abstract: We investigated the association of glycemia and 43 genetic risk variants for hyperglycemia/type 2 diabetes with amino acid levels in the population-based Metabolic Syndrome in Men (METSIM) Study, including 9,369 nondiabetic or newly diagnosed type 2 diabetic Finnish men. Plasma levels of eight amino acids were measured with proton nuclear magnetic resonance spectroscopy. Increasing fasting and 2-h plasma glucose levels were associated with increasing levels of several amino acids and decreasing levels of histidine and glutamine. Alanine, leucine, isoleucine, tyrosine, and glutamine predicted incident type 2 diabetes in a 4.7-year follow-up of the METSIM Study, and their effects were largely mediated by insulin resistance (except for glutamine). We also found significant correlations between insulin sensitivity (Matsuda insulin sensitivity index) and mRNA expression of genes regulating amino acid degradation in 200 subcutaneous adipose tissue samples. Only 1 of 43 risk single nucleotide polymorphisms for type 2 diabetes or hyperglycemia, the glucose-increasing major C allele of rs780094 of *GCKR,* was significantly associated with decreased levels of alanine and isoleucine and elevated levels of glutamine. In conclusion, the levels of branched-chain, aromatic amino acids and alanine increased and the levels of glutamine and histidine decreased with increasing glycemia, reflecting, at least in part, insulin resistance. Only one single nucleotide polymorphism regulating hyperglycemia was significantly associated with amino acid levels.
author: Alena Stančáková; Mete Civelek; Niyas K. Saleem; Pasi Soininen; Antti J. Kangas; Henna Cederberg; Jussi Paananen; Jussi Pihlajamäki; Lori L. Bonnycastle; Mario A. Morken; Michael Boehnke; Päivi Pajukanta; Aldons J. Lusis; Francis S. Collins; Johanna Kuusisto; Mika Ala-Korpela; Markku LaaksoCorresponding author: Markku Laakso, <email@example.com>.
date: 2012-07
references:
title: Hyperglycemia and a Common Variant of *GCKR* Are Associated With the Levels of Eight Amino Acids in 9,369 Finnish Men

Insulin regulates carbohydrate, lipid, protein, and amino acid metabolism (1). Insulin inhibits proteolysis and associated release of amino acids and stimulates amino acid uptake and protein synthesis in skeletal muscle (2,3). Selected amino acids, however, enhance insulin secretion (4,5) or modulate insulin sensitivity (6–10), the two main mechanisms in the regulation of glucose homeostasis.

A recent study reported that three branched-chain amino acids (BCAAs), valine, leucine and isoleucine, and two aromatic amino acids, phenylalanine and tyrosine, predicted type 2 diabetes (11). The risk of diabetes was fivefold higher in individuals in the top quartile of a combination of three amino acids (isoleucine, phenylalanine, and tyrosine) compared with individuals in the lowest quartile. Although some small studies have reported that the levels of amino acids differ between individuals with normal and abnormal glucose tolerance (12,13), previous studies have not investigated the levels of amino acids across the entire range of glucose tolerance.

Amino acids modulate insulin action on glucose transport (9,14–16) and gluconeogenesis (17). High levels of BCAAs, especially leucine, have been shown to associate with insulin resistance (16,18,19) or insulin-resistant states, including diabetes (6,12,13). BCAAs have been shown to downregulate insulin action on glucose uptake by inhibiting critical steps in the postreceptor insulin signaling cascade (10), although other studies have concluded that leucine and isoleucine stimulate glucose uptake (7,8,20). Gluconeogenic amino acids (mainly alanine and glutamine) can enhance hepatic glucose production and thus lead to hyperglycemia (21,22). Finally, amino acids such as arginine, glutamine, leucine, and phenylalanine directly stimulate insulin secretion (4).

Type 2 diabetes is a complex metabolic disease with a significant genetic component, and \>40 gene loci associated with the risk of type 2 diabetes or hyperglycemia have been identified (23–26). The mechanisms by which these loci contribute to the risk of diabetes are only partially known. There are no previous studies on the association of these gene variants with the levels of amino acids.

The aims of our study were *1*) to investigate the relationship between the levels of amino acids and fasting and 2-h glucose across the entire range of glucose tolerance, *2*) to investigate the role of insulin sensitivity and insulin secretion in this relationship, *3*) to investigate the relationship between insulin sensitivity and adipose tissue mRNA expression of genes implicated in the catabolism of amino acids, and *4*) to investigate whether any of 43 risk single nucleotide polymorphisms (SNPs) for type 2 diabetes or hyperglycemia affect serum amino acid levels.

# RESEARCH DESIGN AND METHODS

## Subjects and clinical measurements.

The study included 9,369 nondiabetic or newly diagnosed type 2 diabetic men from the population-based Metabolic Syndrome in Men (METSIM) Study (mean ± SD age, 57 ± 7 years; BMI, 27.0 ± 4.0 kg/m^2^). The study design has been described in detail elsewhere (27).

Glucose tolerance was evaluated according to the American Diabetes Association criteria (28). A total of 3,026 subjects (32.3%) had normal glucose tolerance (NGT), 4,327 (46.2%) had isolated impaired fasting glucose (IFG), 312 (3.3%) had isolated impaired glucose tolerance (IGT), 1,058 (11.3%) had IFG and IGT, and 646 (6.9%) had newly diagnosed type 2 diabetes. Additional analyses were performed in 1,775 nondiabetic subjects re-examined during an ongoing follow-up METSIM study, of which 375 maintained NGT, 1,249 remained nondiabetic, and 151 developed new type 2 diabetes during the mean follow-up of 4.7 ± 1.0 years. None of 9,369 subjects was receiving antidiabetic treatment. BMI was calculated as weight (kg) divided by height (m) squared. The study was approved by the ethics committee of the University of Kuopio and Kuopio University Hospital and conducted in accordance with the Helsinki Declaration.

## Amino acid measurements.

A high-throughput serum nuclear magnetic resonance (NMR) platform operating at 500 MHz was used for amino acid quantification (29). Fasting serum samples collected at the baseline study were stored at −80°C and thawed overnight in a refrigerator before sample preparation. Aliquots of each sample (300 μL) were mixed with sodium phosphate buffer (300 μL). A proton NMR spectrum was acquired where most spectral signals from the macromolecules and lipoprotein lipids were suppressed to enhance detection of the amino acid signals. The eight amino acids (alanine, phenylalanine, valine, leucine, isoleucine, tyrosine, histidine, glutamine) were quantified in standardized concentration units. Details of the NMR experimentation and amino acid quantification have been described previously (29,30).

## Insulin sensitivity and insulin secretion indices.

Results of oral glucose tolerance testing (OGTT) were used to calculate the Matsuda index of insulin sensitivity (ISI) as 10,000/√ (fasting insulin × fasting glucose × mean insulin during OGTT × mean glucose during OGTT) (31). An index of early-phase insulin secretion during an OGTT, insulin area under the curve (InsAUC)~0–30~/glucose (Glu)AUC~0–30~, was calculated as (fasting insulin + 30-min insulin)/(fasting glucose + 30-min glucose) (pmol/mmol) (27).

## Genotyping.

Genotyping of 43 SNPs (29 SNPs associated with risk for type 2 diabetes and 14 associated with increased fasting or 2-h glucose in an OGTT) (23–26) was performed using the Applied Biosystems TaqMan Allelic Discrimination Assay at the University of Eastern Finland or the Sequenom iPlex Gold SBE assay at the National Human Genome Research Institute at the National Institutes of Health. The TaqMan genotyping call rate was 100%, and the discordance rate was 0% among 4.5% DNA samples genotyped in duplicate. The Sequenom iPlex call rate was 90.2–96.9%, and the discordance rate was 0% among 4.2% DNA samples genotyped in duplicate. All SNPs were in Hardy-Weinberg equilibrium at the significance level corrected for multiple testing by Bonferroni method (*P \<* 0.0012). Descriptive data for individual SNPs are shown in [Supplementary Table 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db11-1378/-/DC1).

