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Reelin Can Modulate Migration of Olfactory Ensheathing Cells and Gonadotropin Releasing Hormone Neurons via the Canonical Pathway

One key signaling pathway known to influence neuronal migration involves the extracellular matrix protein Reelin. Typically, signaling of Reelin occurs via apolipoprotein E receptor 2 (ApoER2) and very low-density lipoprotein receptor (VLDLR), and the cytoplasmic adapter protein disabled 1 (Dab1). However, non-canonical Reelin signaling has been reported, though no receptors have yet been identified. Cariboni et al. (2005) indicated Dab1-independent Reelin signaling impacts gonadotropin releasing hormone-1 (GnRH) neuronal migration. GnRH cells are essential for reproduction. Prenatal migration of GnRH neurons from the nasal placode to the forebrain, juxtaposed to olfactory axons and olfactory ensheathing cells (OECs), has been well documented, and it is clear that alterations in migration of these cells can cause delayed or absent puberty. This study was initiated to delineate the non-canonical Reelin signaling pathways used by GnRH neurons. Chronic treatment of nasal explants with CR-50, an antibody known to interfere with Reelin homopolymerization and Dab1 phosphorylation, decreased the distance GnRH neurons and OECs migrated. Normal migration of these two cell types was observed when Reelin was co-applied with CR-50. Immunocytochemistry was performed to determine if OECs might transduce Reelin signals via the canonical pathway, and subsequently indirectly altering GnRH neuronal migration. We show that in mouse: (1) both OECs and GnRH cells express ApoER2, VLDLR and Dab1, and (2) GnRH neurons and OECs show a normal distribution in the brain of two mutant reeler lines. These results indicate that the canonical Reelin pathway is present in GnRH neurons and OECs, but that Reelin is not essential for development of these two systems in vivo.

INTRODUCTION
Neuronal migration relies on complex networks of intracellular signaling pathways, and activation of these pathways is orchestrated by spatially and temporally distributed guidance cue ligands and their receptors. Reelin, a glycoprotein of the extracellular matrix (ECM; D' Arcangelo et al., 1995;Rice and Curran, 2001) regulates neuronal positioning during cerebral development (Tissir and Goffinet, 2003;Katsuyama and Terashima, 2009). Mice with homozygous reelin gene mutations, reeler (Reln rl ), develop a disorganized cortex in which late-born neurons fail to migrate past earlier-born neurons (D'Arcangelo et al., 1995; for review, see Rice and Curran, 2001;Tissir and Goffinet, 2003;Katsuyama and Terashima, 2009). Proposed functions of Reelin include stop, detachment, permissive, attractive, branch-inducing and laminar targeting signals (Tissir and Goffinet, 2003;Soriano and Del Río, 2005;Luque, 2007;Cooper, 2008). All of these processes are involved in neurons reaching their appropriate target location.
The canonical pathway first identified for Reelin was binding to very low-density lipoprotein receptor (VLDLR) and apolipoprotein E receptor 2 (ApoER2; Hiesberger et al., 1999). Binding of Reelin to these receptors leads to the phosphorylation, downstream signaling, and subsequent degradation of the adaptor protein Disabled 1 (Dab1; D' Arcangelo et al., 1999;Hiesberger et al., 1999;Howell et al., 1999;Arnaud et al., 2003). Inactivation of Dab1 by gene targeting or spontaneous mutation caused a reeler-like disorganized cortex (Sheldon et al., 1997;Arnaud et al., 2003) consistent with the Dab1 docking protein being an obligate component of the Reelin signaling pathway (Sheldon et al., 1997;Arnaud et al., 2003). However, non-canonical Reelin signaling has been reported (for review, see Bock and May, 2016). Of the proposed models, only two do not require Dab-1, with one of these models using alternative receptors as well (Bock and May, 2016). Examples of Dab-1 independent signaling include: (1) a Reelin-dependent effect on the migration of early-generated interneurons in the olfactory bulb, which was defective in lipoprotein receptordeficient mice but was not phenocopied in mice lacking Dab1 (Hellwig et al., 2012); (2) hypothalamic Gonadotropin Hormone-Releasing Hormone-1 (GnRH)-positive neurons, with a reduction in number and aberrant position of GnRH neurons in the hypothalamus of reeler, but not Dab1 or ApoER2/VLDLRdeficient mice (Cariboni et al., 2005); and (3) changes in lymphatic vascular development, which is defective in reeler mice but not in mice lacking Dab1 or both ApoER2 and VLDLR (Lutter et al., 2012). In none of these cases were the molecular mechanisms underlying the described Reelindependent phenotypes elucidated.
