Background
==========

Congenital fibrosis of the extraocular muscles (CFEOM) and Duane syndrome (DS) are complex strabismus disorders that present with congenital restrictive ophthalmoplegia with or without ptosis. These disorders were traditionally believed to reflect primary structural extraocular muscle (EOM) anomalies and have been referred to as \'congenital fibrosis syndromes\' \[[@B1]\]. Neuropathology studies of DS \[[@B2],[@B3]\] and one form of CFEOM (CFEOM1) \[[@B4]\], and the identification of *ARIX* as the gene mutated in a second form of CFEOM (CFEOM2) \[[@B5]\], however, support our hypothesis that CFEOM results from maldevelopment of the oculomotor (nIII) and/or trochlear (nIV) nuclei and DS results from maldevelopment of the abducens (nVI) nucleus. The continued definition of these phenotypes and identification of the underlying disease genes will assist clinical diagnostics and lead to a better understanding of the unique developmental features of the oculomotor lower motor neuron unit.

Although several distinct CFEOM phenotypes have been defined \[[@B6]-[@B8]\], each likely resulting from maldevelopment of a unique combination of alpha motor neurons in nIII and/or nIV, most reports of CFEOM families describe a stereotypical clinical phenotype. The affected members of these pedigrees are born with bilateral ptosis and restrictive ophthalmoplegia. The primary vertical position of each eye is downward and cannot be elevated above the midline. On forced duction testing there is resistance to passive movement of the globe. Although the primary position of both eyes is infraducted, there is variability in the secondary position of each eye (i.e. exotropic, esotropic, or neutral), and the degree of residual horizontal movement within the lower quadrants (full to completely restricted). This CFEOM phenotype was first described in the medical literature in 1840 \[[@B9]\] and was recognized to occur as a familial trait in 1879 \[[@B10]\]. Subsequently, families segregating this phenotype have been published under myriad names \[[@B1],[@B4],[@B6],[@B11]-[@B21]\]. We now refer to individuals with this phenotype as having \"classic CFEOM\" and to families in which all affected members have this phenotype as \" CFEOM1 pedigrees\". We previously mapped a CFEOM1 locus, referred to as *FEOM1,* to a ≤ 3 cM region spanning the centromere of chromosome 12, flanked by *D12S1584 (AFM136xf6)* on the p-arm and *D12S1668 (AFMb320wd9)* on the q-arm \[[@B6],[@B15]\].

In addition to families with CFEOM1, we have identified several less common familial CFEOM phenotypes (CFEOM2 and CFEOM3). These phenotypes are classified as CFEOM based on the presence of affected members with congenital restrictive ophthalmoplegia affecting extraocular muscles in the nIII/nIV distribution. By definition, however, one or more affected family members do not have the classic CFEOM phenotype. In families with CFEOM2, the eyes of affected family members are fixed in an exotropic, or outward, position. Thus far, this phenotype segregates as an autosomal recessive trait and maps to the *FEOM2* locus on chromosome 11q13 \[[@B8]\], and affected individuals carry homozygous mutations in *ARIX*\[[@B5]\]. *ARIX* encodes a homeodomain transcription factor required for nIII and nIV development in mice and zebrafish \[[@B22],[@B23]\]. In families with CFEOM3, the CFEOM phenotype is variably expressed. Some affected members have classic CFEOM. Others, however, are unilaterally affected, the primary position of the eye is orthotropic rather than infraducted, and/or the eye can be raised into the upper quadrants. Thus far, this phenotype segregates as an autosomal dominant trait and maps to either *FEOM3* on 16qter \[[@B7]\] or to *FEOM1*\[[@B24]\].

In our attempt to understand phenotype-genotype correlations between specific CFEOM phenotypes and *FEOM* loci, we noted that the CFEOM1 phenotype in all pedigrees reported to date maps to the *FEOM1* locus. To determine if CFEOM1 is indeed genetically homogeneous, we identified all unpublished CFEOM1 pedigrees in our database, analyzed them for linkage to the *FEOM* loci, and found that most but not all were consistent with linkage to *FEOM1.* The two small pedigrees not linked to *FEOM1* were consistent with linkage to *FEOM3.* In addition, to further define the spectrum of human *ARIX* mutations, we identified all CFEOM1 families consistent with linkage to *FEOM2* or sporadic individuals with classic CFEOM and determined that none harbored mutations in the *ARIX* gene.

Results
=======

From our database, 33 pedigrees were of sufficient size and had sufficient clinical data and 14 sporadic individuals had sufficient clinical data to qualify for the study. Of these, 20 pedigrees met CFEOM1 and 5 sporadic individuals met classic CFEOM inclusion criteria. Although not an inclusion criterion for the study, the CFEOM1 phenotype in all 20 families was inherited as an autosomal dominant trait with full penetrance. The phenotype in 9 of the 20 pedigrees was previously demonstrated to map to the *FEOM1* locus \[[@B6],[@B15],[@B25],[@B26]\] (Table [1](#T1){ref-type="table"}). Therefore, the remaining 11 families were included in this study (Figs. [1](#F1){ref-type="fig"} &[2](#F2){ref-type="fig"}, Table [1](#T1){ref-type="table"}, and see additional files 1--3 \[[Additional File 1](#S1){ref-type="supplementary-material"}, [Additional File 2](#S2){ref-type="supplementary-material"}, [Additional File 3](#S3){ref-type="supplementary-material"}\].

![Haplotype analysis of pedigrees BJ, CZ, AG, AJ, AH, T, CT, BC, and E at the *FEOM1* locus. Black symbols denote those individuals who are clinically affected with classic CFEOM. Genotyping data and schematic segregating haplotype bars for chromosome 12cen markers are shown below the symbol for each study participant. Allele sizes here and in figure [2](#F2){ref-type="fig"} were assigned as linkage studies were performed are not equivalent when compared between families. Black bars denote the potential disease-associated region. Diagonally hatched or white bars highlight the inheritance of the non-disease-associated haplotypes. References to specific individuals within the text refer to the generation number (Roman numeral) and position within generation (Arabic numeral). In all 9 pedigrees each family\'s disease-associated haplotype is inherited by all CFEOM1 individuals and by no asymptomatic individuals.](1471-2156-3-3-1){#F1}

![Haplotype analysis for pedigrees K at the **(a)***FEOM1* and **(b)***FEOM3* loci and BT at the **(c)***FEOM1* and **(d)***FEOM3* loci. Symbols are defined in the legend to figure [1](#F1){ref-type="fig"}. In each family the CFEOM1 phenotype is co-inherited with *FEOM3* markers and not with *FEOM1* markers.](1471-2156-3-3-2){#F2}

::: {#T1 .table-wrap}
::: {.caption}
######

Summary of the genetic analysis of CFEOM1 pedigrees
:::

  **Pedigree**   **CFEOM Phenotype**   **Inheritance**   **Forced Ductions**   **Cytogenetic analysis**   ***FEOM1***       ***FEOM3***       ***FEOM2***         ***ARIX* mutations**   **Publication**
  -------------- --------------------- ----------------- --------------------- -------------------------- ----------------- ----------------- ------------------- ---------------------- ------------------------
  A              CFEOM1                AD                \+                    normal                     **LINKED**                                                                     refs. \[[@B6],[@B15]\]
  B              CFEOM1                AD                \+                    normal                     **LINKED**                                                                     refs. \[[@B6],[@B15]\]
  C              CFEOM1                AD                \+                    not done                   **LINKED**                                                                     ref. \[[@B15]\]
  H              CFEOM1                AD                \+                    normal                     **LINKED**                                                                     ref. \[[@B15]\]
  AA             CFEOM1                AD                \+                    normal                     **LINKED**                                                                     ref. \[[@B15]\]
  AC             CFEOM1                AD                \+                    normal                     **LINKED**                                                                     ref. \[[@B15]\]
  AD             CFEOM1                AD                \+                    normal                     **LINKED**                                                                     ref. \[[@B15]\]
  CD             CFEOM1                AD                \+                    not done                   **LINKED**                                                                     ref. \[[@B25]\]
  CB             CFEOM1                AD                not done              not done                   **c/w linkage**   nc/w linkage      NOT LINKED                                 ref. \[[@B26]\]
  BJ             CFEOM1                AD                \+                    normal                     **LINKED**                                                                     current
  CZ             CFEOM1                AD                \+                    normal                     **LINKED**                                                                     current
  AG             CFEOM1                AD                \+                    normal                     **c/w linkage**   NOT LINKED        NOT LINKED                                 current
  AJ             CFEOM1                AD                \+                    not done                   **c/w linkage**   nc/w linkage      nc/w linkage                               current
  AH             CFEOM1                AD                \+                    normal                     **c/w linkage**   nc/w linkage      nc/w linkage        current
  T              CFEOM1                AD                \+                    not done                   **c/w linkage**   nc/w linkage      nc/w linkage                               current
  CT             CFEOM1                AD                \+                    normal                     **c/w linkage**   nc/w linkage      nc/w linkage        current
  BC             CFEOM1                AD                \+                    not done                   **c/w linkage**   nc/w linkage      (c/w linkage)       none                   current
  E              CFEOM1                AD                \+                    normal                     **c/w linkage**   **c/w linkage**   NOT LINKED                                 current
  K              CFEOM1                AD                \+                    normal                     nc/w linkage      **c/w linkage**   **(c/w linkage)**   none                   current
  BT             CFEOM1                AD                \+                    normal                     NOT LINKED        **c/w linkage**   nc/w linkage                               current

AD = autosomal dominant; + = positive forced duction testing for restriction; c/w linkage = consistent with linkage; nc/w li nkage = not consistent with linkage.
:::

The 11 families are ethnically diverse, not consanguineous and, to the best of our knowledge, unrelated. Eight of the families reside in the US and are of mixed European ancestry (BJ, AG, AJ, AH, CT, E, K, BT), while families CZ, T, and BC are of Italian, Irish, and Japanese ancestry, respectively. Family history of CFEOM in several previous generations was documented in two pedigrees (AG, T), and a previous family history was recounted but could not be corroborated in five others (BJ, AJ, AH, E, K). In contrast, in pedigrees CZ, BC, CT and BT, neither the parents nor more distant relatives of the eldest affected family member were reportedly affected. Examination of individuals I--1 and I--2 of pedigrees BC and CT confirmed their unaffected status. Individuals I--1 and I--2 in pedigrees CZ and BT were deceased. These data suggest that the CFEOM1 mutation rate is not negligible. Cytogenetic analyses, when performed, did not reveal abnormalities (Table [1](#T1){ref-type="table"}).

