MicroRNA—implications for cancer

MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression post-transcriptionally. After the discovery of the first miRNA in the roundworm Caenorhabditis elegans, these short regulatory RNAs have been found to be an abundant class of RNAs in plants, animals, and DNA viruses. About 3% of human genes encode for miRNAs, and up to 30% of human protein coding genes may be regulated by miRNAs. MicroRNAs play a key role in diverse biological processes, including development, cell proliferation, differentiation, and apoptosis. Accordingly, altered miRNA expression is likely to contribute to human disease, including cancer. This review will summarize the emerging knowledge of the connections between human miRNA biology and different aspects of carcinogenesis. Various techniques available to investigate miRNAs will also be discussed.
The “small RNA revolution”
Small ribonucleic acid (RNA) can act as a specific regulator of gene expression. This discovery has been an exciting breakthrough in Biological Sciences of the past decade, culminating in last year’s Nobel Prize in Physiology or Medicine awarded to Andrew Fire and Craig Mello. Building on previous work mainly in plants [50], Fire et al. [23] discovered that exogenous double-stranded RNA can be used to specifically interfere with gene function. This phenomenon was called RNA interference (RNAi). They also speculated that organisms might use double-stranded RNA naturally as a way of silencing genes. It was then shown that RNA interference was mediated by 22 nucleotide single-stranded RNAs termed small interfering RNAs (siRNAs) derived from the longer double-stranded RNA precursors [87]. The small interfering RNAs were found to repress genes by eliminating the corresponding messenger RNA transcripts, and thus, preventing protein synthesis.
Over the following years, many new small functional RNAs have been found. RNA is usually thought of as messenger RNA that serves as the template for translation of genes into proteins. In contrast, functional or non-coding RNA molecules are transcribed from a DNA sequence, but not translated into protein. The encoding DNA sequence is often referred to as an RNA gene. Functional RNA genes in the human genome include transfer RNA (tRNA), ribosomal RNA (rRNA), and various other small non-coding RNAs. Several hundred genes in our genome encode small functional RNA molecules collectively called microRNAs (miRNAs). Precursors of these miRNA molecules form structures of double-stranded RNA that can activate the RNA interference machinery. MicroRNAs downregulate gene expression either by degradation of messenger RNA through the RNA interference pathway or by inhibiting protein translation.
The first miRNA was discovered in 1993 by Victor Ambros and colleagues Rosalind Lee and Rhonda Feinbaum [42]. A genetic screen in the roundworm Caenorhabditis elegans, a millimeter-long animal used as a model organism in biological research, identified genes involved in developmental timing [42]. Surprisingly, one of the genes, termed lin-4, did not encode a protein but instead a novel 22-nucleotide small RNA. Seven years later, Reinhart et al. [70] discovered a second 22-nucleotide small RNA of this type, let-7, a gene also involved in C. elegans developmental timing. The lin-4 and let-7 small regulatory RNAs soon became very exciting for two reasons. Firstly, homologs of the let-7 gene were identified in other animals including humans [65]. The conservation of let-7 across species suggested an important and fundamental biological role for this small RNA. Secondly, the mechanism of RNA interference (RNAi) was discovered at that time, and it became clear that miRNA and RNAi pathways were intricately linked and shared common components. Within the following year, more than 100 additional small regulatory RNAs similar to lin-4 and let-7 were identified in worms, the fruit fly Drosophila, and in humans [38, 40, 41]. These small non-coding RNAs were named microRNAs (miRNAs) [38, 40, 41].
Subsequently, many more short regulatory RNAs were identified in almost all multicellular organisms, including flowering plants, worms, flies, fish, frogs, mammals [38, 40, 41, 48, 71], and in single cellular algae and DNA viruses [66, 75]. To date, more than 500 human miRNAs have been experimentally identified. Computational predictions of miRNA targets suggest that up to 30% of human protein coding genes may be regulated by miRNAs [46, 68]. This makes miRNAs one of the most abundant classes of regulatory genes in humans. MicroRNAs are now perceived as a key layer of post-transcriptional control within the networks of gene regulation.
MicroRNAs are sequentially processed from longer precursor molecules that are encoded by the miRNA genes [1] (Fig. 1). MiRNA genes are referred to by the same name (termed mir) written in italics to distinguish them from the corresponding mature miRNA (termed miR) followed by a number, e.g., mir-1 or miR-1. The encoding DNA sequence is much longer than the mature miRNA. Two ribonuclease enzymes, Drosha and Dicer, subsequently process the primary transcripts (or pri-miRNA) to generate mature miRNAs. The primary transcripts contain one or more stem-loop structures of about 70 bases. Stem-loops are double-stranded RNA structures consisting of a nucleotide sequence that can fold back on itself to form a double helix with a region of imperfect base pairing that forms an open loop at the end (Fig. 1a). The ribonuclease Drosha excises the stem-loop structure to form the precursor miRNA (or pre-miRNA) [43]. After export into the cytoplasm, the pre-miRNA is cleaved by the ribonuclease Dicer to generate a short RNA duplex [6, 28]. After untwisting, one RNA strand becomes the mature single-stranded miRNA, while the complementary strand, termed miRNA*, is usually rapidly degraded (Fig. 1b).
Fig. 1The biogenesis and function of miRNAs. a Primary miRNAs (pri-miRNA) are transcribed from longer encoding DNA sequences (miRNA genes). The pri-miRNA contains one or more stem-loop structures of about 70 bases. In the nucleus, the ribonuclease enzyme Drosha excises the stem-loop structure to form the precursor miRNA (pre-miRNA). b After export into the cytoplasm, the pre-miRNA is cleaved by the ribonuclease Dicer to generate a short RNA duplex (miRNA:miRNA*). c The mature single-stranded miRNA is incorporated into the RNA-induced silencing complex (RISC), while the complementary strand (miRNA*) is usually rapidly degraded. The miRNA incorporated into the silencing complex can bind to the target messenger RNA by base pairing, causing inhibition of protein translation and/or degradation of the target messenger RNA
MicroRNAs recognize their targets based on sequence complementarity [10]. The mature miRNA is partially complementary to one or more messenger RNAs. In humans, the complementary sites are usually within the 3′-untranslated region of the target messenger RNA. To become effective, the mature miRNA forms a complex with proteins, termed the RNA-induced silencing complex. The miRNA incorporated into the silencing complex can bind to the target messenger RNA by base pairing. This base pairing subsequently causes inhibition of protein translation and/or degradation of the messenger RNA (Fig. 1c). The potential mechanisms underlying this process were recently reviewed [30, 67]. Protein levels of the target gene are consequently reduced, whereas messenger RNA levels may or may not be decreased. In humans, miRNAs mainly inhibit protein translation of their target genes and only infrequently cause degradation or cleavage of the messenger RNA [1].
The biological role and in vivo functions of most mammalian miRNAs are still poorly understood. In invertebrates, miRNAs regulate developmental timing (e.g., lin-4), neuronal differentiation, cell proliferation, growth control, and programmed cell death [9, 33, 42]. In mammals, miRNAs have been found to play a role in embryogenesis and stem cell maintenance [7], hematopoietic cell differentiation [17], and brain development [59, 60]. To date, knowledge of human miRNAs has been primarily descriptive. MicroRNA expression has been found to be deregulated in a wide range of human diseases including cancer. However, it remains uncertain whether altered miRNA expression is a cause or consequence of pathological processes. The underlying mechanisms of why and how miRNAs become deregulated are largely unknown. Although bioinformatics approaches can predict thousands of genes that are potentially targeted and regulated by miRNAs based on sequence complementarity, only very few miRNA target genes have been functionally validated. Our group is currently investigating the role of miRNAs in mammary gland development and breast cancer pathogenesis. A comparison of miRNA and gene expression identified miRNAs that classify molecular breast cancer subtypes [8]. As cancer is ultimately a consequence of disordered gene expression, miRNAs have been suggested to contribute to the development of cancer [11]. This review will focus on the connection between human miRNA biology and different aspects of carcinogenesis. Various techniques available to investigate miRNAs will also be discussed.
MicroRNAs and cancer
Three important observations early in the history of miRNAs suggested a potential role in human cancer. Firstly, the earliest miRNAs discovered in the roundworm C. elegans and the fruit fly Drosophila were shown to control cell proliferation and apoptosis [9, 42]. Their deregulation may therefore contribute to proliferative diseases such as cancer. Secondly, when human miRNAs were discovered, it was noticed that many miRNA genes were located at fragile sites in the genome or regions that are commonly amplified or deleted in human cancer [14]. Thirdly, malignant tumors and tumor cell lines were found to have widespread deregulated miRNA expression compared to normal tissues [12, 24, 52]. The question remained whether the altered miRNA expression observed in cancer is a cause or consequence of malignant transformation.
MicroRNAs as causal cancer genes at genomic breakpoints
Five years ago, the first direct evidence for an involvement of miRNAs in cancer was reported [13]. Calin et al. studied a well-known deletion on chromosome 13, which is the most frequent chromosomal abnormality in chronic lymphocytic leukemia (CLL). This deletion had long been suspected to contribute to leukemogenesis. However, extensive studies had failed to identify a causal gene. Calin et al. [13] found that two miRNA genes, mir-15 and mir-16, were located within this 30-kb deletion. They subsequently analyzed the expression of miR-15 and miR-16 in blood samples from patients with CLL. Both miRNAs were absent or downregulated in the majority (68%) of cases when compared to normal tissue or lymphocytes. This finding suggested that these two miRNAs were causally involved in the pathogenesis of chronic lymphocytic leukemia.
In 2005, three reports provided the first mechanistic insight into how miRNAs might contribute to carcinogenesis. Two independent studies described the relationship between a miRNA cluster, mir-17-92, and the Myc oncogenic pathway [27, 63]. A third report demonstrated an interaction between let-7 miRNA and the RAS proto-oncogene [32].
The mir-17-92 cluster—small RNAs with oncogenic potential
A cluster of six miRNAs, the mir-17-92 cluster, was found to be located within a region on chromosome 13 that is commonly amplified in human B-cell lymphomas [64]. He et al. [27] demonstrated that the miRNAs from the mir-17-92 cluster were overexpressed in lymphoma cell lines carrying this amplification, and expression levels correlated with gene copy number of the mir-17-92 locus [27]. Further, the miR-17-92 primary transcript was found to be overexpressed in tumor samples from lymphoma patients. To test their hypothesis that mir-17-92 actively contributes to lymphomagenesis, the authors took advantage of a mouse model of human B-cell lymphoma. These mice develop lymphomas due to an overexpression of the Myc oncogene. The Myc oncogene encodes the transcription factor c-Myc that regulates cell proliferation, growth, and apoptosis, and overexpression of c-Myc is common in cancer. He et al. [27] demonstrated that additional expression of the mir-17-92 cluster accelerated c-Myc-induced tumorigenesis in mice. The authors therefore suggested that mir-17-92 was the first potential non-coding oncogene, referred to as oncomir-1.
