonist. This observation, however, is consistent with these previous studies because IL-18 can act as an activator of STAT3 (4). Work from our group previously has shown that members of the IL-6 family of cytokines, which potently activate STAT3, also enhance fat oxidation via AMPK (11,12). Importantly, when mice that harbor a truncation of the COOH-terminal domain that eliminates these tyrosine residues on the gp130 receptor (gp130^∆STAT^ mice) are treated with CNTF, the phosphorylation of STAT3 is abolished, as is the activation of AMPK (12).

Although feeding mice an HFD did not result in differences in body weight, fat mass, or insulin signaling in skeletal muscle and liver when comparing the genotypes, the IL-18R^−/−^ mice nevertheless displayed elevated fasting insulin levels and impaired insulin tolerance as measured by an ITT (Fig. 2). Although speculative, this may be attributable to the fact that under HFD conditions, the activation of the AMPK pathway remained impaired in the IL-18R^−/−^ mice, at least in skeletal muscle and liver (Fig. 4), leading to elevated lipid levels in skeletal muscle of the IL-18R^−/−^ mice under HFD conditions (Fig. 3).

In summary, we have identified that IL-18 can activate AMPK. Moreover, mice that harbor a genetic deletion of a functional IL-18R are prone to weight gain and development of insulin resistance and inflammation in important metabolic tissues such as skeletal muscle and liver. Therefore, our data add IL-18 to a growing list of catabolic proinflammatory cytokines that paradoxically are required to maintain pathways important for fatty acid oxidation and thus prevent insulin resistance.

## ACKNOWLEDGMENTS

This study was supported, in part, by a grant from the National Health and Medical Research Council of Australia (NHMRC grant no. 526606). This study was further supported by the Danish Council for Independent Research–Medical Sciences, the Commission of the European Communities (grant agreement no. 223576-MYOAGE), and by grants from the Novo Nordisk Foundation, Hørslevfonden, the Danish National Research Foundation (#10-083807), Højmosegårdlegatet, Fonden for Lægevidenskabens Fremme, and Direktør Jacob Madsen og Hustru Olga Madsens Fond. The Centre of Inflammation and Metabolism (CIM) and The Rodent Metabolic Phenotyping Center is part of the UNIK Project: Food, Fitness & Pharma for Health and Disease (see [www.foodfitnesspharma.ku.dk](http://www.foodfitnesspharma.ku.dk)) supported by the Danish Ministry of Science, Technology, and Innovation. The CIM is a member of DD2-the Danish Center for Strategic Research in Type 2 Diabetes (the Danish Council for Strategic Research, grant no. 09-067009 and 09-075724). The Copenhagen Muscle Research Centre is supported by a grant from the Capital Region of Denmark. M.A.F. is a Senior Principal Research Fellow, M.J.W. is a Senior Research Fellow, and C.R.B. and V.B.M. are Career Development Fellows of the NHMRC. The CIM is supported by a grant from the Danish National Research Foundation (#02-512-55). B.L. was supported by a grant from the Danish National Research Foundation (#09-063656). B.L. received postdoctoral fellowship support from a grant from the Danish National Research Foundation.

No other potential conflicts of interest relevant to this article were reported.

B.L. designed research, performed and/or analyzed research, and wrote the manuscript. V.B.M., C.B., P.H., T.L.A., E.E., M.J.W., C.R.B., O.H.M., S.S., C.R., J.A., H.P., S.D., T.J.A., and A.N.M. performed and/or analyzed research. J.H. contributed new reagents and analytical tools. B.K.P. designed research. M.A.F. designed research and wrote the manuscript. All authors contributed to the writing of the final submitted version of the manuscript. B.L. is the guarantor of this work and, as such, had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

The authors acknowledge the assistance of Betina Mentz, Ruth Rousing, Hanne Villumsen, Lone Nielsen, Andreas Nygaard Madsen (University of Copenhagen), Steve Risis (Baker IDI Heart and Diabetes Institute), and the Rodent Metabolic Phenotyping Center (University of Copenhagen).

# REFERENCES


author: Elaine R Mardis
date: 2010
institute: 1The Genome Center at Washington University School of Medicine, 4444 Forest Park Blvd, St Louis, MO 63108, USA
references:
title: The \$1,000 genome, the \$100,000 analysis?

Having recently attended the Personal Genomes meeting at Cold Spring Harbor Laboratories (I was an organizer this year), I was struck by the number of talks that described the use of whole-genome sequencing and analysis to reveal the genetic basis of disease in patients. These patients included a child with irritable bowel disease, a child with severe combined immunodeficiency, two siblings affected with Miller syndrome, and several with cancers of different types. Although each presenter emphasized the rapidity with which these data can now be generated using next-generation sequencing instruments, they also listed the large number of people involved in the analysis of these datasets. The required expertise to 'solve' each case included molecular and computational biologists, geneticists, pathologists and physicians with exquisite knowledge of the disease and of treatment modalities, research nurses, genetic counselors, and IT and systems support specialists, among others. While much of the attendant effort was focused on the absolute importance of obtaining the correct diagnosis, the large number of specialists was critical for the completion of the data analysis, the annotation of variants, the interpretive 'filtering' necessary to deduce the causative or 'actionable' variants, the clinical verification of these variants, and the communication of results and their ramifications to the treating physician, and ultimately to the patient. At the end of the day, although the idea of clinical whole-genome sequencing for diagnosis is exciting and potentially life-changing for these patients, one does wonder how, in the clinical translation required for this practice to become commonplace, such a 'dream team' of specialists would be assembled for each case. In other words, even if the cost and speed of generating sequencing data continue their precipitous decreases, the cost of 'team' analysis seems unlikely to immediately follow suit. However, rather than predicting from this reasoning that widespread diagnosis by sequencing is unlikely to occur widely, it is perhaps more fruitful to predict, in my opinion, what is probably required for it to occur. I therefore offer the following as food for thought.