Nine of the 11 CFEOM1 pedigrees contain too few participants to establish linkage to a specific locus. Haplotype analysis of these families using multiple markers that span the critical region of a given locus can, however, eliminate linkage to the locus, determine genetic heterogeneity, and guide future mutation analyses. If the phenotype results from a mutation at a given locus, haplotype analysis at that locus will be consistent with linkage. If the phenotype does not result from a mutation at a given locus, however, haplotype data from a small family may be consistent or inconsistent with linkage. Thus, haplotype data in a small family that is consistent with linkage can result either from a disease mutation at that locus or by chance. Haplotype data in a small family that is inconsistent with linkage strongly suggests that the family\'s phenotype is not linked to the locus.

Linkage to *FEOM1*
------------------

Genetic analyses of the two largest families (BJ, CZ) established linkage of their phenotype to the *FEOM1* locus (Fig. [1](#F1){ref-type="fig"}, Table [1](#T1){ref-type="table"} & see [Additional File 1](#S1){ref-type="supplementary-material"}). Maximum lod scores of 3.01 were obtained at a theta value of zero for the fully informative markers *D12S59* and *D12S1048* in family BJ, and the fully informative markers *D12S1648, D12S345,* and *D12S59* in family CZ.

Linkage to *FEOM1* was ruled out in family BT (see [Additional file 1](#S1){ref-type="supplementary-material"}). A lod score of -2 was obtained at a theta value of 0.04 for the fully informative markers *D12S1621, D12S59* and *D12S1668.* The *FEOM1* critical region is ≤ 3 cM, and a theta of 0.04 corresponds to a genetic distance of approximately 4 cM, thus eliminating linkage of this family\'s CFEOM disease gene to the entire *FEOM1* critical region. Exclusion of linkage to the *FEOM1* locus is further supported by haplotype analysis of this family (Fig. [2c](#F2){ref-type="fig"}). The affected sister and brother inherit different *FEOM1* haplotypes from their affected mother, and the brother\'s affected daughter inherits her unaffected paternal grandfather\'s *FEOM1* haplotype, thus proving non-association between the *FEOM1* haplotype and the disease phenotype.

The eight remaining CFEOM pedigrees were too small to produce statistically significant lod scores; however, seven of the eight families displayed haplotype and linkage data consistent with linkage to the *FEOM1* locus (Fig. [1](#F1){ref-type="fig"}, see [Additional File 1](#S1){ref-type="supplementary-material"}). Genetic and haplotype analysis of all nine families consistent with linkage to the *FEOM1* locus did not reveal any recombination events within the previously defined *FEOM1* critical region.

The smallest family, K, revealed haplotype and linkage data that was inconsistent with linkage to the *FEOM1* locus (Fig. [2a](#F2){ref-type="fig"}, see [Additional File 1](#S1){ref-type="supplementary-material"}). A lod score of -2 was obtained at a theta value of 0.002 for the only two informative markers, *D12S59* and *D12S1090.* These theta values eliminate linkage to only 0.8 cM of the \< 3 cM *FEOM1* critical region and, therefore, this locus cannot be formally ruled out. Nevertheless, the minimum number of recombination events in this family occurs only if the affected son and daughter inherit different *FEOM1* haplotypes from their affected mother, thus strongly suggesting that the disease gene in this family does not map to the chromosome 12 locus.

Genetic heterogeneity was tested taking into account the eleven new families. Admixture analysis of the two-point data with the HOMOG program showed evidence for linkage to *FEOM1* with heterogeneity for both markers tested (*D12S345* and *D12S1048*). Chi-squares of 34.601 and 40.377 were obtained for *D12S345* and *D12S1048* respectively which resulted in significant likelihood ratios of 3.26 × 10^7^ and 5.86 × 10^8^. Alpha (the proportion of linked families) was 0.90.

Linkage to *FEOM3*
------------------

All families except BJ and CZ were analyzed for linkage to the 5.6 cM *FEOM3* locus flanked by *D16S486* and 16qter. Of the seven small families consistent with linkage to *FEOM1,* only the largest (AG) can be definitively excluded from linkage to *FEOM3* (see [Additional File 2](#S2){ref-type="supplementary-material"}). Five of the remaining families (AJ, AH, T, BC, CT) showed haplotype data inconsistent with linkage to *FEOM3,* but the theta values obtained at lod scores of -2 were insufficient to rule out the entire *FEOM3* critical region.

The two families whose phenotype did not map to *FEOM1* (K, BT) had haplotype and linkage data consistent with linkage to *FEOM3* (Fig. [2b](#F2){ref-type="fig"} &[2d](#F2){ref-type="fig"}, see [Additional File 2](#S2){ref-type="supplementary-material"}). In addition, one of the small families consistent with linkage to the *FEOM1* locus (E) had haplotype and linkage data that was also consistent with linkage to the *FEOM3* locus (see [Additional File 2](#S2){ref-type="supplementary-material"}).

Linkage to *FEOM2*
------------------

All families whose phenotype was not linked to *FEOM1* or *FEOM3* were tested for linkage to *FEOM2.* Assuming autosomal dominant inheritance with complete penetrance, two families (AG, E) are not linked and five families are inconsistent with linkage (AJ, AH, T, CT, BT) to the *FEOM2* locus (see [Additional File 3](#S3){ref-type="supplementary-material"}). Family BC is consistent with linkage to both the *FEOM2* and *FEOM1* loci (maximum lod score 0.3 at both loci), and family K is consistent with linkage to both the *FEOM2* and *FEOM3* loci (maximum lod score 0.3 at both loci).

*ARIX* mutation analysis
------------------------

Genomic DNA samples from affected member of pedigree BC and K and 5 sporadic individuals with classic CFEOM were used as templates to sequence the three *ARIX* exons and flanking introns. No mutations were identified.

Discussion
==========

We have established clinical criteria for classic CFEOM and CFEOM1, and have identified 5 sporadic individuals with classic CFEOM and 20 pedigrees with CFEOM1. Of these 20 pedigrees, 18 are linked, or consistent with linkage, to the *FEOM1* locus. Two small CFEOM1 pedigrees are not consistent with linkage, however, establishing that CFEOM1 is genetically heterogeneous.

Eleven of the 20 pedigrees are large enough to establish linkage to a specific locus; we previously reported that the CFEOM1 phenotype in 9 of these pedigrees maps to the *FEOM1* locus \[[@B6],[@B15],[@B25],[@B26]\] and we now demonstrate that the remaining two also map to *FEOM1.* Our analysis of the remaining 9 CFEOM1 pedigrees demonstrates that 6 most likely result from mutations in the *FEOM1* gene. Five of these 6 are consistent with linkage to *FEOM1* and are either not linked or not consistent with linkage to *FEOM2* and *FEOM3.* One is consistent with linkage to *FEOM1* and not *FEOM3* and, although consistent with linkage to *FEOM2, ARIX* mutations were not identified. Therefore, although not proved, the CFEOM1 phenotype in these 6 families seems likely to result from mutations in the *FEOM1* gene. The phenotype of a seventh family, E, is consistent with linkage to both *FEOM1* and *FEOM3* and will be screened for mutations at both these loci.

In contrast to the 18 pedigrees whose CFEOM1 phenotype is consistent with linkage to *FEOM1,* pedigree BT is not linked to *FEOM1* and pedigree K is inconsistent with linkage to this locus. It is notable that haplotype analysis of both these small CFEOM1 families demonstrates co-inheritance with the *FEOM3* locus. In the reported family whose autosomal dominant CFEOM3 phenotype maps to the *FEOM3* locus, 9 of the 17 affected members had classic CFEOM. Our current data now suggests that, at least in small pedigrees, CFEOM1 can also map to the *FEOM3* locus. It will require the identification of additional large CFEOM1 families to determine if they too can map to this locus.

*ARIX,* which encodes a transcription factor critical to nIII and nIV development in mice and zebrafish \[[@B22],[@B23]\], was recently identified as the *FEOM2* gene mutated in affected members of CFEOM2 families \[[@B5]\]. It was unknown, however, if classic CFEOM may also result from mutations in this gene. We now find that we are unable to identify *ARIX* mutations underlying classic CFEOM in either sporadic cases or in individuals from CFEOM1 families.

This finding is consistent with our prediction that, compared to *ARIX,* the genes mutated in CFEOM1 may have a more restricted function in the development of nIII and that their expression may actually be regulated by *ARIX.* This prediction is based on the CFEOM1 phenotype and on the neuropathological study of an affected member of a CFEOM1 pedigree whose disease gene maps to the *FEOM1* locus. This study revealed absence of the superior division of the oculomotor nerve and the corresponding central caudal and medial nIII subnuclei, and marked abnormalities of the levator palpebrae superioris and superior rectus muscles \[[@B4]\]. These findings suggest that while *ARIX* is necessary for both nIII and nIV development, the CFEOM1 genes may be necessary for the development of only these two specific nIII subnuclei.