The cellular function of miR-17-92 was not identified in these experiments. Nevertheless, the pathology of the tumors indicated lower rates of apoptosis as compared to tumors with Myc overexpression alone. Three recent studies contributed towards our understanding of the oncogenic potential of miR-17-92. Two reports demonstrated an anti-apoptotic effect of miR-17-92 through various pathways that promote cell proliferation and growth [55, 76]. A third study identified mir-17-92 as a mediator of angiogenesis in tumors induced by the oncogene c-Myc [19].
O’Donnell et al. [63] independently identified the same cluster of miRNAs, mir-17-92, to be regulated by the transcription factor c-Myc. The transcription factor Myc induces expression of E2F1 growth factor. The mir-17-92 cluster which is also induced by c-Myc does, in contrast, inhibit E2F1 expression. The authors therefore suggested a novel regulatory mechanism by which c-Myc fine-tunes gene expression by activating the transcription of target genes and by simultaneously inducing inhibitory miRNAs that reduce their translation.
The example of the mir-17-92 cluster highlights that a distinction between oncogenic and tumor suppressor miRNAs is likely to be an oversimplification. The same miRNAs may have oncogenic or tumor suppressor activity depending on the context and the cell type they are expressed in. A single miRNA may regulate various unrelated target genes and thereby control opposing activities such as cellular proliferation and apoptosis. The ultimate function of a miRNA may depend on the tissue type they are expressed in and what target genes are present.
MicroRNAs with tumor suppressor potential
The let-7 family of miRNAs was the first group of miRNAs shown to regulate expression of a proto-oncogene, the RAS protein. RAS proteins are membrane-associated signaling proteins that regulate cell growth and differentiation. A miRNA that controls expression of these potentially oncogenic proteins would be predicted to possess tumor suppressor activity.
Mutations in the RAS oncogene are present in approximately 15–30% of all human cancers, and overexpression of the RAS oncogene is common in lung cancer. Johnson et al. [32] showed that overexpression of RAS protein in lung cancer tissue correlated with reduced expression of let-7 miRNA. They experimentally confirmed that let-7 can inhibit RAS expression in human cancer cell lines. Loss or reduction of let-7 in lung cancer leads to RAS overexpression, thus, promoting cellular growth and contributing to tumorigenesis. The authors therefore suggested that let-7 acts as tumor suppressor [32]. Another group independently reported reduced expression of let-7 in lung cancers and found that this correlated with a poor prognosis [77].
Global loss of miRNA expression in cancer
A global decrease in miRNA levels has been observed in human cancers, indicating that small RNAs may have an intrinsic function in tumor suppression. Lu et al. [52] were the first to show that the expression levels of many miRNAs were significantly reduced in cancers compared to the corresponding normal tissues. They analyzed a total of 217 human and mouse miRNAs across 334 human cancers, cancer cell lines, and normal tissues. Cancers had significantly reduced global miRNA expression. Poorly differentiated tumors had lower miRNA levels compared with more-differentiated tumors. The authors hypothesized that miRNAs can function to drive terminal differentiation and prevent cell division. Global changes in miRNA expression may reflect the degree of cell differentiation [52]. A recent study examined the expression of 241 human miRNAs in a comprehensive panel of human cancer cell lines, the NCI-60 panel, and in normal tissues [24]. The authors confirmed the finding that most miRNAs were expressed at lower levels in human tumor-derived cell lines compared with the corresponding normal tissue [24].
Until recently, considerable uncertainty remained as to whether the altered miRNA expression observed in cancer was a cause or consequence of malignant transformation. Earlier this year, a study by Kumar et al. [37] proved for the first time that widespread reduction in miRNA expression does, indeed, promote tumorigenesis. The authors globally reduced the production of mature miRNAs through a knockdown of the miRNA-processing enzymes Drosha and Dicer in cell lines. The mouse and human cancer cells consequently showed decreased steady-state miRNA levels. These cells with global miRNA loss showed enhanced cellular growth in vitro [37]. When injected into nude mice, these cells generated faster growing and more invasive tumors compared to controls. To assess the effect of global miRNA loss in vivo, the authors deleted the miRNA-processing enzyme Dicer in a mouse model of lung cancer. The Dicer mutant mice who had impaired miRNA processing developed an increased tumor burden, with an expansion in tumor number and tumor size, as well as tumors which were less well differentiated compared to controls [37]. Overall, these data clearly suggest that global miRNA loss enhances tumorigenesis. Kumar et al. demonstrated that loss of miRNAs leads to upregulation of proto-oncogenes such as RAS and c-Myc. However, it remains to be elucidated whether loss of all miRNAs is necessary or whether reduction of a subgroup of key tumor suppressor miRNAs, such as let-7, is the event that promotes malignant transformation.
MicroRNAs in the p53 tumor suppressor network
Transcriptional networks are often deregulated in cancer cells and may lead to altered transcription of miRNA genes. Two recent studies identified a miRNA, miR-34, to be regulated by the p53 transcription factor [16, 26]. The p53 protein, also called “the guardian of the genome”, regulates the cellular response to stress and cancer-initiating events such as DNA damage. He et al. [26] found that a miRNA, miR-34, is directly activated by the transcription factor p53 after DNA damage. Expression of miR-34 induces cell cycle arrest and thereby acts together with other effectors of the p53 tumor suppressor network to inhibit inappropriate cell proliferation. Another group independently demonstrated that miR-34 is upregulated by p53 upon DNA damage and promotes apoptosis [16].
Together, these data indicate that altered expression of miRNAs is not simply a secondary event that reflects the less differentiated state of cancer cells. In contrast, at least in some cases, miRNA expression is specifically driven by tumor suppressors and oncogenes.
MicroRNAs with a role in tumor invasion and metastasis
Transcriptional networks may drive miRNA expression in cancers. Recent work from Ma et al. [54] suggested a model by which a pleiotropic transcription factor, Twist, induces expression of a specific miRNA, which suppresses its direct target and in turn activates a pro-metastatic gene, leading to tumor cell invasion and metastasis. The expression of miR-10b induced by the transcription factor Twist promoted cell migration and invasion in mouse and human breast cancer cells. Furthermore, the expression level of miR-10b in primary human breast carcinomas correlated with clinical progression [54]. These findings, if confirmed, suggest that specific miRNAs may have a role beyond the tumor-initiating event and directly participate in tumor progression and metastasis.
Regulation of miRNAs in cancer—who regulates the regulators?
In few cases, the underlying cause of miRNA deregulation in cancer is clear. As discussed above, the overexpression of miR-17-92 correlates with amplification of its gene locus [27]. Similarly, decreased expression of miR-15 and miR-16 is associated with a corresponding chromosomal deletion [13].
Transcriptional or epigenetic regulation of miRNAs has been recently reported [53, 73]. The transcription of a miRNA gene, mir-124a, was shown to be inactivated by hypermethylation of its promoter in various human tumors. This process of epigenetic silencing is a well-known mechanism to inactivate protein-coding genes in cancer cells and may similarly apply to miRNAs. The miRNA gene mir-127 is usually expressed in normal cells but not in cancer cells. Saito et al. [73] demonstrated that miR-127 was highly induced in cultured human cancer cells after treatment with demethylating drugs, suggesting that it is subject to epigenetic silencing through promoter hypermethylation.
A novel mechanism of miRNA regulation was suggested by Mayr et al. [56] and Lee and Dutta [44]. They demonstrated that miRNA function could be regulated through loss of miRNA binding sites in the target gene. Both groups independently demonstrated that chromosomal translocations in a known oncogene, high mobility group A2 (Hmga2), led to loss of the let-7 miRNA binding sites in its messenger RNA. Disrupted repression of Hmga2 by let-7 promoted oncogenic transformation and growth in mammalian cells. These two studies provide the first evidence that disrupting the interaction of a single miRNA and its target can produce an abnormal phenotype in mammalian cells [44, 56].
In addition, there is evidence that miRNAs are regulated indirectly through control of their processing enzymes. Thomson et al. [81] showed that a downregulation of miRNAs in human cancer was not associated with reduced levels of the primary miRNA transcripts. The authors therefore suggested regulation of miRNAs during subsequent processing steps, e.g., through altered function of the enzyme Drosha [81].
MicroRNA profiling—implications for cancer diagnosis
Lu et al. [52] asked the question whether global miRNA expression profiles could classify human cancer. MicroRNA expression profiles clearly differentiated human cancers according to their developmental origin.
Cancers of epithelial and hematopoietic origin had distinct miRNA profiles. A subgroup of gastrointestinal tumors, which arise from endoderm, was distinguished by miRNA expression patterns. Furthermore, tumors within a single cell lineage such as acute lymphoblastic leukemia were further differentiated according to their underlying genetic abnormality into BCR/ABL-positive tumors, T-cell tumors, and those with MLL gene rearrangement [52]. Finally, the authors applied the miRNA expression profiles they had established to an independent series of 17 poorly differentiated tumors of unknown origin. Based on the differential expression of 217 miRNAs, a correct diagnosis could be established in 12 out of 17 of the tumors. In contrast, gene expression profiling based on ∼16,000 messenger RNAs did not accurately classify the tumors [52]. This has potential important clinical implications. If miRNAs prove useful for clinical diagnosis, their key advantage might be their high stability. In contrast to most messenger RNAs, they are long-lived in vivo [49] and very stable in vitro [78], which might allow analysis of paraffin-embedded samples for routine diagnostic applications.
MicroRNAs—novel therapeutic targets?
Regulatory RNAs may also have therapeutic applications by which disease-causing miRNAs could be antagonized or functional miRNAs restored. The most intuitive choice of molecules to correct altered miRNA–messenger RNA interactions are RNA oligonucleotides. These oligonucleotides need to be chemically modified to allow for stability in serum and cellular uptake. Modified antisense oligonucleotides are already being developed to utilize the intrinsic RNAi pathway for delivery of gene therapy. If the delivery problem can be overcome, then miRNA therapies may also be possible.
Two studies have successfully applied 2′-O-Methyl-modified antisense RNAs to inhibit miRNA function in cultured cells [29, 57]. Recent work by Krutzfeldt et al. [36] demonstrated that modified cholesterol-conjugated antisense RNAs designated “antagomirs” could effectively inhibit miRNA function in vivo in the adult mouse. The authors applied three daily intravenous injections of antagomirs and achieved effective inhibition of four miRNAs over a period of weeks in most tissues except brain [36]. A novel approach was recently reported by Ebert et al. [21]. They developed miRNA inhibitors that can be transiently expressed in cultured mammalian cells. These competitive inhibitors termed “miRNA sponges” derepressed miRNA targets at least as strongly as chemically modified antisense oligonucleotides [21]. A different approach was taken by Tsuda et al. [83]. The authors designed synthetic miRNAs to target overexpressed tumor proteins, such as HER-2 protein. A synthetic miRNA targeting HER-2 messenger RNA successfully inhibited HER-2 protein expression in ovarian cancer cells [83]. Together, these studies hold some promise of miRNAs as future therapeutic targets.