## Gene expression analysis.

Total RNA was isolated from 200 subcutaneous fat biopsy samples of METSIM participants using Qiagen miRNeasy kit according to manufacturer's instructions. RNA integrity number values were assessed with the Agilent Bioanalyzer 2100. High-quality samples (RNA integrity number \>7.0) were used for transcriptional profiling with the Illumina Human HT-12 v3 Expression BeadChip. Genome Studio software (2010.v3) was used for obtaining fluorescent intensities. The HT-12 BeadChip contains 48,804 expression and 786 control probes. Expression data from 19,306 probes were removed because of *1*) failure of the probe to align to a genomic or transcriptomic location; *2*) alignment of the probe to multiple genomic or transcriptomic locations; or *3*) presence of SNPs in the probe sequence that may affect hybridization efficiency using the methodology developed by Barbosa-Morais et al. (32). The remaining 29,497 probes were processed using nonparametric background correction, followed by quantile normalization with control and expression probes using the *neqc* function in the *limma* package (R v2.13.0) (33). The 16,223 probes with detection *P* values \<0.01 in any of the 200 samples were used for further analysis. Gene expression data have been deposited to Gene Expression Omnibus (GEO) with the accession number GSE32512.

## Statistical analysis.

Statistical analyses were conducted using SPSS 17 software (SPSS, Chicago, IL). All amino acids, BMI, Matsuda ISI, and early-phase insulin secretion index (InsAUC~0–30~/GluAUC~0–30~) were log-transformed to correct for their skewed distribution. Amino acids were compared across the fasting and 2-h glucose categories using the general linear model adjusted for age and BMI, or additionally for Matsuda ISI or InsAUC~0–30~/GluAUC~0–30~ indices. *P* \< 0.003 (corrected for 16 tests by Bonferroni method) was considered statistically significant. Associations between amino acid levels and indices of insulin sensitivity and insulin secretion were evaluated with Pearson correlation coefficients. The association of amino acid levels with newly developed type 2 diabetes was tested with logistic regression adjusted for confounding factors. Correlations between gene expression levels and phenotypes were calculated using the Pearson correlation coefficient. We used the Benjamini–Hochberg false discovery rate (FDR) method (34) to correct for multiple comparisons, and considered an FDR-adjusted *P*~FDR~ \< 0.05 statistically significant.

For genetic association analysis, unstandardized effect sizes (B \[SE\]) per copy of the minor allele were estimated by linear regression analysis adjusted for age and BMI, using untransformed dependent variables, and percentages of B from the mean were calculated. *P* values were calculated using logarithmically transformed variables when appropriate. A *P* \< 1.45 × 10^−4^ adjusted for multiple comparisons by Bonferroni method was considered to be statistically significant given a total of 344 tests performed (8 traits × 43 SNPs). We had ≥80% power to detect changes in the mean trait value from 1.4 to 5.9% per copy of the minor allele at the significance level of 0.05, depending on the minor allele frequency ([Supplementary Fig. 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db11-1378/-/DC1)). Hardy-Weinberg equilibrium was evaluated by χ^2^ test.

# RESULTS

## Hyperglycemia and the levels of eight amino acids.

We generated categories of fasting (FPG) and 2-h (2hPG) plasma glucose (by 0.5 and 1.0 mmol/L steps, respectively) to investigate the relationship between amino acid levels and glycemia in participants with normoglycemia, IFG, IGT, and type 2 diabetes. Categories with FPG \<5.0 mmol/L and 2hPG \<5.0 mmol/L were set as the reference categories. Across the FPG categories, we observed a significant (*P* \< 0.003) increase in isoleucine level of 38% in the highest glucose category versus the reference category (*P* = 6.7 × 10^−10^ adjusted for age and BMI), tyrosine (+21%, *P* = 1.8 × 10^−15^), alanine (+17%, *P* = 4.3 × 10^−42^), phenylalanine (+15%, *P* = 1.1 × 10^−9^), and leucine (+19%, *P* = 4.2 × 10^−4^) levels, and a significant decrease in histidine (−9%, *P* = 3.3 × 10^−4^) and glutamine (−22%, *P* = 1.9 × 10^−48^; Fig. 1A). Similar trends were seen across the 2hPG categories, with a significant increase in isoleucine (+38%, *P* = 2.7 × 10^−58^), tyrosine (+30%, *P* = 1.1 × 10^−10^), alanine (+14%, *P* = 2.3 × 10^−46^), phenylalanine (+14%, *P* = 1.1 × 10^−13^), and leucine (+15%, *P* = 1.0 × 10^−18^) levels, and a significant decrease in histidine (−5%, *P* = 9.1 × 10^−6^) and glutamine (−12%, *P* = 1.4 × 10^−31^) levels with higher 2hPG (Fig. 1B). These effects were more significant in obese (BMI ≥27 kg/m^2^) than in nonobese (BMI\<27 kg/m^2^) participants, although the trends were similar in both groups ([Supplementary Fig. 2](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db11-1378/-/DC1)). Overall, most amino acids increased, whereas glutamine and histidine decreased with higher FPG and 2hPG.

Table 1 shows that all amino acids that were increased in hyperglycemia negatively correlated with Matsuda ISI (*r* ≤ −0.3, *P* ≤ 1 × 10^−154^) and positively (*r* ≥ 0.2, *P* ≤ 3 × 10^−85^) with the InsAUC~0–30~/GluAUC~0–30~ index of early-phase insulin secretion. For glutamine, which was decreased in hyperglycemia, the correlations were weaker and in the opposite direction (*r* = 0.17 for Matsuda ISI and −0.12 for InsAUC~0–30~/GluAUC~0–30~). Correlations of amino acid levels (except for histidine) with InsAUC~0–30~/GluAUC~0–30~ were weaker than correlations with Matsuda ISI and were largely attenuated after the adjustment of InsAUC~0–30~/GluAUC~0–30~ for Matsuda ISI. Furthermore, additional adjustment for Matsuda ISI attenuated or abolished most of the associations between glucose categories and amino acid levels (with the exception of valine and histidine), whereas adjustment for InsAUC~0–30~/GluAUC~0–30~ attenuated only the associations for histidine ([Supplementary Table 2](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db11-1378/-/DC1)). Thus, insulin sensitivity seemed to at least partly explain the relationship between glucose and amino acid levels.

Pearson correlations between the levels of eight amino acids and indices of insulin sensitivity (Matsuda ISI) and early-phase insulin secretion (InsAUC~0–30~/GluAUC~0–30~) in nondiabetic METSIM participants

![](1895tbl1)

## Association of eight amino acid levels with risk of type 2 diabetes.

We investigated the relationship between amino acid levels and the development of type 2 diabetes during a 4.7-year follow-up, including 526 re-examined METSIM participants (375 with NGT at baseline and follow-up examinations, and 151 with type 2 diabetes). Of eight amino acids measured at baseline, high levels of alanine (*P* = 6.7 × 10^−5^), leucine (*P* = 0.005), isoleucine (*P* = 3.3 × 10^−5^), tyrosine (*P* = 0.001), and phenylalanine (*P* = 0.048) were significantly associated with incident diabetes (adjusted for age and BMI; Table 2). Additional adjustment for Matsuda ISI alone, or Matsuda ISI and InsAUC~0–30~/GluAUC~0–30~, but not for InsAUC~0–30~/GluAUC~0–30~ alone, abolished statistical significances, suggesting that the association of these amino acids with incident diabetes was mostly explained by insulin resistance. Further adjustment for fasting hyperglycemia did not essentially change the association, but adjustment for 2-h glucose made the association for alanine statistically significant (*P* = 0.027). The elevated baseline glutamine level was significantly associated with a decreased risk of newly developed type 2 diabetes (*P* = 4.1 × 10^−6^), and this association persisted after the adjustment for Matsuda ISI (*P* = 1.5 × 10^−4^) or InsAUC~0–30~/GluAUC~0–30~ (*P* = 6.7 × 10^−6^) or both (*P* = 0.048). Additional adjustment for fasting glucose abolished this association (*P* = 0.051). The levels of glutamine were significantly lower at baseline in 440 participants who developed abnormal glucose tolerance (IGT and/or IFG, diabetes) at follow-up, compared with 375 participants who remained normoglycemic (155.6 vs. 159.6; *P* = 0.019, adjusted for age and BMI; [Supplementary Table 3](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db11-1378/-/DC1)). We also performed statistical analyses in a larger sample, including all 1,624 reexamined participants who did not develop diabetes during the follow-up and 151 participants who developed type 2 diabetes. The results remained essentially similar, although *P* values were less significant.