Conclusions
===========

The genetic analysis of the 11 CFEOM1 pedigrees in this report demonstrates that this disorder is genetically heterogeneous. While the phenotype of all large CFEOM1 pedigrees analyzed thus far map to the *FEOM1* locus, smaller CFEOM1 pedigrees may harbor mutations in the *FEOM3* gene. The CFEOM1 phenotype does not, however, appear to result from mutations in *ARIX.* The CFEOM1 families identified in this study contribute critical alleles toward the identification of the mutated *FEOM1* and *FEOM3* genes. Once identified, we anticipate that the study of the function of these genes will contribute to our understanding of midbrain motor neuron development.

Materials and Methods
=====================

Subjects
--------

We established inclusion criteria for this study as follows. First, we established criteria for a \"classic CFEOM individual\" as an individual with congenital nonprogressive bilateral ophthalmoplegia and ptosis, an infraducted primary position of each eye with the inability to raise either eye above the midline, and forced duction testing positive for restriction, if testing was performed. We then established criteria for a \"CFEOM1 pedigree\" as a family in which every affected member met the criteria for \"classic CFEOM\". Second, we reviewed all participants enrolled in our ongoing CFEOM study and determined which individuals and which pedigrees met these two criteria. For a pedigree to be considered, we required clinical examination records and/or photographs/videos of the primary positions of gaze for all affected study participants. Third, from these CFEOM1 pedigrees we identified those with a family structure sufficient for linkage analysis. Pedigrees were required to have affected study participants in three or more generations, or affected study participants in two generations with at least two participating offspring of affected individuals, or two or more affected study participants within one generation. In this way we did not assume a mode of inheritance. Lastly, sporadic individuals with classic CFEOM were screened for *ARIX* mutations. The study was approved by the Children\'s Hospital institutional review board, and all study participants signed informed consent forms. Our methods adhered to the Declaration of Helsinki for research involving human subjects.

Molecular studies
-----------------

Blood samples were obtained from all participating family members, and lymphocyte DNA was extracted using the Puregene kit (Gentra, Research Triangle Park, NC). Chromosome analyses of GTG banded metaphase cells at a 400 band level minimum resolution were performed on one or more affected family members of each family whenever possible to rule out cytogenetic abnormalities. All families were analyzed for linkage to the *FEOM1* locus. Families not linked to the *FEOM1* locus were also analyzed for linkage to the *FEOM2* and *FEOM3* loci. Linkage studies were conducted using three or more locus specific polymorphic DNA micro satellite markers for each family. The *FEOM1* markers included *D12S1648, D12S61, D12S1584, D12S1621, D12S345, D12S59, D12S2080, D12S1048, D12S1668,* and *D12S1090*\[[@B6],[@B15]\]. The *FEOM2* markers included *D11S1337, D11S4162, D11S4196, D11S1314,* and *D11S1369*\[[@B8]\]. The *FEOM3* markers included *D16S539, D16S3077, D16S498, D16S486, D16S476, D16S3063, D16S689, D16S2621, D16S303,* and *D16S3407*\[[@B7]\]. The primer sequences for these polymorphisms are available from the Genome Database <>. Unlabeled primers were purchased from Genosys Biotechnologies, Inc. <>. Fluorescently labeled primers were purchased from Research Genetics, Inc. <>. Radioactive products were made by 30 cycles of PCR amplification of 10-μl reaction volumes containing 10--30 ng of genomic DNA, 40 ng of each primer, 200 μM each of dATP, dTTP, dGTP, and dCTP, 1 μCi α-^32^P-dCTP (3,000 Ci mmol^-1^) and 0.5 U Taq polymerase (Perkin Elmer). The radioactively labeled PCR products were separated on 6% denaturing polyacrylamide sequencing gels, and the alleles were visualized by autoradiography \[[@B6]\]. For the fluorescently labeled products, α-^32^P-dCTP was omitted, fluorescent primers were used, and the products were analyzed in an ABI PrismTM 377 DNA Sequencer (Perkin Elmer) following the manufacturer\'s specifications.

Lod score calculations
----------------------

An individual was scored as affected based on clinical examination records and/or photographs of primary position of gaze. Lod scores were calculated using the Fastlink version 3.0 package of programs \[[@B27]\], assuming autosomal dominant inheritance with complete penetrance, and a disease incidence of 1 in 1,000,000 births, as described previously \[[@B7],[@B8]\]. Because of the absence of specific allele frequencies for each of the ethnic groups represented in the study, we assumed ten marker alleles of equal frequency. To assess nonallelic heterogeneity linkage data from two *FEOM1* markers spanning the *FEOM1* critical region (*D12S345* and *D12S1048*) were analyzed using a HOMOG version 3.35 program \[[@B28]\].

ARIX mutation detection
-----------------------

*ARIX* was PCR amplified from genomic DNA using five primer sets and sequenced on an automated ABI 377 DNA seqeuncer (PE-Applied Biosystems) as described previously \[[@B5]\].

Supplementary Material
======================

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###### Additional file 1

Table 2: Two-point linkage data between CFEOM1 and the *FEOM1* locus
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###### Additional file 2

Table 3: Two-point linkage data between CFEOM1 and the *FEOM3* locus
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###### Additional file 3

Table 4: Two-point linkage data between CFEOM1 and the *FEOM2* locus
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Acknowledgements
================

This work was supported by National Eye Institute EY12498 and EY13583 and by the Children\'s Hospital Mental Retardation Research Center (P30 HD18655).


Background
==========

Identification of species with molecular probes is likely to revolutionize taxonomy, at least for taxa with morphological characters that are difficult to determine otherwise. Among these are the single cell eucaryotes, such as Ciliates and Flagellates, but also many other kinds of small organisms, such as Nematodes, Rotifers, Crustaceans, mites, Annelids or Insect larvae. These organisms constitute the meiofauna in water and soil, which is of profound importance in the ecological network. Efficient ways for monitoring species identity and abundance in the meiofauna should significantly help to understand ecological processes.

Molecular taxonomy with sequence specific oligo-nucleotide probes has been pioneered for bacteria \[[@B1],[@B2]\]. Probes that are specific to particular species or groups of related species can be used in fluorescent in situ hybridization assays to detect the species in complex mixtures or as symbionts of other organisms \[[@B3],[@B4]\]. Alternatively, the microarray technology is increasingly used for this purpose, allowing potentially the parallel screening of many different species. Most of the species-specific sequences that are used so far for this purpose are derived from ribosomal RNA sequences. However, any other sequence is also potentially suitable, as for example mitochondrial D-loop sequences in eucaryotes.

The species-specific probes are usually derived from an alignment of the respective sequences, where conserved and non-conserved regions are directly visible. A program has been developed for ribosomal sequences that helps to build the relevant database, and supports the selection of suitable specific sequences (ARB \[[@B5]\]). In this, a correct alignment is crucial for finding the optimal probes, but alignments are problematical in poorly conserved regions. These, on the other hand, have the highest potential to yield specific probes. Moreover, the current implementation of probe finding calculates only the number of mismatching position to discriminate between the probes, but does not take into account the position of the mismatches within the stretches, which could influence the hybridization behavior. We have therefore devised here a new algorithm that allows working with datasets that need not to be carefully aligned and that takes the position of mismatches along the recognition sequence into account.

The algorithm
-------------

The algorithm includes three parts. The first one aims to provide a function that calculates the relative stability of matching oligos in dependence of the number and position of mismatches. The second one provides a strategy for probe finding that scans all possible sequence combinations, but works time efficient. The third part deals with matches caused by single nucleotide outloops of a given sequence.

Stability function
------------------

Extensive studies exist for assessing the thermodynamic consequences of internal mismatches in short oligo-nucleotides (see fro example \[[@B6],[@B7]\]). These show that there are no simple rules and that the exact influence on the stability of a hybrid depends on the nature of the mismatch, as well as its flanking nucleotides. For example, mismatches including a G (i.e. G-G, G-T and G-A) tend to be less destabilizing than the other types of mismatches \[[@B7]\], although this can not directly be predicted from steric considerations. Comparable systematic studies on the relative influence of the position of the mismatch within the oligonucleotide do not exist yet, although it is clear that the influence is lower at the ends than in more central positions \[[@B7],[@B9]\]. Preliminary evidence with an oligo-dT stretch harboring A mismatches along the sequence suggests that the position dependence could be a continuous function \[[@B8]\]. We have therefore decided to use an *ad hoc* approach for the stability calculation that is mainly designed to discriminate against sequences with more central mismatch positions.

We model the relative stability of mismatched oligos as follows. The position of the mismatch can be considered to be a \"weak point\". The location of the \"weak point\" is expressed as a probability function that takes into account the differential contribution of central versus terminal positions. The probability that the \"weak point\" is at position x is defined by p~1~. Under the experimental conditions of melting, the presence of the \"weak point\" is true, meaning that \[sum(p1) for all x\] = 1.

We assume a Gauss distribution as the respective probability function, with the maximum in the middle of the duplex and the integral value along the duplex length set to 1 (Equation 1).![](1471-2105-3-9-i1.gif)

Equation 1. \"Weak point\" location probability. L -- duplex length, σ -- distribution parameter, x -- duplex position.

Note that the function in Equation 1 refers to discrete positions within the sequence, while the Gauss distribution is continuous and the integration from -∞ to +∞ is set to yield 1. The parameter σ is therefore chosen such that the discrete sum approaches 1 at any intended precision. In the program discussed below the accuracy of the sum value is 0.999.

Although the preliminary experimental evidence \[[@B8]\] suggests that the destabilization function can be approximated with the Gauss distribution, the program implementation allows also to use a flat distribution, i.e. where a position-independent effect on the melting is assumed as an alternative, to compare the outputs of the two different assumptions.