One source of difficulty in using resequencing approaches for diagnosis centers on the need to improve the quality and completeness of the human reference genome. In terms of quality, it is clear that the clone-based methods used to map, assign a minimal tiling path, and sequence the human reference genome did not yield a properly assembled or contiguous sequence equally across all loci. Lack of proper assembly is often due to collapsing of sequence within repetitive regions, such as segmental duplications, wherein genes can be found once the correct clones are identified and sequenced. At some loci, the current reference contains a single nucleotide polymorphism (SNP) that occurs at the minor allele frequency rather than being the major allele. In addition, some loci cannot be represented by a single tiling path and require multiple clone tiling paths to capture all of the sequence variations. All of these deficiencies and others not cited provide a less-than-optimal alignment target for next-generation sequencing data and can confound the analytical validity of variants necessary to properly interpret patient-derived data. Hence, although it is difficult work to perform, the ongoing efforts of the Genome Resource Consortium \[1\] to improve the overall completeness and correctness of the human reference genome should be enhanced.

Along these lines, although projects such as the early SNP Consortium \[2\], the subsequent HapMap projects \[3-5\], and more recently the 1,000 Genomes Project \[6\] have identified millions of SNPs in multiple ethnic groups, there is much more diversity to the human genome than single base differences. In some ways, the broader scope of 'beyond SNP' diversity of the genome across human populations remains mysterious, including common copy number polymorphisms, large insertions and deletions, and inversions. Mining the 1,000 Genomes data using methods to identify genome-wide structural variation should augment this considerably \[7\], with validation playing an important role, as many methods are still nascent. Lastly, devising clever ways to provide all such classes of variants as a 'searchable space' for sequence data alignment remains a significant challenge, as does the development of sequence alignment algorithms that facilitate the analysis of structurally complex loci.

How well do we understand the functions encoded by our genome? Certainly, comprehensive functional information about proteins, including the impact of mutations, is complete for relatively few genes. The development of high-throughput systems for biochemistry and enzymology could have a dramatic impact on this deficiency and would add vitality to these areas of scientific endeavor. Efforts that annotate regulatory protein binding sites, sites of RNA-mediated regulatory mechanisms, and other motifs that contribute to transcriptional regulation in the human genome must continue. Improved understanding of these regions, and thus their annotation, will require the power of model-organism-based systems to identify and characterize functional proteins or mechanisms that are shared with humans. We also must transfer these findings into human cell experimental systems that allow researchers to examine the impact of the mutations or other alterations of the genome on cellular pathways and the resulting disease biology. With functional consequences in hand, we will begin to understand and associate the clinical validity of genomic variants, effectively enabling the correlation of variant(s) with the resultant phenotype(s).

If our efforts to improve the human reference sequence quality, variation, and annotation are successful, how do we avoid the pitfall of having cheap human genome resequencing but complex and expensive manual analysis to make clinical sense out of the data? One approach would emphasize the development of 'clinical grade' interpretational analysis pipelines to perform much of the initial discovery from datasets derived from massively parallel sequencing \[8\]. Although such pipelines already exist in the research setting \[9\], manual checks and orthogonal validation of variants are required because of the ongoing development of the analytical approaches. Towards patient diagnoses, such validation could initially be performed in a clinical laboratory medicine setting, but ultimately we must develop sophisticated analytical approaches and quality filters that enable high-confidence variant detection solely from the primary data. All discovered variants would then be interpreted in the context of the ever-improving human genome annotation and evaluated in the contexts of medical genetics, of demonstrated clinical validity, and of the pharmaceutical databases (when appropriate), to identify causative or therapeutically actionable genes. Ultimately, as in medicine today, the results will require interpretation by a physician, which raises a separate but equally important issue: the significant need to develop and implement training programs in genomics for medical professionals. Pathologists and genetic counselors will be the first in line for training programs focused on genomic diagnostics, and improving the genomics education of medical students will also be a first priority. More challenging will be the genomics education of practicing physicians and other medical professionals, many of whom do not require genetics to perform their valuable role in health care daily, but who will be confronted in the near term by increasingly well informed patients who expect their doctors to be as well versed as they are about genome-guided diagnosis and treatment.

A final word on the important topic of patient access to genome-guided medicine seems necessary and appropriate. The current high cost of whole-genome sequencing and analysis relative to most clinical diagnostic assays, coupled with the fact that these costs are not currently reimbursed by insurers, might mean that only those with the means to pay for the test will be allowed access. Perhaps worse, those with the fattest wallets might pay extra for a place higher in the queue, denying earlier access to patients who more desperately need the information. Although there are no easy answers here, one plausible solution might be the establishment of funds at major medical centers, where genome-guided medicine is likely to be practiced first, that pay for the genomic sequencing, diagnosis and associated costs and thus allow equitable access to this new assay.

# Competing interests

The author declares that they have no competing interests.

## Acknowledgements

I thank Deanna Church, Timothy Ley and W Richard McCombie for their critical reading and suggestions.


date: 2018-05
title: WHO and UNICEF issue new guidance to promote breastfeeding in health facilities globally

**11 APRIL 2018 \| GENEVA -** WHO and UNICEF today issued new ten-step guidance to increase support for breastfeeding in health facilities that provide maternity and newborn services. Breastfeeding all babies for the first 2 years would save the lives of more than 820 000 children under age 5 annually.

The Ten Steps to Successful Breastfeeding underpin the Baby-friendly Hospital Initiative, which both organizations launched in 1991. The practical guidance encourages new mothers to breastfeed and informs health workers how best to support breastfeeding.

Breastfeeding is vital to a child's lifelong health, and reduces costs for health facilities, families, and governments. Breastfeeding within the first hour of birth protects newborn babies from infections and saves lives. Infants are at greater risk of death due to diarrhoea and other infections when they are only partially breastfed or not breastfed at all. Breastfeeding also improves IQ, school readiness and attendance, and is associated with higher income in adult life. It also reduces the risk of breast cancer in the mother.

"Breastfeeding saves lives. Its benefits help keep babies healthy in their first days and last will into adulthood," says UNICEF Executive Director Henrietta H. Fore. "But breastfeeding requires support, encouragement and guidance. With these basic steps, implemented properly, we can significantly improve breastfeeding rates around the world and give children the best possible start in life."

WHO Director-General Dr Tedros Adhanom Ghebreyesus says that in many hospitals and communities around the world, whether a child can be breastfed or not can make the difference between life and death, and whether a child will develop to reach his or her full potential.