In vertebrates, reproduction is dependent on hypothalamic neurons secreting the neuropeptide GnRH, which regulates anterior pituitary gonadotropes and thus gonadal function. During embryonic development, GnRH neurons differentiate in the nasal placode and migrate to the hypothalamus apposed to olfactory-vomeronasal nerves (Schwanzel-Fukuda and Pfaff, 1989;Wray et al., 1989) and olfactory ensheathing cells (OECs). In humans, several monogenic, as well as digenic, disorders leading to idiopathic hypogonadotropic hypogonadisms (IHH) are caused by disruption of GnRH neuronal ontogeny/migration (Pitteloud et al., 2007). Identifying molecules regulating the development of the GnRH system will facilitate understanding pathogenesis of human IHH disorders as well as developmental processes involved in neuronal movement.
The present study was initiated to delineate the non-canonical Reelin signaling pathway used by GnRH neurons. Unexpectedly, VLDLR, ApoER2 and the adaptor protein Dab1 were found in OECs and GnRH neurons during embryonic mouse development. No defects in GnRH neuronal migration, olfactory axons and/or OECS entering the olfactory bulb, were found in reeler mice either prenatally or postnatally. However, chronic immunodepletion of Reelin in explants decreased both GnRH cell and OECs migration distance and this attenuation was rescued by co-application of Reelin. These data indicate that the Reelin pathway can modulate, but is not essential, for the development of GnRH neurons and OECs, and that both of these cell types possess the necessary components for Reelin signaling through the canonical pathway and as such, are not an appropriate model for identifying a non-conventional Reelin signaling pathway.

Animals
All procedures were approved by National Institute of Neurological Disorders and Stroke (NINDS) ACUC and performed in accordance with National Institutes of Health (NIH) guidelines.
In vitro: embryos were obtained from timed pregnant NIH Swiss mice at E11.5. Nasal explants devoid of brain tissue were generated, cultured and maintained as previously described (Fueshko and Wray, 1994;Klenke and Taylor-Burds, 2012). Briefly, nasal pits were dissected under aseptic conditions in Gey's balanced salt solution (Life Technologies Inc., Grand Island, NY, USA) supplemented with glucose (Sigma Chemical Co., St. Louis, MO, USA). Explants were adhered onto coverslips by a plasma (Cocalico Biologicals, Reamstown, PA, USA)/thrombin (Sigma) clot and maintained in a defined serum-free medium (SFM; Wray et al., 1991) in a humidified atmosphere at 37 • C with 5% CO 2 . On culture day 3, SFM was replaced by fresh SFM containing fluorodeoxyuridine (2.3 µM; Sigma) for 3 days to inhibit proliferation of dividing olfactory neurons and non-neuronal explant tissue.
Controls: omission of the first antibody produced no detectable signal (data not shown). The efficiency of the anti-rabbit FAB fragment blocker was verified by omission of the second primary antibody with all other conditions remaining the same. Application of the second fluorescent secondary antibody resulted in no double-labeled structures (data not shown) and glial markers did not stain GnRH cells when both were stained using rabbit antibodies (see Figure 4C).

Nasal Cartilage, GnRH Cell and OEC Isolations and RNA Extraction
Timed pregnant NIH Swiss mice were obtained according to an NINDS ACUC approved protocol. Pregnant mice were euthanized at embryonic day 11.5 and embryos harvested as for making explants (described above). Briefly, embryonic heads were removed, and the nose isolated from the brain by a single coronal cut. From this tissue, the midline nasal cartilage and surrounding mesenchyme was isolated. The midline tissue was then cut in half along the horizontal plane and ''top'' or ''bottom'' transferred to a tube on ice until all embryos from a single litter were processed. Tubes were then placed on dry ice until RNA extraction. One or two litters (average eight animals per litter) were processed for RNA isolation together. GnRH single cell isolation and single-cell RT-PCR were performed as described previously (Kramer et al., 2000). Briefly, nasal explants, were washed twice with PBS (without Mg 2+ or Ca 2+ ) and placed in 2 ml of the same solution. Single GnRH cells were identified under a Carl Zeiss inverted microscope (Zeiss, NY, USA) by their bipolar morphology, position and association with outgrowing axons. At two time points (4 and 10 days in vitro div), single GnRH cells were isolated from nasal explants and cDNA libraries made for both single cells and midline nasal cartilage (Kramer et al., 2000).
Primary cultures OECs were generated from olfactory bulbs of up to 1-week-old neonatal mice. Cells are purified by the differential cell adhesion method (Nash et al., 2001) and cultured for up to 2 weeks. Briefly, olfactory bulbs were collected and degraded in an enzyme mix (hyaluronidase (Sigma), dispase I (Sigma), collagenase type 4 (Worthington), DNAse Worthington; Au and Roskams, 2002) for 35 min (37 • C) with constant agitation. Cells were strained through a 40 µm cell strainer to remove non-dissociated tissue pieces and then washed with media (Dulbecco's minimum essential media (Gibco) and Ham's F-12 (Gibco), at a 1:1 mixture with 10% fetal bovine serum (Gibco), 1% antibiotic mixture PSN, Gibco) to remove enzyme residues. Re-suspended cell solution (4 × 10 6 viable cells/flask) was seeded into uncoated T75 flasks for 18 h to remove fast attaching fibroblasts. The supernatant of the first flask was seeded into another uncoated flask for up to 36 h to allow attachment of astrocytes. The final supernatant was seeded onto poly-L-lysine (Sigma)-coated flasks to grow primary OECs. Media was changed every 2-3 days. Cells were trypsinized, four coverslips seeded for staining to confirm phenotype, and the rest of the cells were processed for RNA extraction (RNAqueous kit; Ambion). Following extraction, cDNA was created using Moloney murine leukemia virus reverse transcriptase (Superscript TM III; Invitrogen). The purity of OECs was determined to be more than 90% based on p75 and S100B immunostaining.

