Food Safety: Perchlorate Exposure: Tip of the Iceberg?

For several years, federal and state agencies have debated over what is an acceptable level of human perchlorate exposure through food and drinking water. Now Food and Drug Administration (FDA) investigators have found the chemical in milk and lettuce from 15 states, including some apparently uncontaminated areas, showing that human exposure may come from more sources than expected. 
 
Perchlorate is used mainly in rocket fuel as well as in some fertilizers and explosives. Perchlorate with no anthropogenic source has been found at 20–60 parts per billion (ppb) in West Texas groundwater and in trace amounts in precipitation, says Texas Tech University chemist Purnendu Dasgupta. This suggests atmospheric reactions may create a low background level of perchlorate. Perchlorate disrupts thyroid function by competitively inhibiting iodine uptake in a dose-dependent fashion, with unquantified effects in humans. 
 
In a November 2004 agency report, FDA scientists wrote of finding an average 7.76–11.9 ppb perchlorate in about 90% of lettuce samples from Arizona, California, Florida, New Jersey, and Texas. They also found an average of 5.76 ppb in 97% of cow’s milk samples collected at stores in 14 states. Until more is known about the health effects of perchlorate and its occurrence in foods, the FDA continues to recommend that people of all ages eat a balanced, healthy diet. 
 
Parts of southern Arizona and California are irrigated with river water containing roughly 4–6 ppb perchlorate, but contamination is not known at the other sites. “The results are surprising—we would have expected lettuce grown in known perchlorate-contaminated areas to have higher concentrations than lettuce from apparently uncontaminated areas,” says Terry Troxell, director of the FDA Office of Plant and Dairy Foods. Troxell says samples with very high and very low values came from the same place. For example, the highest lettuce concentration was 71.6 ppb in iceberg lettuce from Belle Glade, Florida. But another Belle Glade iceberg sample contained 1.3 ppb. 
 
“I don’t think it’s possible to conclude anything about the national food supply from this survey,” says Kevin Mayer, the Environmental Protection Agency Region 9 perchlorate coordinator. Still, says Bill Walker, West Coast director for the nonprofit Environmental Working Group, “The surprising data suggest that this is a national problem and that risk assessments have to account for dietary exposure.” 
 
In January 2005 the National Academy of Sciences reported that more information is needed on food as a source of perchlorate exposure. Meanwhile, the evidence rolls in. In the 26 January 2005 Journal of Agricultural and Food Chemistry, Texas Tech researchers reported finding perchlorate in a variety of forage and edible crops, including alfalfa and cantaloupe. The FDA is also sampling tomatoes, carrots, cantaloupe, and spinach, with results to come.


