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Microarray Detection Call Methodology as a Means to Identify and Compare Transcripts Expressed within Syncytial Cells from Soybean (Glycine max) Roots Undergoing Resistant and Susceptible Reactions to the Soybean Cyst Nematode (Heterodera glycines)

Background. A comparative microarray investigation was done using detection call methodology (DCM) and differential expression analyses. The goal was to identify genes found in specific cell populations that were eliminated by differential expression analysis due to the nature of differential expression methods. Laser capture microdissection (LCM) was used to isolate nearly homogeneous populations of plant root cells. Results. The analyses identified the presence of 13,291 transcripts between the 4 different sample types. The transcripts filtered down into a total of 6,267 that were detected as being present in one or more sample types. A comparative analysis of DCM and differential expression methods showed a group of genes that were not differentially expressed, but were expressed at detectable amounts within specific cell types. Conclusion. The DCM has identified patterns of gene expression not shown by differential expression analyses. DCM has identified genes that are possibly cell-type specific and/or involved in important aspects of plant nematode interactions during the resistance response, revealing the uniqueness of a particular cell population at a particular point during its differentiation process.

Introduction
Microarray analyses are a way to study the expression of thousands of genes simultaneously. Microarray analyses are important because they can provide information on genes that are expressed differentially between a control and an experimental sample [1]. However, part of the problem of differential expression methodology is that genes must be expressed in both sample types, the experimental and control samples, for statistical analyses to be possible. Without gene expression data available for a probe set in each sample, the probe set will be discarded by the analysis procedure. The drawback of the differential expression methodology, therefore, is obvious when specific cell types with vastly different identities are being compared [2].
Microarray analyses, however, do provide useful information on the transcripts that are present or absent within samples [3]. Detection call methodology (DCM) reveals the types of transcripts that are present or absent within samples. The DCM is typically used on a single array to 2 Journal of Biomedicine and Biotechnology answer whether a transcript of a particular gene is present or absent in a sample. Several recent papers have used DCM successfully to understand transcription in various experimental systems [4][5][6]. The DCM is useful when cost is an issue because the method can be performed on a single array. More importantly, DCM can be used to compare transcripts between different cell types or of the same cell type at different points during a time course [2,3,7]. None of these examples compared detection calls during a pathological infection. Nonetheless, DCM can provide extremely useful information about the samples under investigation, especially in the analysis of plant pathological systems where a pathogen interacts intimately with a specific cell population within a complex tissue or organ (e.g., root).
The infection of plants by parasitic nematodes is a major agricultural problem that is currently poorly understood (reviewed in [8][9][10][11][12]). Infection results in damage totaling 157 billion U.S. dollars, annually [13]. Among the most costly is Heterodera glycines infection of Glycine max, accounting for an estimated $460 to $818 million in production losses annually in the U.S. [14]. The G. max-H. glycines system is a powerful research tool because both resistant and susceptible reactions can be studied in the same genotype (e.g., G. max [PI 548402/Peking] ). Information learned through its genetic and genomic studies can be translated directly to improve resistance in one of the most important global agriculturally relevant plants.
The genetic basis of G. max to overcome H. glycines infection (an incompatible reaction resulting in resistance) is complex (reviewed in [15]). Several recessive resistance loci (rhg1, rhg2, and rhg3) [16] and dominant resistance loci (Rhg4) [17] and (Rhg5) [18] have been identified (reviewed in [15]). The understanding of resistance to H. glycines has also been aided by other genetic marker technology (e.g., quantitative trait loci (QTL) mapping). Those studies have identified QTLs that map to 17 linkage groups. G. max [PI 548402/Peking] has nine QTLs that map to different linkage groups (reviewed in [15]). One of those QTLs present in G. max [PI 548402/Peking] that maps to linkage group G explains more than 50% of resistance to H. glycines [19]. It also is responsible for resistance to several different populations of H. glycines. The major QTL that is located on linkage group G was identified using the RFLP marker C006V and is designated as rhg1 [20]. Further studies have shown that molecular marker Satt309 is only 0.4 centiMorgans from rhg1 [21]. Importantly, much of the resistance that has been bred into elite G. max varieties originates from the G. max [PI 548402/Peking] genotype.
Genomic approaches have also identified transcriptional changes in whole roots during infection [22][23][24][25][26]. Importantly, a time course microarray analysis was used to investigate the G. max-H. glycines interaction [23]. The analysis demonstrated that differential expression of genes was occurring in G. max roots undergoing a compatible reaction, a reaction that results in susceptibility. The analysis used time points both prior to and after feeding site selection [23]. Importantly, the differential expression of genes was occurring in G. max roots even before the nematodes had selected their feeding sites [23]. Thus, the plant is reacting in important ways to the presence of the nematode before the nematodes have begun to initiate the formation of their feeding sites during a compatible reaction.
The G. max-H. glycines interaction is an exceptional model because it is possible to compare gene expression occurring during incompatible (resistant) and compatible reactions. The experiments are possible because even resistant genotypes like G. max [PI 548402/Peking] undergo infection [12,[25][26][27][28][29][30][31][32][33]. The comparisons can be made because welldefined incompatible and compatible H. glycines races (populations) are available [12,25,26,[31][32][33][34][35]. A time course microarray analysis has examined H. glycines infection during both an incompatible and a compatible reaction in whole roots at time points both prior to and after nematodes have established feeding sites [25]. Importantly, those microarray analyses were performed in the same G. max genotype (e.g. G. max [PI 548402/Peking] ) by using incompatible and compatible populations of H. glycines [25,26]. Thus, no possibility existed for G. max genotype differences complicating the identification of important gene expression events during those reactions. The analyses have shown that G. max behaves differently as it undergoes the incompatible or compatible reaction and these differences in gene expression are detectable as early as 12 hours post infection (hpi) [25]. The 12 hpi time point is a point before the nematode has selected its feeding site. The analyses also showed how expression of G. max [PI 548402/Peking] genes differs over time between roots undergoing an incompatible or compatible reactions.
The aforementioned investigations were not designed to study gene expression of the syncytium. However, several labs have made histological studies of the infection process. The studies showed that H. glycines infest the roots and migrate through the cortex during the early stages of the infestation process. After 24 hpi the nematodes reach the stele where they select and establish their feeding sites [27][28][29][30]36]. Consequently, the feeding site initial (FS i ), a cell that is usually a pericycle cell, fuses with neighboring cells. The process occurs when the cell walls dissolve and the cytoplasm of adjacent cells (e.g., cortex) merges with the feeding site initial. Cell fusion, thus, results in the formation of a syncytium. Syncytial cells continue to develop in compatible roots into sites from which H. glycines feed (Figure 1) [27][28][29][30]. Conversely, syncytial cells of incompatible roots collapse four to five days post infection (dpi) and the nematodes die [27,28,30].
Understanding the localized resistance reaction at the site of infection may also lead to better measures to control H. glycines parasitism. The problem, however, has been in isolating these cells to some amount of homogeneity for expression analysis. Hand dissections have been performed to obtain giant cells from galls induced by the root knot nematode (Meloidogyne incognita) during a compatible interaction in tomato (Lycopersicon esculentum) [37]. The experiments permitted the isolation of cDNA from those cells [37]. However, relatively few of them turned out to be gall specific [38]. The experiments, nonetheless, demonstrated the efficacy of the approach in isolating RNA from those cell types. Unfortunately, it is not possible to use this method to study syncytium formation during H. glycines infection. Cysts, encasing the eggs, are able to remain dormant in the soil for years. At some point, the eggs hatch. The second-stage juveniles (J2s) migrate toward the root and burrow into it. The infective J2s (i-J2s) then migrate toward the root stele. A stylet emerges from the anterior end of the nematode. The nematode selects a pericycle cell or neighboring root cell, for its feeding site. The i-J2 then presumably releases substances that then cause major changes in the physiology of the root cell. Those root cells (yellow) subsequently fuse with neighboring cells (light blue), producing a common cytoplasm. The repeated cell fusion events produces a syncytium (orange) that contains approximately 200 merged root cells and serves as the H. glycines feeding site. After the establishment of the syncytium, male nematodes feed for several days. Feeding proceeds until the end of their J3 stage. Meanwhile, the males become sedentary. Subsequently, the males stop feeding, followed by a molt into vermiform J4 males. The males burrow out of the root in preparation for copulation. In contrast to the males, the females become and remain sedentary after the establishment of their feeding site. The female nematodes then increase in size while undergoing both J3 and J4 molts. The J4s then mature, becoming adult feeding females. Ultimately, the female develops into the cyst that encases the eggs. (a) Cysts (dark red) with eggs (white) hatch. (b) Second-stage juveniles (J2) (gray) hatch and migrate toward the root. (c) The J2 nematodes burrow into the root and migrate toward the root stele (dark gray). (d) Feeding site selection (yellow). (e) i-J2 nematodes molt into J3 and then J4. The female is shown here in red. During this time, the original feeding site (yellow) is incorporating adjacent cells (magenta) via cell wall degradation and fusion events. Meanwhile, the male discontinues feeding at the end of its J3 stage. (f) The male and female J4 nematodes mature into adults. By this time, the feeding site has matured into a syncytium (green) as shown here where the female is actively feeding. The vermiform male (blue) migrates out of the root and subsequently copulates with the female (red). (g) After ∼30 days, the female is clearly visible externally because its body emerges from the root tissue. The figure is adapted from Klink et al. [11].
Laser capture microdissection (LCM) is an alternative means that affords a high degree of precision and accuracy to isolate homogeneous cell populations that are otherwise recalcitrant to their isolation [39][40][41][42]. The method has proven to be especially valuable to study the development of the syncytium during G. max infection by H. glycines during a compatible and incompatible reaction [26,33,43,44] because H. glycines can be used as an in situ physical marker for the syncytium. Microarray analysis studying gene expression of the syncytium has allowed for the identification of genes that exhibit differential expression in these cell types [26,33]. However, it was unclear whether the true diversity of gene expression was being revealed by the differential expression methodology.
In the analysis presented here, DCM was used to compare detection calls made between the different cell types involved in the formation of the syncytium using samples isolated by LCM. The DCM was used to compare how the cell types under investigation (e.g., the syncytium) differed from the cell type(s) from which they originated (e.g., pericycle). Using a comparative analysis aided by customized computer scripts, a broader understanding was obtained of the differences between (1) syncytia and pericycle cells, (2) syncytia undergoing incompatible and compatible reactions, and (3) syncytia at different points of their development during a compatible reaction as they mature into a functional feeding site.