Microarray Analysis
For microarray experiments on GnRH cells and midline nasal cartilage, all cDNAs were further processed (labeled and hybridized) by the DIRP NIH microarray core facility. GeneChip Mouse Genome 430 2.0 arrays (Affymetrix: Santa Clara, CA, USA) were used. The microarrays were checked for noise and outliers using custom R scripts, including covariance-based principal component analysis (PCA), correlation heat maps, LOWESS analysis and clustering. The data were normalized such that the average expression of genes in all chips was similar and log transformed. Since levels of receptors were generally an order of magnitude lower than housekeeping genes, receptor expression was analyzed as a group to determine background vs. positive signals. Data is given as maximum Robust Multiarray Averaging (RMA; Izadi et al., 2016). Previous work established that GnRH cells robustly expressed CXCR4 chemokine receptors (Toba et al., 2008;Casoni et al., 2012). The RMA value for CXCR4 receptors (5.7) was used for comparison of expression VLDL, ApoER2 and Dab1 values.

Functional Assays in Nasal Explants
To determine the function of Reelin in the developing GnRH/olfactory systems, nasal explants were used. In the first experiment two groups were compared, explants maintained in 1.5 ml of SFM containing 1 µg of either CR-50 monoclonal antibody (100 µg IgG in 100 µl PBS, MBL, International Corp., an antibody known to block the effects of Reelin (Ogawa et al., 1995;Nakajima et al., 1997;Zhao et al., 2004) or mouse IgG (control), added twice daily. Antibodies were diluted in 200 µl SFM, 200 µl removed from explant and antibodies added. Explants were treated at 3, 4, 5 div and fixed in 4% of formaldehyde at 6 div. Immediately after fixation, explants were processed immunocytochemically for GnRH neurons (control N = 12, treated N = 13), olfactory fibers (staining for peripherin, control N = 6, treated N = 5) or OECs (staining for S100 or Sox10, control N = 5, treated N = 3). GnRH cell migration, olfactory axon outgrowth and OEC migration were quantified. For GnRH cells and OECs, stained images were taken on a Nikon Eclipse E800 (Nikon USA, Melville, NY, USA) equipped with an ICCD camera (Retiga, Qimaging, Burnaby, BC, Canada). The digital acquisition was made through IPLab software (IPLab Spectrum, Scanalytics Inc., Rockville, MD, USA). The number of immunopositive cells was counted inside the main tissue mass (cell in, just for GnRH cells) as well as outside the main tissue mass in the periphery of the explant (cell out, both GnRH and OECs; Fueshko et al., 1998) using ImageJ software (Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, MD, USA 1 , 1997-2008). The main tissue mass contained the nasal pit/olfactory epithelial region, surrounding mesenchyme, and nasal midline cartilage (NMC). The periphery refers to the area surrounding the main tissue mass into which cells had spread and/or migrated. For olfactory axons, the distance, as measured by 200 µm zones in which the bulk of the fibers or cells were located, was recorded. Groups were compared using Mann-Whitney, p < 0.05 for significance.
The second experiment, similar to those described above, was performed using three groups: CR-50, G10 (an antibody against Reelin previously used for immunocytochemistry which has not been reported to block function, 100 µg IgG in 100 µl PBS, Millipore) and CR-50 + Reelin (1 µg Cr-50 + 5 µg Reelin/1.2ml; Recombinant mouse reelin protein 100 µg/ml in PBS, R&D Systems). The position of cells in the explant periphery was expressed relative to the tip of the cartilage i.e., the larger the number is, the further the cells migrated from the tissue mass. For each treatment, N = 5. The cell numbers were compared with a two-way ANOVA, followed by post-hoc Tukey's multiple comparisons test. The spatial distributions of the cells between treatments were compared using the non-parametric Kolmogorov-Smirnov (KS) test (p value < 0.001). To identify what proportion of the cells contributed most to the significance, cells were distributed in eight bins (60-120-µm wide, the outliers were combined in the last bin) and chi-square tests were performed (p value < 0.001).