One limitation of antisense RNA therapies is the restricted number of cells that can be targeted. Any approach to knock down a particular miRNA with antisense oligonucleotides will only result in partial knockdown. This may represent a limitation for cancer therapies. It remains to be seen whether indirectly mediated bystander effects on cancer cells that have not been directly targeted may partly overcome this limitation. In contrast, a partial effect on function may be of therapeutic value in neurodegenerative diseases, such as Parkinson’s or Alzheimer’s disease. A partial restoration of dopamine production by antisense therapy might result in a significant clinical improvement in Parkinson patients. Similarly, a partial reduction of the disease-causing proteins in Alzheimer’s disease may lead to a clinical improvement and might be achievable by RNA based or miRNA gene therapy.
Techniques and approaches to study miRNAs
All known miRNAs are registered in a public web-based registry, the “miRBase” database that provides up-to-date information on all published miRNAs [25]. Novel miRNA genes can be discovered by bioinformatics approaches searching for evolutionary conserved stem-loop structures in the genome (reviewed in [3, 5]). Experimentally, miRNAs are discovered by cloning all small RNAs from a certain tissue type or developmental stage and subsequent sequencing to identify the subgroup of small RNAs that fulfill the criteria for miRNAs [38, 51]. Both computational and experimental approaches indicate that many more miRNAs are likely to be identified [4, 5], which is reflected by the rapidly increasing number of annotated miRNAs which increased from less than 300 to more than 4,000 over the past 4 years [58].
MicroRNA expression studies
Northern blot analysis is a well-established technique for studying messenger RNA expression and was soon adapted to detect miRNAs in cells or tissues [42, 84]. Subsequently, conventional DNA microarray technology was modified to form miRNA microarrays, allowing for the detection of multiple miRNAs simultaneously across various samples [15, 60, 62, 82].
Lu et al. [52] developed a novel microarray strategy to improve probe specificity, which is critical due to the short nature of mature miRNAs. They performed hybridization in solution using polystyrene capture beads that are coupled to oligonucleotide probes complementary to the miRNAs of interest. The solution hybrids are then analyzed using a multicolor flow cytometer measuring bead color, denoting miRNA identity, and labeling intensity, denoting miRNA abundance [52].
In parallel to microarray platforms, commercial assays for quantitative reverse transcriptase polymerase chain reaction (RT-PCR) have become available. These allow for the analysis of miRNAs in small tissue samples or even single cells [79], as well as validation of microarray data. In addition to mature miRNAs, these quantitative RT-PCR assays can be applied to analyze miRNA precursors and primary transcripts [31]. In situ hybridization for the detection of mature miRNAs has recently become possible by using special high-affinity locked nucleic acid (LNA)-modified DNA oligonucleotide probes and holds promise for the application on human formalin-fixed and paraffin embedded tissue [35, 61].
Functional characterization of miRNAs
Various strategies have been used to investigate the function of specific miRNAs. In worms and flies, loss-of-function mutants for specific miRNAs or miRNA families allow us to draw conclusions regarding possible physiological functions of miRNAs from the resulting abnormal phenotype [34, 39, 86]. The knockdown of miRNAs or pre-miRNAs using modified antisense oligonucleotides has proven particularly useful in cell lines [29, 45, 57]. LNA-modified antisense oligonucleotides have been successfully utilized to knock down specific miRNAs in cultured cells [22]. This approach allowed identification of a crucial role for a miRNA, miR-223, in granulocytic differentiation [22]. In addition, the modified antisense RNAs (antagomirs) described by Krutzfeldt et al. [36], which inhibit miRNA function in the adult mouse, may provide a potential research tool to study miRNA function in vivo.
In mammals, induced defects in miRNA biogenesis are a useful tool for investigating the biological roles of miRNAs, as loss-of-function mutants are not available for most miRNA genes. Dicer knockout mouse models have revealed essential roles for miRNAs in murine organogenesis [88]. A recent study utilized a combined knockdown of the miRNA-processing enzymes Drosha, Dicer1, and DGCR8 to study the consequences of a global decrease in mature miRNAs in cancer cell lines and in a mouse model for lung cancer [37].
Earlier this year, four independent groups have, for the first time, deleted genes for single miRNAs in mice [72, 80, 85, 88]. Two of the groups deleted the same DNA sequence for mir-155 and described severe immune defects [72, 80]. Mice lacking miR-155 showed impaired function of B and T lymphocytes and dendritic cells [72]. In particular, T helper cell differentiation and the germinal center reaction to produce a T-cell-dependent antibody response were defective [80]. Together, these two studies demonstrated a key role for miR-155 in normal immune function. The two other groups deleted different miRNAs, miR-1-2 and miR-208, and reported cardiac defects. Mice lacking miR-208 showed inadequate cardiac growth in response to stress [85], while mice lacking miR-1-2 had defects in cardiac morphogenesis and electrical conduction [88].
MicroRNA target sites
A validated biochemical strategy for identifying miRNA targets would be highly desirable. Two groups have recently reported promising approaches to experimentally identify miRNA targets. Both approaches apply biochemical methods to purify the effector complexes of miRNAs associated with proteins and bound messenger RNA targets [2, 20].
An increasing number of sophisticated bioinformatics approaches are being developed to predict putative miRNA target genes [3, 10, 69, 74]. This is based on the fact that miRNA target recognition is at least partly based on simple sequence complementarity. Interestingly, exact base pairing between miRNAs and their targets commonly appears to be required only in the first six to eight bases from the 5′ end of the miRNA. The short nature of this designated “seed region” allows a single miRNA to act on up to a hundred different target sites, and all human miRNAs together may regulate up to one third of protein coding genes [10, 47]. A different approach to discovering miRNA target genes is to knock out or overexpress a particular miRNA and use conventional microarrays to identify genes that show changes in expression. This approach is based on the observation that some miRNAs can also downregulate messenger RNA levels in addition to downregulating protein levels of their target genes [49]. Experimental validation of miRNA target sites has been limited to date. A common approach has been to express a miRNA in vivo while simultaneously expressing and monitoring the target messenger RNA linked to a reporter gene, i.e., Luciferase [10, 18, 55, 56, 76]. The fact that a single miRNA can regulate multiple targets and a particular target may be regulated by various miRNAs suggests a highly complex network of miRNA-target interactions, which is only beginning to be unraveled.
Conclusions
Over recent years, miRNAs have emerged as major players in the complex networks of gene regulation and have been implicated in various aspects of human disease. Only 5 years after the first study reported a direct involvement of miRNAs in cancer, these small RNAs have already significantly improved our understanding of carcinogenesis. In addition to protein-coding oncogenes and tumor suppressor genes, we will have to take into account miRNAs and their regulatory networks if we aim to understand the complex processes underlying malignant transformation.

microrna cancer mirna development pathogenesis


Increased Terpenoid Accumulation in Cotton (Gossypium hirsutum) Foliage is a General Wound Response

The subepidermal pigment glands of cotton accumulate a variety of terpenoid products, including monoterpenes, sesquiterpenes, and terpenoid aldehydes that can act as feeding deterrents against a number of insect herbivore species. We compared the effect of herbivory by Spodoptera littoralis caterpillars, mechanical damage by a fabric pattern wheel, and the application of jasmonic acid on levels of the major representatives of the three structural classes of terpenoids in the leaf foliage of 4-week-old Gossypium hirsutum plants. Terpenoid levels increased successively from control to mechanical damage, herbivory, and jasmonic acid treatments, with E-β-ocimene and heliocide H1 and H4 showing the highest increases, up to 15-fold. Herbivory or mechanical damage to older leaves led to terpenoid increases in younger leaves. Leaf-by-leaf analysis of terpenes and gland density revealed that higher levels of terpenoids were achieved by two mechanisms: (1) increased filling of existing glands with terpenoids and (2) the production of additional glands, which were found to be dependent on damage intensity. As the relative response of individual terpenoids did not differ substantially among herbivore, mechanical damage, and jasmonic acid treatments, the induction of terpenoids in cotton foliage appears to represent a non-specific wound response mediated by jasmonic acid.
Introduction
Plants produce a broad range of defense chemicals that act as deterrents or toxins against herbivores and pathogens. Several different strategies have evolved concerning the deployment of such compounds. When the synthesis of defense chemicals occurs solely after initial attack, this reduces metabolic costs (Gershenzon 1994), as defenses are produced only when needed. Such induced defenses have ecological advantages (Agrawal and Karban 1999) and might be a useful strategy for plants that are sporadically attacked. However, plants that risk frequent and heavy damage may be better protected by investing in constitutive defense compounds (Wittstock and Gershenzon 2002). The accumulation of constitutive defenses within plants usually conforms to expectations of the ‘optimal defense theory’ (ODT), which predicts that the highest protection level will be found in parts with the highest fitness value such as young tissues and reproductive organs (McKey 1979; Rhoades 1979). This has been shown for a variety of plant secondary metabolites from different classes such as alkaloids (Hartmann and Zimmer 1986; Baldwin 2001), glucosinolates (Porter et al. 1991. Brown et al. 2003), furanocoumarins (Berenbaum and Zangerl 1999), phenylpropanoids (Harborne 1991; Opitz and Schneider 2002), and terpenes (Gershenzon and Croteau 1991). Under abiotic or biotic stress, plants often exhibit induced responses by enhancing the accumulation of constitutive compounds in certain tissues that increases their deterrence or toxicity to enemies (Baldwin 1988; Wittstock and Gershenzon 2002; Vazquez-Flota et al. 2004). These changes also may be rationalized as a way for plants to reduce the costs of defense by producing high concentrations of constitutive defenses only when and where these are needed. However, more information is required to understand how the levels of constitutive defenses change under different stresses in plants on an organ-by-organ basis.
Among the vast number of defensive compounds in plants, terpenoids form the largest group with over 25,000 known structures (Buckingham 1994). Some of the best-studied examples for constitutive plant defenses are terpenoid volatiles such as monoterpenes (C10), which are considered defensive against herbivores and pathogens (Gershenzon and Dudareva 2007). Monoterpenes are frequently constituents of oils and resins that are accumulated in complex secretory structures such as glandular trichomes, secretory cavities, or resin ducts (Fahn 1979). Among the most comprehensively studied systems are monoterpenes in the glandular trichomes of peppermint (Gershenzon et al. 2000; McConkey et al. 2000). However, less is known about the accumulation of other classes of terpenes.
Cotton (Gossypium hirsutum) is particularly suitable for the study of terpenoid accumulation because a large variety of terpenes are produced constitutively in all photosynthetically active parts of the plant and stored in subepidermal glands. Beside monoterpenes, G. hirsutum produces sesquiterpenes (C15; Elzen et al. 1985) and terpenoid aldehydes, such as hemigossypolone (C15) and the heliocides H1 to H4 (C25; Fig. 1; Hedin et al. 1992). All three classes of terpenoids in cotton are biosynthetically related (Stipanovic et al. 1977, 1978a; Davis and Essenberg 1995). The precursor of sesquiterpenes, farnesyl diphosphate, is formed by addition of a C5 isopentenyl diphosphate unit to geranyl diphosphate, the precursor of all monoterpenes. The C25 heliocides are formed from Diels–Alder-type reactions of the C15 hemigossypolone (derived from δ-cadinene) to the monoterpene myrcene for the formation of heliocides H2 and H3 or the monoterpene E-β-ocimene for the formation of heliocides H1 and H4 (Fig. 2).