Female Index.
The H. glycines  population used in the analyses presented here has been used extensively as race 3 for analyses requiring susceptible reactions in G. max genotype Kent (G. max [Kent] ) [22, 23, 31-33, 43, 45] and resistant reactions in G. max [PI 548402/Peking] [25,26,31,33,46]. For a description of the 16 nematode races and the HG-type test, please refer to Niblack et al. [35]. The HG-type test is derived from the original Index of Parasitism test [47]. The determination of the HG-type is based on the performance of the nematode race to infect indicator lines.  (1, 2, 3, 5, and 14) in the greenhouse [48]. The HG-type test is based off of the presence of an expected number of females, given as a proportion, which will develop on each indicator line. The number is called the female index (FI). The FI is the number of mature females that develop on a test genotype divided by the number of females that develop on a known susceptible genotype (i.e., G. max [Lee] and/or G. max [Essex] ) multiplied by 100. According to the original Index of Parasitism [47] any genotype with a female number less than 10% of the number determined on G. max [Lee] would be considered resistant (−) and any number above 10% would be susceptible (+). The HG-type test as determined by Niblack et al. [35] has changed the Index of Parasitism test [47] and improved race test [34] 7] have always been exposed to the same G. max genotypes. The method virtually eliminated variations among the different G. max genotypes in influencing the experiments.
Seedlings were grown in sterilized sand in 20×20×10 cm flats for a period of one week. The plants were gently removed from the sand and rinsed with sterile water. Seedlings were placed on moistened germination paper (Anchor Paper; St. Paul, MN) inside the flats. Mature female nematodes were harvested by massaging the roots in water. Mature nematodes were collected by filtering the solution through nested 850 and 150 μm sieves. Females were further purified by sucrose flotation [45]. The females were crushed gently with a rubber stopper within a 7.5 cm diameter apparatus containing 250 μm sieves. The process released the eggs. The eggs passed through the sieve into a small plastic tray. Debris smaller than the eggs was removed. Debris removal was done by washing the debris in a 25 μm mesh sieve. The eggs were placed in a small plastic tray containing 1 cm of water. The tray was covered with plastic wrap and subsequently placed on a rotary shaker at 25 rpm. After 3 days, the second-stage juvenile nematodes (J2s) were separated from the unhatched eggs. Separation was done by passing them through a 41 μm mesh cloth. The J2s were concentrated by centrifugation in an IEC clinical centrifuge for 30 seconds at 1720 rpm to 5,000 J2/mL. The nematodes were used to infest the roots. There were 2 mL of nematodecontaining solution added directly on the roots for a final concentration of 2,000 J2/root. The control mock-infested replicates received the same amount of water. The roots were covered with a moistened sheet of germination paper. The plants were placed in a 45 × 50 × 20 cm plastic tray with a one cm of water in the bottom to add humidity. A semitransparent bag was then wrapped around the tray. The trays were then placed under fluorescent lights of 16/8 hour light/dark photoperiod. Light intensities were identical for all experiments. Infested roots were grown for three or 8 dpi. The mock-infested control samples and susceptible and resistant reactions were washed. The process removed the extraneous nematodes that had not yet penetrated the root, preventing additional nematodes from entering the root. The process ensured that tissue that was the most highly infested with nematodes was obtained. The process was then repeated, providing two independent sets of samples. Seven independent replicates were pooled to obtain each replicate for each sample type in the analysis. Thus, there are a total of 14 replicates used in the analysis. At least 50 serially sectioned syncytia were used for each of the 7 replicates. Materials for histological observation to confirm incompatible and compatible reactions were derived from these samples (see below).

Histology.
Histological tissue processing was according to Klink et al. [26,33,43]. Briefly, tissue was fixed in Farmer's solution (FS) composed of 75% ethanol and 25% acetic acid v/v [43,52]. Some replicate samples of roots used for immunohistochemical analyses were killed and fixed in 3.7% w/v paraformaldehyde (PFA) buffered with PEMP buffer (100 μM Pipes, 1 μM EGTA, 1 μM MgCl 2 , and 4% w/v polyethylene glycol MW 8000, pH 6.8) [43,53]. G. max root tissue was harvested and cut into 0.5 cm pieces. Those pieces were vacuum infiltrated with either FS or PFA at room temperature for one hour (h). Fresh fixative (FS or PFA) was then added to their respective samples. Tissue was subjected to an incubation step of 12 hours at 4 • C. PFA fixed tissue was then dehydrated through 10% (v/v), 25% (v/v), 50% (v/v), 75% (v/v) ethanol : water. The remaining procedure was done identically as for FS processed tissue. Fixative was removed from the roots. Dehydration of FS-fixed tissue proceeded through a graded series of 75% (v/v), 85% (v/v), 100% (v/v), 100% (v/v) ethanol : water, 30 minutes each. Ethanol was replaced with 1 : 1 (v/v) xylene : ethanol for 30 minutes. Subsequently, three, 100% xylene incubations (30 minutes each) were done. Xylene was replaced by paraffin. The processing was done slowly by placing the specimens into a 58 • C oven. The roots were infiltrated sequentially in 3 : 1 (v/v), 1 : 1 (v/v), 1 : 3 (v/v) xylene : Paraplast+ tissue embedding medium (Tyco Healthcare Group LP; Mansfield, MA) in each step for three hours. Tissue was cast and subsequently mounted for sectioning. Serial sections of roots were made on an American Optical 820 microtome (American Optical Co.; Buffalo, NY) at a section thickness of 10 μm. Sections were stained in Safranin O (Fischer Scientific) in 50% (v/v) ETOH : water and counter-stained in Fast Green FCF (Fisher Scientific). The tissue was permanently mounted in Permount (Fisher Scientific).

LCM.
Slides were prepared according to Klink et al. [26]. MembraneSlides (Leica, Germany; Cat# 11505158) were placed on a slide warmer set at 40 • C. DEPC-treated RNAse-free water (∼0.5-1 mL) was placed onto the slide and allowed to warm. The tissues used for these analyses were obtained from the same tissue used in whole-root microarray experiments [26]. Serial sections (10 μm) from control mock-inoculated roots and roots undergoing incompatible (3 dpi) and compatible (3 and 8 dpi) reactions were prepared according to Klink et al. [26,33,43]. Serial sections for the independent sample types were placed directly onto the pool of DEPC-treated water. DEPC-treated water was blotted off with a sterile KimWipe after the serial sections were adequately spread. Tissue was allowed to warm on the slide warmer for an additional hour to promote tissue binding to the slide surface. Slides were deparaffinized for five minutes in xylene. The processing was followed by a two-minute incubation in 1 : 1 (v/v) xylene : ETOH. That was followed subsequently with two one-minute incubations in ETOH. Slides were then dried on the lab bench on filter paper covered with KimWipes. The slides were used immediately for LCM after the drying step was done. LCM was performed on a Leica ASLMD microscope (Leica). Microdissection cutting parameters were determined empirically for each session by examining how amenable the tissue was to LCM. However, cutting parameters for dissections performed on the 40× objective were approximately power, 55-85; speed, 2-4; specimen balance, 1-3; and offset, 40. Similar quantities of cells were obtained for each sample type for the analyses.
Tissue was collected in OptiCaps (Leica Cat. number 11505169) and subsequently washed to the bottom of the OptiCap PCR tube. The process was done by micropipetting 20 μL of XB buffer (Arcturus) onto the microdissected tissue. The cap was spun for five minutes at 500 rpm to pellet the tissue into the bottom of the Opticap. LCM tissue was ground with a micropestle. The process was done in 40 μL RNA extraction buffer (Arcturus). The RNA was extracted and subsequently processed using the PicoPure RNA Isolation Kit (Arcturus). The process was done according to the manufacturer's instructions. A DNAse treatment was added, just before the second column wash, using DNAfree (Ambion; Austin, TX). RNA quality and yield were determined. The processing was done using the RNA 6000 Pico Assay (Agilent Technologies; Palo Alto, CA) using the Agilent 2100 Bioanalyzer according to the manufacturer's instructions. RNA amplification of LCM samples was performed with the GeneChip Two-Cycle cDNA Synthesis Kit (Affymetrix; Santa Clara, CA: Cat. number 900432). Probe preparation and hybridizations were performed according to Affymetrix guidelines at the Laboratory of Molecular Technology, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, MD 21701, USA.