Association between the baseline levels of eight amino acids and newly developed type 2 diabetes during the follow-up of the METSIM study participants (logistic regression adjusted for age, BMI, and additional covariates)

![](1895tbl2)

## Gene expression of genes involved in amino acid metabolism in relation to insulin sensitivity.

Analysis of microarray data from subcutaneous adipose tissue samples of 200 METSIM participants showed that Matsuda ISI correlated significantly with mRNA levels of several genes involved in the metabolism of alanine, including a key enzyme, alanine aminotransferase (*r* = 0.46, *P*~FDR~ = 9.7 × 10^−9^); glutamine, including a key enzyme, glutamine synthetase (*r* = 0.44, *P*~FDR~ = 4.0 × 10^−8^); BCAA, including key enzymes of BCAA degradation, branched chain amino-acid transaminase 2 (*r* = 0.36, *P*~FDR~ = 2.5 × 10^−5^) and branched-chain α-keto acid dehydrogenase (BCKDH) A and B (*r* = 0.45 and 0.35, *P*~FDR~ = 1.3 × 10^−8^ and 3.0 × 10^−5^); and phenylalanine, tyrosine, and histidine ([Supplementary Table 4](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db11-1378/-/DC1)). The most consistent results were observed for the BCAA degradation, as enzyme mRNAs correlated positively with Matsuda ISI at almost all steps of the metabolic pathway ([Supplementary Fig. 3](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db11-1378/-/DC1)). Figure 2 shows that the mRNA levels of three key enzymes in BCAA degradation also correlated negatively with ISI, FPG, and 2hFP levels. However, these associations disappeared when controlled for Matsuda ISI. These results indicate that the association between insulin sensitivity and amino acid catabolism (especially BCAA catabolism) also exists at the mRNA level and add further evidence that insulin sensitivity is likely contributing to higher BCAA levels in hyperglycemia.

## Risk variants for type 2 diabetes and/or hyperglycemia and the levels of eight amino acids.

Of the 43 SNPs investigated, only *GCKR* rs780094 was significantly associated with the levels of several amino acids after Bonferroni correction for multiple testing (*P* \< 1.45 × 10^−4^) (Fig. 3 and [Supplementary Table 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db11-1378/-/DC1)). The glucose-increasing C (major) allele was associated with lower levels of alanine (effect size −1.9% per C allele, *P* = 1.6 × 10^−11^) and isoleucine (−2.3%, *P* = 3.1 × 10^−6^), and higher levels of glutamine (+1.2%, *P* = 1.0 × 10^−6^ adjusted for age and BMI). The C allele also had nominally significant effects on leucine (−1.2%, *P* = 0.001), tyrosine (+1.1%, *P* = 0.001), and histidine (+0.8%, *P* = 0.003). These associations did not change significantly after additional adjustment for FPG, 2hPG, or Matsuda ISI (data not shown), traits known to be modulated by SNPs of *GCKR*. Furthermore, the association of amino acids with newly developed type 2 diabetes was not affected by further adjustment for rs780094.

A number of nominally significant associations of other SNPs with amino acids were found; the top ranking was rs8042680 (*PRC1*). The type 2 diabetes risk allele (A) was nominally associated with higher levels of leucine (+1.2%, *P* = 0.002), isoleucine (+1.7%, *P* = 0.001), and valine (+1.2%, *P* = 0.001; [Supplementary Table 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db11-1378/-/DC1)). The most consistent nominally significant associations were seen for rs75789326 (*IRS1*). The nonrisk allele (G) was associated with lower leucine (−0.8%, *P* = 0.019), isoleucine (−1.5%, *P* = 0.003), tyrosine (−0.7%, *P* = 0.037), valine (−0.7%, *P* = 0.047), and histidine (−0.5%, *P* = 0.039) levels.

# DISCUSSION

This is the first large, population-based study aiming to investigate the relationship between hyperglycemia and 43 risk SNPs for type 2 diabetes/hyperglycemia and amino acid levels. We observed that with increasing FPG and/or 2hPG, the levels of alanine, valine, leucine, isoleucine, phenylalanine, and tyrosine increased, whereas the levels of histidine and glutamine decreased. Significant correlations between insulin sensitivity and mRNA expression of genes regulating amino acid metabolism were found. Only 1 SNP (rs780094 in *GCKR*) of 43 risk SNPs for type 2 diabetes or hyperglycemia was significantly associated with the levels of several amino acids.

## Hyperglycemia and levels of eight amino acids.

We demonstrated that the levels of alanine, phenylalanine, valine, leucine, isoleucine, and tyrosine increased and the levels of histidine and glutamine decreased in hyperglycemia, indicating that the changes in amino acid levels parallel closely the changes in FPG and 2hPG levels. Similar trends were also observed in normoglycemia, suggesting that even mild elevations of glucose levels result in changes in amino acid levels. The largest and most significant increase across the FPG and 2hPG categories was observed for isoleucine (increased up to 38 and 30%, respectively, compared with the reference category), and alanine (increased up to 17 and 14%), and the largest decrease was seen for glutamine (by 22 and 12%, respectively). We also showed that obesity did not have a major effect on these associations, although the effects of hyperglycemia on the levels of amino acids tended to be more pronounced in obese individuals.

The levels of all amino acids correlated inversely (except for glutamine correlating positively) with insulin sensitivity (Matsuda ISI). The strongest correlation was found for isoleucine (*r* = −0.49), a BCAA previously shown to be a potent activator of glucose uptake in skeletal muscle (7,8). Insulin resistance is likely to be an important mechanism explaining these associations because the adjustment for Matsuda ISI attenuated or abolished most of the associations between glucose (especially 2hPG) and amino acid levels. Further evidence for the role of insulin resistance mediating the associations of amino acids with hyperglycemia comes from our 4.7-year prospective follow-up of the METSIM cohort. We demonstrated that alanine, leucine, isoleucine, tyrosine, and glutamine were associated with incident type 2 diabetes. Adjustment for insulin sensitivity abolished significant associations, with the exception of glutamine, indicating that insulin resistance is likely to play an important role in the risk of type 2 diabetes induced by amino acids. Insulin secretion or hyperglycemia did not significantly modify these associations. Our study not only supports the recent observations of a relationship between amino acid levels with the risk of diabetes (11) but also offers a possible underlying mechanism to explain this relationship.