For assessing the relative amount of destabilization caused by a certain mismatch, we assume that the mismatch disturbs the surrounding base pairs from (y-n) to (y+n) positions, n can be called a border parameter that will need to be experimentally verified in the future. Because n can currently only be guessed, it is set as a program variable with a default value of 5. n might also depend on the nature of the mismatch, i.e. some types of mismatches might influence the surrounding bases less than the others. We therefore implemented further program variables that allow to define a different n depending on the nature of the mismatch (i.e. it is possible to set a particular n value for each possible type of mismatch).

The overall relative stability of a given duplex is then expressed as a probability function. It is expressed as the sum of products of the individual position probabilities p~1~ (determined by the stability function) and p~2~ (determined by the border parameter). The value of p~2~ it the probability of \"melting\", conditioned that the \"weak point\" is disturbed. (Equation 2).![](1471-2105-3-9-i2.gif)

Equation 2. L -- the length of the duplex, p~1~ -- the \"weak point\" location probability, p2 -- the \"melting\" probability due to the disturbance of the \"weak point\".

p~2~ is a conditional probability of \"melting\" with p~2~ = 1 if the \"weak point\" is disturbed (in the region y ± n) and p~2~ = 0 at non-affected positions. This allows transforming Equation 2 into Equation 3.![](1471-2105-3-9-i3.gif)

Equation 3. y -- the mismatch position, n -- the border parameter

p~1~ can then be substituted by the function in Equation 1, to yield Equation 4.![](1471-2105-3-9-i4.gif)

Equation 4: x -- the duplex position, y -- the mismatch position, n -- the border parameter, σ-distribution parameter

In the case of several mismatches, the summing is done along all the respective mismatch regions. If the mismatches occur next to each other, their disturbed regions simply overlap and the summing is performed across the respective region.

Probe finding
-------------

The probe finding strategy is devised in a way (i) to avoid the need for exact alignments, (ii) to check probe specificity along the whole available sequence and (iii) to optimize performance. The workflow is depicted in Figure [1](#F1){ref-type="fig"}. It starts with a database in which each organism is represented by a single continuous sequence, such as a defined region of the 18S or 28S ribosomal genes. From this it takes first the sequences of the In-group organism(s) for which specific probes should be found and cuts these into short pieces of the specified oligo-nucleotide length (set as a program variable), following an approach proposed by Bavykin et al \[[@B11]\]. This is accomplished by a sliding window scheme with 1-nucleotide shifts across the whole length of the sequence(s). Two separate lists are created in this way. The first list is simply a straight list of all possible fragments from all In-group organisms. The second one consists of an array of lists for each of the In-group organisms (the two lists are identical if only one In-group organism is chosen). All duplicate oligos from the first list are then removed and each of the remaining oligos is checked whether it matches with each of the In-group organisms in the second list. A match is positive, when the relative melting probability is within the range of 0--25%, employing the function of Equation 4. Thus, this first calculation simply ensures that all candidate probes match with all In-group organisms. This calculation would be largely dispensable, if only a single In-group organism is chosen.

![Scheme of the probe finding algorithm. Details are explained in the text.](1471-2105-3-9-1){#F1}

The next step is to subtract all oligos that match in any of the Out-group organisms. To avoid the comparison of all candidate oligos against all Out-group sequences, we identify first a group of sequences that is closely related to the In-group. For this one requires a rough alignment of all sequences, to calculate percentage similarity between them. Note that this serves only to identify a subgroup of sequences for speeding up the calculations, i.e. mistakes in the alignment are of no concern. The similarity calculator in the program extracts this related group of sequences by a simple percentage identity calculation across the given alignment. All sequences that are at least 90% similar to the In-group are used as Related-group. This percentage can be set as a program variable and should be set such that the Related-group does not become more than 5--10% of all sequences.

The sequences of the Related-group are again converted into a fragment list as above, duplicates are removed and all candidate oligos are matched with this list. Now only those oligos are retained, which have a melting probability of at least 75% (the exact percentage values are program variables). The majority of oligos is removed in this step. The remaining candidate oligos are then matched against the remaining sequences in the Out-group with the same cut-off criterion.

This stepwise selection scheme allows to significantly speed up the calculations even for very large datasets, but still ensures that all oligo-nucleotides of the desired length were directly or indirectly matched against all possible other oligos in the database.

Single nucleotide loops
-----------------------

Structure analysis with experimental oligo-nucleotides has shown that in a pair of hybridized oligos, one nucleotide can loop out, without interfering much with the stability of the hybridized pair \[[@B12]\]. This implies that one base of one strand of a duplex can loop out from the duplex and the rest of the strand can shift one position. This is depicted in Figure [2](#F2){ref-type="fig"}. A standard linear scanning algorithm would recognize the situation at the left as one with 11 mismatches, i.e. would suggest it as a specific probe. However, if the single nucleotide loop is taken into account, the match would be perfect and the probe would have to be considered as unspecific. Our scanning algorithm takes this problem into account by re-checking all candidate probes after the completion of the filtering steps. It does this by sequentially removing one nucleotide from the candidate probe and shifting the remainder by one position. The melting probability of the new oligo is then calculated and checked. The removed nucleotide is then reinserted and the cycle is repeated for the next position. The same procedure is done for the target sequence, so that outloops are considered to be possible on both strands of the duplex. Note that outloops of two nucleotides are considered to destabilize the helix too much to warrant a separate analogous calculation.

![Scheme of the single-nucleotide outloop problem; asterisks represent mismatches, columns represent matches.](1471-2105-3-9-2){#F2}

Parallel computation
--------------------

A parallel program version allows probe finding to be done in parallel on several processors. Essentially the same algorithm is used in the parallel version of the program, whereby the parallelism is introduced in the matching steps. Each process takes its own part of the database and performs the matching as well as the stability calculations. The results are then gathered by the root process and superimposed.

Program implementation
----------------------

The algorithm is implemented in a program called PROBE. The program consists of three modules that can be used independently. The first module finds the probes based on the given task (specificity group, length of probes, source database).

The second one is the analytic module, which can be used if it is impossible to design a probe for a given organism group. This module depicts the situation with the given In-group and enables to find the closest group for which the task can be accomplished. The use of the analytic mode comes into play when PROBE fails to identify a set of probes for the given organism group. Such a failure can have two reasons -- either there is no probe, which identifies all organisms in the specificity group, or there is another organism outside the specificity group, which is also identified by all candidate probes suitable for the specificity group.

For the first case, the specificity group must be broken down into several subgroups and the probes must be identified for these subgroups separately. For the second case, the organism that is very similar to the specificity group should be added to the specificity group and this may then have to be broken down into smaller subgroups.

The analytic module creates a table with the organisms of the specificity group as well as the most related organisms. This table depicts then the matching or non-matching patterns for each of the possible probes, allowing a simple visual inspection of the best specificity groups. The output can be viewed and modified with spreadsheet programs such as Excel.

The third module provides a report for the identified probe, including the mismatches in the duplexes within the specificity group, the best match out of the group and some other information.

The program is written in standard C++ in a platform independent manner. Therefore, the program can be easily compiled for Linux and Windows without any modifications. The program binary files for Linux and Windows are available from the <> as freeware accompanied with all its source files, and a manual that describes further details.

Results
=======

As an example of the performance of the program we have used the full SSU database (RDP, release 8.1) \[[@B13]\] containing approximately 16.000 sequences to find a specific oligo-nucleotide probe with a length on 20 nt for *Thermotoga maritima.* The search was done on a Pentium III (800 MHz, 512 MB RAM) PC and took about 1.5 hours without outlooping and 16 hours with outlooping, indicating that the most time intensive step is the outlooping subroutine. The parallel version running on a cluster with 24 nodes (with the slowest node being a Pentium II -- 400 MHz with 256 MB RAM) took 2 hours for the same full task.

Figure [3](#F3){ref-type="fig"} depicts the output from the check module, which allows comparing the oligos and their specificity that were found in this particular comparison. It shows that ARB suggests two oligos that are rejected by PROBE either because of mismatches occurring only at the ends, or under the outloop routine. Both programs find one oligo with acceptable high specificity.

![Comparison of specific oligos suggested by ARB and PROBE for *Thermotoga maritima,* in comparison to the whole SSU database. **A)** Oligo suggested by ARB, but found to have lower than 70% melting probability in two other species. This was therefore rejected by PROBE because of insufficient specificity. **B)** Oligo suggested by ARB, but found to have lower than 70% melting probability when outlooping is considered. This was therefore also rejected by PROBE because of insufficient specificity. **C)** Oligo suggested by both programs, whereby the best outgroup matches have a higher than 70% melting probability.](1471-2105-3-9-3){#F3}

Discussion
==========

The algorithm presented here does not take into account the effect of relative GC content and stacking interactions of neighboring bases on the melting temperature of the oligo-nucleotides. Accordingly, the oligo-nucleotides suggested by the program can differ significantly in melting temperature. However, as this can easily be adjusted after the selection is made, we have not included a subroutine that takes GC content into account during the primary search, because this would slow down the calculations. Furthermore, we expect that GC content differences may be of less importance for the applications envisioned here, because they can be largely compensated by the choice of experimental conditions, such as buffers that compensate stability differences \[[@B13]\].