Fig. 1Terpenoid products of three structural classes, which are accumulated in glands of the foliage of Gossypium hirsutum. 1 α-pinene, 2 (E)-β-ocimene, 3 myrcene, 4 (E)-β-caryophyllene, 5 γ-bisabolene, 6 α-humulene, 7 hemigossypolone, 8 heliocide H1, 9 heliocide H2Fig. 2Proposed scheme for the biosynthesis of heliocide H1 in cotton
The accumulation of terpenoid aldehydes in cotton leaves was shown to be increased after herbivory (Bezemer et al. 2004), and these compounds have been considered to function as feeding deterrents on generalist herbivores such as Spodoptera exigua (McAuslane et al. 1997). The heliocides specifically have been correlated with antifeedant effects and are locally and systematically induced in response to herbivore attack (Karban and Carey 1984; Croxford et al. 1989; Alborn et al. 1996). However, besides the terpenoid aldehydes, little information is available on the accumulation of other terpenes found in subepidermal glands. In studying the control of terpenoid accumulation in cotton, it is of interest to determine if higher concentrations arise from increases in gland number or from increases in the amount of terpenoids per gland (McAuslane et al. 1997). Increasing numbers of subepidermal glands in cotton leaves were observed after attacks of spider mites and caterpillars (McAuslane et al. 1997; Agrawal and Karban 2000).
To learn more about the control of terpenoid accumulation in cotton, we compared the response of plants to herbivore damage, mechanical damage, and the application of jasmonic acid (JA), a ubiquitous mediator of defense responses in the plant kingdom (Browse 2005). Differences in the reaction of plants to these treatments were investigated with respect to plant growth, gland production, and accumulation of major compounds from three classes of terpenoids: terpenoid aldehydes, monoterpenes, and sesquiterpenes at the level of the individual leaf. We also examined how the accumulation was influenced by the number of glands in the total foliage or the relative filling of glands with terpenoids.
Methods and Material
Plants G. hirsutum L. of a single variety, ‘Deltapine acala 90’ (Delta and Pine Company—Hollandala, MS, USA), were reared in a growth chamber under high light intensity [1 mmol (m2)−1 s−1 of photosynthetically active radiation], a 15-h light period, 28°C/23°C (day/night), and 65% relative humidity. Plants were established in pots containing ‘Seramis’-clay granulate (Masterfood GmbH, Verden/Germany), which were watered daily and fertilized weekly with a complete fertilizer [1 g/l Flory 3 (N/P/K, 15:10:15), Euflor/Germany]. Under these conditions, optimal growth and production of the various terpenoids was obtained.For all experiments, leaves were numbered sequentially such that number 1 refers to the first true leaf initiated right after the cotyledons (Fig. 3). In this study, leaves 1–7 were investigated and collectively referred to as total foliage.
Fig. 3Sketch of a Gossypium hirsutum plant at the end of experiment 1, approximately 1-month-old. Leaves are numbered according to the order of development. Grid pattern indicate leaves, which were damaged either mechanically or by caterpillars of Spodoptera littoralis
Herbivores Eggs of the generalist herbivore, Spodoptera littoralis (Boisd.; Lepidoptera, Noctuidae), Egyptian cotton worm, were obtained from Syngenta (Basel, Switzerland). After hatching, larvae were reared in 2-l plastic boxes containing artificial diet, which consisted of 500 g of bean flour, 500 ml water, 9 g ascorbic acid, 5 g ethyl-4-benzoic acid, 0.6 ml α-tocopherol, 9 ml Mazola germ oil, 4 ml 3.7% formaldehyde, and 650 ml 7.5% agar solution. Fresh diet was applied every third day. Larvae were reared at constant 20°C, 50–60% relative humidity, and ambient light conditions. Two weeks after hatching, larvae reached the third instar and were used for the study.
Experiment 1
Treatment of Cotton Plants Three weeks after germination, when plants had developed four to five true leaves, the length of the midrib of leaf 4 was determined as a good measure for growth stage. Next, 24 plants at comparable growth stage were transferred into a second growth chamber (Vötsch, Germany) with abiotic conditions identical to the chamber where plants were reared. Here, plants were maintained separately in 250-ml plastic pots and watered twice a day to ensure adequate water supply. After 24 h, plants were divided into four treatment groups of six individuals each. Plants of one group were damaged mechanically three times on leaf 2 on the first day and three times on leaf 3 on the fourth day by using a fabric pattern wheel. In every case, leaves were scratched four times in parallel to the midrib (two times on each side). For a second group, three caterpillars of Spodoptera littoralis were caged on each plant on leaf 2 during the first day and on leaf 3 during the fourth day until larvae consumed at least one third of the available leaf material. Plants of the third group were watered with a 100-μM JA solution in water (Sigma) during the first day. JA concentrations in this range are commonly used to induce the biosynthesis of secondary products in plants (Koch et al. 1999; van Poecke and Dicke 2002). Afterward, this solution was replaced by pure tap water. A control group of plants was not treated at all. One week after the start of treatments, all true leaves were harvested separately, frozen in liquid nitrogen, and stored at −20°C until further analyses.
Foliar Glands Just after removal, all leaves were scanned to calculate leaf areas by using the program ImageJ (Wayne Rasband, National Institutes of Health, USA). In this procedure, herbivore-damaged leaves were used as templates to reconstruct the areas of undamaged leaves. In addition, surface view pictures were taken of two circular 24-mm2 sections from the center of each leaf half with a stereomicroscope connected to a digital camera. In doing so, leaves were illuminated from below with a lamp to visualize filled subepidermal glands as pigmented spots. After the number of filled glands was counted, gland densities for 24-mm2 sections were calculated. Based on these sections, gland numbers and gland densities per leaf and per total foliage were extrapolated. These values were slightly underestimated because of higher gland densities at leaf edges (personal observations).
Analysis of Monoterpenes and Sesquiterpenes True leaves were separately analyzed for their content of constitutive terpenes. Depending on available material, 30–100 mg of ground frozen leaf were extracted with 300–500 μl pentane/n-hexane 1:1. Additionally, 2 μg of nonylacetate were added as internal standard before extracts were shaken for 3 h at room temperature. After extracts were cooled to 4°C, they were filtered through cotton and Na2SO4 to remove remaining water. A subset of extracts was analyzed qualitatively by gas chromatography–mass spectrometry (GC–MS) on a Hewlett-Packard 6890 gas chromatograph (injector temperature 220°C; 1 μl splitless injection) coupled to a Hewlett-Packard 5973 quadrupole mass selective detector. Separations were performed on a DB-5MS column (30 m × 0.25 mm × 0.25 μm film; Agilent Technology) with 2 ml min−1 helium as the carrier gas. The following temperature gradient was used: 40°C for 3 min, increased to 90°C at 5°C/min, further increased to 140°C at 40°C/min, followed by an increase to 160°C at 4°C/min, maintained at 160°C for 3 min, and a final heating to 300°C for 3 min. Mass spectrometry was performed with an ionization potential of 70 electron volts and a scan range of m/z from 50 to 300. Compound identification was based on comparisons with mass spectra in the Wiley and National Institute of Standards and Technology (NIST) libraries or on direct comparison of mass spectra and retention times with available standards. For quantification, all samples were analyzed on a Hewlet-Packard 6890 gas chromatograph and a flame ionization detector (temperature 250°C). The separation procedure was identical to the method for GC–MS analysis. Terpenes were quantified by using nonylacetate and corrected with response factors according to Scanlon and Willis (1985). Concentrations and levels of terpenes per subepidermal gland were calculated for single leaves and for total foliage. Eight individual monoterpenes and sesquiterpenes were analyzed, which represent more than 90% of the total terpenes in G. hirsutum.
Analysis of Terpenoid Aldehydes True leaves were separately analyzed for their content of terpenoid aldehydes according to Stipanovic et al. (1988). Depending on available material, 30–100 mg of ground frozen material were extracted ×3 with 3 ml of ethyl acetate/n-hexane (1:3) and 50 μl of 10% HCL. Extracts were shaken at 5°C for 3 h. Afterwards, extracts were removed from the leaf material, evaporated with nitrogen, and redissolved in 40% H2O and 60% solvent B (see below) from the following high-performance liquid chromatography (HPLC) procedure. Reversed phase HPLC analysis was performed on a Chromolith LC-18 column (Merck; 5 μm; 100 × 4 mm) with single wavelength detection at 272 nm. The column was eluted with H2O + 0.05% trifluoracetic acid (solvent A) and EtOH/MeOH/MeCN/EtOAc/iPrOH, 26.6:7.4:40.5:6.1:19.4 (solvent B), with the following gradient: 50% B (0 min)–50% B (5 min)–61% B (5.1 min)–61% B (20 min). Flow rate was held constant at 2 ml min−1, and temperature was kept at 25°C. Five compounds, the hemigossypolone (C15) and the heliocides 1–4 (C25), were identified by their retention times according to the literature (Stipanovic et al. 1988) and additionally by liquid chromatography–mass spectrometry (LC–MS) measurements. Quantification was done by using standard curves of purified compounds. Concentrations and levels per subepidermal gland were calculated for single leaves and for the total foliage. Hemigossypolone and the four heliocides represent approximately 90% of the total terpenoid aldehyde content. As a sesquiterpene aldehyde, hemigossypolone was included in our analysis with other terpenoid aldehydes rather than with the sesquiterpenes. In comparison to other cotton varieties (Bezemer et al. 2004), the terpenoid aldehyde gossypol (C30) was found only in traces by LC–MS and was not quantified.
Experiment 2
Eighteen 2-wk-old plants at the growth stage of two true leaves were chosen and prepared for treatments in a way comparable to the first experiment. Plants were divided into three groups. Plants of one group were damaged mechanically nine times on leaves 1 and 2 during six consecutive days by using a mechanical fabric pattern wheel (procedure see experiment 1). For a second group, two caterpillars of Spodoptera littoralis were caged on each plant on one cotyledon during day 1, on leaf 1 during day 3, and on leaf 2 during day 5 until larvae consumed at least one third of the available leaf material. A control group was not treated at all. One week after the start of treatments, leaf 4 from each plant was harvested. Leaf areas and gland number per leaf were determined according to experiment 1. Afterwards, leaves were frozen in liquid nitrogen and stored at −20°C until further analyses of mono- and sesquiterpenes. Levels of terpene classes were calculated based on the levels of eight individual compounds that represent more than 90% of the accumulated monoterpenes and sesquiterpenes in G. hirsutum.