"Hospitals are not there just to cure the ill. They are there to promote life and ensure people can thrive and live their lives to their full potential," says Dr Tedros. "As part of every country's drive to achieve universal health coverage, there is no better or more crucial place to start than by ensuring the Ten Steps to Successful Breastfeeding are the standard for care of mothers and their babies."

The new guidance describes practical steps countries should take to protect, promote and support breastfeeding in facilities providing maternity and newborn services. They provide the immediate health system platform to help mothers initiate breastfeeding within the first hour and breastfeed exclusively for six months.

It describes how hospitals should have a written breastfeeding policy in place, staff competencies, and antenatal and post-birth care, including breastfeeding support for mothers. It also recommends limited use of breastmilk substitutes, rooming-in, responsive feeding, educating parents on the use of bottles and pacifiers, and support when mothers and babies are discharged from hospital.

**Note to editors**

The Ten Steps are based on the WHO guidelines, issued in November 2017, titled Protecting, promoting and supporting breastfeeding in facilities providing maternity and newborn services.

Early initiation of breastfeeding, within one hour of birth, protects the newborn from acquiring infections and reduces newborn mortality. Starting breastfeeding early increases the chances of a successful continuation of breastfeeding. Exclusive breastfeeding for six months has many benefits for the infant and mother. Chief among these is protection against gastrointestinal infections and malnutrition, which are observed not only in developing but also industrialized countries.

Breast-milk is also an important source of energy and nutrients in children aged 6–23 months. It can provide half or more of a child's energy needs between 6-12 months, and one-third of energy needs between 12-24 months. Breast-milk is also a critical source of energy and nutrients during illness, and reduces mortality among children who are malnourished.

Children and adolescents who were breastfed as babies are less likely to be overweight or obese.

Available from: <http://www.who.int/mediacentre/news/releases/2018/promote-breastfeeding-globally/en/>


abstract: The high levels of intelligence seen in humans, other primates, certain cetaceans and birds remain a major puzzle for evolutionary biologists, anthropologists and psychologists. It has long been held that social interactions provide the selection pressures necessary for the evolution of advanced cognitive abilities (the 'social intelligence hypothesis'), and in recent years decision-making in the context of cooperative social interactions has been conjectured to be of particular importance. Here we use an artificial neural network model to show that selection for efficient decision-making in cooperative dilemmas can give rise to selection pressures for greater cognitive abilities, and that intelligent strategies can themselves select for greater intelligence, leading to a Machiavellian arms race. Our results provide mechanistic support for the social intelligence hypothesis, highlight the potential importance of cooperative behaviour in the evolution of intelligence and may help us to explain the distribution of cooperation with intelligence across taxa.
author: Luke McNally; Sam P. Brown; Andrew L. Jackson\*Author for correspondence (<email@example.com>).
date: 2012-08-07
institute: 1Department of Zoology, School of Natural Sciences, Trinity College Dublin, Dublin 2, Republic of Ireland; 2Centre for Biodiversity Research, Trinity College Dublin, Dublin 2, Republic of Ireland; 3Centre for Immunity, Infection and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JT, UK
references:
title: Cooperation and the evolution of intelligence

# Introduction

Natural selection never favours excess; if a lower-cost solution is present, it is selected for. Intelligence is a hugely costly trait. The human brain is responsible for 25 per cent of total glucose use, 20 per cent of oxygen use and 15 per cent of our total cardiac output, although making up only 2 per cent of our total body weight \[1\]. Explaining the evolution of such a costly trait has been a long-standing goal in evolutionary biology, leading to a rich array of explanatory hypotheses, ranging from evasion of predators to intelligence acting as an adaptation for the evolution of culture \[2–4\]. Among the proposed explanations, arguably the most influential has been the 'social intelligence hypothesis', which posits that it is the varied demands of social interactions that have led to advanced intelligence \[4–12\].

In recent years, the cognitive demands of reciprocity, one of the mechanisms posited as important in the maintenance of cooperation in humans and other intelligent taxa, have been suggested to be a causal factor in the evolution of advanced intelligence and human language. This has been particularly apparent in the evolutionary game theory literature, where conjecture regarding this relationship is frequent \[13–16\]. Indeed, there is a rich history of work relating intelligence and reciprocity in the game theory literature, though most of this work has focused on the cognitive abilities required for the evolution of cooperation, rather than the possible role that the negotiation of these interactions has in the evolution of intelligence \[17–24\]. As well as the cognitive abilities required for the coordination of partners during cooperative acts, both direct (decisions based on what you do to me) and indirect (decisions based on what you do to others) reciprocity have additional demands in terms of the ability to remember previous interactions and to integrate across these interactions to make decisions in cooperative dilemmas \[25–31\]. These cognitive demands, combined with the occurrence of cooperative behaviour between unrelated individuals in intelligent taxa, suggest that selection for these mechanisms of cooperation could, at least in part, be responsible for advanced cognitive abilities \[26\].

The many subfields within the social intelligence hypothesis have shown a rich elaboration of verbal arguments, and data from comparative studies support many of their predictions \[32–34\]. However, verbal reasoning and comparative analysis alone are not sufficient to assess the relative merit of competing hypotheses \[35\]; mechanistic models are needed to assess the plausibility of these different explanations for advanced cognition.

Here, we use an artificial neural network model to focus on the potential for direct reciprocity, a behaviour that is widespread in humans, to select for advanced cognitive abilities. Rather than manufacturing some form of functional relationship between intelligence and fitness, we allow this relationship to emerge based on the demands of decision-making in two social dilemmas, and analyse the consequences for the evolution of intelligence.