Background
Studies during the past decade have shown that the product of the gene myostatin (GDF8) (a muscle-specific TGFβ family member) is an inhibitor of muscle development and of the maintenance of muscle mass. Mutations in myostatin [1,2] result in double-muscling (DM) in both cattle and rodents. In cattle, several disruptive myostatin mutations have been identified in different breeds [3,4]. These mutations truncate the protein product resulting in functional inactivation. For example, in the Belgian Blue breed, an 11-bp deletion [nt821 (del11)] has occurred in the third exon in a region encoding the bioactive domain. Similarly, the Q204X mutation (a C to T transition), which results in a premature stop codon in the N-terminal LAP (Latency Associated Peptide) domain, is frequently found in the Charolais breed or in the INRA95 genotype [4].
Myostatin-null mice exhibit enlarged skeletal muscles relative to wild-type littermates because the numbers (hyperplasia) and area (hypertrophy) of muscle fibers are increased [1]. In cattle breeds, double-muscling is primarily due to hyperplasia [5] as early as the fetal period [6,7]. Myostatin expression is regulated throughout gestation [2,8]. It was found to be located in the most recently differentiating cells throughout bovine fetal development [9]. Myostatin may negatively regulate the number of fastglycolytic (IIX) fibers, which is therefore increased in DM muscles at the expense of oxidative fibers [8]. Furthermore, the properties of DM muscles differ from those of normal ones (NM) owing to lower collagen and intramuscular fat contents [10]. In adult muscle, myostatin is specifically expressed in satellite cells and behaves as an important regulator of satellite cell activation and renewal, thereby controlling muscle mass (reviewed in [11,12]).
Examination of the molecular action of myostatin has revealed that it inhibits the proliferation of myogenic cells through the control of cell cycle progression [13]. This provides an explanation for the higher proliferation rates of DM fetal myoblasts than controls in vitro [8,14]. Myostatin also protects myoblasts from apoptosis and delays their terminal differentiation [15,16]. Functional myostatin binds to the activin type IIB transmembrane receptor, which then recruits and activates the ALK type I co-receptor by phosphorylation. This results in recruitment of the SMAD signaling pathway [17]. An alternative pathway involving p38 MAPK signaling has also been proposed to account for growth inhibition [18]. The inhibition of myogenesis is mediated partially through a decreased expression of Myogenic Regulatory Factors (reviewed in [12]). Myogenin and p21CKI have been identified as the major physiological targets of endogenous myostatin in murine cells [19]. Proteomics has also revealed novel differentially-expressed proteins associated with double-muscling in bull calves [20], suggesting the existence of unidentified myostatin targets.
In order to identify differences in gene expression, and hence novel genes or networks that may be myostatin targets liable to be involved in muscle differentiation, we examined the transcriptional profiling of the semitendinosus (ST) muscle of DM fetuses vs Non-Double-Muscled (NM) at 260 days of gestation.

Microarray experiments
We performed microarray analyses of the ST muscle of DM of the INRA95 genotype and NM fetuses (n = 3 per group) using heterologous oligonucleotide chips (Myochips, human and murine sequences) dedicated to muscle and cardiac gene expression. The ST muscle was chosen since it is highly hypertrophied in DM. The rationale for studying gene expression in late fetuses was that expression of many genes is affected during the last third of gestation [21], a key period in the fiber differentiation/ specialization process in cattle, which are mature at birth with regard to muscle physiology [22]. The arrays have the advantage of high quality and specificity together with a large number of genes. Some 75% of their oligonucleotide sequences are human, which was another advantage for the study since the comparative coverage of the bovine and human genomes is about 91% [23]. Myostatin expression in ST muscle was monitored by quantitative RT-PCR ( Figure 1) and was found to be lower in DM than NM muscle. However, it was more elevated in one of the DM foetuses, which was found to be heterozygous for the Q204X mutation.
Hybridization of bovine targets on to the Myochips enabled us to recover 75-84% of valid expression values.
Myostatin expression in the ST muscle of experimental fetuses Figure 1 Myostatin expression in the ST muscle of experimental fetuses. Myostatin expression was assessed by quantitative RT-PCR using the Sybergreen method. Values are means ± S.E. for n = 6. The highest crossing point (Ct) corresponds to the lowest expression level. DM: double-muscled fetus; NM: non-double-muscled fetus. DM108 is heterozygous for the Q204X mutation. Examination of microarray data from DM and NM fetuses allowed us to identify major differences in gene expression amongst individuals and genotypes.
Hierarchical clustering of the data allowed the genotype groups to be clearly discriminated (DM/NM, Figure 2). However, it revealed a transcriptional profile for the heterozygote DM108 fetus more closely related to that of the NM animals (Figure 2A, B). Principal component analysis confirmed this finding ( Figure 2C). Thus, muscle gene expression appeared to be altered in fetuses harbouring the Q204X mutation. However, this influence differed according to whether one or two impaired myostatin allele(s) were present, illustrating the autosomal recessive character of myostatin in cattle [24], as already shown for muscle protein expression [20].
Expression data were filtered for 20% missing values and processed by ANOVA. Eight clusters were selected. They included 189 genes the expression of which varied according to the presence of one or two functional myostatin allele(s) as shown by the hierarchical clustering. Only 142 of them had a GO annotation. Some of these could be potential candidate targets of functional myostatin. Twelve were also found to be differentially expressed postnatally in the ST muscle of DM vs NM cows (our unpublished data). Using FatiGO+, an evolution of FatiGO, we searched for GO functional annotation and the KEGG pathway. Some genes were annotated for carbohydrate metabolism (e.g. Foxc2, SDS, APM1), for lipid, fatty acid and steroid metabolism (e.g. PLIN, APM1), or for protein metabolism and modification (e.g. Mrpl36, CTBP1, PAK1, SMAP1). Interestingly, these putative myostatin targets were predicted to belong to 51 different KEGG pathways (Table 1) such as focal adhesion, axon guidance, calcium signaling pathway, cell cycle, or lastly Wnt signaling, which was recently shown to be altered in myostatin knocknull-out mice [25].