Our study found that the levels of BCAAs (particularly isoleucine) were strongly correlated with mRNA levels of genes involved in BCAA catabolism. BCAAs are thought to be early indicators of insulin resistance (6,10,18,35). High levels of BCAAs promote insulin resistance by interfering with the insulin-signaling pathway (10) or by directly inhibiting muscle glucose transport and/or phosphorylation in skeletal muscle (9), whereas insulin resistance in turn may contribute to high levels of amino acids by increased release of BCAAs (and other amino acids) due to impaired ability of insulin to suppress proteolysis in skeletal muscle (36). BCAAs are oxidized in skeletal muscle and adipose tissue (37) to form alanine and glutamine (38). Oxidation of BCAAs was increased in individuals with type 2 diabetes (21). In animal models of type 2 diabetes, BCAA catabolism is downregulated due to low activity of BCKDH complex (39), the key enzyme of BCAA oxidation. In our study, adipose tissue mRNA expression of *BCKDHA* and *BCKDHB* (encoding α and β polypeptides of BCKDH E1) as well as of *BCAT2* (branched chain amino-acid transaminase 2 mitochondrial, the first enzyme in BCAA catabolism) and other genes involved in BCAA catabolism correlated positively with Matsuda ISI and negatively with glucose levels. This suggests that the insulin resistance-related decrease in degradation of BCAAs could be one of the mechanisms leading to the elevation of BCAA levels in hyperglycemia in humans. Other possible mechanisms for the elevation of BCAAs in hyperglycemia could be a decreased uptake and increased release of BCAAs from skeletal muscle due to increased protein catabolism in insulin resistance.

Alanine is a nonessential amino acid synthesized from pyruvate and amino acids (mainly BCAAs) primarily in skeletal muscle and gut and used for gluconeogenesis in the liver. Alanine is the main precursor for gluconeogenesis in the liver (38,40) and a stimulator of glucagon secretion (41). Therefore, high plasma levels of alanine may contribute especially to fasting hyperglycemia by enhancing gluconeogenesis. Our study shows that elevated levels of alanine in hyperglycemia, both in obese and nonobese individuals, could be mediated by insulin resistance, which is supported by a significant positive correlation between insulin sensitivity and mRNA level of alanine aminotransferase, the key enzyme in alanine metabolism.

Glutamine is a nonessential gluconeogenic amino acid synthesized from glucose or amino acids or released by proteolysis from skeletal muscle (42). Unlike alanine, glutamine is primarily taken up by the gut and kidneys, where it is processed to glucose via the gluconeogenesis pathway. Glucagon, but not insulin sensitivity, seems to play a role in the uptake of glutamine by the gut (43). Differential regulation of glutamine metabolism, compared with other amino acids, could possibly explain the opposite associations between glutamine and glucose metabolism. We found that plasma levels of glutamine decreased with elevated levels of FPG and 2hPG and that a low level of glutamine was significantly associated with future diabetes, independently of obesity and insulin resistance. Baseline glutamine levels were already decreased in normoglycemic participants who developed abnormal glucose tolerance at follow-up compared with those who remained normoglycemic. Our results are in agreement with previous studies showing that patients with type 2 diabetes (12) and lean insulin-resistant normoglycemic offspring of patients with type 2 diabetes have lower levels of glutamine than normoglycemic individuals (44).

## Risk variants for type 2 diabetes and/or hyperglycemia and the levels of eight amino acids.

A common variant in *GCKR* was the only SNP among 43 loci associated with the levels of several amino acids. *GCKR* encodes the glucokinase regulatory protein, which inhibits the effects of glucokinase (GCK) on glycogen synthesis and glycolysis in the liver (45). SNPs at the *GCKR* locus have been associated with fasting glycemia (24,46), risk of type 2 diabetes (46,47), insulin resistance (46–48), and elevated hepatic glucose uptake (49). In our study, the glucose-increasing major C allele of the intronic SNP rs780094 was significantly and unexpectedly associated with decreased levels of alanine and isoleucine and elevated levels of glutamine. Mechanisms explaining the relationship of rs780094 of *GCKR* with amino acids are unknown, but the association with low levels of alanine could be a consequence of reduced glycolysis induced by *GCKR* resulting in decreased production of pyruvate and its conversion to alanine. On the basis of our study, it remains unclear whether the effects of rs780094 of *GCKR* on amino acid metabolism are primary or secondary. Although in our study the C allele of rs780094 was associated with both FPG and 2hPG, as well as with Matsuda ISI, the association of *GCKR* with the levels of amino acids was independent of these variables (data not shown). The *GCKR* variant did not affect the association of amino acids with newly developed type 2 diabetes, which agrees with its modest effect on amino acid levels.

Although several type 2 diabetes/hyperglycemia risk SNPs showed nominally significant associations with the levels of selected amino acids, the most consistent results were found for rs7578326 near *IRS1*. The major A allele previously linked to type 2 diabetes risk in a genome-wide association study (26) was nominally associated with elevated levels of several amino acids (valine, leucine, isoleucine, tyrosine, histidine). *IRS1* encodes the insulin receptor substrate 1, an important component of the insulin-signaling pathway, and its gene variant affects insulin resistance and adiposity (50). The association of *IRS1* with amino acid levels, if confirmed, could contribute to evidence for a causal relationship between insulin resistance and amino acid metabolism. The type 2 diabetes risk allele of the rs8042680 in *PRC1*, encoding protein regulator of cytokinesis 1, was associated specifically but nominally with higher levels of all three BCAAs. *PRC1* is not known to have a function in amino acid metabolism. These results suggest that the relationship between the levels of glucose and amino acids could be determined, at least in part, by genes regulating hyperglycemia or the risk of diabetes.

This study has limitations. Only Finnish men were included, and therefore, we do not know whether our results are applicable to women and to different ethnic or racial groups. We had only a modest statistical power to demonstrate statistically significant associations of gene variants with amino acids.

In conclusion, our large, population-based study shows that levels of branched-chain, aromatic amino acids and alanine increase whereas glutamine and histidine decrease with increasing FPG and/or 2hPG levels. These associations seemed to be mediated by insulin resistance, at least in part, supported by correlations of expression of genes involved in amino acid metabolism with the Matsuda ISI. Among the 43 loci associated with risk for hyperglycemia or type 2 diabetes, only *GCKR* rs780094 was significantly associated with several amino acids, especially with the levels of alanine.

## ACKNOWLEDGMENTS

This work was supported by the Academy of Finland (A.S., M.L., SALVE program to M.A.-K.), the Finnish Diabetes Research Foundation (M.L.), the Finnish Cardiovascular Research Foundation (M.L., M.A.-K.), the Jenny and Antti Wihuri Foundation (A.J.K.), an EVO grant from the Kuopio University Hospital (5263), DK062370 to M.B., 1Z01 HG000024 to F.S.C., and National Institutes of Health Grant HL-095056 to P.P. and Grant HL-28481 to P.P. and A.J.L.

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

A.S. wrote the manuscript and researched the data. M.C., N.K.S., P.P., and A.J.L. performed the mRNA experiments, analyzed the data, and reviewed and edited the manuscript. P.S. conceived, designed, and performed the NMR experiments, analyzed the data, and reviewed and edited the manuscript. A.J.K. analyzed the NMR data, contributed analysis tools, and reviewed and edited the manuscript. H.C., J.Pa., and J.Pi. researched the data and reviewed and edited the manuscript. L.L.B., M.A.M., and F.S.C. designed and performed genotyping and reviewed and edited the manuscript. M.B. contributed analysis tools and reviewed and edited the manuscript. J.K. designed the study and reviewed the manuscript. M.A.-K. conceived and designed the NMR experiments, analyzed the data, and reviewed and edited the manuscript. M.L. designed the study, contributed to discussion, and reviewed and edited the manuscript. M.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.