# Material and methods

## The social dilemmas

In order to consider the dynamics of cooperative social interactions, we use the framework of two classic social dilemmas: the iterated prisoner's dilemma (IPD) and the iterated snowdrift game (ISD). In both games, two players must choose between cooperation and defection during repeated rounds. In the event of mutual cooperation or mutual defection, both players receive payoffs *R* or *P*, respectively, while a defector exploiting a cooperator gets *T* and the cooperator gets *S*. In the prisoner's dilemma, the benefit of an individual's cooperative behaviour goes to their opponent, while they pay all of the costs (e.g. food sharing, reciprocal coalitionary behaviour). This results in a payoff order of *T* \> *R* \> *P* \> *S*. Here, the worst possible outcome for an individual is to cooperate while their opponent defects, while the best outcome is to defect while the opponent cooperates. In the snowdrift game, the benefits of cooperative behaviours are shared between opponents, and the costs are shared if both individuals cooperate (e.g. cooperative hunting, coalitionary behaviour with shared benefits). This results in a payoff order of *T* \> *R* \> *S* \> *P*. Again, the best outcome for an individual is to defect while their opponent cooperates, though the worst possible outcome for an individual is for neither them nor their opponent to cooperate. In both games, the overall payoff (sum of both individual's payoffs) is greatest for mutual cooperation and lowest for mutual defection.

All of this means that the equilibrium frequency of cooperation for a single interaction (single-interaction Nash equilibrium) will be zero in the prisoner's dilemma but will be non-zero for a single-interaction snowdrift game \[36\]. These single-interaction Nash equilibria provide a useful benchmark against which to assess the effects of contingent behaviours (i.e. those that depend on the behaviour of others) in repeated interactions.

## The neural network model

Any attempt to define a metric of intelligence will always be a contentious matter. However, comparative studies across taxa have usually focused on two main classes of brain properties as proxies of intelligence: metrics based on relative or absolute size of the brain or certain brain regions, and metrics based on more specific properties such as numbers of cortical neurons \[37\]. It is with this tradition in mind that we develop our artificial neural network model, with evolving network structure, using the number of neurons, *i*, as our proxy for intelligence. Each individual can display varying levels of intelligence, from simply being characterized by a binary response of always cooperate or always defect to large neural networks that possess complex neuronal structure, allowing for computations to inform decisions based on payoffs and the integration of longer-term memory into their current decision-making processes.

Each individual in our simulated populations possesses a neural network that determines their behaviour in social dilemmas (illustrated in figure 1). The networks each have two input nodes (which receive the payoffs of the individual and their opponent in the previous round as inputs) and one output node (giving the probability that they cooperate during their next interaction). The hidden layer of each individual's network has an evolving structure, possessing different numbers of cognitive and context nodes \[38\] (figure 1). Cognitive nodes allow for computation based on the values of network inputs and context nodes, which in turn allow for the build-up of memory based on previous states of their associated cognitive nodes.

Computation in the network is implemented via synchronous updating of nodes. The value of each input node is passed to each of the network's cognitive nodes, multiplied by the weight linking the two nodes. Each cognitive node is also passed the current value of their associated context node (if they possess one) multiplied by the weight linking the two nodes. The cognitive nodes sum across all of the weighted values that they receive and pass this value through a sigmoidal squashing function, resulting in a value between 0 and 1, analogous to a probability of activation. All context nodes are then passed the value of their associated cognitive nodes. This allows the context nodes to build up memory of previous interactions without having to store the actual sequence of events that have occurred. The internal states of these context nodes could be considered analogous to emotional states. Finally, the values at all cognitive nodes are then passed to the output node (multiplied by their weights), summed and again passed through a sigmoidal squashing function. This output gives the probability that the individual will cooperate in the current round. As the sigmoidal function asymptotes to 0 at –∞ and 1 at ∞, there will always be inherent noise in the network's probabilistic decision. This property of the function also means that it is easy to minimize noise in the network's behaviour if that behaviour shows a lack of contingency (as the node can always be near one of the asymptotes), while contingent behaviour will show greater noise (as switching is more difficult to achieve near the asymptotes). This formulation has intuitive appeal over simply adding extraneous noise to individual decisions, as in nature we would expect individuals to make few mistakes when their behaviour is non-contingent, while more complex decisions would be expected to be more error-prone. As the network cannot make decisions without an input, each individual has an additional trait encoding whether they cooperate or defect in the first round.

We allowed networks to evolve according to natural selection using a genetic algorithm where fitness is the mean payoff per round from the iterated games minus a penalty for the individual's intelligence, *i*. When individuals reproduce, mutations allow for the gain and loss of nodes from the hidden layer of their network with a fixed probability. Context nodes could only be gained if there was already a cognitive node present without an associated context node. The loss of a cognitive node with an associated node resulted also in the loss of the associated context node.

The addition of extra cognitive nodes gives networks the potential to perform complex computation based on payoffs by increasing the dimensions of internal representation of the network. The addition of context nodes gives the potential for the integration of longer-term memory of previous interactions in these computations. If an individual possessed no hidden layer nodes in its network, its behaviour in all rounds was decided by its first round move (i.e. they either always cooperated or always defected). The weights of each node in the network (arrowed lines in figure 1) and the threshold of each node (see the electronic supplementary material) were encoded as continuous genetic traits, again subject to mutation during reproduction. This means that, while the number of nodes in the network constrains the possible behavioural repertoire, it is the way that the constituent parts of the network interact that actually decides the individual's behaviour. In this way, our metric of intelligence assesses the potential for complex behaviour that the individual possesses, rather than the appropriateness or 'wisdom' of their behaviour, similarly to the measures of intelligence used in comparative studies.

## Model implementation

In order to elucidate when selection favoured intelligence, we ran 10 replicates of our model for both the IPD and ISD, with each replicate lasting 50 000 generations. The payoff values used for all simulations were *R* = 6, *P* = 2, *T* = 7 and *S* = 1 for the IPD, and *R* = 5, *P* = 1, *T* = 8 and *S* = 2 for the ISD. The genetic algorithm was implemented as follows (see the electronic supplementary material for further details):

1.  — an initial population of random networks was generated;

2.  — each individual played every other individual in the population (50 individuals) in an IPD or ISD;

3.  — each individual network's fitness was calculated as their mean payoff per round minus a fitness penalty for their level of intelligence, *i*;

4.  — individuals were selected to reproduce asexually with probability proportional to their fitness;

5.  — newly produced offspring underwent mutation of their network weights, node thresholds and network structure with constant probabilities;

6.  — the previous generation died; and

7.  — the algorithm returned to step 2 until 50 000 generations was reached.