Quantitative analysis of GnRH neurons was performed as a function of location with GnRH cells counted in three regions (nasal compartment, nasal/forebrain junction and brain). GnRH cell number/region and total number were analyzed using an ANOVA; p < 0.05 for significance (no difference was found as a function of age (p = 0.43) thus E13.5 and E14.5 were grouped; N = 5 control and 7 reeler). Serial coronal sections (16 µm, 4 series) from ≥PN 26 reeler and WT mice (N = 4 each, B6C3Fe a/a-Reln/J) were cut and labeled for GnRH as described above (GnRH immunoreactivity visualized using DAB substrate). Total number of GnRH cells were calculated and combined to give group means ± SEM. For examination on OECs and olfactory sensory axons, sections were stained with GFAP, Sox10 or p75 (for OECs), Tyrosine hydroxylase (glomeruli) or peripherin (olfactory axons). The section containing fibers entering the olfactory bulb were photographed, imported in ImageJ, digitized and density of fibers measured. Values were analyzed using an ANOVA; p < 0.05 for significance.

Reelin Immunodepletion Disrupts Migration of GnRH Neurons and OECs
A previous report suggested that Reelin plays a role in guiding GnRH neurons via a non-conventional signaling pathway (Cariboni et al., 2005). The present study was initiated to characterize this non-conventional pathway. Primary GnRH neurons maintained in nasal explants allow one to examine ligand/receptor/signaling pathways in a controllable environment (Fueshko and Wray, 1994;Klenke and Taylor-Burds, 2012). GnRH cells migrate in this model system, recapitulating many of the events seen in vivo. Expression of reelin transcript and protein in the nasal midline mesenchyme has been detected at E12.5 in mouse (Schnaufer et al., 2009). Since nasal explants are generated from E11.5 mice, the presence of reelin transcript and protein was first verified. Microarray analyses were conducted on material obtained from nasal midline cartilage. For this purpose, tissue samples were harvested at E11.5, a stage corresponding to the beginning of the GnRH migratory process. Reelin transcript was identified in this midline screen (5.47-6.22 mean RMA values) and immunocytochemistry confirmed expression of the protein in nasal explants (Figure 1). At 3-4 div, GnRH neurons have migrated from the inner tissue mass out into the periphery (Wray, 2010). At this stage, Reelin immunoreactivity was detected in a group of cells at the tip of the NMC, where GnRH cells emerge (Figures 1A,B). To determine whether perturbation of Reelin signaling would alter GnRH neuronal migration, explants were chronically treated from 3-6 div with CR-50, a Reelin antibody that recognizes the fulllength protein and interferes with Reelin homopolymerization thereby blocking the effects of Reelin (Ogawa et al., 1995;D'Arcangelo et al., 1997;Cariboni et al., 2005). The concentration of the antibody used to neutralize the activity of Reelin (1 µg/1.5ml media) was similar to that used by others in in vitro experiments (Cariboni et al., 2005). Treatment of 3-6 div was chosen because it is the temporal window during which large (B) Immunostaining with antibody against Reelin in a 3 div explant, shows strong expression of Reelin (brown, arrow) at the tip of NMC. Reelin is expressed by mesenchymal cells in the area where GnRH neurons migrate into the periphery (Compare large arrows in A-D). 7 div explants immunostained with antibody against GnRH (brown) after chronic treatment (from 3-6 div) either with vehicle (CTR) (C) or CR-50 antibody (D), an antibody known to block the effect of Reelin. Dash lines indicate border between the main tissue mass and the periphery of the explant, asterisk indicates tip of NMC. Under control (CTR) conditions, GnRH-positive cells are dispersed outside the main tissue mass (C, arrows), in the periphery of the explants. After CR-50 treatment, the majority of the GnRH neurons remained within the main tissue mass (D, arrows) and fewer cells migrated into the periphery. (E) Quantitative analysis of GnRH cell distribution in the inner tissue mass and in the periphery under the two experimental conditions. The total number of GnRH cells did not change between control and treated explants. However, following CR-50 treatment significantly more GnRH neurons remained within the inner tissue mass (Cells In) as compared to control conditions ( * P < 0.01; Student's t-test). Concomitantly significantly less cells under CR-50 treatments were counted outside the main tissue mass (Cells Out) compared to CTR ( * P < 0.01; Student's t-test). Scale bar = 100 µm in (C) (same magnification A,D). Scale Bar = 50 µm in (D).