A more general problem is our way of calculating the relative stability factor. This does currently not take the nucleotide composition into account either. The reason is that there are too few experimental data as yet, that would allow to unequivocally include this in the calculations. The current experimental data sets focus on the types of mismatches in particular contexts, but not systematically on position specific effects \[[@B7],[@B15]\]. Moreover, they deal with relatively short model oligos only (up to 12 nt). However, the probes used for species identification are longer and the different effects can currently not be accurately assessed from experimental data for such longer probes. In our equation, it is mainly the border parameter n that would be affected by base composition and nearest neighbor interactions and we have therefore left this as a variable that can be set according to experimental results. In principle, it seems possible that n differs for different sequence compositions, i.e. GC-rich stretches have a smaller n than AT-rich ones. Thus, if one chooses a low n, one would risk that GC-rich oligos are suggested as specific probes that still show cross hybridization. However, it seems that these can easily be eliminated after the selection is made. Still, if experimental data indicate that this is a major problem, the program could easily accommodate such new insights.

Finally, the stability function proposed in Equation 1 could possibly also have other shapes than Gaussian. Again this is a factor that needs further experiments. If it turns out that other functions are more appropriate, one can include this as additional options into the program. At the present we offer the extreme, namely a flat function, as an alternative option.

Conclusion
==========

We have designed a versatile algorithm for finding optimal species- and group-specific probes for molecular taxonomy that is sufficiently open to implement further experimental insights into the nature of the stability of mismatched oligo-nucleotides.

Acknowledgements
================

We are grateful to Dr. Lysov from the Engelgardt Institute of Molecular Biology, Russian Academy of Sciences for the supporting A.P. in the initial phase of the project. We thank Prof. Speckenmeyer at the Institute of Informatics, University of Cologne for providing access to their LINUX cluster and Jens Rühmkorf for his help with installing the parallel version. This project was supported by a grant from the Ministerium für Schule Wissenschaft und Forschung des Landes Nordrhein-Westfalen.


Background
==========

Nerve growth factor (NGF), a prototypical neurotrophic factor and a member of the neurotrophin family, promotes a wide range of responses in its target cells. These range from neuronal differentiation, maintenance of survival, and regulation of metabolic activities \[for review see \[[@B1]-[@B3]\]. Many of these actions include and require transcriptional regulation \[[@B4],[@B5]\]. However, the greater part of the changes in gene expression that underlie the NGF response remain to be elucidated. The PC12 line of rat pheochromocytoma cells \[[@B6]\] has proved to be a particularly favorable system for detecting NGF-responsive changes in gene expression \[[@B7]-[@B11]\]. These cells resemble sympathico-pheochromo-blasts and upon exposure to NGF cease proliferation and acquire, in a transcription dependent mechanism, many of the properties of post-mitotic sympathetic neurons including neurite outgrowth and electrical excitability. The robust nature of the response of PC12 cells to NGF coupled with their capacity to be examined both before and at various times after exposure to the factor has greatly facilitated study of the NGF mechanism of action, including gene regulation \[[@B12]\].

To obtain a comprehensive and quantitative over-view of NGF-promoted gene regulation, we \[[@B11]\] have used SAGE (Serial Analysis of Gene Expression). In this technique \[[@B13]-[@B15]\] cellular transcripts are converted to SAGE \"tags\" which are sequenced, quantified and, in many cases, matched with known genes. By comparing SAGE profiles for cells in different states (as for example before and after exposure to NGF), it is thus possible to obtain a comprehensive view of gene expression and regulation. Moreover, if a sufficient number of SAGE tags are analyzed, changes in expression levels of individual transcripts can be associated with a high level of statistical significance \[[@B13]\].

Initial analysis of approximately 157,000 SAGE tags from PC12 cells cultured without or with NGF for 9 days revealed nearly 800 transcripts (of a total of at least 21,000) that are regulated by ± 6-fold or greater in response to NGF \[[@B11]\]. Of these, approximately 150 were assignable to named genes of known functions that regulate cellular behaviors ranging from actin and microtubule cytoskeleton assembly/disassembly, gene transcription, RNA processing, neurotransmission, and energetics. A variety of criteria supported the reliability of the quantitative findings revealed by our SAGE analysis \[[@B11]\].

In the present study, we have extended our SAGE profiling of naïve and long-term NGF-treated PC12 cells to over 163,000 tags representing over 22,000 unique transcripts. Analysis of these reveals the presence of transcripts encoding 74 different ribosomal proteins (RPs). Surprisingly, we find that long-term NGF exposure leads to statistically significant changes in relative abundance of at least half of these transcripts by factors of up to nearly 5-fold. In addition, a time course for one of the RP transcripts (encoding RP L9) reveals that its relative abundance begins to change within 1 hr and is maximally regulated by 8 hr of NGF exposure.

Results
=======

SAGE libraries
--------------

SAGE libraries were generated and analyzed as previously described \[[@B11]\] from matched sets of PC12 cells before and after 9 days of exposure to NGF. The present analysis is based on 76,280 15-mer tags from NGF-untreated PC12 cells and 87,004 tags from NGF-treated cells (after exclusion of duplicate ditags, mitochondrial transcripts and repetitive elements). Consideration of tags observed twice or more between the two libraries indicated the presence of approximately 22,000 unique transcripts. Of these, approximately 10% were regulated by more than 3-fold in response to NGF and approximately 4% by 6-fold or more.

Detection and quantification of transcripts encoding ribosomal proteins
-----------------------------------------------------------------------

Transcripts represented by SAGE tags were identified by direct matches of tags with the appropriate sequences of known rat genes (present in GenBank) or through matches of tags with appropriate sequences of rat ESTs (present in NCBI Unigene) that were in turn found to overlap with known rat genes. Positive identification required that the transcript or EST have a poly adenylation signal and poly A tail, and that the tag followed the most 3\' CATG of the transcript. In this way 74 tags were unambiguously assignable to known rat transcripts encoding proteins described as ribosomal components. Available sequences encoding rat ribosomal proteins L15 and S8 do not contain a CATG sequence and hence SAGE tags for these could not be identified. In addition, rat sequence data for transcripts encoding RPs L2, L25, L33 and S1 are not presently available in GenBank or Unigene.

Table [1](#T1){ref-type="table"} lists the tags in our libraries corresponding to transcripts for ribosomal proteins along with their relative abundances. Considering that the eukaryotic ribosome contains approximately 82 proteins, our analysis includes transcripts encoding a major proportion of the known ribosomal proteins.

::: {#T1 .table-wrap}
::: {.caption}
######

The effect of NGF (9 days treatment) on expression levels of transcripts for various ribosomal proteins in PC12 cell cultures
:::