During simulations we recorded the frequency of cooperation in the population, the intelligence of individuals (*i*) and assessments of the behaviour of individuals against a pre-determined test set of moves (see the electronic supplementary material). We then analysed the gradients of selection for intelligence across these simulations by taking selection for intelligence as the covariance between fitness and intelligence in any given generation \[39\]. As 50 million individual neural networks were simulated in our study, and individuals were not constrained to base their behaviour only on the previous move, our simulations generated a great diversity of strategies. In order to gain a coarse-grained overview of the strategic composition of the population, we clustered individuals based on their proximity to four canonical strategy types: always-defect-like, always-cooperate-like, tit-for-tat-like (do what your opponent did to you) and Pavlov-like (if your payoff is over a threshold, repeat your previous move). Assignment to each of these strategy types was based on which of these four strategies each individual network clustered closest to based on its behaviour against the test set. While this clustering is only a coarse-grained view, it allows assessment of the effects of shifts towards contingent cooperative strategies on selection for intelligence. Additionally, contingent human cooperation has previously clustered as either tit-for-tat-like or Pavlov-like \[40\], though longer-term memory is often included \[41\]. For full details of our data analysis, we direct readers to the electronic supplementary material.

# Results

Our model shows the spontaneous evolutionary emergence of behaviours similar to strategies known to perform well in the IPD and ISD, such as tit-for-tat and Pavlov, as well as simple always-cooperate or always-defect strategies (figure 2) \[42\]. Although our networks' behaviours are similar to these strategies, they often show integration over many previous rounds to decide on their next moves. For example, manual interrogation of networks revealed that, of the tit-for-tat type strategies that emerge, many are tit-for-2(or more)-tats, and many of the Pavlov-like strategies also show a threshold mechanism, switching to constant defection against opponents that show behaviour close to an always-defect strategy. Behaviour was observed that appeared to be close to many other strategies—for example, grim variants (cooperate until the opponent defects, then defect forever), though often requiring more than one defection to trigger permanent defection; false cooperator (cooperate first then switch to defection), though often giving another cooperative move after many defections; and many other variants of tit-for-tat such as 2-tits-for-1-tat and 2-tits-for-2-tats. It is worth noting that strategies of these types that use longer-term memory are observed in behavioural experiments of repeated games with noise \[41\]. These responsive strategies require greater cognitive abilities in order to carry out computations based on payoffs, memorize past rounds and integrate across them to make decisions, in comparison with the lower requirements of simply always cooperating or defecting. We hasten to add, however, that the strategies emerging only resemble these strategies; the strategies vary in a continuous manner and often incorporate memory over more rounds. Our goal here is not to describe the strategies that can emerge in repeated games, as there is already extensive literature on this topic (see table 2.1 in \[43\]), but rather to elucidate the potential effects of their evolution on selection for cognitive capacities.

In order to elucidate the causal factors leading to the evolution of more complex strategies, we analyse the gradient of selection for intelligence in response to population features such as the prevalence of cooperative acts. We find that the selection for intelligence is maximized as the level of cooperation in the population moves above the single-interaction Nash equilibria towards more cooperative regimes (figure 3). In the IPD, this maximum occurs during increases in cooperation from the single-interaction Nash equilibrium, whereas in the ISD selection for intelligence is maximized at levels of cooperation just above the single-interaction Nash equilibrium. This discrepancy between the games is explained by the different natures of their single-interaction Nash equilibria. In the IPD, this equilibrium is zero, meaning that declining cooperation near this equilibrium is caused by increases in the frequency of individuals that always defect, requiring only little cognitive ability. In the ISD, the equilibrium is non-zero (0.23 in our simulations), meaning that decreases in cooperation back towards the equilibrium can be caused by 'meaner' contingent strategies (e.g. 2-tits-for-1-tat and false-cooperator variants), as well as individuals that always defect. As a result, transitions back to the single-interaction Nash equilibrium in the ISD can in principle select for intelligence, while this is very unlikely in the IPD.

We also find that increasing intelligence decreases the mean frequency of cooperative acts in the IPD (Spearman's rank correlation test; *ρ* = −0.2333, *p* \< 0.001; figure 4*a*), while slightly increasing cooperation in the ISD (Spearmans' rank correlation test; *ρ* = 0.0089, *p* \< 0.0001, figure 4*b*). Increasing intelligence increases the variance in the frequency of cooperative acts in the population in both the IPD (Breusch–Pagan test; intercept = 0.0294, slope = 0.0582, *p* \< 0.0001; figure 4*a*) and the ISD (Breusch–Pagan test; intercept = 0.0309, slope = 0.0384, *p* \< 0.001; figure 4*b*), showing that intelligence can facilitate greater extremes of cooperation. These results can be explained by assortment of individuals' cooperative acts \[44\]; the contingent strategies facilitated by increased intelligence allow an individual to increase the probability that they assort their cooperative acts with other cooperative acts. This leads to a synergistic process, where this increase in cooperation due to increased intelligence creates further opportunities for intelligent individuals to engage in mutual cooperation. However, as levels of cooperation increase further this feedback can break down, as there may be enough cooperation occurring for unconditional cooperators to increase in the population, allowing in turn for the invasion of unconditional defectors or 'meaner' intelligent strategies (e.g. grim variants, false-cooperator variants, etc.). This results in intelligence-facilitating cycles in the levels of cooperation seen in the population (figure 2), which increases both the variance in, and the maximum level of, cooperation.