# REFERENCES


abstract: A report on the 13th International Congress on Yeasts (ICY), held at Madison, Wisconsin, USA, 26-30 August 2012.
author: Daniela Delneri
date: 2012
institute: 1Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UK
title: The yeast factory

Almost 400 scientists attended the 13th International Congress on Yeasts (ICY), where advances in yeast physiology, genomics, biotechnology, ecology and taxonomy were reported. In addition, the meeting celebrated the achievements accomplished with yeast in the past two decades, with the keynote speaker, Steve Oliver (University of Cambridge, UK), giving an overview of the scientific findings in yeast genomics, starting from the pioneering sequencing of *Saccharomyces cerevisiae* chromosome III in 1992 to the recent translational studies using yeast as a model to predict copy number variation phenotypes in human cancer cell lines. The plenary speaker Bernard Dujon (Institut Pasteur, France) also superbly summarized the history of comparative genomics with yeasts, the achievements of the Génolevures consortium (<http://www.genolevures.org/>) and the evolutionary insights gained in the hemiascomycetes lineage.

Here, I highlight some contributions from three key themes of the meeting, including genomic diversity, comparative studies and evolution; biogeography; and taxonomy.

# Genomic diversity, comparative analysis and evolution

*S. cerevisiae* yeasts form diversified groups, usually defined by the source from which they were isolated. Justin Fay (Washington University, USA) analyzed a highly differentiated group of *S. cerevisiae* isolated from grapes to assess whether the level of phenotypic variation between natural and domesticated strains could be a consequence of limited gene flow between sympatric wild and vineyard strains. Chardonnay must (pressed grapes) was fermented with both oak tree isolates and wine strains of yeast, and differences in the aroma and flavor of the resulting wine were assessed by 51 human volunteers. Not only was the oak tree wine the least palatable, but genome comparative analysis also showed that the wild isolates were exchanging genes with vineyard strains, potentially contaminating wine production with off-flavors. Fay concluded there is no clear link between the genetic diversification of vineyard yeasts and domestication.

Understanding the molecular basis of phenotypic differences among yeast strains may lead to the engineering of new strains of biotechnological relevance. Amparo Querol (IATA-CSIC, Valencia, Spain) presented her studies on differential expression profiles during wine fermentation of 29 yeast strains (14 fermentative yeasts and 15 wild isolates). The detection of differences in transcript levels between homologous genes from natural and wine strains can help the selection of properties that are desirable in enology (fermentation at low temperature, high production of glycerol and lower ethanol yield).

Comparative genomics studies have also explored the evolution and reconstruction of yeast carbon metabolism. Jure Piskur (Lund University, Sweden) investigated the origin of the Crabtree effect, which enables some species of yeast to produce ethanol, rather than biomass, at high sugar concentrations, by analyzing 40 different yeasts in the Saccharomycetes class, covering 200 million years of history. Interestingly, he found that species that originated before the whole genome duplication, such as *Lachancea* species, were 'Crabtree positive', producing as much ethanol as biomass. He concluded that the ability to produce ethanol from sugar is independent from the whole genome duplication, and originated about 120 million years ago, when the first modern flowering plants and fruits appeared on our planet.

Dawn Thompson (Broad Institute, USA) compared transcriptome data for 15 fungal species grown in a variety of conditions in order to study how regulation evolves and how network rewiring can lead to phenotypic adaptation. Growth rate, expression profiles, metabolites and nucleosome organization profiles were analyzed and compared to reconstruct molecular networks of ancestral and extant species.

Interspecies hybridization is a source of phenotypic diversity for the colonization of new environments. Eladio Barrio (University of Valencia, Spain) analyzed the genomes of hybrids between *S. cerevisiae* and *Saccharomyces kudriavzevii*, focusing on mitochondrial inheritance. There was a tendency for the hybrids to lose the portion of the genome derived from *S. kudriavzevii*, but also to retain the mitochondria from this species. Phylogenetic reconstruction of mitochondria based on the cytochrome-c oxidase subunit II (*COX2*) sequence showed that mitochondrial DNA often recombines in a region between *COX2* and the *ORF1* sequence (a large ORF related to the group I introns), and that introgression from *S. paradoxus* is present.

Serge Casaregola (CIRM-Levures, INRA, France) carried out a population genomics study of two species of the CTG clade (yeasts that translate CTG as serine instead of leucine), *Millerozyma farinosa* and *Debaryomyces hansenii*, and showed that several cryptic species, which could not be distinguished using ribosomal DNA analysis, were present. New hybrids, such as those between *M. farinosa* and *Millerozyma miso*, were also discovered. These hybrids seemed to undergo loss of heterozygosis readily during sporulation. Casaregola concluded that the diversity among yeasts of the CTG clade is wider than previously thought, with the coexistence of many cryptic species, haploids, heterozygote diploids and hybrids.

Yeast has long been used as model to study the phenotypic effects of copy number variation and mitochondrial diseases. Maitreya Dunham (University of Washington, USA) analyzed the origins and consequences of copy number variation in yeast strains grown in a chemostat under different nutritional limitations. By introducing a collection of barcoded plasmids containing a copy of every gene into the yeast cells, she was able to study the effect of duplications. She identified a set of genes whose copy number increase was beneficial to the cell in all the conditions tested, and others that were environment-dependent. Dunham also reported a new inverted tandem repeat structure that is inconsistent with the currently accepted mechanisms of DNA repair. She proposed a new model for origin-dependent inverted repeat amplification, suggesting that such tandem architecture can be caused by an error in the DNA replication fork.

Monique Bolotin-Fukuhara (Université Paris Sud, France) described the generation of a collection of yeast strains with mutations in mitochondrial transfer RNAs that mimicked human pathological diseases. She showed that the severity of the human phenotype was recapitulated in yeast, and that the nature of the mitochondrial sequence neighboring the engineered mutations affected the severity of the phenotype.

# Biogeography

Significant advances on the ecology and natural distribution of *Saccharomyces* *sensu stricto* species were made recently. José Paulo Sampaio (Universidade Nova de Lisboa, Portugal), explained how the belief that *Saccharomyces* was a purely domesticated organism, living mainly in human-made environments such as fermenting grapes, has hampered the detection of truly ecological species for several years. Improvements in the isolation methodologies from natural substrates are gradually changing this situation, allowing the identification of several new yeast isolates. In fact, yeasts are widely distributed on oaks and on species of the closely related *Nothofagus* genus, found in Patagonia, the Philippines, South Australia and New Zealand. The cryotolerant species *Saccharomyces arboriculus*, *Saccharomyces eubayanus* and *Saccharomyces uvarum* have all been isolated from *Nothofagus* species. Sampaio's study of *S. uvarum* biogeography has shown the existence of three different populations, found in Europe, South America and Australasia, respectively. Interestingly, the Australasian population is highly divergent, and hybrid crosses of Australasian strains with those from the South American populations have as little as 36% spore viability. Although *Saccharomyces paradoxus* and *S. cerevisiae* seem to have a global distribution, other lineages such as *S. kudriavzevii* and *Saccharomyces mikatae* were isolated from restricted geographical areas.

Christopher Hittinger (University of Wisconsin, USA) reported on the discovery and the genome analysis of *S. eubayanus* (isolated from *Nothofagus* sp. in Patagonia), the missing parental contributor to the lager-brewing yeast *Saccharomyces pastorianus*. A total of 200 *S. eubayanus* and *S. uvarum* strains were isolated in Argentina and were sequenced to determine the genetic diversity and population structure of this species. Interestingly, Hittinger pointed out that both *S. eubayanus* and *S. uvarum* have retained some parts of the RNA interference machinery in their genomes, such as the *Dicer 1* (*DCR1*) gene, which are lost in the other *Saccharomyces sensu stricto* species. Hittinger also mentioned the *Saccharomyces sensu stricto* consortium (SSS), which includes improved and assembled genome sequences for these yeasts (<http://www.saccharomycessensustricto.org>).