Microarray Analyses and G. max Probe Set Annotations.
The GeneChip Soybean Genome Array (Affymetrix; Cat. number 900526) containing 37,744 G. max transcripts (35,611 transcripts) was used for the microarray analyses. Details of the GeneChip soybean genome array can be obtained (http://www.affymetrix.com/index.affx). Annotations were made by comparison to the Arabidopsis thaliana gene ontology (GO) database [54] based on their best match obtained by BLAST searches [55]. They were updated (2009).  All microarray hybridizations were performed at the Laboratory of Molecular Technology, SAIC-Frederick, National Cancer Institute at Frederick, Frederick, MD 21701, USA. Local normalization was used. The presence or absence of a particular probe set's (gene's) transcript on a single array was determined using the Bioconductor implementation of the standard Affymetrix DCM. In summary, the DCM consists of four steps: (1) removal of saturated probes, (2) calculation of discrimination scores, (3) P-value calculation using the Wilcoxon's rank test, and (4) making the detection (present/marginal/absent). Ultimately, the algorithm determines if the presence of a probe set's transcript is provably different from zero (present (P)), uncertain (marginal (M)), or not provably different from zero (absent (A)). Details of the standard Affymetrix DCM can be found in their Statistical Algorithms Description Document (http://www.affymetrix.com/support/technical/whitepapers/ sadd whitepaper.pdf.). For a particular condition (e.g., 3 dpi syncytia during the incompatible response), a probe set was considered present only if it was present on both replicate microarrays corresponding to that condition. Otherwise, it was considered to be absent. All original data sets, the normalized data sets, statistics, and data supplemental to each table and figure are available at the MAIME compliant [http://bioinformatics.towson.edu/SGMD3] [56].

Histological Analysis of Incompatible and Compatible
Responses in the Whole Root. Morphological and anatomical details of compatible and incompatible disease responses by G. max to H. glycines infection have been published previously [27-29, 36, 57-61]. Infection during the first 8 dpi (Figure 2) was focused on for this analysis because syncytial cells complete the incompatible reaction by 8 dpi under the experimental conditions in G. max [PI 548402/Peking] . During a compatible reaction, nematodes have selected and are establishing feeding sites by 3 dpi (Figure 2(a)) that are continuing to develop by 8 dpi (Figure 2(b)). During an incompatible reaction, nematodes have also selected and are establishing feeding sites at 3 dpi (Figure 2(c)). However, during an incompatible reaction, syncytial cells collapse by 8 dpi (Figure 2(d)). Syncytial cells (Figure 3(a)) for the various analyses were collected by LCM (Figure 3(b)). numbers of present transcripts within a sample, and (3) a comparison of the present transcripts between the different sample types while estimating the differences between those samples (4) the identification of whether transcripts that are common between the two sample types under comparison had been identified in a prior differential expression analysis [26]. Only probe sets that measured detection on both arrays for a particular sample type ( Figure 4) were evaluated further (see below).

The Use of Detection Calls to Identify Genes Present in Syn
While detection calls are generally used for single array analyses, the DCM presented here used two arrays for each sample type in a comparison. Thus, for a particular comparison between cell types, four arrays were taken into consideration. Detection calls were analyzed for each of the two arrays for each sample type (e.g., pericycle). Detection calls were made for each of the two arrays independently to determine if the probe sets were consistently measuring present or absent for a particular sample type. For example, the probe set had to obtain a like measurement (e.g., present/present; absent/absent) for each of the two arrays for each sample type to be considered for subsequent analyses ( Figure 4). The arrays that measured present on both arrays within a sample type are considered present. The arrays that measured absent on both arrays within a sample type are considered absent ( Figure 4). The probe sets that failed to produce like measurements (e.g., any combination of present/absent; present/marginal for the two arrays) and those that measured marginal amounts of a   transcript for a particular probe set on each of the two arrays (e.g., marginal/marginal) were excluded from the analysis ( Figure 4). The resulting probe sets used in the subsequent analyses were measuring present/present detection calls for each of the two arrays for a particular sample type (e.g., 3 dpi incompatible syncytia).
Comparisons were made between the different sample types (e.g., 3 dpi incompatible syncytia versus pericycle). In those comparisons, four arrays would be compared ( Figure 5). Probe sets that measured detectable amounts Only genes that are present in both A and B can be used for differential expression analyses. The red circle represents the genes that are present in both pools and are also differentially expressed, measuring induced gene activity. The blue circle represents the genes that are present in both pools that are differentially expressed, measuring suppressed gene activity. The remaining genes (white) are present in both samples (common) but are not differentially expressed.
of a transcript on the four arrays under comparison were considered common and present between two sample types (e.g., pericycle and 3 dpi incompatible syncytia-common).
The probe sets measuring absent on the four arrays (common and absent), although potentially interesting, were not taken into consideration in this analysis. The samples whose probe sets measured present for both arrays but only in one of the two sample types would be considered present and unique for a particular sample type (e.g., pericycleunique or 3 dpi incompatible syncytia-unique) ( Figure 5). Probe sets that measure detectable amounts of gene activity in both sample types can either be differentially expressed or not differentially expressed ( Figure 6). The differential expression calls used in some of the comparative analyses had been presented previously [26]. The DCM analysis presented here is employed as a different way of examining the data with the goal of identifying genes at low thresholds of expression that are missed in differential expression analyses. More importantly, DCM is also a way of identifying genes that may be expressed at high thresholds in one sample type and are undetectable in a second sample type used for comparative purposes in a differential expression analysis. In cases like these, statistical analyses cannot be done because no expression data is available for the second sample type and thus the probe set is excluded from the differential expression analysis. Therefore, probe sets that measured detectable amounts of a transcript uniquely in one sample type (e.g., unique-present) ( Figure 5) cannot measure differential expression ( Figure 6). An example of genes identified in a comparative analysis of two hypothetical gene pools (Figure 7) illustrates the different gene categories investigated in the analysis ( Figure 8). As illustrated, all genes that are identified as differentially expressed had to be present in each gene pool ( Figure 7). It became clear from the analysis that many genes that were unique to a specific sample type (e.g., A or B) were being excluded from the differential expression analysis because the probe sets measured detectable levels of gene activity only in one of the two sample types (Figure 7). The probe sets that match this criterion, A or B and not A U B, became the focus of the analysis (Figure 8).

The DCM Identifies Many Genes Expressed in the Experimental Cell
Types. The DCM identified a total of 13,291 transcripts as being present between the pericycle, 3 dpi incompatible syncytium, 3 dpi compatible syncytium, and the 8 dpi compatible syncytium samples. Direct comparisons were made between each of the sample types. The analyses focused on two types of transcripts that were determined to be present. The transcript types are (1) unique and (2) common. Unique transcripts were defined as those that were present and found in only one of the two sample types being compared. Common transcripts were defined as those that were present and overlap between the two sample types being compared. Data from five of the comparisons (   Table 1  Gene count Gene count Gene count Gene count and common between the pericycle and 3 dpi incompatible syncytium sample (Figure 8(a) (supplementary Table 2)).
Only the genes that are present and common can be used for differential expression analyses because expression data was available for each sample type. An analysis identified 940 genes present and unique to the 3 dpi incompatible syncytium sample (Figure 8(a) (supplementary Table 3)). Therefore, in the analysis presented here, a total of 1,942 genes within 3 dpi incompatible syncytial cell samples were present. Customized computer scripts were written to make seven comparisons of those genes. Some of these comparisons were made to genes identified previously as being differentially expressed between the two cell types under investigation [26]. A histogram of the functional categorizations of the 940 genes present and unique to the 3 dpi incompatible syncytium sample is presented (Figure 8(b)).

3 dpi Syncytia Undergoing a Compatible Reaction.
The DCM was used to compare genes within the 3 dpi microdissected syncytia undergoing a compatible reaction to the pericycle sample (Figures 8(c) and 8(d)). A total of 4,925 genes were present in these two samples. From these analyses, 711 genes were identified that were present and unique to the pericycle sample (Figure 8(c) (supplementary Table 4)). Further analysis identified 2,257 genes that were present and common between the pericycle and 3 dpi compatible syncytium sample (Figure 8(c) (supplementary Table 5)).
Only these genes could be used for differential expression analyses because expression data was available for each Table 1: Select genes that were unique to the 3 dpi syncytia undergoing an incompatible reaction but that were not differentially expressed as compared to a pericycle control sample (Figure 8(b) Figure 8) (supplementary Table 6)). Therefore, in the analysis presented here, a total of 4,214 genes were present within 3 dpi compatible syncytial cell samples. A histogram of the functional categorizations of the 1957 genes present and unique to the 3 dpi compatible syncytium sample described in this section is presented (Figure 8(d)). Selected gene lists comprising the (1) Disease and Defense, (2) Signaling, and (3) Transcription categories are provided ( Table 2 (supplementary Table 6)).

8 dpi Syncytia Undergoing a Compatible Reaction.
The DCM was used to compare genes within the 8 dpi microdissected syncytia undergoing a compatible reaction to the pericycle sample (Figures 8(e) and 8(f)). A total of 4,823 genes were present in these two samples. From these analyses, 656 genes that were present and unique to the pericycle sample were identified (Figure 8(e) (supplementary Table  7)). Further analysis identified 2,312 genes that were present and common between the pericycle and 8 dpi compatible syncytium sample (Figure 8(e) (supplementary Table 8)).
Only these genes could be used for differential expression analyses because expression data was available for each sample type. The detection call analysis identified 1,855 genes present and unique to the 8 dpi compatible syncytium sample (Figure 8(e) (supplementary Table 9)). Therefore, in the analysis presented here, a total of 4,167 genes within 8 dpi compatible syncytial cell samples were present. A histogram of the functional categorizations of the 4,167 genes present and unique to the 8 dpi compatible syncytium sample described in this section is presented (Figure 8(f)). Selected gene lists comprising the (1) Disease and Defense, (2) Signaling, and (3) Transcription categories are provided (Table 3 (supplementary Table 9)).

Direct Comparison: 3 dpi Incompatible versus 3 dpi Compatible Syncytia.
The DCM was used to compare genes within the 3 dpi microdissected syncytia undergoing an incompatible reaction directly to the 3 dpi syncytia undergoing a compatible reaction (Figures 8(g) and 8(h)). A total of 4,793 genes were present in these two samples. From these analyses, 2,851 genes were identified that were present and unique to the 3 dpi compatible syncytium sample (Figure 8(g) (supplementary Table 10)). Further analysis identified 1,363 genes that were present and common between the 3 dpi syncytia undergoing compatible and incompatible reactions (Figure 8(g) (supplementary Table 11)). Only these genes could be used for differential expression analyses because expression data was available for each sample type. The detection call analysis identified 579 genes present and unique to the 3 dpi incompatible syncytium sample (Figure 8(g) (supplementary Table 12)). A histogram of the functional categorizations of the 579 genes present and unique to the 3 dpi incompatible syncytium (as directly compared to the present and unique to the 3 dpi compatible syncytium sample genes) described in this section is presented (Figure 8(h)). Selected gene lists for the incompatible syncytium (Table 4) and compatible syncytium (Table 5) comprising the (1) Disease and Defense, (2) Signaling, and (3) Transcription categories are provided.