In addition to dependency on the prevalence of, and change in, cooperative actions (figure 3), we find that intelligence can be subject to a Machiavellian runaway process \[11,12\]. In the ISD, as the frequency of contingent (intelligent) strategies increases, so too does selection for intelligence in the population (tit-for-tat-like strategies: Spearman's *ρ* = 0.2025, *p* \< 0.0001; Pavlov-like strategies: *ρ* = 0.2352, *p* \< 0.0001; see figure 5; electronic supplementary material, table S1 and figure S1). In the IPD, increasing frequencies of tit-for-tat and Pavlov reduces selection for intelligence at low levels of cooperation (frequency of cooperation \<0.5; tit-for-tat: *ρ* = −0.0945, *p* \< 0.0001; Pavlov: *ρ* = −0.0529, *p* \< 0.0001), but does increase selection for intelligence when cooperation is more frequent (frequency of cooperation ≥ 0.5; tit-for-tat: *ρ* = 0.5491, *p* \< 0.0001; Pavlov: *ρ* = 0.3187, *p* \< 0.0001). The reason for this distinction between the IPD and the ISD is that there must already be some cooperation occurring for cooperative contingent strategies to be favoured via their ability to assort cooperative acts. In the ISD, the partially cooperative single-interaction equilibrium provides sufficient baseline cooperation for tit-for-tat and Pavlov to be favoured, whereas in the IPD the single-interaction equilibrium of zero cooperation means that unless contingent strategies (or random drift) have already increased cooperation, tit-for-tat-like and Pavlov-like strategies cannot be favoured, and hence cannot lead to an arms race. Note that it is still the case that intelligence is selected for in the IPD when cooperative acts are rare yet increasing (figure 3*a*). However, the cooperative acts that drive selection for intelligence in this case are generated by less cooperative contingent strategies, which cluster with always-defect as their closest pure strategy (*ρ* = 0.1095, *p* \< 0.0001; see electronic supplementary material, table S1 and figure S1). This means that there is a succession in the arms race in the IPD, with 'mean' contingent strategies initially increasing selection for intelligence at low cooperation, and 'kind' contingent strategies increasing selection for intelligence as cooperation increases.

It is not any particular single strategy that drives these arms races; rather, as the complexity of the strategies in the population increases, there is selection for other complex strategies to outwit them. Unlike previous analyses where fixed strategies or strategies with constrained memory were used, our open-ended system allows for near infinite strategic variations to outwit opponents. In this way, selection for intelligence occurs owing to a constantly shifting strategic environment, where the 'best response' to the population of strategies can be shifting from generation to generation. Increases in memory allow for the potential of the recognition of opponents' strategies, allowing for the alteration of one's own strategy in response (e.g. Pavlov-like strategies that can recognize individuals that always defect). In turn this can allow for attempts to deceive opponents regarding one's own strategy (e.g. false cooperators).

# Discussion

It is important here to note the closed nature of our model system; individuals can only choose within one social task (whether to cooperate or defect against another individual based on their behaviour in previous interactions with them). However, our results may apply in principle to other social scenarios where individuals use strategies to decide who to cooperate with or when to cooperate, such as indirect reciprocity \[17,18\], policing/punishment \[45,46\] and partner choice \[47–49\]. Along with kin selection, these are the major mechanisms thought to lead to transitions to cooperative groups. As the intelligence of an individual increases, it is likely that more of these behavioural repertoires will become available to them, with increased intelligence acting as a pre-adaptation. For example, increased intelligence owing to selection for direct reciprocity could facilitate the evolution of indirect reciprocity or partner choice, highlighting the contingent nature of social evolution in multiple strategic dimensions \[50\]. The facilitation of new social behaviours due to emergent intelligence could allow for a perpetuated Machiavellian arms race leading to ever-greater levels of intelligence. Additionally, in our simulations populations evolved to play only a single game (either the IPD or ISD). The simultaneous play of multiple games could greatly increase strategic complexity, with the possibility of the integration of information from previous interactions in games with different payoff structures into the decision-making process.

It has previously been suggested that the pinnacles of cooperative behaviour in nature show a bimodal distribution with intelligence, with the most cooperative species displaying either limited cognition (e.g. microbes, social hymenoptera) or exceptional intelligence (e.g. humans and other primates, certain cetaceans and birds) \[26\]. It is clear in the former case that cooperation has evolved primarily owing to combinations of kin selection and ecological factors \[51\]. However, in the latter case, kin selection is not the only mechanism leading to cooperation, and may not even be the most important. A recent study has in fact suggested that relatedness was too low in human hunter–gatherer groups for kin selection to drive the evolution of human cooperation \[52\]. In highly intelligent species, contingent behaviours (reciprocity, partner choice, etc.) appear to have been important in the evolution of cooperation \[26\]. Our results may help us to explain this pattern by showing that the selection for appropriate behavioural assortment of cooperative acts can lead to selection for greater cognitive abilities and Machiavellian arms races, and that intelligence facilitates greater extremes of cooperation. Additionally, although kin selection is still of importance in highly intelligent taxa, high relatedness may hinder the evolution of intelligence by driving unconditional cooperation to fixation in the population, without any need of contingent behaviours.

A trait as complex as advanced intelligence is likely to have evolved owing to a combination of several factors rather than a single factor \[4\]. Along with the social intelligence hypothesis, many other theories attempting to explain the evolution of advanced intelligence have been suggested, among them that intelligence is an adaptation for tool use \[53,54\], that intelligence is an adaptation for social learning and the accumulation of culture \[55–57\], and that intelligence is the result of sexual selection \[58\]. All of these theories are supported by evidence from at least some of the most intelligent animals. However, the difficulty lies in disentangling the traits that are causal factors in the evolution of intelligence from those that are by-products of advanced intelligence. The combination of game theoretic frameworks and artificial neural network models presented here may provide a framework for the evaluation of the mechanistic strengths of these different hypotheses. While previous models have sought to relate cooperation and intelligence, the focus has most frequently been on the cognitive requirements of cooperation, rather than on the selection for intelligence. Many of these models have lacked an explicit brain structure \[17–22\], and among those studies that have used artificial neural networks, we know of no examples where the network structure was allowed to freely evolve or implications of selection for decision-making strategies for the evolution of intelligence were directly addressed \[23,24\]. While artificial neural networks are not real brains, relying on abstraction of the activity of millions of real neurons down to a manageable number of artificial neurons, they can provide insight into the dynamics of cognitive evolution and allow for the flexible evolution of behaviour \[59\]. Our results show that, in a freely evolving system, selection for efficient decision-making in social interactions can give rise to selection pressures for advanced cognition, supporting the view that the transition to the cooperative groups seen in the most intelligent taxa may be the key to their intellect.

## Acknowledgments

We thank G. D. Ruxton, C. J. Tanner, I. Donohue, N. M. Marples, K. Irvine, R. Bshary and two anonymous reviewers for helpful comments on a previous version of this manuscript. We also thank F. J. Weissing, S. Gavrillets, J. A. R. Marshall and R. Kümmerli for helpful discussions. L.M. was supported by a scholarship from the Irish Research Council for Science, Engineering and Technology. S.P.B. was supported by the Wellcome Trust.