numbers of GnRH neurons migrate from the inner tissue mass into the periphery of the explant (Fueshko and Wray, 1994). Treated and control explants were fixed at 7 div and stained for GnRH (Figures 1C,D). No significant differences were found in total number of GnRH cells present in control and CR-50treated explants, arguing against mitogenic or survival effects of Reelin on GnRH neurons. However, application of Reelin antibody severely stunted the migration of GnRH neurons, with significantly more GnRH cells remaining on the inner tissue mass of the explants as compared with controls (Cells In, Figure 1E; p < 0.01). In CR-50 treated explants, clusters of GnRH neurons were detected along the midline cartilage (Figure 1D, arrows). Concomitantly, fewer GnRH neurons were located in the periphery (Cells Out, Figure 1E). To determine whether Reelin receptive cells might be altering GnRH cell migration indirectly, rather than via a non-conventional signaling pathway, two other major components of this system were evaluated, olfactory sensory neurons and OECs. It is known that perturbation of olfactory axon extension can alter GnRH cell migration (Wray, 2010). In addition, a guidance role for OECs has been suggested (Cummings and Brunjes, 1995) and impaired maturation of OECs has been linked to olfactory axon tangle formation and misrouting, and defective GnRH neuron migration to the brain (Forni and Wray, 2012). Thus, olfactory sensory neurons and/or OECs could be responding to decreased Reelin and indirectly decreasing GnRH cell movement during treatment. Treatment of explants was performed as described above and olfactory axon extension examined after immunostaining with peripherin and OEC location examined after immunostaining with BLBP. In these experiments, the explant periphery was divided into 200 µm zones extending from the tip of the cartilage (Giacobini et al., 2004), and the zone with the bulk of the sensory axons or OECs recorded. On average, olfactory axons extended 6.3 zones (1.26 mm) in control explants (N = 6) and 5.6 zones (1.12 mm) in treated explants (N = 5). Although extension was less in treated explants, no statistical difference was found (p = 0.36; Mann-Whitney). In contrast, a significant decrease was detected in migration of OECs (Control: 1.13 mm, N = 5; treated: 0.84 mm, N = 3; p = 0.05; Mann-Whitney). These data indicated that Reelin signaling was altering movement of both GnRH cells and OECs. To evaluate whether OECs expressed the canonical signaling Reelin components and might be altering GnRH cell migration indirectly, immunocytochemistry was performed.

Reelin Receptors and Dab1 Are Expressed by OECs and GnRH Neurons
Immunocytochemistry for VLDLR, ApoE2 and Dab1 in nasal explants showed co-expression in OECs (Figures 2A1-2,B,C respectively). In addition, these experiments revealed that cells associated with OECs were also positive for these markers (boxed area in 2A1, arrowheads 2C). Based on their location and morphology these cells appeared to be GnRH neurons and subsequent staining (for both GnRH ( Figure 2D) and TUJ1 (a neuron-specific marker that strongly labels olfactory and vomeronasal structures, as well as GnRH neurons (De Carlos et al., 1996;Roskams et al., 1998 ; Figures 2E,F), revealed that the majority of GnRH-positive cells co-expressed VLDLR (Figures 2D1,D2; arrows), ApoE2 (Figures 2E1,E2) and Dab1 (2F1,F2). As expected, non-GnRH cells, positive for all three markers were detected (Figures 2D2,F2, green arrows), consistent with both GnRH cells and OECs expressing the Reelin canonical signaling pathway components. In contrast, olfactory axons exhibited VLDLR staining (green fibers in Figure 2D2; arrowheads) but appeared negative for ApoER2 ( Figure 2E) and Dab1 (Figure 2F). To confirm expression of these signaling molecules in GnRH neurons, microarray data previously generated in our lab was used (Kramer et al., 2000; OECs (green, S100, arrows). In addition, VLDLR highlighted positive cells associated with OECs (boxed area A1) that did not stain for OEC markers. OECs (B,C Sox10, red) co-expressed ApoER2 (B, green, example shown by arrow) and Dab1 (C, green, example shown by arrow). Sox10 negative Dab1 positive cells (arrowheads) were associated with OECs. GnRH cells (D-F, red, white arrows), co-expressed VLDLR (D, green) ApoER2 (E, green) and Dab1 (F, green). Numerous non-GnRH positive VLDLR cells (D2, green arrows) and Dab1 cells (F2, green arrow) were detected, consistent with OEC expression described above. TUJ1 was used to stain both GnRH cells and olfactory axons in (E,F). Although GnRH cells co-expressed ApoER2, olfactory fibers were devoid of staining (no fibers detected in E1 and fibers only red in E2). Dab1 was absent from the majority of olfactory fibers as well (F). Scale Bar = 100 µm in (A-C). Scale Bar = 50 µm in (D1,E1,F1); 100 µm in (D2,E2,F2), double labeled images of (D1,E1,F1), respectively. Insets in (D2,E2) correspond to black arrow in lower magnification. Giacobini et al., 2008) and single cell PCR performed. Microarray values for these receptors was compared to the chemokine receptor CXCR4, known to be robustly expressed by GnRH cells (Toba et al., 2008;Casoni et al., 2012). The RMA value for CXCR4 was 5.7. The RMA values for VLDLR, ApoE2 and Dab1 were 8.9, 5.9 and 5.0, respectively, indicating robust transcript levels for both VLDLR and ApoE2. Since Dab1 had the lowest microarray chip value (log scale), and is essential for the canonical Reelin pathway, presence of its transcript was verified by PCR in both OECs and GnRH cells (Supplementary Figure S1).
Since only 5% of GnRH neurons in E18 rat brains was reported to co-express ApoER2 (Cariboni et al., 2005) and none of the GnRH neurons were reported to be immunoreactive for Dab1, experiments were performed at developmental stages when GnRH cells and OECs are actively migrating (E12.5-E14, Figures 3-5), to ensure that expression of the Reelin canonical signaling pathway molecules in GnRH neurons and OECs maintained in explants reflected that occurring in vivo.