Statistics—Experiment 1
Total Foliage The effects of mechanical damage, herbivory, and JA application on foliage area, levels of total monoterpenes, sesquiterpenes, and terpenoid aldehydes, as well as on levels of individual major terpenoids, were analyzed with analyses of variance. Data were tested for normality and equal variances. For normally distributed data, one-way analyses of variance (ANOVAs) were used. Differences between treatments and controls were tested for significance by using Dunn’s post hoc tests. Data not normally distributed were analyzed by using Kruskall–Wallis one-way ANOVAs on ranks and Dunn’s post hoc tests. Analyses were performed with Sigma Stat 2.03. The effect of foliage area and treatment on the total number of glands was tested by analysis of codeviance. Generalized linear models (GLM) with the log link as link function were performed. To deal with overdispersion, a quasi-Poisson distribution was used in the models instead of Poisson distribution. The models were simplified by removing non-significant terms and by factor-level reduction (Crawley 2002). These analyses were performed in R, version 2.4.1. All data are presented as mean ± SE except when it is mentioned otherwise.
Single Leaves The effects of leaf position, treatment, and their interactions on levels of terpenoids per gland were tested by using nested two-way ANOVA. For this purpose, terpenoid data were root- or log-transformed to normalize them. For single leaf positions, the effects of the treatments on the leaf area were tested with one-way ANOVAs. The effect of leaf area and treatment on the total number of glands was tested by analysis of codeviance (GLM see above). To analyze the effect of leaf area and treatment on gland density, levels of terpenoids per gland and terpenoid concentrations per milligram fresh leaf material ANCOVAs were used. These analyses were performed in R, version 2.4.1. Changes in per gland concentrations of individual terpenoids at specific leaf positions after treatments were tested with Sigma Stat 2.03. For normally distributed data, one-way ANOVAs were used. Differences between treatments and controls were tested for significance by using Dunn’s post hoc tests. Data not normally distributed were analyzed using Kruskall–Wallis one-way ANOVAs on ranks and Dunn’s post hoc tests.
Statistics—Experiment 2
Single Leaves The effect of the treatments on the areas of leaf 4 was tested with one-way ANOVA. The effect of leaf area and treatment on the total number of glands was tested by analysis of codeviance (GLM see above). To analyze the influence of leaf area and treatments on the accumulation of mono- and sesquiterpenes an ANCOVA was achieved. All analyses were performed in R, version 2.4.1.
Results
Experiment 1
Terpenoid Accumulation in Total Cotton Foliage after Induction The accumulation of terpenoid classes among the four different treatments was first compared on the basis of total foliage (Fig. 4). For monoterpenes, sesquiterpenes, and terpenoid aldehydes, there was a gradual increase in the following order: control, mechanical damage, herbivory, and JA treatment (ANOVA on ranks: monoterpenes, H
= 9.420, P
= 0.024; sesquiterpenes, H
= 14.140, P
= 0.003; terpenoid aldehydes, H
= 19.760, P < 0.001). Levels of all three classes were elevated in herbivore-damaged (Dunn’s tests: monoterpenes, Q
= 2.572; sesquiterpenes, Q
= 2.490; aldehydes, Q = 2.939; all P < 0.05) and JA-treated plants (Dunn’s tests: monoterpenes, Q
= 2.735; sesquiterpenes, Q
= 3.674; aldehydes, Q
= 4.164; all P
< 0.05) in comparison to the controls.
Fig. 4Mean (±SE, N = 6) levels of terpenoids in the total foliage of 4-week-old cotton plants, which were either damaged at leaves 2 and 3 mechanically (Mech), fed upon by larvae of Spodoptera littoralis (Herb), treated with jasmonic acid (JA), or left as untreated controls (Ctrl) 7 days previously. Asterisks indicate significant differences between treatment and control plants based on Kruskall–Wallis one-way ANOVAs on ranks and Dunn’s post hoc tests. *P < 0.05
All individual compounds showed the same pattern that was seen for total terpenoid accumulation: a gradual increase from control to mechanical damage, herbivory, and JA treatment. In 11 out of 13 compounds, this increase was significant (Table 1) for herbivore and JA-treated plants compared to the control plants, while mechanically damaged plants showed tendencies for increased accumulation. Among monoterpenes, considerable differences in accumulation after treatment were shown. (E)-β-ocimene increased up to eightfold after both herbivory and JA treatment in comparison to controls, whereas myrcene levels slightly but significantly increased after herbivory and JA treatment, and the amounts of α- and β-pinene did not show significant differences between treatments and control. Little variation in the induction pattern occurred when comparing individual sesquiterpenes. Levels of all major sesquiterpenes, including β-caryophyllene, α-humulene, γ-bisabolene, and β-bisabolol doubled after herbivory and were 2.0–2.6-fold higher after JA treatment in comparison to control plants. Among terpenoid aldehydes, considerable differences appeared. Whereas the amounts of hemigossypolone were doubled, levels of heliocides, especially H1 and H4, increased much more after herbivory and JA treatment compared to the controls. Heliocide H4, for example, showed up to a ninefold increase after herbivory and up to a 15-fold increase after JA treatment in comparison to control plants. For H1, these increases were approximately five- and tenfold, respectively. Meanwhile, the highest levels of H2 and H3 were found in the JA-treated plants, reaching up to 3.3-fold that of control levels.
Table 1Mean levels (±SD, N = 6) of terpenoid products in total foliage of 1-month-old G. hirsutum plants that were either mechanically damaged with a fabric pattern wheel, damaged by larvae of Spodoptera littoralis (herbivory), treated with jasmonic acid (JA), or left as untreated controlsCompoundANOVA (df = 3)Levels of terpenoids (mg)F/HP ValueControlMechanical damageHerbivoryJAMonoterpenesα-Pinene7.2130.0651.11 ± 0.081.26 ± 0.111.31 ± 0.251.45 ± 0.44β-Pinene7.6470.0540.20 ± 0.020.23 ± 0.020.25 ± 0.050.26 ± 0.08Myrcene10.1670.0170.49 ± 0.060.68 ± 0.040.78** ± 0.150.80** ± 0.33(E)-β-Ocimene17.487<0.0010.06 ± 0.020.25 ± 0.060.47** ± 0.110.49** ± 0.26Sesquiterpenes(E)-β-Caryophyllene16.340<0.0010.50 ± 0.070.76 ± 0.120.95** ± 0.211.32** ± 0.50α-Humulene9.509<0.0010.14 ± 0.020.22 ± 0.030.27 * ± 0.060.36 * ± 0.13γ-Bisabolene18.007<0.0010.23 ± 0.040.34 ± 0.060.45** ± 0.100.60** ± 0.19β-Bisabolol14.3400.0020.46 ± 0.050.59 ± 0.090.82** ± 0.180.91** ± 0.26Terpenoid aldehydesHemigossypolone15.2870.0021.59 ± 0.302.16 ± 0.443.14** ± 0.403.10** ± 0.83Heliocide 120.487<0.0010.59 ± 0.141.65 ± 0.373.35** ± 1.115.95** ± 1.93Heliocide 221.031<0.0010.97 ± 0.211.45 ± 0.421.83 * ± 0.373.06 * ± 0.74Heliocide 323.938<0.0010.37 ± 0.070.56 ± 0.150.70 * ± 0.151.23 * ± 0.30Heliocide 420.247<0.0010.17 ± 0.080.59 ± 0.251.47** ± 0.392.60** ± 0.78The effect of treatment on terpenoid levels were tested using one-way ANOVAs. For normally distributed data, one-way ANOVAs were performed (F values). For not normally distributed data, Kruskall–Wallis one-way ANOVAs on ranks were performed (H values).*P < 0.05, significant differences from the control according to Bonferoni post hoc tests**P < 0.05, significant differences from the control according to Dunn’s post hoc tests
Growth and Subepidermal Gland Production of Cotton Foliage ANOVA showed an effect of the treatments on the total leaf area (F = 10.44, P < 0.001). Whereas controls, mechanically and herbivore-damaged plants were highly comparable in size (mean, 496–513 cm2), JA-treated plants (mean = 306 cm2) showed a reduction in the total area of foliage down to 60% that of the controls (Bonferoni post hoc test. P < 0.05).
Because of significantly smaller leaves in the JA-treated plants, the effect of treatment on the production of subepidermal glands was tested by using a GLM with leaf area as a covariable. For all treatments, this covariable showed an impact on the number of glands (t = 8.113, P < 0.001; Fig. 5). In comparison to the controls, an increase in the number of glands was found for herbivore-damaged plants (t = 4.147, P < 0.001) and for plants that were treated with JA (t = 5.585, P < 0.001). Because there were no differences between mechanically damaged and non-treated plants, the data of both groups were combined in this analysis.
Fig. 5Effect of leaf area and treatment on the number of glands in the total foliage of 4-week-old cotton plants, which were either damaged at leaves 2 and 3 mechanically (Mech), fed upon by larvae of Spodoptera littoralis (Herb), treated with jasmonic acid (JA), or left as untreated controls (Ctrl) 7 days previously. Regressions were drawn based on transformed data from a generalized linear model (GLM) with a quasi-Poisson error distribution. Data for control and mechanically damaged plants were combined due to factor level reduction
Single Leaf Analysis To assess more precisely plant response to the treatments, analyses were carried out at the level of individual leaves. For all treatments, the distribution of leaf areas followed a consistent pattern within plants with the biggest leaves at intermediate positions (Fig. 6a). Whereas no differences in leaf area were evident among mechanically damaged, herbivore-damaged, and control plants, plants treated with JA exhibited significant smaller areas for leaves 3–6 in comparison to corresponding control leaves (ESM Tables 3 and 4). Because of the negative impact of JA treatment on the leaf area, the following analyses of the effect of treatments on gland and terpenoid production were performed by using leaf area as covariable (statistical results see ESM Tables 3 and 4).
Fig. 6Mean (±SE, N = 3–6) leaf area, number of glands, gland density, terpenoid level per gland, and terpenoid concentration of 4-week-old cotton plants, which were either damaged at leaves 2 and 3 mechanically (Mech), fed upon by larvae of Spodoptera littoralis (Herb), or treated with jasmonic acid (JA) 7 days previously. Areas of herbivore-damaged leaves 2 and 3 were extrapolated from remaining leaf material. For each leaf position, asterisks indicate significant differences from corresponding control leaves based on one-way ANOVAs (a), on analyses of codeviance with leaf area as covariable (b), and ANCOVAs with leaf area as covariable (c–e). *P < 0.05
Gland number per leaf revealed a similar distribution pattern among leaves for all treatments: The oldest leaf 1 contained the lowest gland number, which increased gradually toward leaf 5 and again decreased in the expanding leaves 6 and 7 (Fig. 6b). Young leaves (5–7) of herbivore-treated plants showed significantly elevated numbers of glands in comparison to corresponding leaves of control plants. Lower but still significant increases in gland numbers were found for leaves 2, 3, and 7 (youngest leaf) of plants that were treated with JA compared to control leaves. Mechanical damage showed nearly no effect on gland number.