Differentially expressed genes according to myostatin lossof-function
Using SAM, a differential analysis of the hybridization data was carried out between the two groups of three fetuses (dataset 1, Table 2). It was also performed between two groups of two extreme fetuses (dataset 2 excluding the DM108 heterozygote and an NM0423 animal, the myostatin expression and gene profiling of which were intermediate) to maximize the difference between the two genotypes ( Table 2). The latter analysis was chosen in order to identify true differentially expressed genes with confidence. Analyses allowed a false discovery rate (FDR) that accepts that 5‰ of the genes declared differentially expressed will be false positives. In both analyses, a substantial number of genes were differentially expressed and this number varied according to the fold change value (FC). More than 93% of the genes identified from dataset 1 were declared to be differentially expressed from dataset 2 ( Table 2). Taking a FC ≥ 2, the same genes were identified in both datasets. However, dataset 2 allowed an additional 53 down-and 86 up-regulated genes to be identified with a FC ≥ 1.4 ( Table 2). The rationale for taking differential expression with a FC ≥ 1.4 and FDR < 5‰ into account was to ensure that greater numbers of differential genes were retained with high confidence, using 4 technical replications per animal.
Examples of genes declared differential by SAM from dataset 2 are presented in Table 3 (up-regulated genes) and Table 4 (down-regulated genes). ANOVA confirmed that the expression of 93% of these genes was statistically significant (F>11, p < 0.005). The genes with FC ≥ 2 had pvalues < 5‰ by ANOVA, except one (2410044K02Rik, pvalue < 0.02). Some differential expressions were confirmed by real-time RT-PCR (Table 5). A search for homology between the oligonucleotides representing differential genes with a FC = 1.4 and the bovine genome was carried out using BLASTN and BLASTX searches. More than 80% of the oligonucleotides were found to have a homology greater than 70% with bovine sequences (data presented partially in Tables 3 and 4). These data confirmed the appropriateness of the oligochips even though they were made of human and murine oligonucleotide sequences.

Function of differential genes in DM and biological relevance
The data were further explored using Gene Ontology (GO) information (Biological Process and Molecular Function terms). This predicted that 95 of the down-regulated genes with GO annotations encoded mainly ribosomal proteins, ECM/cell interaction proteins or sarcomeric slow contractile proteins. Conversely, the genes up-regulated in DM muscle were annoted for regulation of the cell cycle, transcription and DNA metabolism. Only 136 of the 242 up-regulated genes had a GO annotation at the Biological Process level. Of these, 30 were annotated for "regulation of transcription", of which 24 were annotated with the GO term "Transcription Factor". They included regulators of muscle-specific gene expression such as MEF2A, ID1 or ZFX1B (ZEB2/SIP1) and also MyoD1 for which the FC (1.35) was just under our FC threshold (1.4).
Some down-regulated genes were found to belong to KEGG pathways (Table 6), e.g. the ribosome, oxidative phosphorylation and ATP synthesis, calcium signaling pathway and extracellular matrix (ECM)/receptor interaction. Up-regulated genes were mainly involved in the insulin pathway, cytokine/receptor interaction, Wnt signaling, cell cycle, apoptosis and axon guidance ( Table 6).
Hierarchical clustering and Principal Components Analysis of expression data reveal the influence of heterozygosity for the Q204X mutation and the variability of gene expression among individuals Altogether, these results indicated that myostatin loss-offunction was associated with the alteration of many biological pathways in DM muscle.