  TAG           RP         ACC\#       \#TAGS-   \#TAGS+   FOLD CHANGE +NGF   *P* value
  ------------- ---------- ----------- --------- --------- ------------------ -----------
  GGACCGCTCAA   L3         X62166      44        27        ↓ 1.6              0.06
  TTGAAGCTGAA   L4/L1      X82180      49        39        ↓ 1.3              0.2
  CTGCTATCCGA   L5         X06148      40        28        ↓ 1.4              0.15
  TACCCTCACAA   L6         X87107      16        24        ↑ l.5              0.05
  AGATCTATACA   L7         Ml 7422     8         7         ↓ 1.1              0.5
  CACCACTGTTG   L7A        X15013      63        47        ↓ 1.3              0.2
  AATCCTGTGGA   L8         P25120      119       58        ↓ **2.1**          0.0002
  ATCAAGGGTGT   L9         X51706      7         19        ↑ **2.7**          0.01
  TTCAATAATAA   L10        X87106      24        17        ↓ 1.4              0.2
  GGCAAGCCCCA   L10A       X93352      26        30        ↑ 1.2              0.2
  CGCTGGTTCCA   L11        X62146      18        23        ↑ .3               0.1
  ACATCATAGAT   L12        R7RT12      59        15        ↓ **3.9**          \< 0.0001
  GCCCGAGCCAA   L13        X78327      48        57        ↑ .2               0.1
  AGGTCGGGTGG   L13A       X68282      87        118       ↑ **1.3**          0.002
  AGGAGGCTACA   L14        X94242      31        51        ↑ **1.6**          0.003
  GCACGGGAATA   L17        X58389      8         26        ↑ **3.2**          0.0007
  GGTGTTGACAT   L18        M20156      47        12        ↓ **3.9**          \< 0.0001
  AAGGTGGAAGA   L18A       JC4231      56        33        ↓ 1.7              0.03
  GATCAGTCATT   L19        X82202      65        63        ↓ 1.0              0.3
  GCCTAATGTAT   L21        M27905      43        32        1.3                0.2
  TTTTGTATTAA   L22        X78444      5         4         1.3                0.7
  GTGATGGCCAC   L23        X58200      72        63        ↓ 1.1              0.4
  AAGGTCGAGCT   L24        X78443      95        28        ↓ **3.4**          \< 0.0001
  CCCAGTTTTCA   L26        X14671      31        19        ↓ 1.6              0.1
  CCCACAAGGTA   L27        X07424      16        30        ↑ 1.9              0.007
  ATCCGAAAAAA   L28        X52619      42        10        ↓ **4.2**          0.0001
  GCCAAGGGTCG   L29        X68283      97        40        ↓ **2.4**          \< 0.0001
  CCAGAACAGAC   L30        X52619      36        9         ↓ **4.0**          0.0002
  AAGGAGATGGG   L31        X04809      64        111       ↑ **1.7**          \< 0.0001
  CTGCCTAGCGG   L32        X06483      26        20        ↓ 1.3              0.2
  TGCGCCAAGTG   L34        X14401      27        30        ↑ 1.1              0.2
  AAGAGAAGCTG   L35        X51705      115       70        ↓ **1.6**          0.009
  GTTCGTGCCAA   L35A       X03475      33        16        ↓ 2.1              0.04
  CGGAAGGCGGC   L36        X68284      38        77        ↑ **2.0**          \< 0.0001
  GATTCCGTGAA   L37        X66369      58        79        ↑ **1.4**          0.01
  AAAACAGTGGC   L37A       X14069      55        90        ↑ **1.6**          0.0003
  TGACTATTAAA   L38        X57007      25        21        ↓ 1.2              0.5
  TCTTCTCACAA   L39        X82551      15        27        ↑ 1.8              0.03
  CAGATCTTCGT   L40        X82636      89        32        ↓ **2.8**          \< 0.0001
  AGAGCGAAGTG   L41        X82550      185       80        ↓ **2.3**          \< 0.0001
  CAAGGTGACAG   S2         U92700      52        47        ↓ 1.1              0.4
  CCTCAGCCAGT   S3         X51536      21        35        ↑ 1.7              0.03
  GTGAAGGCGGT   S3A        X75161      46        43        ↓ 1.1              0.4
  ATGAAATCAAA   S4         X14210      44        20        ↓ **2.2**          0.01
  CCTTTGAGATC   S5         X58465      72        34        ↓ **2.1**          0.004
  GCAGAGTGCGC   S6         NM_017160   63        27        ↓ **2.3**          0.0006
  TTCAGCTCGAG   S7         X53377      41        31        ↓ 1.3              0.3
  CCCGTGTGCTC   S9         X66370      54        32        ↓ 1.7              0.06
  CAGTCTCTCAA   S10        X13549      57        36        ↓ 1.6              0.02
  TCTGTGCACCT   S11        K03250      23        18        ↓ 1.3              0.4
  TATGTCAAGCT   S12        M18547      92        52        ↓ **1.8**          0.006
  GTGTGGCACAG   S13        X53378      65        65        1.0                0.2
  TTGGCTGCCCA   S14        X15040      99        50        ↓ **2.0**          0.0008
  GTGGGTGTGTA   S15        NM_017151   254       60        ↓ **4.2**          \< 0.0001
  AAGAGGCAAGA   S15A       X77953      35        48        ↑ 1.4              0.04
  TGGCCCAAATT   S16        X17665      155       48        ↓ **3.2**          \< 0.0001
  GGCCGCGTTCG   S17        K02933      18        39        ↑ **2.1**          0.003
  CAGAACCCACG   S18        X57529      101       26        ↓ **3.9**          \< 0.0001
  ACCAAGATCTA   S19        X51707      136       28        ↓ **4.8**          \< 0.0001
  CCTACCAAGAC   S20        X51537      8         16        ↑ 2.0              0.07
  GGTCTGGCTAG   S21        X79059      0         3         ↑ 3.0              na
  CCGTGGGTGAT   S23        X77398      52        68        ↑ 1.3              0.03
  GCCTTTATGAG   S24        X52445      310       87        ↓ **3.6**          \< 0.0001
  CCGCCCAAAGA   S25        X62482      90        43        ↓ **2.1**          0.0003
  GAAAAATAAAA   S26        X02414      35        16        ↓ 2.2              0.03
  CACAAACGGTA   S27-1      AF184893    99        46        ↓ **2.2**          0.0004
  GGTAGCCACTT   S27A       X81839      33        77        ↑ **2.3**          \< 0.0001
  GAATGACCTGC   S28        X59277      40        37        ↓ 1.1              0.4
  CTAGTCTTTGT   S29        X59051      125       123       ↓ 1.0              0.3
  GTTCTCTGGCT   S30        X62671      29        33        ↑ 1.2              0.3
  GGATTCGGTCT   P0         Z29530      40        55        ↑ 1.4              0.04
  TCCAATAAAGA   P1         R5RT12      96        48        ↓ **2.0**          0.0002
  GGATTTGGCCT   P2         X15098      83        84        ↑ 1.0              0.3
  GGAGGTTATGC   40 KD RP   D25224      83        34        ↓ **2.4**          \< 0.0001

Rat SAGE tags and the corresponding ribosomal proteins (and GenBank accession numbers) are given along with the number of times each tag was detected. For cells before and after NGF treatment, a total of 76,280 and 87,004 11 bp tags were analyzed, respectively. Tag numbers for non-treated cultures were normalized against those for NGF treated cultures. *P* values were calculated by Monte Carlo simulations using SAGE software. Fold changes in which *P* ≤ 0.01 are expressed in **bold.**
:::

Relative expression of transcripts for ribosomal proteins
---------------------------------------------------------

Among the information provided by analysis of SAGE data is the relative abundances of transcripts. The data given in Table [1](#T1){ref-type="table"} and Figure [1](#F1){ref-type="fig"} show the relative abundances of transcripts for PC12 cell ribosomal proteins with respect to one another as well as with respect to the total cell complement of transcripts. As noted above, our analysis has detected at least 22,000 unique transcripts in PC12 cells. The 74 ribosomal protein transcripts identified here thus account for no more than 0.33% of this total. By contrast, for NGF-untreated and -treated PC12 cells, tags corresponding to the 74 identified RP transcripts represent 5.2% and 3.5%, respectively, of total tags analyzed. As anticipated, this clearly places transcripts for ribosomal proteins as a whole in the high abundance category.

![Relative abundances of RP transcripts before and after long-term NGF treatment. **A.** Transcripts for RP L3--L41 and P0--P2. **B.** Transcripts for RP S2--S30 and 40 KD RP. Relative abundances were calculated on the basis of total numbers of tags evaluated and numbers of tags corresponding to each RP transcript.](1471-2202-3-3-1){#F1}

The most abundantly expressed ribosomal transcripts in NGF-untreated PC12 cells included those encoding ribosomal proteins S15, S24 and L41. These each accounted for approximately 0.2--0.35% of total cell transcripts. The high relative abundance of these ribosomal transcripts in NGF-untreated cells can be appreciated by the observation that of the 4 tags encountered more than 200 times in our analysis, 2 (S15 and S24) encoded ribosomal proteins; moreover, RP transcripts represented 6 of the 13 tags encountered from 100--200 times and 23 of the 36 tags encountered from 50--100 times.

The data in Table [1](#T1){ref-type="table"} and Figure [1](#F1){ref-type="fig"} show that NGF treatment resulted in an overall decrease in relative abundance of transcripts encoding RPs (from 5.2% to 3.5% of total). This may reflect in part our observation that NGF treatment increases the numbers of transcripts representing low-abundance genes \[[@B11]\]. Nevertheless, transcripts for ribosomal proteins remained among the most highly expressed in the NGF-treated cells. For instance, tags for RPs L13A, L31, L37A, S24 and S29 had relative abundances of 0.1% or more. Of the 56 most abundantly expressed tags in NGF-treated cells, 21 represent ribosomal transcripts.

NGF selectively regulates the expression of transcripts encoding ribosomal proteins
-----------------------------------------------------------------------------------

In addition to yielding an overall decrease in the abundance of RP transcripts relative to total cellular transcripts, long-term NGF treatment also promoted selective changes in relative expression of transcripts encoding individual RPs (Table [1](#T1){ref-type="table"}, Figs. [2](#F2){ref-type="fig"},[3](#F3){ref-type="fig"}). Monte-Carlo simulation analysis of the SAGE data indicated that nearly half (a total of 35) of the 74 RP transcripts underwent changes that were significant at the *P* ≤ 0.01 level (see Table [1](#T1){ref-type="table"}). Of these 35 transcripts, 2/3 were down-regulated in response to NGF.

![Distribution of changes in RP transcript expression caused by long-term exposure to NGF. Changes in expression are expressed as fold up- or down-regulation by NGF. Up-regulated transcripts are given as the ratio of normalized tag numbers +NGF/-NGF and are expressed as positive values. Transcripts that are down-regulated by NGF are given as the ratio of normalized tag numbers -NGF/+NGF and are expressed as negative values.](1471-2202-3-3-2){#F2}

![**Incremental changes in expression of RP transcripts evoked by long-term treatment with NGF.** NGF-promoted changes in expression of RP transcripts are given in incremental units. Up-regulated transcripts are given as \[(number of normalized tag numbers + NGF/number of normalized tag numbers - NGF) - 1\] and are thus expressed as positive values. Transcripts that are down-regulated by NGF are given as --\[(number of normalized tag numbers - NGF/number of normalized tag numbers + NGF) - 1\] and are thus expressed as negative values. For transcripts that do not change expression in response to NGF, the incremental change is zero. **A.** Transcripts for RP L3-L41 and P0-P2. **B.** Transcripts for RP S2-S30 and 40 KD RP.](1471-2202-3-3-3){#F3}

Figure [2](#F2){ref-type="fig"} shows the distribution of fold-changes in transcript levels in response to NGF. 47 of the 74 RP transcripts were either invariant or showed changes of 2-fold or less; 10 individual transcripts decreased by 3--5 fold (L12, L18, L24, L28, L30, S15, S16, S18, S19 and S24) and one was elevated by more than 3-fold (L17).

In a past study \[[@B11]\], the differences in NGF-promoted gene expression revealed by SAGE analysis of our libraries were found to be highly reliable based on 1) the absence of regulation shown by a number of anticipated \"housekeeping\" genes including β-actin, 2) the observation that the vast majority of transcripts were not responsive to NGF, 3) the detection of anticipated changes in expression of a number transcripts previously reported to be NGF responsive, and 4) the agreement in relative expression of 20 genes as revealed by SAGE and northern blot analyses. To further confirm the reliability of our SAGE findings, we used real time quantitative PCR to compare levels of 5 different RP transcripts in PC12 cells treated with or without NGF for 9--12 days. As shown in Figure [4](#F4){ref-type="fig"}, both techniques revealed similar changes in expression.