Co-expression of Dab1 in some migrating GnRH cells (Figure 3A1) was detected. In addition, many Dab1 positive non-GnRH cells were intermingled with GnRH cells along these tracts (Figure 3A2, red arrow). Immunofluorescence (Figures 3B1,B2) also revealed low levels of co-expression of GnRH and ApoER2 (Figure 3B2, yellow arrow). As with Dab1, other elements along the migratory pathway were also positive for ApoER2 (Figure 3B, white arrows). Double-labeling for GnRH and VLDLR ( Figure 3C) indicated that most GnRH cells robustly co-labeled with VLDLR ( Figure 3C, yellow arrow). Consistent with earlier data (Schnaufer et al., 2009), VLDLR was not detected in cells in the OE/VNO but was found on non-GnRH cells along the tracks (Figure 3C, red arrows) as well as potentially in the axonal tracks themselves. Examination of Reelin signaling molecules in OECs in vivo (labeled with S100 or GFAP) gave similar results to that found in explants. The majority of OECs along the migratory tracts expressed Dab1, ApoER2 and VLDLR (Figure 4) suggesting canonical Reelin signaling can impact OEC migration, as well as GnRH neuronal migration. It was unclear whether cells in the olfactory and vomeronasal organ epithelium expressed low levels of Dab1 or VLDLR (Figures 4A, 5, VNO). Dab1, if expressed on olfactory axons, was at a level much lower than that of surrounding OECs and GnRH cells (Figure 5A, red structures still detected, white arrowheads). VLDLR expression was detected on the axonal tracks ( Figure 5B2), but also at a level lower than that of surrounding OECs and GnRH cells (Figure 5B, asterisk). ApoER2 was not detected on cells in the sensory epithelium or on outgrowing axons. Thus, the in vitro and in vivo expression patterns demonstrate that OECs and GnRH neurons express the Reelin signaling pathway as they migrate into CNS, supporting a role for Reelin as a guidance cue for early migratory GnRH cells and/or for developing OECs. At E18, Cariboni et al. (2005) in fact, reported a decrease in the GnRH cell number in the brain of reeler mice compared to WT. To investigate the functional relevance of Reelin in the early development of the GnRH system, OEC development and GnRH cell migration was evaluated in reeler mice.

OEC Development and GnRH Neuronal Migration Is Normal in Reeler Mutants
Previously described phenotypes of reeler mice were verified in our specimens before subsequent analysis (Figures 6A-D). The first reeler mice analyzed in this study are characterized by a complete gene deletion of Reelin (strain B6C3Fe a/a-Reln/J; Jackson Laboratories). Previous studies showed that at E12.5 and E14.5, prominent expression of Reelin by with GnRH cells from the vomeronasal organ (VNO), and without GnRH cells from the olfactory epithelium), crossing to the nasal/forebrain junction (large arrow); ob, olfactory bulb; fb, forebrain. High magnification of area boxed in (A). Double labeling for Dab1 (red, A2) showed expression in some GnRH-GFP cells (green, yellowed merge, A3). Dab1-immunoreactivity was often confined to the rim of the GnRH cells but co-expression in leading processes was detected (A3, yellow arrow). In addition, Dab1 positive non-GnRH cells were located along the axonal tracts (A2, red arrow). (A3) is higher magnification of area shown by white arrow in (A2). (B) Immunofluorescence for ApoER2 (red) and GnRH-GFP (green) revealed co-expression in GnRH cells crossing the nasal mesenchyme (B2, higher magnification of B1, merged, yellow arrow, white arrow is same location in both B1,B2). ApoER2 was also detected on other elements along the migratory pathway (B, white arrows). (C) Double labeling for VLDLR (red) and GnRH-GFP (green) indicated that the majority of GnRH neurons expressed VLDLR (Boxed area in C1, shown at higher magnification, merged in C2, yellow and red arrows indicate same location in C1,C2). As with Dab1 and ApoER2, VLDLR was also detected on other elements along the migratory pathway (C, red arrows). Scale bar = 200 µm in (A1), 20 µm in (A2) (same in B1,C1) and 40 µm in (A3) (same in B2,C2).
Cajal-Retzius cells was detected just beneath the pial surface of the cerebral cortex corresponding to the marginal zone or the future layer I (Stanfield and Cowan, 1979;Ikeda and Terashima, 1997). The specificity of the reeler phenotype was confirmed immunohistochemically. Staining was detected in WT littermates but no Reelin-immunoreactive cells were detected in the marginal zone of the embryonic cortex (Figures 6A,B; Katsuyama and Terashima, 2009) or mitral cell layer of the OB of ≥PN26 mutant mice (Figures 6C,D; see arrows), confirming the mutant genotype.