# Taxonomy

The theme of taxonomy was an important part of the meeting given the rapid and increasing number of newly discovered yeast species. In his plenary lecture, Teun Boekhout (CBS-KNAW, Utrecht, The Netherlands) called for a revision of the phylogenetic classification not only of the basidiomycetous yeasts, but also of the highly polyphyletic *Candida* genus, in which the majority of species are harmless and have no relation to the human pathogen *Candida albicans*. The systematics of yeast species taxonomy should be carried out using refined multigene phylogenies or whole genome analysis, because the morphological and physiological criteria are often misleading, conflicting with the gene tree. It follows that there is a need for whole genome sequencing projects at the species level, building on from the existing genomic characterization of several strains belonging to *S. cerevisiae* and *S. paradoxus* species (<http://www.sanger.ac.uk/research/projects/genomeinformatics/sgrp.html>). Boekhout also discussed his large-scale effort to determine unusual phenotypes in less explored non-conventional yeasts that may be of industrial relevance (the EU Cornucopia project).

Vincent Robert (CBS-KNAW, Utrecht, the Netherlands) gave an update on the DNA barcoding of all the 9,000 strains in the CBS collection by sequencing the D1/D2 and ITS region of the ribosomal DNA. This barcode method is useful to revise identifications and select strains for further exploration. Moreover, MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry) techniques, based on differential peptide fingerprinting patterns, are being developed and applied for strain classification and quality control analysis of the CBS collection.

The recent discovery of the new species *S. eubayanus*, one of the original parents of *S. pastorianus*, necessitated a revision of the nomenclature and phylogeny of the *sensu strictu* species, with the old *Saccharomyces bayanus* var. *uvarum* becoming a recognized species called *S. uvarum*, and the old *S. bayanus* var. *bayanus* becoming *S. bayanus*, presenting a hybrid genome from different strains and species belonging to *S. uvarum*, *S. eubayanus* and *S. cerevisiae*. Huu-Vang Nguyen (INRA, AgroParisTech, Thiverval-Grignon, France) showed that a small number of specific markers (*NTS2*, *MAL31*, *MEL1*, *MTY1*) are sufficient to discriminate between pure lines of *S. uvarum* and *S. eubayanus* and the hybrids *S. bayanus* and *S. cerevisiae*. By PCR and restriction fragment length polymorphism analysis, he was able to reclassify the *S. bayanus* strains CBS 424, CBS 425 and CBS 3008, showing that they were inbred lines of *S. uvarum* and *S. eubayanus*, and that they did not contain the *S. cerevisiae* portion of the genome usually present in *S. bayanus* strains. This method is a quick and reliable approach to differentiating between different hybrids and species belonging to the *Saccharomyces* *sensu stricto* group.

In conclusion, the ICY meeting showed that there is an exciting future for yeast research, with an ever expanding repertoire of species and strains to study and set of genomics tools with which to do so.


abstract: Recent advances in shotgun sequencing and computational methods for genome assembly have advanced the field of metagenomics, the culture-independent cloning and analysis of microbial DNA extracted directly from an environmental sample, to provide glimpses into the life of uncultured microorganisms.
author: Patrick D Schloss; Jo Handelsman
date: 2005
institute: 1Department of Plant Pathology, University of Wisconsin, Madison, WI 53706, USA
references:
title: Metagenomics for studying unculturable microorganisms: cutting the Gordian knot

The estimate that fewer than 1% of the prokaryotes in most environments can be cultivated in isolation \[1\] has produced a quandary: what is the significance of the field of modern microbial genomics if it is limited to culturable organisms? Until recently, this limitation meant that the genomes of most microbial life could not be dissected because more than half of the known bacterial phyla contain no cultured representatives, and the archaeal kingdoms are likewise dominated by uncultured members. The problem can be likened to the Gordian knot of Greek legend, which was impossible to unravel. The knot, which was constructed with interwoven strands with no ends exposed, served as a source of great pride of the citizens of Gordium where it was displayed. It was Alexander the Great who finally cut the massive knot and called the act his greatest victory. One strategy to expose the rest of the microbial world to the eye of the microbiologist - analogous to attempting to untie the knot - is to coax more bacteria into pure culture. The alternative approach - which could cut through it as Alexander the Great did - is metagenomics.

Metagenomics is the culture-independent analysis of a mixture of microbial genomes (termed the metagenome) using an approach based either on expression or on sequencing \[2,3\]. Recent studies in the Sargasso Sea \[4\], acid mine drainage \[5\], soil \[6\], and sunken whale skeletons \[6\] have used the shotgun-sequencing approach to sample the genomic content of these varied environments. In each study, environmental samples were obtained and the microbial DNA was extracted directly from the sample, sheared, cloned into *Escherichia coli*, and random clones were sequenced. In some of the studies sequence overlaps were then used to assemble contigs or scaffolds of genomic sequence. The Sargasso Sea study \[4\] resulted in nearly 2,000,000 random sequence reads, a massive total \[7\]; the acid-mine-drainage community sequence, more modest in size but impressive in the analytical insights gained, was based on 100,000 sequence reads (Table 1). Assembling so many sequence reads, while simultaneously accounting for heterogeneities between genomes, introduced unique challenges for each study. In the hyper-diverse soil metagenomic sequencing project, fewer than 1% of the 150,000 sequence reads could be assigned to a contig \[6\], whereas the acid-mine-drainage sequencing project successfully assigned 85% of the sequence reads to one of 1,183 scaffolds \[5\]. The genome sequences of uncultured microorganisms residing in mixed communities can now realistically be determined.

# A simple oceanic community

The most extensive metagenomic sequencing effort has been the attempt by Venter *et al.* \[4\] to sequence the prokaryotic genomes in the water of the Sargasso Sea, a well characterized region of the Atlantic near Bermuda that has unusually low nutrient levels; this study has already spawned numerous other meta-analyses (for example, \[8-11\]). Among one billion nucleotides of sequenced DNA, Venter *et al.* \[4\] identified more than 1.2 million open reading frames (ORFs), including 782 that had significant similarity to rhodopsin-like proteins. This was a surprise because the rhodopsins were previously thought to be present in only a small group of organisms, and the Sargasso Sea study broadened the spectrum of species known to have them. One intriguing problem in metagenomics is that most ORFs cannot be assigned to gene families of known function \[2\]. In the Sargasso Sea sequences, for instance, 69% of the ORFs had no known function \[4\]. This analysis points to a major limitation in annotating sequences from uncultured microorganisms: if no relative of the organism being sequenced has ever been sequenced, then the likelihood of matching each of the newly identified genes to genes of known function is low. The choice of database used for comparison determines the answer, as demonstrated by the identification by Venter *et al.* \[4\] of 16S rRNA sequences from the Sargasso Sea by querying a database containing only 16S rRNA gene sequences from genome sequences of Bacteria and Archaea. As they limited the comparative database to cultured microorganisms, it was not surprising that they did not identify any 16S rRNA gene fragments from any phyla with no cultured representatives. A further limitation of this study was presented by Delong \[12\], who pointed out that the two genomes that Venter *et al.* \[4\] were able to complete were probably contaminants in the sea-water sample. Obvious examples of assembly error (for example, contigs containing bacterial 5S and 23S rRNA genes adjacent to an archaeal 16S rRNA gene) suggest an insidious assembly problem throughout the sequence collection \[12\]. Perhaps the next stage of the project will profit from the mistakes of this 'pilot' sequencing attempt \[4\].

# An even simpler biofilm community

Although the nutrient-limited Sargasso Sea was selected for metagenomics because it was thought to contain a simple community \[4\], the community was not simple enough to allow assembly of most of the sequence reads into contigs. Tyson *et al.* \[5\] selected a far simpler community, that of a biofilm found in the very acidic waste water from an iron mine (termed acid mine drainage), which contains three bacterial and three archaeal lineages. By grouping the assembled contigs into 'bins' according to their GC content and the number of reads per contig, they were able to assign each bin to an organism. The near-complete genome sequences (ten-fold coverage) of *Ferroplasma* type II and *Leptospirillum* group II members enabled Tyson *et al.* \[5\] conceptually to model the metabolic processes that each genome contributes to the broader community.