![Comparison of effects of long-term treatment with NGF on expression of RP transcripts as determined by SAGE and real-time PCR. Changes in expression are expressed as fold up- or down-regulation by NGF. Up-regulated transcripts are given as the ratio of normalized tag numbers +NGF/-NGF and are expressed as positive values. Transcripts that are down-regulated by NGF are given as the ratio of normalized tag numbers -NGF/+NGF and are expressed as negative values. Values for real-time PCR are given as means ± SE (n = 4-6) and were normalized against the levels of beta actin message.](1471-2202-3-3-4){#F4}

Rapid regulation of an RP transcript by NGF
-------------------------------------------

We next determined the time course with which NGF regulates an RP transcript. For this purpose, we studied RP L9 which undergoes a 2.7-fold elevation in response to long-term NGF treatment. Because the changes in expression are relatively small, we used real time PCR for this end. As shown in Figure [5A](#F5){ref-type="fig"}, the relative abundance of transcripts encoding RP L9 were significantly upregulated by 1 hr of NGF treatment (1.8 ± 0.2-fold, n = 14) whereas there was no significant change in transcripts encoding RP S29, which does not undergo long-term NGF regulation. A time course (Figure [5B](#F5){ref-type="fig"}) revealed that elevation of L9 transcripts reaches maximal levels within 8 hrs of NGF exposure.

![Rapid regulation of RP L9 transcripts by NGF. **A.** Elevation of RP L9 transcripts after 1 hr of NGF exposure. PC12 cell cultures were treated with or without NGF for 1 hr and used for preparation of RNA and cDNA. Relative levels of L9 and S29 transcripts were determined by real-time PCR with normalization against levels of transcripts for GAPDH. Data are given as ratios for values of \[NGF treated/untreated\] and are expressed as means ± SE (n = 14 for L9 and n = 6 for S29). **B.** Time course for NGF response of transcripts encoding RP L9. PC12 cultures were treated with NGF for the indicated times and used for preparation of RNA and cDNA. Relative levels of L9 transcripts were determined by real-time PCR with normalization against levels of transcripts for GAPDH. Values are given as means ± SE (n = 3).](1471-2202-3-3-5){#F5}

Discussion
==========

NGF regulates expression of RP transcripts
------------------------------------------

In the present work, we used SAGE profiling to identify and quantify the relative numbers of transcripts for 74 ribosomal proteins in rat PC12 cells before and after long-term exposure to NGF. This has permitted us to detect selective changes in transcripts encoding specific RPs.

A number of criteria support the reliability of our findings. The first regards the assignment of SAGE tags to specific RP transcripts. We required that all matching ESTs or transcripts have clearly definable poly A tails and poly adenylation signals. Due to the extensive data base for rat RP transcripts, it was possible to make all matches with rat sequences. In addition, we used 15 mer (CATG +11) base SAGE tags for our analysis; we have reported \[[@B11]\] that this leads to a significantly more reliable matching of SAGE tags to genes than with the often-used 14-mer tags. A second indicator of reliability stems from prior analysis of our SAGE libraries and data obtained from them \[[@B11]\]. For instance, we have shown that our SAGE libraries show little or no NGF-promoted changes in tag numbers for transcripts encoding a number of housekeeping proteins. Moreover, a number of transcripts previously found by alternative technologies to respond to NGF treatment, showed similar changes in our SAGE profiling study. Finally, Northern blot analysis verified SAGE-predicted responses of over 20 transcripts to NGF treatment. A third criterion for reliability was that Monte Carlo simulation indicated that many of the NGF-promoted responses of RP transcripts were at a probability of *P* \< 0.01. Because of the relatively high abundance of many RP transcripts, even comparably small changes in expression could be detected at this level of significance. A last criterion was that we used real time RT-PCR to verify effects of NGF on five RP transcripts.

A previous study by Lee et al. \[[@B8]\], based on comparison of a total of approximately 7,000 random ESTs from naïve and NGF-treated PC12 cells, reported an NGF-promoted increase in expression of RPL7 transcripts and a decrease in RPL19 transcripts. This contrasts with the current data which revealed no significant change in expression of either of these transcripts. The reason for this discrepancy is unclear, but could in part originate from the relatively small number of transcripts that were surveyed in the former study. To our knowledge, there are currently no other reports regarding effects of NGF on levels of ribosomal transcripts.

Relative levels of RP transcripts
---------------------------------

Our observations indicate that there is a wide range in the numbers transcripts per cell that encode individual RPs. For instance, both before and after NGF treatment, there was over a 10-fold difference in the relative numbers of transcripts for RPs L7, L22, S21 as compared to those encoding RP S29. Because of the general scarcity of antisera prepared against mammalian RPs, we do not know whether this is reflected at the protein level. However, for a number of other NGF-regulated transcripts in PC12 cells, there is a good correlation between relative levels of message and protein \[[@B11],[@B16],[@B17]\]. Thus, it may be that although levels of individual RPs are assumed to be similar to one another, some may be present in limiting numbers. Alternatively, there may be a considerable disparity between relative abundances of RP transcripts and their corresponding proteins

A recent SAGE study of targets for N-myc in a human neuroblastoma cell line \[[@B18]\], reported relative abundances for 66 RP transcripts. The existence of such data permit us to compare the relative levels of RP transcripts in two cell types (neuroblastoma and pheochromocytoma) of related origin (i.e., neural-crest-derived) that both have the potential for neuronal differentiation. The 74 RP transcripts detected here represented 5.2% of the total transcripts in NGF-untreated PC12 cells and 3.5% after NGF treatment. By contrast, the 66 RPs reported in the neuroblastoma study accounted for 4.1% of total transcripts for cells without N-myc over-expression and 12.6% for such cells transfected with N-myc. Thus, for non-N-myc transfected neuroblastoma cells and PC12 cells (± NGF) the contribution of total RP transcripts lies in a similar range and this parameter is greatly elevated in neuroblastoma cells by N-myc over-expression. With respect to transcripts for individual RP\'s, there are many similarities as well as several striking differences between the two cell types. When NGF-untreated PC12 cells are compared with non-transfected neuroblastoma cells, of the 55 RP transcripts detected in common for both systems, about half (a total of 28) have relative abundances within a factor of 2. Twenty transcripts are more than 3-fold higher in relative abundance in PC12 cells. In two of these cases (S9 and S17) no tags were detected in the non-transformed neuroblastoma cells; in several other cases (S12, S24, L24) the relative abundance in PC12 cells was over 20-fold that in the neuroblastoma cells. For most of these, expression of N-myc elevated the neuroblastoma levels to relative abundances within 2-fold of those in PC12 cells. However, for L24 and S24, the relative abundance in N-myc-expressing neuroblastoma cells was still 1/3 that in PC12 cells. Finally 7 RP transcripts (L7, L11, L21, L30, S7, S8 and P2) were of 3--5 times lower abundance in PC12 cells than in neuroblastoma cells and this difference was appreciably enhanced when the NB cells were transfected with N-myc.

We have also compared our RP results with those for SAGE carried out with pooled human adult brain (tissue supplied by Gregory J. Riggins: <>). Comparison with data for NGF-treated cells reveals similarity in relative abundance within a factor of 2 for 39 of the 57 RP transcripts in common between the libraries and 48 of 57 to within a factor of 3. However, several large differences do occur. For example the relative abundances of transcripts encoding RPs L9, L39, S24, S13, and S17 are 5--10-fold higher in NGF-treated PC12 cells than in the pooled brain library and transcripts for RP L28 are 8-fold lower. These observations reinforce the notion that expression of individual RP transcripts can be significantly variable from cell type to cell type as well as sensitive to extrinsic signals.

Potential significance of RP transcript regulation
--------------------------------------------------

Although the ribosome has been considered as a \"molecular machine\" \[[@B19]\], it is of interest that the transcripts encoding individual proteins of this organelle are subject to regulation by NGF. Two related questions emerge regarding these findings: how do these changes compare with previous reports for regulation of RP transcripts and what might be the functional consequences of these changes?

A number of studies have employed a variety of techniques to detect changes in gene expression associated with the oncogenic state and have reported selective elevation of specific RP transcripts in tumors. Examples include RPs L7a, L37 and S14 in prostate tumors and cell lines \[[@B20]\]; RPs L5, L7A, L18, S3, S6, S8, S12, S13, S28, P0 and P1 in colorectal cancers and cell lines \[[@B21]-[@B26]\]; L5 in astrocytomas \[[@B27]\]; L18a in squamous cell carcinoma \[[@B28]\]; L19 in breast tumors that over-express erbB-2 \[[@B29]\]; RPs S3A, S4, and S17 in lymphoid malignancies \[[@B30]\]; L38, S4, P0, and P1 in rhabdomyosarcoma cell lines \[[@B31]\]; and P0, P1, P2, L5, L9, L35, L39, S3A, S10, and S17 in liver tumors \[[@B32]\]. In addition, as noted above, a recent SAGE study identified targets of N-myc in a human neuroblastoma cell line \[[@B18]\]. Of 114 up-regulated genes detected, 66 encoded RPs with elevations ranging from 40% to 37-fold. Several of these genes were also up-regulated by c-myc. Taken together, these findings indicate that the transition to the transformed state is associated with elevation of various RP transcripts. A potential interpretation of such observations is that this up-regulation reflects the enhanced rate of cell division in tumor cells and the requirement for greater levels of protein synthesis. In agreement with this possibility, transcripts encoding RPs L6 and S7 are reported to be up-regulated in regenerating liver \[[@B33]\]. However, for the study involving responses of neuroblastoma cells to N-Myc, despite the massive up-regulation of RP transcripts, there was no over-all increase in the rate of protein synthesis \[[@B18]\].

Before considering potential functional consequences of the changes in RP expression described here as well as elsewhere, it must be conceded that changes in transcript abundance may not necessarily lead to changes in protein expression. The present lack of available antibodies/antiserum to most mammalian RPs hampers such a determination. However, even if cases occur in which altered RP transcript expression does not lead to changes in expression of the corresponding protein, our and others\' findings regarding selective regulation of RP transcripts would then raise the interesting issue as to why, and the mechanism by which, transcript and protein expression are uncoupled.