Changes in leaf area and gland numbers after treatment should have an impact on gland densities. Independent of treatment, gland densities increased gradually from the oldest leaf 1 toward the youngest leaf 7 (Fig. 6c). This pattern was enhanced in herbivore-damaged and JA-treated plants because of significantly higher gland densities in young leaves (5, 6, 7) compared to those leaves from the control plants. Mechanical damage did not cause an increased gland density in any leaf.
To analyze how terpenoid production was altered by treatment, total terpenoid levels per gland were calculated. Treatments had a considerable effect on the pattern of terpenoids per gland among leaves (Fig. 6d). Control plants showed highest levels in the oldest leaf and lowest levels in the youngest one. In contrast, mechanically and herbivore-damaged plants exhibited highest values in younger leaves, having significantly elevated total terpenoid levels in comparison to those of control leaves. Plants that were treated with JA showed significantly elevated levels of total terpenoids per gland in young and intermediate leaves in comparison to the controls.
The increase in total terpenoid and gland production caused by the treatments as well as altered leaf areas had a considerable impact on the pattern of the total terpenoid concentrations in leaves. Total terpenoid concentrations showed a consistent distribution pattern among leaves for all treatments (Fig. 6e). There was a gradual increase starting from the oldest leaf 1 toward the youngest leaves 6 and 7. All treatments enhanced this pattern by causing significantly higher concentrations in intermediate and young leaves in comparison to the leaves of corresponding control plants. The lowest effect was found for mechanically damaged plants, whereas plants treated with JA reached the highest concentration with mean levels of approximately 16 μg terpenoids per milligram fresh weight.
The analyses of individual terpenoids revealed a significant impact of treatment, leaf position, and interactions between these two factors on levels of all compounds per gland (Table 2). The lowest effects of treatment were found for the monoterpenes α- and β-pinene, whereas the biggest changes appeared for (E)-β-ocimene and heliocides 1 and 4. Differences of single terpenoid levels per gland among leaves appeared to be biggest for (E)-β-ocimene and heliocides 4, but in case of the latter, this was due to its unusual absence in leaves 2 and 3 (Fig. 7). Significant interactions between treatment and leaf position revealed that changes in levels of total and single terpenoids of induced plants were not consistent in all leaves, which is illustrated in Fig. 7. As with total terpenoid levels per gland (Fig. 6d), mechanical damage and herbivory caused significantly elevated accumulation of individual compounds per gland in the youngest leaves in comparison to control plants. In contrast, JA treatment induced elevated levels in young but also in intermediate leaves compared to control leaves. JA treatment led to the highest increases of most compounds, especially for heliocides, in comparison to those in the corresponding leaves of control plants.
Fig. 7Mean (±SE, N = 3–6) concentrations of terpenoids per gland among true leaves of 4-week-old Gossypium hirsutum plants, which were either damaged at leaves 2 and 3 mechanically (Mech), fed upon by larvae of Spodoptera littoralis (Herb), treated with jasmonic acid (JA), or left as untreated controls (Ctrl) 7 days previously. Asterisks indicate significant differences (P < 0.05) from corresponding control leaves based on one-way ANOVA’s and Bonferoni post hoc tests. Pound signs indicate significant differences (P < 0.05) from corresponding control leaves based on Kruskall–Wallis one-way ANOVAs on ranks and Dunn’s post hoc testsTable 2Results of nested two-way ANOVA for the effects of treatment (mechanical damage, herbivory, jasmonic acid application), leaf position, and interactions on levels of accumulated terpenoids per gland in plants of G. hirsutumCompoundTreatment (df = 3)Leaf Number (df = 6)Treatment × Leaf Number (df = 18)FPFPFPTotal terpenoids37.81<0.00117.45<0.0016.78<0.001Monoterpenesα-Pinene6.70<0.00139.98<0.0014.02<0.001β-Pinene4.340.00642.48<0.0013.67<0.001Myrcene10.25<0.00151.60<0.0014.06<0.001(E)-β-Ocimene106.72<0.00179.24<0.0016.50<0.001Sesquiterpenes(E)-β-Caryophyllene24.13<0.00113.30<0.0016.59<0.001α-Humulene20.56<0.0019.66<0.0013.21<0.001γ-Bisabolene37.08<0.00111.33<0.0015.74<0.001β-Bisabolol23.15<0.00112.48<0.0014.99<0.001Terpenoid aldehydesHemigossypolone10.16<0.00115.48<0.0016.47<0.001Heliocide 164.63<0.00113.78<0.0018.88<0.001Heliocide 227.13<0.00117.65<0.0014.94<0.001Heliocide 330.60<0.00117.66<0.0014.83<0.001Heliocide 455.70<0.001104.05<0.0018.59<0.001
Experiment 2
Morphological and Chemical Changes in Young Leaves after Induction In addition to herbivore-treated plants and untreated controls, this supplementary experiment also included mechanically damaged plants that were injured more frequently and over a longer period than in experiment 1. ANOVA showed an effect of the treatments on areas of young leaves (leaf 4; F = 5.97, P = 0.012; Fig. 8a, ESM Table 5). Herbivory reduced the areas of these leaves in comparison to those of the corresponding leaves of the control plants (t = −3.445, P = 0.004). Therefore, the effect of mechanical damage and herbivory on the production of glands were tested by using a GLM with leaf area as a covariable. For both treatments, leaf area showed an impact on the number of glands (t = 2.256, P = 0.041). In comparison to the control plants, a significant increase in the number of glands was found for mechanically damaged plants (t = 2.599, P < 0.021) and an even more pronounced increase for herbivore-treated plants (t = 3.938, P = 0.002; Fig. 8b).
Fig. 8Mean (±SE, N = 6) leaf area, number of glands, and monoterpene- and sesquiterpene levels of leaf 4 from 3-week-old Gossypium hirsutum plants, which were either damaged mechanically at leaves 1 and 2 (Mech), fed upon by larvae of Spodoptera littoralis (Herb) on one cotyledon, leaves 1 and 2, or left as untreated controls (Ctrl). Asterisks indicate significant differences from corresponding control leaves based on one-way ANOVAs (a), on analyses of codeviance with leaf area as covariable (b), and ANCOVAs with leaf area as covariable (c, d). *P < 0.05; **P < 0.01; ***P < 0.001
The accumulation of mono- and sesquiterpenes in leaf 4 after treatment followed a comparable pattern: a gradual increase from control to mechanical damage and herbivory (ANOVA: monoterpenes, F = 8.13, P = 0.004; sesquiterpenes, F = 27.43, P < 0.001; Fig. 8c,d). Levels of monoterpenes were elevated after herbivory in comparison to the controls (t = 4.029, P = 0.001). Levels of sesquiterpenes were elevated after mechanical damage (t = 5.115, P < 0.001) and herbivory (t = 7.197, P < 0.001) in comparison to the controls.
Discussion
Cotton (G. hirsutum) accumulates a large variety of terpenes constitutively, including monoterpenes (C10), sesquiterpenes (C15), and terpenoid aldehydes (C15, C25, and C30). This study is the first to describe the accumulation of all three of these classes after actual or simulated herbivory. Increased levels of terpenoids were observed in the total foliage 1 week after mechanical damage, feeding by Spodoptera littoralis caterpillars, or treatment with JA, in comparison to the untreated controls. A closer look revealed that, after damage to older leaves (leaves 2 and 3 numbered from the base), the increase was restricted mainly to young leaves (leaves 5–7). Similarly, studies on cotton that investigate the effects of herbivory by larvae of S. exigua and the wireworm Agriotes lineatus also showed elevated concentrations of terpenoid aldehydes in young cotton leaves (McAuslane et al. 1997; McAuslane and Alborn 1998; Bezemer et al. 2004). Such an induction pattern is in agreement with ODT, which predicts that an increase in the accumulation of defense chemicals occurs preferentially in plant parts with the highest fitness value such as young tissues or reproductive organs (McKey 1979; Frischknecht et al. 1987; Ohnmeiss and Baldwin 2000).
All three classes of cotton terpenoids are stored in subepidermal pigment glands found in leaves and other organs. After treatment, G. hirsutum displayed two ways to achieve elevated terpenoid accumulation: (1) production of additional glands and (2) increased filling of existing glands (Fig. 6). We showed that production of additional glands was restricted to leaves that were still under development or newly formed after treatment. This trend also has been observed after attack by spider mites or larvae of S. exigua on cotton (McAuslane et al. 1997; Agrawal and Karban 2000). In other plants, such as birch and tomato, an elevated number of defense structures like glandular trichomes is also produced in young leaves after induction by herbivores (Boughton et al. 2005; Valkama et al. 2005). Besides the fact that, according to ODT, plants have been selected to increase their resistance to herbivores especially in young tissue, the formation of subepidermal pigment glands in cotton might be necessarily restricted to newly developing leaves because of developmental constraints.
In counting the number of cotton leaf glands under a stereomicroscope, we may have overestimated the production of new glands after treatment if herbivory triggered the filling of pre-existing glands that were unpigmented (H. T. Alborn, personal communication). However, by using several microscopic techniques, we did not find any evidence for unfilled glands in the leaves analyzed. Therefore, we assume that actual or simulated herbivory does indeed induce increased numbers of glands in cotton.
The increased filling of existing glands with terpenoids was shown in the youngest leaves of induced plants for all major terpenoid classes. However, considerable differences in the magnitude of this additional accumulation were observed among individual terpenoid compounds. Among terpenoid aldehydes, levels of heliocides H1 and H4, showed the highest increase after treatments. Other studies on cotton species also have demonstrated that these two heliocides increase more than H2, H3, and hemigossypolone (HGQ) after herbivory by Spodoptera species (McAuslane et al. 1997; McAuslane and Alborn 1998; Agrell et al. 2004; Bezemer et al. 2004). Here, we showed that levels of (E)-β-ocimene increased in a magnitude similar to H1 and H4, and that this compound increased most among monoterpenes and sesquiterpenes. Interestingly, (E)-β-ocimene is a direct precursor in the formation of H1 and H4, combining with hemigossypolone in a Diels–Alder-type reaction (Stipanovic et al. 1978a; Fig. 2). Therefore, in non-treated plants, the biosynthesis of this monoterpene could represent a limiting step in the production of the appropriate heliocides. This proposition is supported by the fact that, in cotyledons of G. hirsutum, which do not contain any heliocides, hemigossypolone is present (S. Opitz, unpublished), but (E)-β-ocimene is absent. A comparable regulation mechanism that involves myrcene could determine the levels of H2 and H3 (Stipanovic et al. 1977, 1978b).