Direct Comparison: 8 dpi Compatible versus 3 dpi Compatible Syncytia.
The DCM was used to compare genes within the 8 dpi microdissected syncytia undergoing a compatible reaction to the 3 dpi syncytia undergoing a compatible reaction (Figures 8(i) and 8(j)). A total of 5,475 genes were present in these two samples. From these analyses, 1,308 genes were identified that were present and unique to the 3 dpi compatible syncytium sample (Figure 8(i) (supplementary Table 13)). The detection call analysis identified 2,906 genes that were present and common between the three and 8 dpi syncytia undergoing compatible reactions (Figure 8(i) (supplementary Table 14)). Only these genes could be used for differential expression analyses because expression data was available for each sample type. Further analysis identified 1,261 genes present and unique to the 8 dpi compatible syncytium sample (Figure 8(i) (supplementary Table 15)). A histogram of the functional categorizations of the 1,261 genes present and unique to the 8 dpi compatible syncytium sample (as directly compared to the present and unique to the 3 dpi compatible syncytium sample genes) described in this section is presented (Figure 8(j)). Selected gene lists comprising the (1) Disease and Defense, (2) Signaling, and (3) Transcription categories are provided ( Table 6 (supplementary Table 15)).

Discussion
Microarray experiments typically rely on differential expression analysis methods to identify differences in relative levels Table 2: Select genes that were unique to the 3 dpi syncytia undergoing a compatible reaction but that were not differentially expressed as compared to a pericycle control sample (Figure 8(d)   of gene expression. However, it is possible that very large differences in gene activity are present when the analysis involves comparing gene expression within homogeneous populations of cells, especially cells that are at different stages of differentiation or become genomically reprogrammed as a consequence of a pathological infection. In the analysis presented here, the DCM was used as an alternative method to identify genes that are expressed in a particular cell type but not active in another cell type. Importantly, the resulting DCM analysis identified a group of genes that were present uniquely within a sample type. However, those same genes were eliminated by differential expression analyses methods because differential expression analyses require expression data from each sample in the comparison. The DCM is a statistically sound method based on a fourstep procedure. The procedude incorporates (1) removal of saturated probes, (2) calculation of discrimination scores, (3) P-value calculation using the Wilcoxon's rank test, and (4) making the detection call. The DCM has been used in a variety of analyses to understand gene expression in various experimental systems [4][5][6]. The DCM analyses have demonstrated the utility of the method. The DCM takes into consideration only the presence of the transcript as measured by the probe set on the microarray. Thus, DCM can be used as a measurement of the diversity of transcripts within those samples. In the analysis presented here, DCM identified thousands of genes in the 3 dpi incompatible and compatible syncytium samples, the 8 dpi compatible syncytium samples, and the pericycle samples that were isolated by LCM. The DCM, along with customized computer scripts, was then used to compare the transcripts present in those samples. The method allowed for the identification and comparison of transcripts that were found in those samples. The DCM analyses presented here identified transcripts that are found in the incompatible or compatible syncytium samples that did not meet the criteria in a differential expression analysis [26]. Thus, DCM provided a broader (or different) estimate of the similarities and differences in those samples. In all of the comparisons made, the samples exhibited substantial differences in transcript composition. The DCM demonstrated vast differences in transcripts when directly comparing 3 dpi incompatible to compatible syncytia, even though the anatomy of these cells at that time appears similar [26][27][28]33].
Comparisons of detection calls between the pericycle control and syncytia undergoing an incompatible reaction resulted in the identification of a disproportionate number of transcripts in the syncytia undergoing an incompatible reaction belonging to the "No Homology to Known Proteins" category. Conversely, the pericycle control had a disproportionate number of transcripts in the "Metabolism" and "Hypothetical Protein Supported by cDNA" categories ( Figure 8(b)). Detection calls can also be used to determine other features of the cells under study. For example, detection calls can be used to arrive at an estimate of how different (or similar) two cell samples or sample types are from each other. Thus, when examining the development of specialized cell types like the syncytium, certain types of gene activity can be identified and used for comparative purposes by using DCM. For example, rapid elicitation of gene expression can be followed by a return to preinfestation levels during a reaction to a pathogen [62,63]. The DCM may allow for the identification of genes expressed at lower levels that are not identified in a differential expression analysis. The DCM will also identify gene expression that is at a high threshold in one sample and absent in the other. This category of genes would be excluded in a differential expression analysis because no statistics can be performed on probe sets lacking expression data.
In this study, DCM aided in identifying additional genes expressed during nematode infection. In the analyses many genes from (1) Disease and Defense, (2) Signaling, and (3) Transcription categories that were unique to one sample type and thus excluded from a differential expression analysis were focused on because of their obvious importance in a variety of plant defense pathways. The analyses here focus on the incompatible analyses. Recent proteomic work by Afzal et al. [64] provides an additional bank of genes to obtain a broader understanding of H. glycines infection of G. max. The genes identified in the analysis make reasonable candidates for further functional tests [32].

DCM Is Used to Compare the 3 dpi Incompatible
Syncytium to Pericycle Cells. The DCM analyses identified genes that were present only in the incompatible syncytial cell sample as compared to the pericycle sample. The genes included various defense response genes. Some of these were DRRG49-C (CF809336), Pto-interacting-kinase (Pti) (BI970555), BOTRYTIS-INDUCED KINASE 1 (BIK1) (AW311265), and two leucine-rich repeat genes (LRRs) (AW348555, AW317282). LRRs near rhg1 exist [65]. The DRRG49-C gene is induced during pathogen attack [66]. The Pti-kinase [67] and BIK1 [68] are examples of genes that are induced during a response to pathogenic attack and are Table 3: Select genes that were unique to the 8 dpi syncytia undergoing a compatible reaction but that were not differentially expressed as compared to a pericycle control sample (Figure 8(f)   involved in important defense responses. The identification of a Pti-like kinase was particularly interesting. In L. esculentum, Pti4 and Pti5 are induced by the virulent Pseudomonas syringae pv. tomato, the nonhost pathogenic bacterium Xanthomonas oryzae pv. oryzae (strain PXO A avrXa10), and the nonpathogenic bacterium Pseudomonas fluorescens (strain 2-79) [69]. Interestingly, Pti kinases are observed at 3 dpi in both compatible and incompatible reactions. The Pti-kinase identified in the 3 dpi incompatible reaction was most closely related to Pti1 isolated from tomato [70]. In that analysis, Pti1 was shown to be phosphorylated by Pto and to be involved in the hypersensitive response [70]. The LRR genes have a long history as being important for plant defense [71,72]. The genes also have been shown to confer resistance to parasitic nematodes [73][74][75][76][77][78]. Thus, due to the transient nature of expression of some of these genes in other systems, it is not surprising that they were not identified as being differentially expressed in syncytium samples [26].

DCM Is Used to Compare the 3 dpi Incompatible Syncytium Directly to the 3 dpi Compatible Syncytium.
The DCM analyses identified genes that were present only in the 3 dpi incompatible syncytium as compared directly to the 3 dpi compatible syncytium sample (supplementary Table 12). The probe sets included genes like Table 4: An analysis compared the 3 dpi syncytia undergoing an incompatible reaction directly to the 3 dpi compatible syncytium samples. Selected genes that were unique to the 3 dpi syncytia undergoing an incompatible reaction but that were not differentially expressed as compared directly to the 3 dpi compatible syncytium samples (Figure 8(h)) comprising the Disease and Defense, Signaling, and Transcription categories are provided.
I-3 dpi genes in the comparison of I-3 dpi to C-3 dpi  Table 5: An analysis compared the 3 dpi syncytia undergoing an incompatible reaction directly to the 3 dpi compatible syncytium samples. Selected genes that were unique to the 3 dpi syncytia undergoing a compatible reaction but that were not differentially expressed as compared directly to the 3 dpi incompatible syncytium samples (Figure 8(h)) comprising the Disease and Defense, Signaling, and Transcription categories.
C-3 dpi genes in the comparison of I-3 dpi to C-3 dpi  Journal of Biomedicine and Biotechnology  haem peroxidase (AW234624), DRT100 (AI443411), thaumatin (CF807955), defender against cell death-1 (DAD-1) (CA936764), polyphenol oxidase (BE659266), calcium dependent protein kinase (AW350917), constitutive photomorphogenic 9 (COP9) subunit 3 (BI968757), WRKY 52 gene (AW394946), and Nonexpressor of PR genes 1 (NPR1) (AW349633) in syncytia undergoing an incompatible reaction at 3 dpi. Importantly, these comparisons were made directly to syncytia undergoing a compatible reaction at 3 dpi. Haem peroxidase [79], DRT100 [80,81], thaumatin [82], DAD-1 [83][84][85], polyphenol oxidase [86][87][88], calcium dependent protein kinase [89,90], COP9 subunit 3 [91,92], WRKY [93][94][95][96][97][98][99], and NPR1 [100] all perform important roles in defense and/or stress tolerance. Genes like polyphenol oxidase are known to exhibit intense, but transient expression after wounding [87] while a WRKY homolog (WRKY45) exhibits intense, but transient expression after infection [99]. The identification of genes that are known to experience rapid elicitation of gene expression as a consequence of wounding or infection followed by a rapid decline in expression is consistent with their absence from differential expression analyses. The absence could be due to the chronology of infection and syncytium establishment and maintenance [27-29, 36, 57-61].  [107]. Quantification of PPO protein functionality showed a 5-to 10-fold increase in PPO activity in the transgenic plants [107]. Consequently, the overexpressing PPO transgenic lines produce 15-fold fewer lesions as well as strong inhibition of bacterial growth [107]. Bacterial population growth counts demonstrate at least a 100-fold reduction of bacterial populations in the infected leaves [107]. Thus, PPO could provide a terminal step in plant defense and may provide a localized resistance reaction to H. glycines infection. WRKY transcription factor homologs, involved directly in plant defense, are also identified in syncytia undergoing a resistant reaction. WRKY transcription factors are important in defense [93-96, 98, 108]. Shen et al. [108] demonstrated that WRKY genes are important to the resistance response in the specific cells that contain the signaling proteins that are secreted by the pathogen. Shen et al. [108] demonstrated that this is accomplished through leucine rich repeat receptorlike kinase genes (LRRs) involved in resistance. Many LRRs are essential in gene-for-gene resistant (R) interactions [72]. Shen et al. [108] demonstrated that the signals were transduced through R-genes to WRKY transcription factors, resulting in resistance to the pathogen. Importantly, R genes have been shown to confer resistance to parasitic nematodes [73,75]. WRKY gene expression in the syncytial cells during the resistance response is consistent with their suggested roles in plant defense.