Examination of embryos at E14.5 and adult brains from these reeler mice showed no obvious differences in ingrowth of olfactory axons (peripherin staining, data not shown), presence of OECs (Sox10, GFAP and S100 staining, data not shown) or glomeruli formation (tyrosine hydroxylase staining, data not shown) as compared to WT. In addition, Reelin-null mice at E13.5/14.5 revealed a normal complement of GnRH neurons in all anatomical regions analyzed and the total number of GnRH cells was similar between mutant mice and controls FIGURE 5 | Olfactory sensory neurons express Dab1 and VLDLR during mouse embryonic development. Sagittal sections of E14.5 mice immunostained with indicated antibodies. Olfactory axons identified by antibodies to peripherin (green). Dab1 (A1-A3, red), if expressed on olfactory axons, was at a level much lower than that of surrounding OECs and GnRH cells (A3, red structures still detected, white arrowheads). VLDLR expression (B1-B3) was detected on the axonal tracks (B2 inset), but also at a level lower than that of surrounding OECs and GnRH cells (Large cluster of VLDLR positive migratory cells is indicated by asterisk). Scale bar in (B1) = 25 µm (same in A1). Scale bar in (B3) = 50 µm (same in A1,A2,B1).
( Figure 6E). Since these data were in contrast to that reported by Cariboni et al. (2005), a later time point, ≥PN26 mice, was examined. The total number of GnRH cells in the brain was similar between reeler and WT mice ( Figure 6F; p > 0.05). The distribution of GnRH neurons, the morphology of the cells, as well as their terminals in the median eminence was unaffected when compared to WT mice from the same litter (Figures 7C-F), whereas abnormalities in the DGC layer lamination in the Reelin-null mice were detected as previously described (Figures 7A,B; Stanfield and Cowan, 1979;Förster et al., 2002;Weiss et al., 2003). In the Cariboni study (Cariboni et al., 2005) a different strain of reeler mutant mice was used. To insure our data was not mutant line specific, adult animals from the line used in the Cariboni study were obtained. This strain of reeler mice (reeler/Orleans, Relnrl-Orl) possesses a truncated Reelin protein, which is produced but not secreted (de Bergeyck et al., 1997). In adult Relnrl-Orl mice, the total GnRH cell number was 586 in heterozygous mice (N = 2) and 603 ± 99 in mutants (N = 3). The two different mutant mouse lines had a similar total number of GnRH neurons (p = 0.69). These experiments indicate that the lack of Reelin in vivo does not alter development  (Derer, 1985;Ogawa et al., 1995). (C) Sagittal OB section of PN26 WT mouse immunostained for Reelin (blue) and tyrosine hydroxylase (TH; brown). Strong Reelin-immunoreactivity is detectable in the mitral cell layer (mcl; arrows) as well as in the olfactory nerve layer (arrowheads). TH-immunostaining identifies the glomerular layer (gl) of the OB. (D) In the OBs of reeler mice, Reelin is totally absent. (E) Analysis of GnRH neurons location at E13.5/E14.5 revealed no differences in the total number of cells or in the distribution of the GnRH-positive cells across their migratory route in WT and in reeler embryos. The areas of analysis for GnRH neuron location along the migratory pathway were the nasal compartment (nose), the nasal/forebrain junction (n/fb j) and the brain, respectively. (F) Quantitative analysis of GnRH neuronal population in ≥PN26 brains revealed no differences in the Reelin mutants as compared to WT (P > 0.1). Quantitative data are mean cell number ± SEM. Bar in (C) = 100 µm (same in D).
or final position of GnRH neurons when analyzed as early as E13.5.
Since depleting Reelin in vivo vs. in nasal explants gave different results, a second set of experiments was performed in nasal explants to validate that the CR-50 antibody was blocking the Reelin pathway. Three explants groups were run using G10 (an antibody with no known functional effects, control), CR-50 (mutant-like) and CR-50 + Reelin (rescue). None of the treatments had an effect on the total number

DISCUSSION
Non-canonical signaling pathways of Reelin remain unclear (Senturk et al., 2011;Bock and May, 2016;Pohlkamp et al., 2016;Talebian et al., 2018). Based on a previous report (Cariboni et al., 2005), GnRH cells were a candidate for studying this phenomenon. However, this report documents the presence of the Reelin canonical signaling pathway (VLDLR, ApoER2 and Dab1) in GnRH cells as they migrate from nasal areas to the brain during embryonic development in mouse. In addition, we document VLDLR, ApoER2 and Dab1 in developing OECs. Chronic perturbation of Reelin in explants generated from E11.5 embryos decreased OEC migration and GnRH neuronal migration but not olfactory axon outgrowth, consistent with the expression data. However, normal migration of GnRH cells was found in two different lines of reeler mutant mice and no changes in OECs within the olfactory bulb were detected, suggesting that these two systems are rescued in reeler mice at a developmental time point after E11.5.