If at least some of the NGF-promoted alterations in RP transcript abundance lead to changes in expression of the corresponding proteins, what might be the functional consequences? In the present system, NGF converts proliferating PC12 cells to a non-proliferating neuronally differentiated state. Our findings reveal that the relative overall abundance of transcripts for RP proteins fell by 1/3 in response to NGF. In addition, nearly half of the individual RP transcripts detected showed significant changes in expression and approximately 2/3 of these were decreases. This overall decrease in NGF-promoted RP transcript expression and the preponderance of decreases in expression of specific RP transcripts compared to increases would favor the interpretation that the changes observed here reflect, at least in part, the transition to the non-dividing phenotype. On the other hand, we observed that many of the RP transcripts did not show significant responses to NGF and that a number showed increases in expression. This suggests that the situation is likely to be more complex with individual RPs perhaps playing specific roles not simply associated with the state of cell proliferative capacity. In agreement with this, we found no significant change in expression of RP S29 transcripts even though these have been reported to be present at low levels in growth phase cells and elevated in quiescent cells \[[@B34]\].

In addition to leaving the cell cycle, NGF-treated PC12 cells undergo neuronal differentiation. This raises the possibility that some of the observed changes, as for many other NGF regulated genes, is related to acquisition of the neuronal phenotype. An analysis of gene regulation in human NTERA2 cells induced to leave the cell cycle and neuronally differentiate in response to retinoic acid revealed decreases in RP transcripts including L3, L7, L8, L10, L13, L39, S2, S6, S13, S16, S20, S19, S23, S27A and P0 \[[@B35]\]. Of these, In the present study only RP transcripts L8 and S6, S16, S19 showed significant down regulation in response to NGF whereas the others found in the NTERA2 study either did not show changes that were significant at the *P* ≤ .01 level or underwent a significant increase (RP S27A). Curcic et al. \[[@B36]\] reported that differentiation of BC3H1 myocytes is accompanied by a drop in L32 gene transcription; in contrast, we found no significant change in transcripts encoding this RP. In another study, RPs L35a and S5 were down regulated during murine erythryolukemia cell differentiation \[[@B37]\]; we also observed decreases in these transcripts. Mutation of RP S19 is associated with Blackfan\'s anemia and therefore appears to play a selective role in differentiation/proliferation of erythropoetic cells \[[@B39]\]. Here, NGF resulted in a nearly 5-fold drop in S19 transcripts. Thus, although changes in expression of specific RP genes may be associated with differentiation, the pattern of such changes may reflect the particular cell type and differentiation stimulus that is involved.

Another condition associated with changes in RP expression is apoptotic death. RP L4/L1 transcripts, which were not significantly affected by NGF, are selectively upregulated in PC12 cells prior to 5 aza cytosine-induced death and over-expression of this gene in COS-7 cells induces apoptosis \[[@B39]\]. Over-expression of L7, L13A, S29 have been also reported to induce apoptosis \[[@B40],[@B41]\]. In the present study, NGF promoted little if any change in expression of these RP transcripts. Thus, although NGF is an effective anti-apoptotic factor, these actions do not appear to be mediated by down-regulation of potentially death-inducing RPs.

At present, in contrast to studies on bacterial and archael ribosomes, relatively little is known about the functions of individual mammalian ribosomal proteins (\[[@B42]\]. However, assuming that changes in message abundance lead to alterations in protein levels, it is reasonable to anticipate that the NGF-promoted effects observed here may affect the protein synthetic capacity of the cell in some manner. Such changes are unlikely to be global; NGF does affect the cellular rate of protein synthesis, but this seems to be due at least in part to post-translational modification of the translational machinery \[[@B43]\]. The alternative is that the changes reported here may result in selective effects on translation of specific messages.

A final, and important possibility to consider is that a number of RPs appear to possess extraribosomal functions \[[@B44]\] and consequently that the changes reported here may lead to responses that do not directly relate to ribosomal function. For example, RP L18, which was significantly down-regulated by NGF, has been reported to negatively regulate double-stranded RNA (dsRNA)-activated protein kinase (PKR) \[[@B45]\]. As an additional example RP S19, which is also down-regulated by NGF, has been implicated in erythropoiesis and has also been described as a monocyte chemotactic factor \[[@B46]\]. As one last example, rat RP L9 which we found to be up-regulated by NGF starting within a few hours and maximally by 8 hrs, has been shown to be the ortholog of the yeast gene *grc5* which appears to be involved in multiple cellular functions including growth control, cytoskeleton control and energy metabolism \[[@B47]\]. Identification of NGF-regulated RPs should now facilitate exploration of their potential roles in the responses of cells to neurotrophins.

Conclusions
===========

1\. SAGE analysis provides a reliable, quantitative picture of ribosomal protein expression in PC12 cells before and after long term exposure to NGF.

2\. Transcripts for ribosomal proteins are among the most abundant transcripts in the cells; however there is a wide range between numbers of transcripts for individual RP transcripts.

3\. NGF promotes an overall decrease in relative RP transcript expression (for the 74 RP transcripts detected, from approximately 5% of total transcripts to 3.5%). This drop represents a decrease in relative expression of individual RP transcripts as well as an increase in message complexity in NGF-treated cells.

4\. Long term NGF treatment of PC12 cells promotes statistically significant changes in expression of over half of the transcripts encoding ribosomal proteins. Decreases in expression outnumbered increases by a ratio of approximately 2:1. The largest observed changes in expression are in the range of 3--5 fold.

5\. For at least one RP transcript (RPL9) the response to NGF is rapid; an elevation is detected within 1 hr of NGF exposure and reaches maximum regulation by 8 hrs.

6\. Examination of the literature reveals many other reports in which expression of individual or groups of RP transcripts are regulated in response to growth factors, differentiation agents or malignant transformation. Comparison with the present data indicate that changes in ribosomal protein transcripts is regulated in a cell and state dependent fashion with a large diversity in the particular RP transcripts that are subject to regulation. Thus, although the ribosome may be regarded as a \"machine\" there is a good deal of potential for plasticity with respect to the expression of its various components. This raises the possibilities that growth factors and other cell regulators may affect ribosomal function, and thereby the capacity of cells to transcribe specific transcripts.

7\. In the absence of information about the specific functions of most individual RPs in mammalian cells, one can only speculate at present on the physiologic significance of the reported changes. However, it seems highly plausible that the observed responses may play roles in NGF-promoted neuronal differentiation. This may be mediated in part by selective effects on translation of certain messages. In addition, past findings support the possibility that at least some of the regulated RPs have extra-ribosomal actions that may affect neuronal differentiation and function.

Materials and Methods
=====================

SAGE and matching SAGE tags with RP transcripts
-----------------------------------------------

SAGE libraries were prepared from naïve and 9-day NGF-treated PC12 cells and sequenced as previously described \[[@B11]\]. To match SAGE tags with RP transcripts, tags were initially analyzed with the National Center for Biotechnology Information (NCBI) rat SAGE tag to gene mapping database <>, which matches possible 14-mer tags with known rat genes and expressed sequence tags (ESTs). With the use of sequences present in the NCBI UNIGENE rat database <> potential matches were further scrutinized to determine whether there was a match at the 15th base and to determine whether the matched sequence was at the most 3\' end of a known rat transcript or EST. We considered only cases in which a clear poly(A) tail and a polyadenylation signal were present at the 3\' end of the transcript or EST. Appropriate ESTs were further analyzed by an advanced BLAST search for matches with known rat RP genes. Monte-Carlo simulation analysis of the SAGE data was carried out using the SAGE 300 software package \[[@B13]\].

Real time quantitative PCR analysis
-----------------------------------

PC12 cells were cultured as previously described \[[@B12]\] in complete medium (85% RPMI 1640 medium, 10% horse serum, 5% fetal bovine serum). Replicate cultures were treated with 50 ng/ml rhNGF (kindly donated by Genentech, Inc.) for the indicated times. Total cellular RNA was isolated as described previously \[[@B11]\] and 1.2 -- 5 μg was used for reverse transcription with 5\'-T~30~NN-3\' primer using Superscript II Rnase H-RT according to the manufacturer\'s specifications (Life Technologies, Grand Island, NY). Quantitative real time PCR reactions were performed as described by Troy et al. \[[@B48]\]. GAPDH was used to normalize input cDNA for samples originating from short-term NGF treatments and β-actin was used for this purpose for samples with long-term NGF exposure.

Forward and reverse primers pairs used for quantitative PCR were: *S29,* 5\'GGTATCACAGGGTAGACAGT3\' and 5\'GGTATCACAGGGTAGACAGT3\'; *L9,* 5\'GAACTCTGAGGAGGGACTTC3\' and 5\'AAACTGTACTTGTTATCAGGAT3\'; *S27-1,* 5\'CGGCACGAGCGACCTCCCTA3\' and 5\'GTTCCCACTCATCTTGAATC3\'; *L24,* 5\'CAAGAAAGGACAGTCGGAAA3\' and 5\'TTCACAGGCTTCACAATCTT3\'; *S19,* 5\'TAACCAGCAGGAGTTCGTCA3\' and 5\'TTTGTTCTAATGCTTCTTGTT3\'; β-*Actin*, 5\'ATCCTGACCCTGAAGTACCC3\' and 5\'TACGACCAGAGGCATACAG3\'; and *GAPDH,* 5\'GAAACCTGCCAAGTATGATGA3\' and 5\'TCTCTCTTGCTCTCAGTATCC3\', respectively.

Acknowledgements
================

We thank Claudine Bitel for her excellent technical assistance. Supported in part by grants from the National Institutes of Health.