DCM Identifies Genes Involved in Defense in the Syncytia
The nonexpressor of PR genes (NPR1) (also known as nim1 (for noninducible immunity 1) and sai1) [109,110] is the regulator of salicylic acid-mediated defense. Mutants of NPR1 block SA signaling in A. thaliana [100,[110][111][112]. In the uninduced state, NPR1 exists in the cytoplasm as an oligomer. The oligomer is formed through intermolecular disulfide bonds [113]. Oligomerization is mediated by Snitrosylation of NPR1 by S-nitrosoglutathione which occurs at cysteine-156 [114]. During systemic acquired resistance (SAR), NPR1 experiences a thioredoxin-mediated reaction that results in its monomerization [114]. This monomerization is induced by mutations at residues Cys82 and Cys216 that facilitated NPR1 monomer accumulation. It also resulted in constitutive nuclear localization. Importantly, the monomerization promoted NPR1-mediated gene expression in the absence of the pathogen [114]. Experiments in A. thaliana using mutants in NPR1 (npr1-2 and npr1-3), impaired in SA signaling, demonstrate an increased susceptibility to the beet cyst nematode H. schachtii 24 Journal of Biomedicine and Biotechnology Table 6: Select genes that were unique to the 8 dpi syncytia undergoing a compatible reaction but that were not differentially expressed as compared directly to the 3 dpi compatible syncytium samples (Figure 8(j) [115]. Conversely, the npr1-suppressor mutation sni1 shows decreased susceptibility to the nematode [115]. Thus, the highly induced expression of thioredoxin during the resistance responses of G. max [PI 548402/Peking] is consistent with functional tests involving npr1-2 and npr1-3 in A. thaliana. Induced levels of NPR1 are not observed in syncytium samples of G. max [PI 548402/Peking] . Thioredoxin has been shown to be involved in this process [114]. Therefore, it is possible that thioredoxin transcription accompanies infection. Thus, thioredoxin could be recruited during the defense response to monomerize NPR1 already present in root tissues to accomplish the resistant reaction. Calmodulin dependent protein kinases (CDPKs) such as calmodulin kinase II (CaMKII) are proteins reliant on calcium for their proper function. The identification of CaMKII indicates that calcium may be playing important roles in resistance. Calcium performs many interesting cellular roles. Calcium, as a second messenger, encodes information through Ca +2 gradients, amplitude, and oscillation frequency [116]. Thus, proteins relying on Ca +2 gradients and calmodulin may be important during the establishment of the resistant reaction. CaMKII functions by decoding Ca +2 oscillation frequencies [117]. At the cellular level, calmodulin is implicated in successful plant-pathogen interactions by its interaction with CDPKs. For example, the arbuscular mycorrhizal interaction in Medicago truncatula requires the CDPK, DMI3 [118]. Other symbioses as well are dependent on CDPKs [118,119]. The expression analyses show that calmodulin may be performing some function analogous to those observed for the arbuscular mycorrhizal interaction in M. truncatula. 4.4. Orthogonality of the DCM. The DCM has resulted in the identification of probe sets that measure detectable amounts of gene activity in one cell type (present) while absent in the other cell type (Figures 7 and 8). The DCM analysis has also identified genes that were common to the two cell types under investigation. As would be expected, there is orthogonality of the DCM probe set lists as compared to probe set lists obtained by the differential expression analysis method. However, since statistical analyses for differential expression analyses can only happen if statistically significant (e.g., measuring present) amounts of gene activity are present in the two cell types under study (e.g., A B), many genes are eliminated from differential expression analyses. The elimination of the genes occurs because measureable amounts of gene activity as measured by a particular probe set are not present in one of the two samples under study. The exclusion of genes from differential expression analyses is probably less common and less of a problem when the RNA under study is obtained from a whole organism or whole organs (i.e., roots). The problem would be minimized in analyses of whole organisms or organs because they are composed of heterogeneous cell populations, each having unique gene expression programs. The RNA pools of those individual cell types become homogenized during the RNA extraction procedures. In contrast, LCM purifies cells to near homogeneity. Thus, gene expression of homogeneous samples of one cell type may be very different from gene expression found in their neighboring cells or a cell at an earlier point during its developmental process. As shown here, many genes are excluded from a differential expression analysis of nearly homogeneous populations of pericycle cells as compared to syncytia at various stages of their resistant or susceptible reactions. The genes identified in the DCM analyses that are present, but not differentially expressed, became the focus of the analysis presented here. As shown in the multiple analyses, genes that pertain to important classes of genes involved in various plant defense responses to pathogens have been identified by DCM.

Summary
The DCM was used to compare syncytium and pericycle samples isolated by LCM. The comparisons presented here are an alternative method of examining microarray gene expression data and are different from those presented in a differential expression analysis of the syncytium [26]. The DCM comparisons are powerful when considering that the cells under investigation are homogeneous (e.g., syncytia). The power of DCM is that it reveals that nearly homogeneous populations of cells have gene activity that is unique to each type. Importantly, differential expression analyses would miss the uniqueness of gene activity of the various cell types because gene expression data is required from each cell type for the analysis to be performed. Therefore, differential expression analyses actually may be underestimating the uniqueness of gene activity profiles for the different cell types under study. The genes identified here represent an additional and significant pool to take into consideration and explore with regard to the interaction between G. max and H. glycines. The genes can be investigated in functional analyses to study the interaction between G. max and H. glycines [31,120]. In the broader sense, DCM should be seriously considered as an analysis tool when comparing homogeneous populations of cells.

Abbreviations
EST: Expressed sequence tag hpi: Hours post inoculation dpi: Days post inoculation J2: Second stage juvenile FS: Farmer's solution PFA: Paraformaldehyde DEPC: Diethylpyrocarbonate LCM: Laser capture microdissection MRS: Moisture replacement system DCM: Detection call methodology.


extfieldsofstudy:
Biology
Computer Science
Medicine

s2fieldsofstudy: Biology Chemistry

text:
Virtual Screening of potential drug-like inhibitors against Lysine/DAP pathway of Mycobacterium tuberculosis

Background An explosive global spreading of multidrug resistant Mycobacterium tuberculosis (Mtb) is a catastrophe, which demands an urgent need to design or develop novel/potent antitubercular agents. The Lysine/DAP biosynthetic pathway is a promising target due its specific role in cell wall and amino acid biosynthesis. Here, we report identification of potential antitubercular candidates targeting Mtb dihydrodipicolinate synthase (DHDPS) enzyme of the pathway using virtual screening protocols. Results In the present study, we generated three sets of drug-like molecules in order to screen potential inhibitors against Mtb drug target DHDPS. The first set of compounds was a combinatorial library, which comprised analogues of pyruvate (substrate of DHDPS). The second set of compounds consisted of pyruvate-like molecules i.e. structurally similar to pyruvate, obtained using 3D flexible similarity search against NCI and PubChem database. The third set constituted 3847 anti-infective molecules obtained from PubChem. These compounds were subjected to Lipinski's rule of drug-like five filters. Finally, three sets of drug-like compounds i.e. 4088 pyruvate analogues, 2640 pyruvate-like molecules and 1750 anti-infective molecules were docked at the active site of Mtb DHDPS (PDB code: 1XXX used in the molecular docking calculations) to select inhibitors establishing favorable interactions. Conclusion The above-mentioned virtual screening procedures helped in the identification of several potent candidates that possess inhibitory activity against Mtb DHDPS. Therefore, these novel scaffolds/candidates which could have the potential to inhibit Mtb DHDPS enzyme would represent promising starting points as lead compounds and certainly aid the experimental designing of antituberculars in lesser time.