This thorough sequencing and metabolic analysis provided the starting point for a 'proteogenomic' analysis. Protein was extracted from biofilms found in the acid mine drainage and digested with trypsin \[13\]. Applying shotgun mass spectrometry to the fragmented proteins, Ram *et al.* \[13\] obtained a sequence of part of the proteome. By combining the proteome and metagenome sequences \[5\], they linked one or more peptide sequences to approximately 49% of the ORFs from the five dominant genomes \[13\]. The most powerful outcome of this analysis was the identification from the *Leptospirillum* group II sequences of a novel acid-stable iron-oxidizing c-type cytochrome with an adsorption maximum wavelength at 579 nm (Cyt~579~). Cyt~579~ is the primary iron-oxidizing enzyme in the microbial community and mediates the rate-limiting step in acid production. In this relatively simple community, the proteogenomic approach enabled Ram *et al.* \[13\] to quantify protein production from each ORF, validate the DNA-derived metabolic model, and identify a process that potentially acts as a keystone for the whole ecosystem.

# First metagenomic analyses of complex microbial communities

A fundamental challenge in understanding microbial communities is to chronicle genetic conservation across time and location and to delineate the smallest complement of genes conserved in genomes across different communities \[4\]. Tringe *et al.* \[6\] tackled this problem by sequencing microbial communities sampled both from soil from a Minnesota farm and from three deep-sea communities living on sunken whale skeletons ('whale-fall') and comparing them with the Sargasso Sea sequence collection. ORFs from each metagenomic sequence were assigned to clusters of orthologous genes (COGs \[14\]), operons, pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) \[15\], and COG functional categories \[14\].

The relative enrichment found using each of the four annotation methods between the Sargasso Sea, deep-sea whale fall, and Minnesota farm soil was then determined, resulting, in essence, in an *in silico* subtractive hybridization. The over-representation of rhodopsin ORFs in the Sargasso Sea and ORFs encoding cellobiose phosphorylase in the Minnesota farm soil make biological sense, because marine microorganisms are more likely to use light-driven energy transduction systems and soil microorganisms are more likely to encounter plant-derived oligosaccharides such as cellobiose. The large number of ORFs of no known function that were over-represented in each community may indicate as-yet unknown functional systems. Generating copious sequence information from a community is intrinsically valuable, but this comparative analysis \[6\] is a worthy example of how metagenomics may move beyond descriptive, annotation-based analyses toward meaningful inference about ecological phenomena.

# Dealing with complexity and contamination

Application of molecular biology methods to cultured organisms has led to striking insights into the life of microbes in mono-species culture. But genomics has failed to elucidate the functions of microbial communities, where most microorganisms on Earth spend most of their time and that provide the platform from microorganisms shape plant, animal, environmental and human health. Metagenomics, coupled with gene arrays, proteomics, expression-based analyses, and microscopy, will give insights into problems such as genome evolution and the membership of particular niches that are currently hindered by our inability to culture most microorganisms in pure culture \[16\]. To realize the full potential of metagenomics, however, a number of obstacles need to be overcome. Perhaps the most significant of these is the microbial complexity in most communities. The successful analysis of the acid mine drainage community was predicated on its simplicity. In contrast, the Minnesota farm soil probably contains more than 5,000 species and 10^4^-10^5^ strains, making it inevitable that the over 150,000 sequence reads could not be assembled into contigs \[6\] (Table 1). It is likely that 2-5 gigabase-pairs of sequence are necessary to obtain eight-fold coverage of the dominant species in the community, suggesting that inventive approaches are needed to enrich DNA sequences from less abundant organisms or from members that are unique to a community \[3\].

Another focus for improvement in metagenomics is the use of robust sampling and DNA-extraction procedures. Methodology that guards against contamination such as that revealed in the Sargasso Sea samples is essential. Making the metagenomic studies ecologically meaningful will require sampling strategies that account for spatial and temporal variability, thereby enabling comparisons between communities \[17\]. These comparisons will also require standardized and aggressive methods for extracting DNA. It is unfortunate that all of the large metagenomic sequencing projects used chemical extraction methods to obtain DNA, whereas the technique of 'bead beating', which applies high shear forces to cells, is more effective than chemical lysis methods at breaking tough cells (for example, \[18\]). The studies that used chemical lysis methods therefore include DNA from only a subset of the organisms that can be accessed by modern methods.

This is an exciting time for metagenomics, as many projects are underway to sequence the metagenomes of biologically interesting environments. The US Joint Genome Institute (JGI) has essentially sequenced the metagenomes of the microbial communities associated with two extinct ancient cave bears, which contained less than 2 and 6% cave bear DNA, respectively \[19\]. The JGI is also currently sequencing metagenomic DNA for more than ten studies through their scientific Community Sequencing Program \[20\], and the J. Craig Venter Foundation is sequencing the metagenomes of samples taken along a path intended to simulate the voyage of Darwin's ship *The Beagle*, as well as samples of New York City's air \[21\]. A future prospect is completing the human genome by sequencing the metagenome of the 10^12^ microbial cells that are associated with the human body \[22\]. Each of these studies will unearth secrets unique to the environment being examined, and comparison of results of these studies will provide a meta-understanding of the recurrent and unique themes in community structure and function.

## Figures and Tables

Summary of metagenomic sequencing projects

| Community | Estimated species richness | Thousands of sequence reads | Total DNA sequenced (Mbp) | Sequence reads in contigs (%) |
|----|----|----|----|----|
| Acid mine drainage | 6 | 100 | 76 | 85 |
|  |  |  |  |  |
| Deep sea whale fall |  |  |  |  |
| Sample 1 | 150 | 38 | 25 | 43 |
| Sample 2 | 50 | 38 | 25 | 32 |
| Sample 3 | 20 | 40 | 25 | 47 |
|  |  |  |  |  |
| Sargasso Sea |  |  |  |  |
| Samples 1-4 | 300 per sample | 1,662 | 1,361 | 61 |
| Sample 5-7 | 300 per sample | 325 | 265 | \<1 |
|  |  |  |  |  |
| Minnesota farm soil | \>3,000 | 150 | 100 | \<1 |


abstract: We need to familiarize ourselves with the facts of evolution, so that we can mount a spirited defense against creationism and the forces of ignorance.
author: Gregory A Petsko
date: 2008
institute: 1Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA 02454-9110, USA.
title: It is alive

They're at it again. Armed with another new idea from the Discovery Institute, that bastion of ignorance, right-wing political ideology, and pseudo-scientific claptrap, the creationist movement has mounted yet another assault on science. This time it comes in two flavors: propaganda and legislative.

The propaganda is in the form of a poorly written, badly acted movie produced by Ben Stein, an attorney and entertainment figure who once served as a speechwriter for US Presidents Gerald Ford and Richard Nixon. As if working for Nixon didn't do enough to demonstrate his faulty judgment, he has become an ardent critic of evolution and an advocate for 'intelligent design', which is creationism poorly disguised as 'science'. He co-wrote and stars in the film *Expelled: No Intelligence Allowed*, which attempts to link evolution to the eugenics movement in Nazi Germany and to the Holocaust, and portrays advocates of intelligent design as champions of academic freedom and victims of discrimination by the scientific community. The famous evolutionary biologist and atheist Richard Dawkins has a spirited attack on the film on his website <http://richarddawkins.net>, and there's also a lively critique from the National Center for Science Education <http://www.expelledexposed.com>.