# References


abstract: Two new approaches have identified physiological nonsense-mediated mRNA decay (NMD) substrates, and suggest that NMD functions as a multipurpose tool in the modulation of gene expression.
author: Gabriele Neu-Yilik; Niels H Gehring; Matthias W Hentze; Andreas E Kulozik
date: 2004
institute: 1Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Im Neuenheimer Feld 150, 69120 Heidelberg, Germany; 2Molecular Medicine Partnership Unit, EMBL/University of Heidelberg, Im Neuenheimer Feld 156, 69120 Heidelberg, Germany; 3EMBL Heidelberg, Gene Expression Programme, Meyerhofstrasse 1, 69117 Heidelberg, Germany
references:
title: Nonsense-mediated mRNA decay: from vacuum cleaner to Swiss army knife

Nonsense-mediated mRNA decay (NMD) is a specific pathway for the degradation of mRNAs that have premature termination codons (PTCs) in their open reading frames (ORFs). Its importance is highlighted by its conservation in all eukaryotes. NMD counteracts the potentially harmful impact of mRNAs that have PTCs as a result of errors at various levels of gene expression, such as nonsense and frameshift mutations, transcriptional errors and faulty splicing. Thus, NMD serves as a 'cellular vacuum cleaner' that protects the cell from the potentially harmful effects of truncated proteins by eliminating mRNAs with PTCs in a sequence of events that is not yet fully understood. In recent years numerous biochemical and cell-biological investigations in *Saccharomyces cerevisiae* \[1\], *Drosophila melanogaster* \[2\], *Caenorhabditis elegans* \[3\] and human \[4,5\] cells have helped to elucidate some of the mechanistic details underlying the NMD pathway. A role for NMD in the regulation of mRNA metabolism beyond the mere vacuum cleaner function for faulty mRNAs has been suspected, and was foreshadowed by work on the splicing factor SC35 and some ribosomal proteins \[6,7\]. Now, 'genome-wide' approaches - one in yeast using microarrays and another *in silico*, analyzing information mined from mRNA and protein databases - have added powerful evidence to suggest that NMD may serve multiple purposes in gene expression \[8-11\].

# Inspirations from yeast

In yeast, NMD depends on the expression of the Upf1, Upf2 (Nmd2) and Upf3 proteins. Single or simultaneous inactivation of the UPF genes stabilizes nonsense-containing mRNAs, indicating that their protein products interact functionally in the same pathway. He *et al*. \[8\] used high-density oligonucleotide arrays to analyze genome-wide expression profiles of yeast strains containing single deletions of the *UPF1*, *UPF2* or *UPF3* genes, as well as of the *DCP1* and *XRN1* genes which encode proteins with activities thought to be involved in the NMD pathway - an essential component of the mRNA decapping enzyme and the 5'-3' exonuclease, respectively. They also tested double deletions of the *XRN1* gene in combination with each of the UPF genes. Two-dimensional clustering analysis of the expressed genes for the *Δupf1*, *Δupf2* and *Δupf3* strains yielded several interesting results.

The deletion of *UPF1*, *UPF2* or *UPF3* generated nearly identical expression profiles. Thus, all three gene products act on the same targets, consistent with the function of Upf1, Upf2 and Upf3 in a single, linear pathway in yeast. The abundance of most mRNAs upregulated in Upf-deficient cells was also increased in *Δdcp1* and *Δxrn1* strains, suggesting that these mRNAs are largely degraded by decapping and subsequent 5'-3' exonucleolytic decay. This approach also identified a considerable number of NMD-regulated transcripts (765 out of the 7,839 genes represented on the microarrays) and showed that NMD substrates are generally expressed at below-average levels. In addition, most NMD substrates were found to be upregulated upon NMD inactivation, but some were downregulated, pointing to the existence of higher-order NMD targets (or additional functions of the Upf proteins in alternative gene-regulation pathways). Finally, only one third of the identified transcripts can be classified into structural or functional groups, some of which are surprising and hitherto unrecognized. Representatives of previously described NMD-substrate categories were identified, including mRNAs with nonsense mutations, transcripts resulting from faulty or regulated alternative splicing, mRNAs subject to leaky scanning during translation initiation, and mRNAs with an upstream ORF or with AATGA or ATGAA motifs immediately upstream of their translation initiation codons. More intriguing is the discovery of several new classes of NMD targets, including mRNAs that use translational +1 frameshifting, bicistronic mRNAs and, most interestingly, two classes of noncoding RNAs: pseudogene transcripts and transcripts encoded by transposable elements or their long terminal repeat (LTR) sequences.

A significant fraction of the protein-encoding transcripts upregulated in the strains with mutations in the NMD-pathway genes \[8\] could be grouped into clusters of proteins that act in similar pathways. Among these are proteins coordinately involved in telomere maintenance, pre-mRNA splicing, peroxisomal function and DNA repair. This suggests the exciting possibility that NMD could orchestrate the expression of functional groups of genes. Several important results of the work by He *et al*. \[8\] confirm findings obtained by another lab using a similar approach several years ago \[12\]. Notably, the coordinate upregulation of genes involved in telomere maintenance by NMD inactivation has already sparked interesting follow-up investigations. The yeast NMD pathway has been shown to accelerate the rate of senescence promoted by the loss of the telomerase enzyme or by the erosion of telomeres that results from altering the stochiometry of telomere-cap components \[13-15\]. At least one likely primary target of NMD is the mRNA of Stn1p, an essential protein involved in chromosome-end protection. The observation by He *et al*. \[8\] that 35.9% of all ORFs encoded in the telomere region were upregulated in strains with mutations in components of the NMD pathway further illustrates that NMD controls many genes near telomere ends, although probably indirectly. These genes are usually silenced and may be derepressed when the protection of chromosome ends is disturbed by the loss of NMD. This enlightening example demonstrates how NMD can affect whole pathways by regulating the expression of one or a few primary target mRNAs, with consequences for groups of downstream secondary effectors. The discovery of additional examples of NMD-mediated control of functional pathways can be expected, promoting NMD to a gene-expression tool with many utilities. The cellular vacuum cleaner has therefore become a Swiss army knife (Figure 1).