Background
Causing massacre especially in Asia and Africa, Tuberculosis (TB) prevalence and mortality rates have probably been mounting globally for last several years [1]. Further, association of TB with HIV patients and emergence of multiple drug-resistant Mycobacterium tuberculosis (Mtb) to isoniazid and rifampicin and extensive drug-resistant Mtb to any floroquinolone, amikacin and capreomycin is a growing alarm. Despondently, more than two million people happen to be victim of TB annually and globally [2][3][4]. World Health Organization (WHO) 2008 report has mentioned the statistics regarding the occurrence of 9.2 million new cases and 1.7 million deaths from TB in 2006, out of which 0.7 million cases and 0.2 million deaths were in HIV-positive patients [5]. These numbers observed to be boosted compared with those reported by the WHO for the previous years. Therefore, discovery of novel unexploited drug target enzymes and their inhibitors besides generating analogues of existing drugs is a major challenge in the field of drug discovery and designing.
The amino acids play a major role in defining the cellular growth, cell wall and protein synthesis of bacterial system. Importantly, the absence of de novo synthesis of protein building blocks and requirement of amino acids as dietary components in mammals implies that specific inhibitors of amino acid biosynthetic pathways would display a novel class of antibacterial agents through inhibition of cell wall and protein synthesis with no mammalian toxicity. For past few years, Lysine/DAP biosynthetic pathway has been gaining high attention due to its foremost feature in the synthesis of D, Ldiaminopimelic acid (meso-DAP) and lysine. Both components are essential for cross-linking peptidoglycan chains to provide strength and rigidity to the bacterial cell wall [6][7][8]. It has been observed that Mycobacterium cell walls are characterized by an unusual high DAP content. Moreover, gene-knockout experiments with Mycobacterium smegmatis has demonstrated the essentiality of DAP pathway for the bacteria, where the absence of DAP results in cell lysis and death [9]. In view of its importance, the designing of potential inhibitors against any enzyme of this pathway may display a novel classes of antitubercular agents.
Till date, designing of inhibitors against DHDPS (mainly from E. coli) is being carried out using experimental procedure; however, no potent inhibitors have been reported. However, analogues of pyruvate such as αketobutyrate, α-ketoglutarate, glyoxylate and fluoropyruvate have been shown to be competitive inhibitors of DHDPS with respect to pyruvate. Additionally, few inhibitors based on DHDP or HTPA structures showing weak to moderate inhibitory activity is also reported [22][23][24]. Recently, Mitsakos et al [25] has demonstrated that several experimentally known inhibitors displayed a clear differentiation in inhibition of DHDPS enzymes from different bacterial species, hence, suggested that designing of inhibitors against DHDPS should be specific to bacterial species rather than a broad-spectrum inhibitor.
Keeping in view, the importance of DAP pathway in Mtb and low outcome of DHDPS inhibitors using experimental procedures, we have made an attempt to screen inhibitors of Mtb DHDPS using virtual screening procedures. The present work of screening of DHDPS inhibitors is reported for the first time, hence, would be a great help in aiding the experimental studies and rational development of novel drugs against Mtb.

Generation of combinatorial library of pyruvate analogues
The LeadGrow module of VLifeMDS [26] provides facility for the generation of combinatorial library starting with a template structure (also called as core/ scaffold) to which the side chains are attached/substituted from the list of given substituents. Thus, if X1 and X2 are two substitution sites on the template, where X1 has L and X2 has M choices for the attachment/ substitution of atoms/groups, then a library with L × M number of compounds can be generated. In the present study, the substrate pyruvate was used as a template for the library generation and three substitution sites namely X1, X2, X3 were chosen as shown in Figure 1, where the atoms/groups can be substituted. For X1 site, the substitutions were carried using non-toxic element such as N, O, F, P, S, Cl, Br, I, whereas for X2 and X3 the alkyl (ethyl, isopropyl, butyl, isobutyl, tertiary butyl, methyl), alkene (vinyl, allyl), acids (carboxyl, carbonic), ketone (methylketone), carbomethoxy (acetate, carbomethoxy), charged (carboxylate), and others (amine, carbonyl, -O-CH 3 , -OC 2 H 5 , amide, cynide, cynate, isocynate, -C = N, -N = C, azo, hydrazo, nitroso, nitro, sulfone, -S = OOO, oxime, CH 2 -C = N) subsitutions were applied. The generated combinatorial library was subjected to Lipinski's rule of five filters [27] to get drug-like orally-bioavailable compounds. Finally, we were left with 4088 pyruvate analogues to be docked for the screening of potential inhibitors against DHDPS.

Pyruvate-like molecules
Since the similarities in structures are indicative of similarities in bioactivities, therefore, structure based searching of databases/libraries has been gaining high demand nowadays [28,29]. In the present study, VLifeMDS package was used for carrying out 3D flexible search (using pyruvate as a template) against most popular databases i.e. National cancer Institute (NCI) Open database with~2,60,071 compounds, and a library of sub/superstructures of pyruvate from Pub-Chem database which constituted 21,061 compounds. These databases were first subjected to Lipinski's rule of five constraints (mentioned earlier), which reduced their size to~160,000 and 17,157 compounds respectively. The searching was based on 3D superimposition, where the query pyruvate and the compounds present in the databases were structurally superimposed for rmsd calculations, in order to check whether the atoms in the match mapping meets the spatial constrains (distance, angle, dihedral) in a query or not.
The databases generally store only a single low-energy conformation or a limited number of conformations for each compound, which may lead to the reduction in the hit rates. Therefore to make the search more effective, the 3D flexible search was carried out in the present study. Here, the conformers for each compound was generated using Metropolis Monte Carlo simulations, which explores the compound's conformational search space using random moves by altering torsion angle values. Here, the tolerance limit (which defines the rmsd cutoff) was set to 50%, such that the hits with rmsd value greater than 50% were discarded. Following the 3D search, 291 and 2349 compounds from NCI and PubChem libraries were retrieved. Finally, we got total 2640 molecules, which are structurally similar to pyruvate molecules. In this study, these molecules will be called pyruvate-like molecules.

Anti-infective molecules
Additionally, 3847 anti-infective compounds, consisting of 1743 antibacterials were retrieved from PubChem database. Out of 3847, only 1750 anti-infectives satisfied the Lipinski's rule of five constraints. These compounds were highly diverse from the pyruvate such that none of the anti-infectives showed 2D/3D similarity with pyruvate. Hence, the docking of these compounds would help to screen the diverse classes of antitubercular agents against Mtb DHDPS.

Ligand-receptor flexible docking
To find the binding affinities between target receptor and screened out compounds, an automated flexible docking of ligands at the flexible active site of receptor was carried out using AutoDock (v.4.0) software [30]. The software facilitates the internal degree of freedom along with the values of translation and rotation for the side chains of selected active residues as well as for the ligands in search of its suitable bound conformations. Undoubtedly, introduction of flexibility makes the docking process computationally more expensive but more superior than rigid ligand-receptor docking. Before docking process, several separate pre-docking steps: ligand preparation, receptor preparation and grid map calculations were performed. The ligand and receptor preparation stage involved the addition of hydrogen atoms, computing charges, merging non-polar hydrogen http://www.biomedcentral.com/1471-2105/11/S1/S53 atoms and defining AD4 atom types to ensure that atoms conformed to the AutoDock atom types. Next, information about rotatable torsion bonds that defines the bond flexibility was acquired. The ligands and receptor molecule preparation was followed by grid construction using AutoGrid module. During grid construction, atom types of the ligand, which acted as probes in the calculation of grid maps, were identified. The grid with default volume of 40 × 40 × 40 Å with a spacing of 0.375 Å centered on the receptor was prepared. For conformational search, the docking calculations using the genetic algorithm (GA) procedure with default parameters was performed. The GA computed the fitness of a docked candidate every time by measuring the minimum values of free energy binding (ΔG) based on different types of energy evaluations. In the present study, the python scripts were used for carrying out automated docking process.

Results and discussion
In the present study, different virtual screening approaches were used for selecting potential inhibitors against Mtb DHDPS. The first approach employed the generation of combinatorial library i.e. analogues of pyruvate. In the second approach pyruvate-like molecules generated using 3D flexible similarity search against available databases/libraries. Thirdly, to screen diverse classes of antitubercular agents, drug-like 1750 anti-infectives available at PubChem database were retrieved. Finally, these three sets of compounds i.e. generated pyruvate analogues, pyruvate-like molecules and anti-infectives were docked into the active site of receptor with the purpose of sorting potential inhibitors of Mtb DHDPS.
The three-dimensional crystal structure of Mtb DHDPS stored in the PDB file (code: 1XXX) [20] was obtained from protein databank. Mtb DHDPS is a homotetramer and each subunit with 300 amino acid residues comprises: N-terminal (β/α) 8 -barrel domain (residues 1-233) and a C-terminal domain (residues 234-300), which consists of three α-helices. The residues bounded the active site are THR54, THR55 TYR143, ARG148 and LYS171. Particularly, LYS171 responsible for substrate binding and catalysis are located at the centre of each monomer in the (b/α) 8 -barrel domain facing the central cavity of the tetramer. In E. coli DHDPS enzyme the equivalent residue is LYS161. Using PYMOL software, all the 1587 water molecules, eight DTT molecules, eight Mg 2+ and eight Clions were removed.
For pyruvate analogues, ΔG values in the range of -9.07 to -3.37 (kcal/mol) was obtained. Approximately 374 compounds were found to be docked with ΔG values better than pyruvate. Importantly, top 10 hits exhibited strong binding by yielding free energy binding values superior than -8.43 (kcal/mol) (Table 1). Further, we The top ligands were found to be lying deep into the binding cavity exhibiting all major interactions such as hydrogen bonding, Van der Waals (VDW) and hydrophobic (shown in Figure 3A and 3B). Nevertheless, hydrogen bonding with LYS171 as well as with other conserved active site residues was found to be dominating. For instance, the predicted inhibitors formed hydrogen bonds with LYS171-NH2, ARG148-NH2, TYR143-OH, THR54-OH and THR55-OH. Here, we have shown the docking poses of top 3 compounds (or their conformers) i.e Pyruvate_16012, Pyruvate_14540 and Pyruvate_10444 (Figure 4), revealing the binding of these compounds at the active site much better than substrate and experimentally known inhibitors. Though, the importance of ARG148 has not been elucidated in Mtb DHDPS, however, the conserved equivalent residue i.e. ARG138 from E. coli DHDPS has been demonstrated to be playing imperative role in binding, specifically feed back inhibition for (S)-lysine. Hence, the present observation i.e. involvement of ARG148 in binding with few potential inhibitors could be further exploited experimentally. The structures of top 10 compounds docked with highly favorable scores i.e. more negative values than substrate receptor complex are also given in Table S1 (see Additional file 1). Most of these hits hold the chemical structure feature group such as oxime, sulfone, carboxylate, carbonic, carboxyl, nitroso, and nitro. Furthermore, we have also calculated the correlation between molecular weight and ΔG values for these top hits. However, very low correlation value of -0.28 was obtained which indicated that the predicted binding affinity was mainly due to specificty and not due to molecular size.