Genes involved in protein metabolism
One notable finding of our study was that an important subset of the differential genes was involved in protein metabolism or encoded ribosomal proteins. Interestingly, some authors have specifically looked for genes that are differentially regulated in early DM embryos compared to normal ones [26]. As in the present study, they identified differential expression of ribosomal proteins, suggesting that NM and DM animals may differ in protein degradation and synthesis.

Genes involved in contractile and metabolic function
The functional categories of the genes down-regulated in DM illustrate the so-called phenotypic muscle characteris-tics of these animals. First, the findings highlighted a marked down-regulation of genes encoding slow contractile proteins in fibers (e.g. cardiac and slow troponin C and T isoforms, MYH7 and MYL2, TPM3; Table 4), slow twitch proteins (e.g. PLN, and SERCA2; Table 4) and MB (myoglobin), the differential expression of some of these genes being confirmed by real-time RT-PCR ( Table 5). The Bibliosphere Pathway Edition web tool of the Genomatix Suite predicted that, in rodents or humans, some of the down-regulated genes (SERCA2, MyH7, S100A4, VIM, FN1, COL1A2) had an NFKB1 site in their promoter. Interestingly, the NFKB1 gene was found to be up-regulated in DM (FC = 1.33, FDR < 5‰, p value < 5‰), suggesting that NFKB1 expression could contribute to negative regulation of their expression as already shown for collagen COL1A2 [27]. Other genes involved in contraction (MYH7, MB, TNN C, desmin) were also predicted to be targets of TEF-1 at MEF2 elements during fast-toslow muscle conversion, as reported for humans [28].
Conversely, the study also showed up-regulation of slc16a10 (Table 3), which encodes a transporter catalyzing the transport of many monocarboxylates including lactate and pyruvate [29], and of LDH-A, which encodes lactate dehydrogenase, although with an FC below our threshold (1.37). Moreover, it confirmed that the expression of MyBP-H, which encodes a component of the thick filaments in fast skeletal muscles, was up-regulated (Table 3) in the ST muscle of DM fetuses, as shown postnatally by proteomic approaches [20]. All in all, these data indicated that DM muscles were shifted towards a more glycolytic Dataset 1 comprised expression data from 3 fetuses per group; dataset 2 excluded the data from the DM108 heterozygote fetus and from the NM0423 fetus. fast metabolism. A similar observation was reported in myostatin-null mice [25,30]. Moreover, it has been demonstrated that the muscles of DM cattle contain a higher proportion of fast glycolytic IIX fibers and a lower proportion of slow I fibers than NM muscles as early as late gestation [31]. Proteomics showed increased expression of fast proteins and lowered expression of slow proteins in the ST muscle of DM bull calves [20]. Accordingly, our study revealed gene expression profiles that may be molecular signatures of the DM fast-type phenotype. Such features are likely to originate from a high proportion in the secondary generation of muscle fibers following the loss of myostatin function, as early as Day 110 of fetal age in cattle [8].