# *In silico* veritas?

In a series of three recent publications \[9-11\] Steve Brenner and colleagues suggested, by data-mining, that one-third of reliably inferred human splice products form a major class of natural targets for NMD. Alternative splicing is thought to occur in 30-60% of human genes, expanding the coding repertoire of the limited number of genes in the genome and modulating tissue-specific and developmental gene functions. Brenner and colleagues \[9-11\] now suggest that alternative splicing provides a mechanism to generate PTC-containing splice products that are subsequently degraded by NMD and, as a consequence, that cooperation between alternative splicing and NMD pathways offers a major and currently underappreciated way to regulate gene expression.

For their *in silico* analysis, they mapped well-characterized human RefSeq \[16\] and LocusLink \[17\] mRNA sequences to genomic sequences, and then performed high-stringency alignments between these 'RefSeq-coding genes' and expressed sequence tags (ESTs). The reliably inferred splice variants were only accepted as likely NMD targets when they conformed to the '50-nucleotide rule': this hallmark of mammalian NMD predicates that stop codons located at least 50 nucleotides upstream of the last exon junction will be interpreted as 'premature' and trigger NMD. This approach leads to an underestimate of potential NMD targets, because some PTCs (for example, in T-cell receptor gene transcripts) will trigger NMD even when they are not followed by a sufficiently distant intron \[18\]. Moreover, Brenner and colleagues \[9-11\] also excluded mRNA variants that are indistinguishable from products of faulty splicing. These studies have unearthed several groups of functionally related proteins whose expression appears to be regulated by NMD, including translation factors and ribosomal proteins. This is in remarkable contrast to the yeast data of He *et al*. \[8\], where proteins with a function in translation were, if anything, underrepresented in the pool of NMD-regulated genes.

NMD is not (yet) on everyone's mind. As a consequence, Brenner and colleagues \[11\] found several entries for truncated proteins in the Swiss-Prot database. In some of these cases the available experimental evidence confirms that the mRNAs that encode these truncated proteins are *bona fide* NMD substrates. We are left with a consolation and a surprise. The consolation is that traces of NMD can be uncovered even though they had been overlooked before. Having recently had to accept that the number of genes in the human genome is too limited to explain the far higher number of proteins (not to mention other gene products), we then had to learn that one plausible and elegant explanation lies in alternative splicing, which enables a gene to code for a whole family of related, or sometimes antagonistic, proteins. And now the surprise is that a large portion of this effort is supposedly expended only to direct many of the primary products to decay. Several conclusions can be drawn from these observations. First, databases need to be read and annotated with a full realization of the implications of NMD; and second, NMD seems to serve as a tool for rapidly switching off gene expression. This view extends the idea of NMD as a mechanism for ridding the cell of the potentially harmful products of faulty splicing. But there may be more to it. NMD rarely downregulates the expression of a transcript completely; more commonly, 10-30% of the PTC-containing transcripts survive and may allow the production of physiologically relevant levels of truncated protein products.

For NMD researchers it has always been hard to reconcile these observations with the presumed protective role of NMD, especially as very low levels of biological products can sometimes have enormous effects. Given the problems of detecting the low levels of proteins or peptides produced from downregulated transcripts, in addition to some lingering lack of awareness of NMD, examples to prove otherwise are hard to come by. A recent publication \[19\] describes a PTC-containing transcript of the high-affinity immunoglobulin E (IgE) receptor, FcεRIβ, arising from retention of an intron. This alternative transcript not only conforms to the 50-nucleotide rule but its expression levels are very low compared to those of the full-length transcript, as would be expected for an NMD target. Nonetheless, the truncated protein is not only detectable, it even competes effectively with the full-length protein to control FcεRIβ expression on the cell surface. Thus, even low endogenous expression levels of NMD targets can suffice to generate a product with a biological function. A similar example of the utility of a *bona fide* NMD substrate is illustrated by the *unc-49* locus in *C. elegans*. This locus uses alternative splicing to produce three GABA-receptor subunits, two of which (A and B) undergo several splice events in their 3' UTRs, rendering them predicted NMD substrates \[20\]. While A-form transcripts are hardly detectable, B-form transcripts represent the most abundant form and code for a protein essential for the worm's locomotion. Either the B-form transcript escapes NMD, or the residual mRNA left after NMD suffices for the necessary protein production.

Among the examples presented by Brenner and colleagues \[11\] or in other studies \[21\] are genes with complex alternative splice patterns resulting in multiple transcript isoforms with or without PTCs. The lower abundance of these isoforms when compared to the full-length form supports the notion that they are targeted to the NMD pathway. But is the possibility that cells engage in complex alternative-splicing procedures generating multiple products, just to finally dispose of them, the only conceivable option? Four out of five PTC-containing isoforms of the mRNA for the LARD death receptor are readily detectable in non-activated lymphocytes, whereas only the full-length form is expressed in activated lymphocytes \[11\]. While it is entirely possible that the PTC-containing forms are generated to switch off receptor expression in resting lymphocytes, there are attractive alternatives: as in the case of the FcεRIβ receptor \[19\], the PTC-containing mRNAs may produce proteins or peptides with relevant, although currently unknown, functions. Thus, quantitative control of the expression of low amounts of protein isoforms could represent yet another facet of the function of the NMD pathway.

A role for NMD in controlling the levels of noncoding RNAs (including noncoding alternative splice products) must also be considered. RNA accounts for more than 95% of the human genome's output and there is increasing evidence that noncoding RNAs (including introns, and spliced and polyadenylated transcripts) can have a function, for example as a modulating network or an additional layer of information \[22,23\]. Importantly, noncoding RNAs have been discovered among natural NMD targets in yeast \[8\], where only a relatively small portion of the genome is transcribed into noncoding RNAs. What might be the role of NMD in mammals, which transcribe a far higher percentage of their genome into noncoding RNAs \[22,24,25\]? Clearly, the recent insights into the RNA-substrate spectrum of the NMD system should enhance the appreciation of NMD as a versatile, multipurpose mechanism that controls the transcriptome qualitatively and quantitatively.

## Figures and Tables