Docking of pyruvate-like molecules
The virtual screening using 3D similarity based search could provide two main advantages-i) helps to narrow down the size of large databases/libraries to be screened, which eventually reduces the computational time required to dock each library compound and ii) 3D searching retrieves the compounds, out of which some are the same class of compounds to which query  belongs, but some others may be entirely new classes of compounds, which may directly lead to the discovery of novel lead compounds.
The docking of 291 and 2349 pyruvate-like compounds (structurally similar to pyruvate) retrieved from NCI and PubChem was carried out using the same docking protocol as mentioned earlier for pyruvate analogues. For NCI library, 50 pyruvate-like compounds provided free energy binding values better than pyruvate control, wheras 8.9% of the compounds were not able to fit perfectly into the binding pocket, yielding positive free energy binding values. The ΔG values in the range of -9.03 to -8.16 (kcal/mol) and ids of the top 10 NCI compounds are summarized in the Table 2. The structures of these hits are given in Table S2 (see Additional file 1). All these top hits were characterized by aromaticity along with presence of heteratoms i.e., N, Cl, Br in few compounds. These detailed analysis revealed that these 10 inhibitors fit very well into the binding pocket by establishing bonded and nonbonded interactions with active site residue as shown in Figure 5. Similar to the docking of pyruvate analogues, hydrogen bonding was found to be dominant interactions with LYS171, TYR143, THR54 and THR55. Furthermore, the correlation between molecular weight and ΔG values for these 10 inhibitors yielded lower value of -0.55.
The docking ΔG values for top 5 pyruvate-like compounds of PubChem are listed in Table 3. Out of 2349, only 250 compounds exhibited docking with free binding energy values better than the substrate. The tops hits were mainly aromatic, however only one compound (PUB20975287) was found to be aliphatic, which displayed strong binding by yielding ΔG value of -8.40 (kcal/mol). The interactions of these inhibitors with active site residue were mainly through hydrogen bonding, VDW and hydrophobic as shown in Figure 6 for the best docked compound i.e. PUB20975287. The structures of these top 5 hits are given in Table S3 (see Additional file 1)

Docking of anti-infectives
Besides, docking of very diverse 1750 anti-infectives structures was also carried out. Interestingly, 28.5% of the compounds provided positive ΔG values, revealing their inadequacy in adjusting into the binding pocket comfortably. Most of the remaining compounds though provided negative ΔG values, however lower than the substrate, hence, did not fit well in DHDPS binding pocket. Very few 25 anti-infectives provided ΔG values better than substrate and experimentally known inhibitors. Interestingly, the top 2 anti-infectives though provided -29 and -23 kcal/mol of ΔG values; however these failed to establish any interactions with LYS171. Table 4 and  Binding poses for top three pyruvate analogues. to fit comfortably in the binding pocket of DHDPS establishing bonded and non-bonded interactions very well as shown in Figure 7. The anti-infective PUB475318 is known by the name Cefmetazole, a semisynthetic cephamycin antibiotic with a broad spectrum of activity against both gram-positive and gram-negative microorganisms [31]. The compound was also explored during the high throughput screening for the identification of inhibitors against Mtb H37Rv [32]. On the other hand, PUB455194, which was ranked second during the screening process, is known as antiviral agent inhibiting virion. We have also shown in Figure 8, the docking poses of these top 2 anti-infectives establishing hydrogen bonding at the active site of DHDPS. Hence, docking of anti-infectives helped us in the identification of compounds with more diverse topology in comparison to pyruvate analogues or pyruvate-like molecules.

Figure 5
Docking of pyruvate-like NCI compounds. Top predicted inhibitors from NCI databases establishing interactions with active site residues of Mtb DHDPS.

Conclusion
To conclude, we have employed several virtual screening protocols such as generation of combinatorial library, 3D flexible search and molecular docking to identify potential inhibitors against Mtb DHDPS. Several potential drug-like inhibitors have been screened out showing strong binding affinity to Mtb DHDPS. Additionally, few anti-infectives with highly diverse topology from the pyruvate also displayed strong binding. Though experimental studies are indispensable to mark them as lead compound for the development of novel drugs against Mtb, however, screened out inhibitors would undoubtedly aid the experimental designing of antitubercular agents expeditiously.


extfieldsofstudy: Biology Medicine

text:
Study of Oostatic Peptide Uptake and Metabolism in Developing Ovaries of the Flesh Fly, Neobellieria bullata

The uptake and metabolism of the oostatic pentapeptide analogue of trypsin modulating oostatic factor (TMOF), H-Tyr-Asp-Pro-Ala-Pro-OH (5P), in ovaries of Neobellieria bullata (Parker) (Diptera: Sarcophagidae) were analyzed during their developmental stages. During selected stages of yolk deposition, the fate of [3HPro3]5P after its in vivo injection was compared to its uptake after in vitro incubation of dissected ovaries. The ovaries were analyzed from 30 s to 180 min after incubation. A detection sensitivity of 60–100 fmol of the labeled 5P was achieved using radio-high performance liquid chromatography. While the uptake of the applied radioactivity strongly depended on the stage of vitellogenesis, especially for the in vitro experiment, degradation of 5P was very quick and independent of whether the label was injected or incubated with the ovaries, regardless of the developmental stage of ovaries. No tracers of 5P were detected at 30 s after applying the labeled 5P in all tests.

Introduction
Insect ovaries undergo periodic changes during the course of their development. Polytrophic ovaries of flies pass through 14 developmental stages based on the deposition of yolk in their oocytes (King 1970). From the 1 st to the 6 th stage they contain no yolk; in stages 7-10 the growing oocytes continuously fill with yolk filling up to half of the egg chamber volume. In this process, the follicular cells that form a single layer surrounding the oocyte and nutritive cells play an important role. Nutrients (proteins, peptides, lipidic substances, and carbohydrates) are synthesized and stored in the fat body from which they are transported to the growing ovaries by the hemolymph. During previtellogenesis, the tightly packed cells of the follicular epithelium shrink under hormonal influence (Sevala and Davey 1993;Davey 2000;Pszczolkowski et al. 2005) and form intercellular spaces where cytoskeletal structures and protuberancies appear (Telfer et al. 1982;Fleig 2001). This patency of follicular cells enables the transport of nutrients to the growing oocyte and takes part in the synthesis of yolk components in the oocyte cytoplasm (Huebner et al. 1975;Kelly and Telfer 1979;Brennan et al. 1982;Fausto et al. 2005). The activity of follicular cells may vary with the demand of particular nutrients by the oocyte during a reproductive cycle (Davey 1996).
A study using the trypsin modulating oostatic factor (TMOF) of the mosquito, Aedes aegypti L. (Diptera: Culicidae), and the flesh fly, Neobellieria bullata (Parker) (Diptera: Sarcophagidae), for the investigation of their sterilizing effect on a partly autogenous strain of N. bullata had negative results, and, consequently, a new study was done of peptides with the C-terminal shortened sequence of Aed-TMOF (H-Tyr-Asp-Pro-Ala-Pro 6 -OH; Borovsky et al. 1990Borovsky et al. , 1994. The evaluation of morphological changes was done on the structures of the first and second egg chambers of N. bullata during the reproductive cycle. The greatest effects on developing ovaries were found after injection of the respective pentapeptide 5P (H-Tyr-Asp-Pro-Ala-Pro-OH) or tetrapeptide 4P (H-Tyr-Asp-Pro-Ala-OH) . In studies of the effects of 5P on vitellogenic stages (Hlavá ek et al. 1997(Hlavá ek et al. , 1998Bennettová et al. 2002;Slaninová et al. 2004) differences were found between morphological changes of both egg chambers: in the first one there were no visible effects, while in the second, proliferation of the follicular epithelium into the inner space of egg chamber, followed by resorption was observed ( Figure 1). Therefore, its 3 H-labeled forms were used for further studies on radioactivity accumulation and degradation of the oostatic peptides in the flesh fly N. bullata and other insects (Tykva et al. 1999Slaninová et al. 2004;Hlavá ek et al. 2007). On the other hand, the native TMOF of N. bullata, hexapeptide H-Asn-Pro-Thr-Asn-Leu-His-OH (Bylemans et al. 1994;de Loof et al. 1995), has no structural similarity to the above oostatic peptides and lacks any oostatic effects, nor does its isosteric analogue H-Asn-Pro [CH 2 O]-D-Thr-Asn-Leu-His-OH, (Bennettová, unpublished results;. Oostatic peptides represent an effective tool for insect control by inhibiting egg development Tykva et al. 2007).
Relative to other biologically active substances (e.g. pesticides, fungicides or juvenogens), oostatic peptides are relatively simple to synthesize and have no negative environmental impact . They are also soluble in water. However, their mode of action is not understood.
In this study, the uptake of an in vivo injected oostatic pentapeptide (5P) into ovaries of N. bullata was estimated at different stages of vitellogenesis (7-10) using radiolabeled peptide. Isolated ovaries were also incubated with radiolabeled peptide in vitro. The radioactive metabolites in the ovaries after in vivo and in vitro uptake of radiolabeled peptide were compared.