ECM-specific genes
It is notable that genes encoding ECM/cell interaction proteins, e.g. COL1A1, COL1A2 and COL3A1 (collagen type I and III), FN1 (murine and human fibronectin 1), LAMB1 (laminin beta 1), and BGN (biglycan, a known TGFβ target gene [32]) are down-regulated in DM muscles. ECM has a profound influence on the differentiation of muscle cells and can regulate growth factor function in skeletal muscle (for a review, see [33]). Our data are in accordance with the reduced connective tissue content in DM muscles [34,35] and the decreased expression in DM fetal muscles of the major collagen isoforms, namely the type I and III collagens, as shown by in situ experiments [36]. Conversely, a novel result of the present study was the up-regulation of genes encoding type IV collagen, the major structural component of basement membrane surrounding and supporting skeletal muscle cells, and PLOD3, an enzyme playing an essential role in the supramolecular assembly of collagen IV [37]. Collagen IV was shown to be located mainly in the endomysium whereas collagen I and III isoforms are located both in the perimysium and endomysium [36]. Thus, the ultrastructure of the DM muscle connective tissue could differ from that of NM at both the endomysium and the perimysium levels.

Gene marker of adipocyte differentiation
Lastly, the most novel and interesting result of our study is the down-regulated expression of C1QTNF3 (Table 4), the differential expression of which was confirmed by RT-PCR (Table 5). This gene encodes an adipocyte differentiation marker with striking homologies with adiponectin [38]. Similarly, decreased C1QTNF3 expression was detected in DM cows (our unpublished data). The finding that a loss-of-function mutation of myostatin is associated with decreased adipocyte differentiation is consistent with the low fat depots in myostatin-null mice [39] and in DM cattle [35]. Recently, myostatin has been shown to promote the commitment of mesenchymal stem cells to the adipogenic lineage [40]. Potts et al. [26] have also reported decreased expression of HMGA2, a transcription factor involved in fat cell proliferation, in DM cattle embryos. The expression of another adipocyte differentia- Genes were declared differentially expressed by SAM (FDR < 5‰) and by GeneANOVA with a p-value < 2% from dataset 2. Percentage homologies between spotted oligonucleotides and bovine sequences were determined using BLASTN and BLASTX searches. ND: None determined (unknown bovine sequence). Results are means of duplicate experiments. *All differential expressions are statistically significant at P < 5% for array experiments (using SAM analysis; comparison of 2 NM vs 2DM animals) or by real-time RT-PCR (using the Mann Whitney U Test). Genes were declared differentially expressed by SAM (FDR < 5‰) and by GeneANOVA with a p-value < 1‰ from dataset 2. Percentage homologies between spotted oligonucleotides and bovine sequences were determined using BLASTN and BLASTX searches. ND: none determined (unknown bovine sequence). -a, -b indicate two different oligonucleotides designed for a same gene.
tion marker (A-FABP) was found to be lower in the muscles of DM Belgian Blue bulls compared to bulls with no myostatin mutation [41], illustrating a reduction in intramuscular adipocyte numbers. Since the number of intramuscular adipocytes is a major factor in determining muscle marbling of beef [42], it could partly explain the lower intramuscular fat development of DM muscles [35].

Conclusion
In conclusion, transcriptomic analysis enabled us to demonstrate that the biological traits of DM muscles are associated with specific gene profiles at the time of fiber differentiation and/or specialization in late fetuses. On the one hand, our results confirm previous data obtained postnatally by classical biochemical and molecular biological approaches. On the other hand, they reveal altered gene expression in the three major muscle compartments, namely fibers, connective tissue and intramuscular adipose tissue. This may help us to understand how myostatin loss-of-function affects so many qualitative properties of muscles. Lastly, this study revealed novel putative myostatin targets, e.g. ECM constituents such as type IV collagen, C1QTNF3 mainly associated with adipose tissue development, and genes encoding transcription factors (ZFH1XB, ...). Work is in progress to determine whether these genes are direct or indirect targets targets of myostatin throught the examination of putative gene networks.

Animals
Animals were obtained as described previously [43]. Only 260-day-old fetuses were used in this study. Three DM fetuses were obtained by artificial insemination of Charolais heifers by transplantation of frozen embryos of strain INRA95. This strain comprised a mixture of breeds and the transplanted embryos contained around 75% Charolais. Three normal Charolais fetuses were obtained by artificial insemination of Charolais heifers using Charolais sperm from non-DM sires. After slaughter, the fetuses were collected and the semitendinosus muscle was excised, snap frozen and stored at -80°C prior to analysis.