Our study compared the terpenoid content of total cotton foliage after three different treatments and showed a gradual increase in terpenoid levels in the following order: mechanical damage with a fabric pattern wheel, herbivory by S. littoralis caterpillars, and application of JA. However, a single incidence of mechanical injury is hard to compare with continuous feeding damage by an insect. Studies on lima bean have demonstrated that both the intensity and frequency of mechanical damage can alter the reaction of a plant (Mithöfer et al. 2005). This is consistent with the results of experiment 2 where more frequent mechanical damage caused significantly higher numbers of glands (Fig. 8), a response not detectable after the less frequent mechanical damage in experiment 1 (Fig. 5 and 6). However, in both experiments, herbivory induced stronger reactions of plants than mechanical damage. Elicitors found in the regurgitate of herbivores are known to trigger the biosynthesis of defense metabolites (Alborn et al. 1997). If such elicitors play a role in cotton defense reactions, mechanical injury may never result in terpenoid accumulation comparable to that caused by herbivory.
The strongest increase in both terpenoid and gland production appeared in plants that were treated with JA, a ubiquitous plant hormone known to mediate defense responses to biotic and abiotic stresses (Browse 2005). A previous study with cotton showed that the application of the methylated derivative, methyl jasmonate, induced the synthesis and emission of volatile terpenes, a response also observed for herbivore-damaged plants (Rodriguez-Saona et al. 2001). Obviously, JA plays an important role in mediating damage-induced signaling in G. hirsutum. Given the effects of exogenous application, herbivory can be assumed to trigger elevated internal levels of JA as in other species (Baldwin et al. 1997; Creelman and Mullet 1997). For example, in tobacco, lima bean, or maize, internal JA levels were determined in ranges of single nanograms per gram fresh weight, showing 20- to 40-fold increases within the first hours after induction (Baldwin et al. 1997; Koch et al. 1999; Schmelz et al. 2003). However, internal JA levels of cotton still need to be investigated. If the irrigation of plants with 100 μM JA in our study led to a non-physiological high internal concentration of JA, this could explain why JA treatment induced terpenoid accumulation to a greater extent than herbivory. We observed additionally that JA inhibited leaf development, a response also seen in other plants (Sembdner and Parthier 1993).
Studies on the induction of chemical defense in plants after herbivory always raise questions about the specificity of such reactions to particular enemies. Certain plants have been shown to induce different spectra of defenses in response to different herbivores (Turlings et al. 1998; Traw and Dawson 2002; Delphia et al. 2007). However, in this study, we did not find substantial differences in the pattern of induction among treatments for the major terpenoids measured (see Table 1). The amounts of single compounds varied in magnitude, leading to changes in the terpenoid profile, but these profile changes tended to be comparable among treatments. The induction of elevated terpenoid accumulation in leaves of G. hirsutum may thus be a non-specific reaction of plants to damage. Similar non-specific reactions to damage are known for terpenes in other species (Banchio et al. 2005), as well as for alkaloids (Frischknecht et al. 1987; Baldwin et al. 1997), phenolics (Cipollini 1997), and glucosinolates (Bodnaryk 1992).
The fact that cotton plants react to damage with elevated levels of terpenoids accumulated in the subepidermal pigment glands of their foliage suggests that these compounds function in plant defense. Caterpillar species such as S. exigua or Heliothis virescens prefer feeding on glandless instead of glanded cultivars of G. hirsutum, thus supporting the argument that the gland terpenes play a key role in defense of cotton against herbivores (Montandon et al. 1986; McAuslane and Alborn 2000). Leaf material from plants of glanded cultivar lines that had been induced by herbivory showed increased deterrent or toxic effects on Spodoptera species compared to material from uninduced plants, a finding thought to be due to their elevated contents of terpenoid aldehydes (Alborn et al. 1996; McAuslane et al. 1997; Anderson et al. 2001). Indeed, terpenoid aldehydes such as gossypol, hemigossypolone, and the heliocides H1 and H2 exhibit strong toxicity to caterpillars of H. virescens and Pectinophora gossypiella after addition to artificial diet (Elliger et al., 1978). While the feeding deterrent effects of induced cotton foliage are usually attributed to elevated terpenoid aldehydes contents (Hedin et al. 1992; McAuslane et al. 1997; McAuslane and Alborn 2000), we showed that cotton plants also increase their levels of monoterpenes and sesquiterpenes. Thus, these compounds may also participate in defense against herbivores. A sesquiterpene, caryophyllene oxide has been demonstrated to synergize the negative effect of gossypol, the dominant terpenoid aldehyde in roots and seeds of cotton, on larval development of H. virescens (Gunasena et al. 1988). Additionally, caryophyllene retarded the growth and delayed the time of development of these larvae. Other studies have shown that single sesquiterpenes such as β-bisabolene impair the development of insect herbivores and deter them from feeding (Bowers et al. 1976; Gonzalez-Coloma et al. 1995; Zipfel 2007).
Beside their function against herbivores, terpenoids are also considered to play a defensive role against fungi or pathogens. Because plants are exposed especially to infestation at wound sites, the elevated terpenoid levels in damaged leaves might have a critical role in helping plants to cope with pathogens. For cotton, studies have shown that certain terpenoid aldehydes possess antifungal and antipathogen activities (Zhang et al. 1993; Abraham et al. 1999). A good example for the antibiotic activity of terpenoids in other species comes from conifers where the growth and germination of bark beetle associated pathogens is inhibited (Keeling and Bohlmann 2006). In addition, analyses of terpene-rich essential oils from a variety of plant species have shown antibacterial and antifungal activities, indicating the potential role of these substances in plant defense (Oyedeji and Afolayan 2005; Ozer et al. 2007).
In summary, our results demonstrate that elevated levels of terpenoids in cotton leaves after real and simulated herbivory represent a general wound response that is mediated by JA. The increase in terpenoids is due to the production of additional glands, in which terpenoids are stored, as well as the increased filling of existing glands. As all three classes of terpenoids (monoterpenes, sesquiterpenes, and terpenoid aldehydes) were elevated after damage, these substances may act synergistically in defense against herbivores or pathogens (Stipanovic et al. 1988).
Electronic supplementary material
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cotton
gossypium hirsutum
pigment glands
monoterpenes
sesquiterpenes
terpenoid aldehydes
spodoptera littoralis
mechanical damage
jasmonic acid
induction
constitutive plant defense


Secretion of Streptomyces mobaraensis pro-transglutaminase by coryneform bacteria

We previously reported on the secretion of Streptomyces mobaraensis transglutaminase by Corynebacterium glutamicum ATCC13869 (formerly classified as Brevibacterium lactofermentum). In the present work, we investigated whether any other coryneform bacteria showed higher productivity than C. glutamicum ATCC13869. We found that most coryneform species secreted pro-transglutaminase efficiently. Moreover, we confirmed that Corynebacterium ammoniagenes ATCC6872 produced about 2.5 g/l pro-transglutaminase over a 71-h period in a jar fermentor. Our findings suggest that some other coryneform bacteria, especially C. ammoniagenes ATCC6872, are potential hosts for industrial scale protein production.
Introduction
The secretion of heterologous proteins into culture medium is a potentially useful method of production (Choi and Lee 2004), although the amounts obtained through this approach are generally small. Thus, there remains a need to develop an efficient secretion system for industrial proteins. Streptomyces mobaraensis transglutaminase (TG) has been used in the food industry to modify proteins (Yokoyama  et al. 2004). It is utilized in binding meat and fish and in gelled food products such as jelly, yogurt, and cheese. Moreover, it has great potential for use in manufacturing the materials found in cosmetics, thermostable microcapsules, and carriers for immobilized enzymes (Yokoyama et al. 2004). However, so far, an efficient production system for TG has been lacking.
A large number of different coryneform bacteria have been isolated from soil, animals, and plants (Liebl 2005). Some are used in biotechnological production processes; for example, Corynebacterium glutamicum and Corynebacterium ammoniagenes are well-known industrial producers of amino acids and nucleotides, respectively (Liebl 2005). C. glutamicum is a Gram-positive, non-sporulating bacterium with about 53.8% guanine–cytosine (GC) DNA content and its genome has been sequenced (Ikeda and Nakagawa 2003; Kalinowski et al. 2003). This species is extensively used in the industrial production of amino acids, such as glutamate and lysine, which have been applied in human food, animal feed, and pharmaceutical products for several decades (Hermann 2003; Krämer 1994; Liebl 2005). However, there had been only few reports concerning heterologous protein secretion in C. glutamicum (Billman-Jacobe et al. 1995; Liebl et al. 1992; Salim et al. 1997). Recently, we demonstrated that TG could be efficiently secreted in an active form using a signal peptide derived from a cell-surface protein of coryneform bacteria in C. glutamicum ATCC13869 (Date et al. 2003, 2004; Kikuchi et al. 2003). We showed that C. glutamicum ATCC13869 could also secrete an active form of human epidermal growth factor, which consists of 53 amino acid residues, including six cysteine residues that form three disulfide bonds (Date et al. 2006). More recently, it has been shown that the twin-arginine translocation (Tat) pathway, which is a recently detected protein secretion pathway, is active in C. glutamicum ATCC13869 and that the Chryseobacterium proteolyticum pro-protein glutaminase, which is not secreted via the Sec pathway in C. glutamicum ATCC13869, is efficiently secreted by the Tat pathway (Kikuchi et al. 2006, 2007). These results demonstrate that protein production using C. glutamicum might be useful on an industrial scale. However, little is known about heterologous protein production by other species of coryneform bacteria.
In this report, we show that most other coryneform bacteria also secrete pro-TG efficiently and that C. ammoniagenes ATCC6872 is the best producer species, accumulating about 2.5 g/l pro-TG over a period of 71 h in a jar fermentor.
Materials and methods
Bacterial strains, plasmids, and culture medium
The bacterial strains used are shown in Table 1. The plasmids were as follows: pPSPTG1 (Kikuchi et al. 2003), which contains the cspB promoter derived from C. glutamicum ATCC13869; the CspA signal peptide sequence derived from a cell-surface protein of C. ammoniagenes ATCC6872; and the coding region of pro-TG, derived from S. mobaraensis, and pPKPTG1 (Kikuchi et al. 2003), in which the signal peptide for pro-TG secretion is replaced by the CspB signal peptide sequence from C. glutamicum ATCC13869. The coryneform bacteria were aerobically grown in CM2G medium (Kikuchi et al. 2003) at 30°C in a shaking test tube. Plasmids were introduced by electroporation with a Gene Pulser (Bio-Rad) using the standard protocol for Corynebacterium species. For pro-TG production in a test tube culture, we used MMTG medium containing 60 g glucose, 1 g MgSO4·7H2O, 30 g (NH4)2SO4, 1.5 g KH2PO4, 0.01 g FeSO4·7H2O, 0.01 g MnSO4·4H2O, 450 μg thiamine hydrochloride, 450 μg biotin, 0.15 g dl-methionine, and 50 g CaCO3 per l distilled water, adjusted to pH 7.5 (Kikuchi et al. 2003). Kanamycin (25 mg/l) was added to the culture medium when required.