Radiolabeled peptide and developmental stages
Tritiated oostatic pentapeptide (5P) H-Tyr-Asp-[ 3 H]Pro-Ala-Pro-OH, [ 3 HPro 3 ]5P, 1.44TBq/mmol, with radiochemical purity > 98%, as described earlier Hlavá ek et al. (2007) was used in the study. Different stages of yolk deposition were classified according to the scale developed by King (1970). For in vivo applications the ovaries were divided into 2 groups (one with eggs in the 7 th and 8 th stages of development and the second with eggs in the 9 th and 10 th stages of development). For the experiments in vitro, each of these four tested stages of egg development were evaluated individually.
In vivo experiments [ 3 HPro 3 ]5P was injected in 5 μl (37 kBq) of physiological solution into left upper part of the thoraces of ether-anesthetized female N. bullata. Flies were then dissected at given time intervals, and 12 pairs of ovaries of the same developmental stage (7, 8, 9 and 10) were selected for each interval. Each pair of ovaries was placed into a separate scintillation vial and covered with 0.5 ml of tissue solubilizer (NCS II, Amersham International, www.gelifesciences.com). After six days, 10 ml of liquid scintillator EcoLite (ICN Biochemicals Inc.) were added and the radioactivity determined in the spectrometer Beckman 6500. The highest and lowest values of the sets were eliminated and the 10 remaining samples were used for evaluation of the mean values and their standard deviations. Three such experiments were carried out independently and from all of them the total mean values with their standard deviations (from ± 16% to ± 25%) were calculated. For metabolite determination by radio-HPLC, in each time interval 10-15 pairs of ovaries were dissected from the injected N. Bullata, pooled into groups according to developmental stage and frozen at -70°C until extraction.

In vitro experiments
Twelve dissected pairs of ovaries of identical stage and appearance were placed into a solution of [ 3 HPro 3 ]5P (555 kBq in 450 μl of physiological solution) in small embryo dishes at room temperature. At time periods equal to those for the in vivo experiments, the ovaries were removed, washed twice in physiological solution, and each pair was placed into individual scintillation vials for determination of total radioactivity and treated as described for the in vivo experiment. From three independent experiments, the total S.D. was calculated from ± 13% to ± 24%. Simultaneously, incubation for the selected time period was done for metabolite determination, and the sample was frozen as for the in vivo experiments.

Extraction for metabolite determination
An ice-cold solution (0.4 ml) of protease inhibitor cocktail Complete Mini (Roche Applied Sciences, www.roche-appliedscience.com) (1 tablet dissolved in 3.5 ml of 50 mM HEPES buffer pH 7.6) was added to the frozen pooled ovaries in an Eppendorf tube, and the contents were homogenized for 1 min using a Teflon pestel. After centrifugation, the supernatant was removed and either immediately analyzed by radio-HPLC or frozen and analyzed later.

Analysis of 5P metabolites
All radio-HPLC analyses were performed using a Waters liquid chromatograph (Waters, www.waters.com). A programmable UV detector was connected on-line to a radiometric flow-through detection system (Beckman 171, www.beckman.com). The stainless steel analytical column (250 x 4 mm) LiChroCART (Merck, www.merck.com), packed with LiChrosphere WP-300, with a particle size of 5 μm was used. The column was protected with a (4 mm x 4 mm) guard column packed with LiChrosphere 100 RP-18, particle size 5 μm (Merck). The mobile phase was composed of the aqueous phase (0.035% TFA in redistilled water) and the organic phase (0.05% in acetonitrile). After passing through the UV detector, the eluent was continuously mixed with the liquid scintillator Ready Safe (Beckman Coulter) with a ratio of 1:2.5 (v/v) in an on-line mixer. The mixture was run through a 500 μl detection cell. The radiometric detector threshold was set at 0.02%. The UV detector was set at 230 nm, 0.05 AUFS. Separation was performed at ambient temperature using a 30 min linear gradient from 0% to 30% organic phase using a flow rate of 0.8 ml/min and continuous degassing with helium. A sample volume of 20 to 80 μl was used. The area of each peak was evaluated as the ratio of its counting rate to the totally measured counting rate in all peaks of the appropriate radiochromatogram (relative concentration crel in percentage). The stability of the [ 3 HPro 3 ]5P was checked before each experiment. Samples were centrifuged for 5 min, and an aliquot of the supernatant was analyzed. Standard unlabeled peptides ) as well as non-active proline were detected by UV, and their retention times were compared to that of the peaks in the radiochromatogram. The retention times of radioactive fractions were corrected to the time delay between the UV and the radiometric detector (0.55 min). The precision of the method was expressed as the coefficient of variation in percentage that varied from 1.3 to 7.7%. It was determined by analyzing five replicates of the same biological sample within one day.
The extraction procedure recovery was evaluated using [ 3 HPro 3 ]5P calibration solutions of three different concentrations (42, 150 and 370 kBq/ml) by comparison of the extracted and the applied radioactivity. The average recovery was between 86.6% and 97.2% (n = 4) with precision range from 3.3 to 5.2 (% coefficient of variation). The linearity of the radiometric detector response was verified using [ 3 HPro 3 ]5P calibration solutions in the range of 0.2-10 kBq with an average correlation coefficient of 0.997 (n = 4). The absolute detection limit in the system, defined by a signal-to-noise ratio of 3, was assigned for the 5P in the range of 85-150 Bq that corresponds to 59-104 (rounded 60-100) fmol.

Results and Discussion
In this study, attention was focused on the uptake of the oostatic pentapeptide 5P (H-Tyr-Asp-Pro-Ala-Pro-OH) by ovaries of N. bullata. As a continuation of previous studies , the uptake was monitored in relation to the stage of egg development (vitellogenesis or yolk deposition) and tested in vitro. The total radioactivity in the ovaries ( Figure 2) was determined, and the radioactive components in extracts of ovaries were analyzed. Radiolabeled metabolites of [ 3 H-Pro 3 ]5P found in ovaries were identified using synthetic standards of non-labeled sequences ) as illustrated in Figure  3.
As can be seen in Figure 2, the time course of radioactivity uptake was different for in vitro and in vivo experiments. In the latter case, the radioactivity increased until 10 min after application, then decreased, and after 30 min  King (1970): 1: 7th and 8th; 2: 9th and 10th; 3: 7th; 4: 8th; 5: 9th; 6: 10th. Sets of 10 samples in 3 independent experiments were evaluated for each point, S.D. of the presented means from ± 16% to ± 25%. High quality figures are available online.
it was almost constant. No statistically significant differences were found between the two experimental groups (curves 1 and 2 in Figure 2). Such a time coarse could be explained by a single injection of [ 3 HPro 3 ]5P. On the contrary, for the in vitro experiments, radioactivity continuously increased because there was permanent contact of 5P with the ovaries. Nevertheless, there were statistically significant differences between the two following groups. With small yolk deposition (Figure 2, curves 3 and 4), the radioactivity increased slowly and continuously during the measured time interval. In the later developmental stages (Figure 2, curves 5 and 6), a rapid increase was found until 30 min, at which time the radioactivity was practically continuous, which was similar to the results of the in vivo experiment. In this time interval of practically stable radioactivity, the concentration of the 5P metabolites seemed to reach its maximum.
Independently of application method, the metabolites were qualitatively identical, and no 5P was found by 30 s after application (Table 1). This finding suggests an effective enzymatic system for peptide degradation. Such system may be located in the interfollicular spaces once their patency was evoked by a juvenile hormone or some other hormonal action (Davey 2000), on the oocyte membrane adjacent to the apical part of the follicular cells (Telfer et al. 1982), or possibly in the oocyte cytoplasm. The process of follicular cell patency is finished prior to the onset of vitellogenesis (Telfer at al. 1982). The active peptide intake increases with continuous yolk deposition even though a receptor responsible for oostatic peptide transport was not previously found .
Regarding the metabolites, the composition of radioactive substances was qualitatively almost identical during the entire period followed (30 s-180 min), both after incubation with the peptide in vitro and also after administration of the peptide in vivo. As can be seen in Table 1, there was a very rapid degradation of the 5P, which was not detectable in either case after 30 s. The same results were found for all tested stages during egg development. These results suggest that there is an extremely rapid metabolic cleavage of the N-terminal Tyr 1 , and the remaining 4P The differences between the in vivo and in vitro experiments may be explained by the partly metabolized 5P (in vivo), which is incorporated into the fat body. Then, as with other nutrients, it is delivered by hemolymph to the ovaries ). On the contrary, the in vitro intake of nonmetabolized 5P begins in the incubated ovaries, where only the 5P alone is available and the metabolism of ovaries takes place without any nutrient.
Very interesting is the fact that isolated ovaries with no connection to the tracheal system, the nervous system and/or the hemolymph were still able to use the 5P by a way other than simple diffusion and and to metabolize it to qualitatively identical metabolites as occured in intact ovaries. Previous studies revealed rich innervation of ovaries by branches of the median nerve that originate in thoracic ganglium, and, via the lateral oviduct, innervate each ovariole along the ovarian sheath (Bennettová-e ábová 1971). Neurosecretory cells were located in close connection with follicular cells and granules of neurosecretory material were present (Bennettová and Mazzini 1989). Such findings might be responsible for the independent functioning of ovaries in vitro, at least for a limited amount of time.

Conclusions
These results showed that the uptake of the oostatic 5P metabolites into the ovaries of N. bullata depended on the stage of the vitellogenic ovaries. Unlike the case after in vivo injection, the in vitro experiments show that ovaries that have low amounts of yolk also accumulate the metabolites at a slower rate. On the other hand, in later vitellogenic stages radioactivity quickly reached a maximum and then stayed almost constant, which was similar to the in vivo assay. In all tested ovaries, no 5P was found by 30 s after application, and the same 5P radiometabolites were detected. The results of these analyses of [ 3 HPro]5P and its metabolites seem to point towards the existence of an enzymatic system that very effectively degrades the 5P TMOF analogue during the transport into the egg chamber. An active intake of the analyzed sequences into the intercellular spaces between follicular cells may be assumed.