Microarray experiments
Transcriptomic analysis was performed with a microarray of around 6,000 genes expressed in muscle; these so called "Myochips" are available from West Genopole [44]. The Myochips were made from a set of relevant genes (probes). They were composed of 919 control spots and 6,473 oligonucleotides (50-mers) representing genes preferentially and/or differentially expressed in normal and diseased striated mice or human muscles and heart. These genes encode proteins belonging to all the main functional categories in striated muscle [44]. They were classified at the biological process level according to Gene Ontology annotations. Three replicates of each gene were spotted on to Myochips, which allowed detailed statistical studies of the reproducibility of the hybridization experiments. Microarray experiments were performed according to recently proposed standards (MIAME consortium [45]). Data were incorporated into the BASE database and the NCBI Gene Expression Omnibus (GEO) [46] and are accessible through GEO Series accession number GSE5456.
The protocol used was that described in the DNA Chips platform protocols. Total RNA was extracted from muscle tissue samples with TRIZOL ® reagent (Life Technologies) according to the manufacturer's recommendation. Each individual sample was compared to a reference pool consisting of skeletal muscle transcripts isolated from the semitendinosus muscles of five 260-day-old NM fetuses and of 16 Charolais heifers. Total RNA (15 µg) was reverse Genes were declared differentially expressed by SAM (FDR < 5‰) and by GeneANOVA with a p-value < 1‰ from the reduced dataset. The gene lists were submitted to FatiGO+ analysis and compared for KEGG pathway.
transcribed using the CyScribe cDNA Post Labelling kit (Amersham Pharmacia Biotech) using random nanomers for priming. During reverse transcription, aminoallyl-dUTP was incorporated to perform labeling with cyanins (Cy5 for the reference sample and Cy3 for individual samples). Four chips were hybridized per sample comparison. After washing, the chips were scanned on an Affymetrix 428™ Array Scanner.

Data analysis
After acquisition, the scanned images were analyzed using GenePix Pro V6 software (Axon instrument, Inc). Raw signal intensity data were normalized using the MADSCAN lowess fitness method [47]. In order to identify differentially expressed genes, the Cy3/Cy5 ratios were statistically analyzed using SAM [48]. Data were also analyzed by standard analysis of variance (ANOVA) using Gene-ANOVA software [49]. In order to identify similar expression patterns, gene expression data were analyzed with Genesis [50] using hierarchical clustering (Average linkage and Euclidian distance).
Putatively involved pathways were explored using the FatiGoplus web tool [51], which is an extension of FatiGO [52] to other types of relevant biological knowledge, and using the Bibliosphere Pathway Edition web tool of the Genomatix Suite [53]. Rv primer: TCTCGTCCGCTACTCCAAGT), PLOD3 (Fw primer: AACGGGGCTTTAGATGAGGT; Rv primer: CGTGGTA-CACCTCGTTGTTG) using a LightCycler FastStart DNA Master SYBR Green I kit (Roche Diagnostics GmBH, Mannheim, Germany) according to the following procedure: Mg 2+ added at a final concentration of 2 mM; pre-incubation step at 95°C for 10 min; amplification step (40 cycles) including denaturation at 95°C for 10 s, annealing at 60°C for 7 s, extension at 72°C for 10 s; melting curve including denaturation at 95°C for 0 s, annealing at 70°C for 20 s, continuous melting at 98°C for 0 s (slope = 0.1°C/s); cooling step at 40°C for 30 s. For MSTN, annealing was at 56°C. Results are expressed in pg/µmol relative to a standard curve of purified cDNA for each gene. Expression data from the 2 homozygote DM fetuses only and the 3 NM fetuses were analyzed using the Mann Whitney U Test and the difference was declared significant for U= 0 (p = 5%). For MSTN, annealing was at 56°C. Results are expressed as Ct values.