Table 1Pro-TG accumulation by coryneform bacteria carrying pPSPTG1 in MMTG medium at 30°C for 72 hStrainPro-TG (mg/l)Corynebacterium ammoniagenes ATCC6872454Corynebacterium glutamicum ATCC13032147Brevibacterium taipei ATCC13744201Corynebacterium glutamicum (Micrococcus glutamicus) ATCC1376166Brevibacterium roseum ATCC13825269Corynebacterium glutamicum (Brevibacterium flavum) ATCC13826314Corynebacterium herculis ATCC1386854Corynebacterium glutamicum (Brevibacterium lactofermentum) ATCC13869118Corynebacterium acetoacidophilum ATCC13870369Corynebacterium glutamicum (Brevibacterium divaricatum) ATCC14020391Brevibacterium saccharolyticum ATCC14066238Brevibacterium immariophilium ATCC14068257Microbacterium ammoniaphilum ATCC15354423Corynebacterium glutamicum (Corynebacterium lilium) ATCC15990246Corynebacterium callunae ATCC15991759Brevibacterium thiogenitalis ATCC19240156
Jar fermentation
Fermentor growth was carried out in a 1-l jar fermentor (300 ml working capacity). Glycerol stock suspensions of bacteria were stored at −80°C in 20 v/v glycerol. Glycerol stock suspension (50 μl) was inoculated into 50 ml CM2G medium in a 500-ml Sakaguchi flask, and cultured at 27°C for 24 h. Then, 15 ml of the culture was inoculated into 300 ml MMTG-J medium containing 120 g glucose, 1 g MgSO4·7H2O, 30 g (NH4)2SO4, 1.5 g KH2PO4, 0.01 g FeSO4·7H2O, 0.01 g MnSO4·4H2O, 450 μg thiamine hydrochloride, 450 μg biotin, 0.15 g dl-methionine, and 0.2 g total nitrogen of soybean hydrolyzate per l distilled water, adjusted to pH 7.2. The jar fermentor operating conditions were as follows: agitation rate = 600 rpm, airflow rate = 1/2 vvm; temperature = 27°C. The culture was automatically maintained at pH 7.2 by the controlled addition of ammonia gas with airflow. The glucose concentrations were analyzed using an AS-210 glucose analyzer (Sakura Seiki, Tokyo, Japan), and the optical densities were measured at 625 nm with a DU640 photometer (Beckman Coulter).
Protein analysis
Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) in a 4–20% gradient polyacrylamide gel was carried out as described by Laemmli (1970), with Coomassie brilliant blue R-250 staining. The secreted pro-TG was measured by reverse-phase high-performance liquid chromatography, using purified native TG as a standard (Yokoyama et al. 2002). For processing the pro-TG to convert to an active form, the secreted pro-TG in a culture supernatant was incubated with purified SAM-P45 (Suzuki et al. 1997) at 30°C for 2 h, at a 100:1 ratio of pro-PG to SAM-P45. The purified SAM-P45 was a gift from Dr. S. Taguchi. The active-form TG was purified according to the method described previously (Yokoyama et al. 2002). TG activity was measured by the calorimetric hydroxamate procedure using N-carbobenzoxy-L-glutaminylglycine as described previously (Yokoyama et al. 2002). For determining the N-terminal amino acid sequence of the pro-TG, after separation by SDS-PAGE, proteins were transferred to polyvinylidene difluoride membrane by electroblotting. The membrane carrying the pro-TG was directly subjected to a gas phase protein sequencer (model PSQ; Shimadzu, Kyoto, Japan) equipped with an in-line amino acid analyzer (model RF-550; Shimadzu) as described previously (Kikuchi et al. 1997).
Results and discussion
Secretion of pro-TG by coryneform bacteria using C. ammoniagenes ATCC6872 CspA signal peptide
We transformed the bacteria listed in Table 1 with pPSPTG1, cultured each transformant in MMTG medium at 30°C for 72 h, and then subjected the supernatants to SDS-PAGE. As shown in Fig. 1, each transformant could stably secrete the pro-TG, and high levels of pro-TG with the anticipated molecular weight (42 kDa) were detected in the supernatants for all of the bacteria tested (Table 1). The pro-TG was most efficiently secreted by C. ammoniagenes ATCC6872, Corynebacterium acetoacidophilum ATCC13870, C. glutamicum ATCC14020 (formerly classified as Brevibacterium divaricatum), Microbacterium ammoniaphilum ATCC15354, and Corynebacterium callunae ATCC15991.
Fig. 1SDS-PAGE analysis of pro-TG secretion by coryneform bacteria carrying pPSPTG1. Each track contained a 10-μl aliquot of supernatant mixed with an equal volume of sample buffer. After electrophoresis, the gel was stained with Coomassie brilliant blue R-250. aLane 1, molecular weight markers; lanes 2 and 3, B. saccharolyticum ATCC14066; lanes 4 and 5, B. roseum ATCC13825; lanes 6 and 7, B. immariophilium ATCC14068; lanes 8 and 9, C. glutamicum ATCC13826; lanes 10 and 11, C. glutamicum ATCC15990; lanes 12 and 13, C. callunae ATCC15991; lanes 14 and 15, C. herculis ATCC13868; lanes 16 and 17, C. acetoacidophilum ATCC13870; lanes 18, C. ammoniagenes ATCC6872. bLanes 1 and 2, C. glutamicum ATCC13761; lane 3, molecular weight markers; lanes 4 and 5, C. glutamicum ATCC14020; lanes 6 and 7, M. ammoniaphilum ATCC15354; lanes 8 and 9, B. taipei ATCC13744; lanes 10 and 11, B. thiogenitalis ATCC19240; lanes 12 and 13, C. glutamicum ATCC13869; lanes 14 and 15, C. glutamicum ATCC13032; lane 16, C. ammoniagenes ATCC6872
We tested 16 strains of coryneform bacteria for the secretion of S. mobaraensis pro-TG. As shown in Fig. 1 and Table 1, all of the bacteria secreted pro-TG. We have previously shown that the C. glutamicum system has two advantages for heterologous protein secretion: a lack of proteolytic activity in C. glutamicum culture supernatants and a small amount of protein secretion by the bacteria themselves (Date et al. 2006; Kikuchi et al. 2003). As a result, the purification of secreted heterologous proteins is relatively easy. From the results shown in Fig. 1, it is clear that the other coryneform bacteria share these advantages for heterologous protein secretion: their culture supernatants contain little endogenous protein and no degradation of pro-TG is detected in the supernatants.
Effect of signal peptide sequence on pro-TG secretion
Next, to investigate whether the secretion efficiency depended on the signal peptide sequence, we examined the effect of using the CspA signal peptide from C. ammoniagenes ATCC6872 (Fig. 2a) or the CspB signal peptide from C. glutamicum ATCC13869 (Fig. 2a) for the pro-TG secretion. C. ammoniagenes ATCC6872, C. acetoacidophilum ATCC13870, C. glutamicum ATCC14020, M. ammoniaphilum ATCC15354, and C. callunae ATCC15991 could efficiently secrete the pro-TG using the CspA signal peptide from C. ammoniagenes ATCC6872 in this study. These strains and C. glutamicum ATCC13869 were transformed with pPKPTG1 harboring the CspB signal peptide from C. glutamicum ATCC13869. As shown in Fig. 2b, the substitution of the CspB signal peptide had little effect on pro-TG accumulation in C. acetoacidophilum ATCC13870, C. glutamicum ATCC14020, and M. ammoniaphilum ATCC15354. However, in C. callunae ATCC15991, the pro-TG accumulation (403 mg/l) was approximately halved (746 mg/l) while in C. ammoniagenes ATCC6872, it was about 2.8-fold higher.
Fig. 2Effect of signal peptide sequence on pro-TG secretion in coryneform bacteria. a Amino acid sequence alignment of C. ammoniagenes CspA signal peptide and C. glutamicum CspB signal peptide using Vector NTI software. Identical and similar residues are shown on black and gray backgrounds, respectively. b Accumulation of secreted pro-TG by coryneform bacteria carrying pPSPTG1 or pPKPTG1. The solid bars indicate pro-TG accumulation using the CspA signal peptide from C. ammoniagenes ATCC6872, and the open bars indicate pro-TG accumulation using the CspB signal peptide from C. glutamicum ATCC13869
The amount of pro-TG accumulation by C. ammoniagenes ATCC6872 using the CspB signal peptide from C. glutamicum ATCC13869 was about 2.8-fold higher than with its own CspA signal peptide (Fig. 2b). Interestingly, the difference between the effects of the two signal peptides was the opposite in C. glutamicum ATCC13869: the amount of pro-TG accumulation using the CspA signal peptide from C. ammoniagenes ATCC6872 was about 1.5-fold higher than with the CspB signal peptide from C. glutamicum ATCC13869 (Kikuchi et al. 2003). We found the same to be true for all of the heterologous proteins examined so far (data not shown). The reason for the differing effects of these signal peptides is unclear.
Cultivation of C. ammoniagenes ATCC6872 carrying pPKPTG1 in a jar fermentor
As C. ammoniagenes ATCC6872 carrying pPKPTG1 was the best producer strain in our study, we subjected it to a fermentation test in a jar fermentor under the conditions described in the “Materials and methods” section. The MMTG-J medium contained 120 g/l glucose; after the consumption of glucose for 71 h under these conditions, the optical density at 625 nm reached approximately 140, and the dry cell concentration was approximately 50 g/l. During the exponential growth phase, little pro-TG accumulated in the culture supernatant; however, the rate of accumulation increased linearly after entry into the stationary phase, and approximately 2.5 g/l pro-TG was produced (Fig. 3). The N-terminal amino acid of the secreted pro-TG was Asp, as in native pro-TG (Kikuchi et al. 2003), demonstrating that the CspB signal peptide had been correctly processed in C. ammoniagenes ATCC6872. We then incubated the culture supernatant with purified SAM-P45, which is a subtilisin-like protease from Streptomyces albogriseolus, for 2 h at a 100:1 ratio of pro-TG to SAM-P45 in order to process the pro-domain (Kikuchi et al. 2003). The pro-TG secreted by C. ammoniagenes ATCC6872 carrying pPKPTG1 was cleaved by purified SAM-P45 to the C-side of 41-Ser of the pro-domain and converted to an active form and the conversion yield of this enzymatic process was approximately 100%. The specific activity of the purified active-form TG, with additional Phe-Arg-Ala-Pro residues, was similar to that of the native TG (about 23 U/mg; Kikuchi et al. 2003).
Fig. 3Jar fermentation experiment employing C. ammoniagenes ATCC6872 carrying pPKPTG1. Growth (optical density at 625 nm), glucose concentration, and pro-TG accumulation are indicated by open circles, open squares, and closed triangles, respectively
The results of the fermentation experiments in the 1-l jar fermentors showed that the pro-TG secreted by C. ammoniagenes ATCC6872 was correctly folded. Thus, protein production using C. ammoniagenes ATCC6872 could be useful for industrial scale protein production.

transglutaminase
corynebacterium glutamicum
corynebacterium ammoniagenes
protein secretion