Fortunately, the film is sinking faster than the *Lusitania*. As far as I can discover, it has grossed less than US\$8 million in ticket sales to date, far less than its cost, and is playing to virtually empty houses in the few theaters that are still showing it. Whether this is because people recognize its ideas as rubbish or because it is simply a bad movie, I don't know. So we can probably ignore it, as it so richly deserves. But the legislative attack is much more serious.

On 11 June 2008, the Louisiana House of Representatives voted 94:3 in favor of a bill that would promote 'critical thinking' by students on topics such as evolution, the origins of life, global warming, and human cloning. The Louisiana Senate already passed a similar bill, Senate Bill 733, by a vote of 35:0, but an amendment adopted by the House, which would allow the state Board of Elementary and Secondary Education to prohibit supplemental materials it deems inappropriate, means that the Senate must pass the bill again. If they do, and this seems a certainty, then the bill will be sent to Louisiana governor Bobby Jindal, at 36 the youngest governor in the United States and the first Indian-American to serve as the head of a state government. A former Hindu who converted to Catholicism in high school, Jindal attended Oxford University on a Rhodes Scholarship. Jindal was a biology major at Brown University, so he should understand the science at stake here, but he opposes stem-cell research and has publicly supported the teaching of 'intelligent design' in public schools. He has not stated whether or not he will sign Bill 733. A fascinating subtext to this story is that Jindal is reportedly under consideration by Republican presidential nominee John McCain as a possible vice-presidential nominee.

The bill is cleverly worded: it states in section 1C that it "shall not be construed to promote any religious doctrine, promote discrimination for or against a particular set of religious beliefs, or promote discrimination for or against religion or nonreligion." In an interview with the conservative newspaper *The Washington Times* (12 June 2008), Jason Stern, vice-president of the Louisiana Family Forum, a Christian right-wing lobby group, insisted "It's not about a certain viewpoint. It's allowing \[teachers\] to teach the controversy."

Let me say this as clearly as possible, so there can be no mistake about what I mean: there is no controversy. Just because a few misguided so-called scientists question the validity of the concept of evolution doesn't mean there is a controversy. There are still some people who believe the Earth is flat (there's even a 'Flat Earth Society'), but that doesn't mean that a grade-school science teacher should teach his or her students that the Earth might be flat. The fact that some people believe nonsense does not give that nonsense scientific validity. A challenge to existing scientific principles must be based on evidence, not on belief, and there isn't a shred of evidence to support either creationism or intelligent design. Those ideas belong in a religion or philosophy class, not in a science class.

By the way, speaking of religion class, if we accept the creationists' own rationale for this bill, then shouldn't right-wing fundamentalist Christian schools be forced to 'teach the controversy' about religion? It's a much more controversial subject than science. Shouldn't their students be forced to consider the possibility that there is no God, or that the Muslim faith, or the Hindu faith, or the Jewish faith might be the true one? Or that there are so many different translations and versions of the Bible that there is no way of knowing which one is the 'word of God'? You can see how quickly their argument breaks down.

What about the academic freedom argument? If someone wants to teach creationism in a science class, shouldn't they have the right to do so? Certainly - if they want to get fired. Because if they do that they deserve to get fired. It has nothing to do with academic freedom; it's about basic competence. Consider, for example, a science teacher who taught that the Sun revolves around the Earth. Even the intelligent-design advocates would probably have to admit that such a science teacher was incompetent and ought to be dismissed. That teacher might counter with a claim that his or her academic freedom was being infringed, but no court would uphold it, any more than a court would uphold a similar claim from a history teacher who taught that the Allies lost World War 2 or that Napoleon Bonaparte was emperor of Japan. Science, and history, may welcome speculation, but the speculation must be based on facts, and when it isn't, then it doesn't belong in that subject. Any 'science' teacher who teaches that the Earth might have been created about 6,000 years ago and that all the material evidence that it's billions of years old is controversial is simply incompetent. If the state of Louisiana wants its children taught by such people then they deserve the kind of workforce and citizenry they are going to get.

It's worth pointing out that in 1987, in the case of Edwards *versus* Aguillard, the US Supreme Court ruled as unconstitutional the idea of equal time for "creation science" and evolution in biology classes. That precedent will almost certainly be used as the basis for a constitutional challenge to the Louisiana law if it passes. Also, in the state of Pennsylvania, the 'Kitzmiller *versus* Dover' case in 2005 put to rest the idea of intelligent design as an alternative to evolution being taught in biology classes - the judge there, in a brilliantly reasoned opinion, demonstrated that intelligent design was just creationism by another name. Although not a Supreme Court case, this decision was strong enough to cause creation science advocates to switch tactics to arguments about academic freedom, the focus of the current legislation at issue in Louisiana.

Lest you think this is merely some Bible Belt aberration, let me assure you that the creationists are marshalling this argument in other states as well. In Michigan, Senate Bill 1361, introduced in the Michigan Senate on 3 June 2008, and referred to the Senate Committee on Education, is yet another 'academic freedom' bill aimed squarely at the teaching of evolution. Identical to Michigan House Bill 6027, which is still in the House Committee on Education, Senate Bill 1361 would, if enacted, require state and local administrators "to create an environment within public elementary and secondary schools that encourages pupils to explore scientific questions, learn about scientific evidence, develop critical thinking skills, and respond appropriately and respectfully to differences of opinion about controversial issues" and "to assist teachers to find more effective ways to present the science curriculum in instances where that curriculum addresses scientific controversies" by allowing them "to help pupils understand, analyze, critique, and review in an objective manner the scientific strengths and scientific weaknesses of existing scientific theories pertinent to the course being taught." And in Texas (why is it not a shock that the state that gave us George W Bush would show up here), the Texas State Board of Education is again considering mandating a science curriculum that teaches the "strengths and weaknesses" of evolution. On 7 June 2008, the Houston Chronicle wrote that "strengths and weaknesses" language is "a 'teach the controversy' approach, whereby religion is propounded under the guise of scientific inquiry". The editorial went on to say: "What students really need is to be able to study science from materials that have not been hijacked by creationists whose personal agenda includes muddying the science curriculum. Creationism is not a 'system of science'."

As scientists, we need to protest with our feet and our wallets. I am about to become the president of the American Society for Biochemistry and Molecular Biology, a scientific society with about 12,000 members. Our 2009 annual meeting is scheduled to take place in New Orleans. If Bill 733 becomes law in Louisiana, it will be too late to move the meeting to another state. But we need to see to it that no future meeting of our society will take place in Louisiana as long as that law stands, nor should we hold it in any other state (are you listening, Michigan and Texas?) that passes a similar law. I call upon the presidents of the American Chemical Society, the American Association of Immunologists, the Society for Neuroscience, and all the other scientific societies in the US and around the world to join me in this action and make clear to the state legislators in Louisiana, the governor of the state, and the mayor and business bureau of New Orleans that this will be the consequence. You can do the same. Governor Jindal can be reached through his website <http://www.bobbyjindal.com> and Ray Nagin, mayor of New Orleans, can be reached through the Mayor's office <http://www.cityofno.com/Portals/Portal35/portal.aspx>.

In its ability to rise again just when we think we've got it licked, creationism is like Frankenstein's monster. "Come see, villagers! It is alive!" We'll never be rid of it by being silent and doing nothing, so one important thing is to force governments that ally themselves with this monster to pay for their folly by denying them our business. In addition, we must all arm ourselves with the one weapon we have that, in the end, the monster cannot overcome: the truth. All of us need to familiarize ourselves with the facts of evolution so that we can mount a spirited defense against the forces of ignorance and the charlatans who would exploit human insecurity and need for certainty.

Carl Sagan memorably called science "a candle in the dark". Well, the darkness is always around us, closer than you think sometimes. Yes, it is alive. Creationism's alive because some of our fellow men and women keep it alive. In the dark.