With respect to the GnRH cells, our data are in contrast to earlier work that found no Dab1 and little Reelin receptor expression in GnRH neurons (Cariboni et al., 2005). However, it should be noted that Cariboni et al. (2005) looked at the expression of these molecules in an immortalized mouse GnRH cell line and in vivo in GnRH cells in rats at a later developmental stages (E18). The expression of the full canonical Reelin pathway in OECs has not been previously reported. In agreement with an earlier study (Schnaufer et al., 2009), VLDLR expression was detected in olfactory axons but Dab1 highlighted non-axonal structures (GnRH cells and OECs), suggesting that olfactory sensory neurons do not have a functional canonical signaling pathway for Reelin. In nasal explants, peripherin, which marks olfactory sensory axons, was used to measure olfactory axon outgrowth. Chronic treatment with CR-50 did not significantly alter olfactory axon extension. These data are consistent with a report by Teillon et al. (2003) that found that the vomeronasal nerves were indistinguishable in normal and reeler mutant mice. In addition, Wyss et al. (1980) reported no significant alteration in either the layer of olfactory nerve fibers or the glomeruli themselves in reeler mutant mice (Wyss et al., 1980). In contrast to olfactory sensory cells, this report identifies that OECs, like GnRH neurons, express VLDLR, ApoER2 and Dab1. Although Antal et al. (2015) examined olfactory placode development in human fetal tissue and performed double immunostaining for Dab1 and S100 protein (which marks OECs), they were not able to conclude anything definitive about Dab1 expression in OECs. Notably in nasal explants, the migration of OECs was examined after chronic treatment with CR-50, and a significant decrease in OEC migration into the periphery was found. These data support our expression data and indicate that a functional Reelin signaling pathway is present in OECs. However, no changes in OECs within the olfactory bulb were observed in reeler mice, suggesting again that a rescue event occurs in reeler mice at a developmental time point after E11.5.
Examination of Reelin null mice did not show any deficits in the migration of GnRH neurons. Moreover, adult brains from reeler mice contained the normal complement and a similar distribution of GnRH cells. These results are in contrast with previous findings revealing a reduction of GnRH-immunoreactive neurons in the hypothalamus of reeler/Orleans mice compared to WT controls (Cariboni et al., 2005). Certainly, a plethora of Reelin-null models exists and have been successfully used to investigate the role of Reelin during development, and in the adult in regions of neuronal plasticity (Rice and Curran, 2001;Tissir and Goffinet, 2003;Katsuyama and Terashima, 2009). It is possible that different genetic backgrounds of the reeler mutants could result in phenotypes with various degrees of severity depending on the cellular compartment. Thus, the phenotype of the reeler/Orleans was analyzed. These two lines differ in that the first is characterized by a complete gene deletion of Reelin (B6C3Fe a/a-Reln/J; Jackson Laboratories) while the other one (reeler/Orleans) possesses a truncated Reelin protein which is produced but not secreted (de Bergeyck et al., 1997). In both lines, similar results were obtained. Normal development of the GnRH system was found, while other brain regions known to be affected in reeler mice (i.e., the hippocampus, the developing cortex and the olfactory bulb) were significantly compromised in both transgenic lines. The reasons for the discrepancies between the present study and Cariboni et al. (2005) are unknown. However, we often find variability in immunostaining of GnRH cells shortly after birth and either do counts later in development or verify with in situs. In mice, inactivating mutations of Dab1 (scrambler mice), and double mutations of ApoER2 and VLDLR generate reeler-like phenotypes (Tissir and Goffinet, 2003) but maintained the normal complement of GnRH neurons in the hypothalamic region. These data are consistent with our findings that the GnRH system is normal in reeler mutants.
It is clear that the adhesion, pathfinding and the rate of migration of GnRH neurons across the nasal mesenchyme is regulated by multiple factors expressed along their migratory route, including ECM proteins, adhesion molecules, chemokines, guidance molecules and neurotransmitters (Tobet and Schwarting, 2006). Therefore, compensatory mechanisms are likely to exist in the development of the GnRH as well as the olfactory system of Reelin-null mice since no deficits were found in these mice by E13.5. Functional analysis was performed in nasal explants, which are generated at E11.5 and lack brain tissue. These explants have been successfully used for functional studies (Fueshko et al., 1998;Kramer et al., 2000;Giacobini et al., 2004Giacobini et al., , 2007 since they maintain large numbers of GnRH neurons and OECs, migrating in a similar manner to that observed in vivo, as well as directed olfactory axon outgrowth (Fueshko and Wray, 1994). Reelin was expressed in the nasal cartilage of the explants, and the expression paralleled the in vivo distribution. When the activity of Reelin was abolished with chronic treatment with the CR-50 antibody, the GnRH cell migratory behavior was significantly altered as well as that of the OECs. Both cell groups did not migrate as far. However, no changes were detected in outgrowth of olfactory axons. The same concentration of this antibody has been previously used to interfere with the aggregation of Reelin and to block its function in vitro and in vivo (Ogawa et al., 1995;Miyata et al., 1997;Nakajima et al., 1997;D'Arcangelo et al., 1999;Kubo et al., 2002;Cariboni et al., 2005). In addition, we showed that the effects of CR-50 on GnRH cells and OECs could be blocked by addition of exogenous Reelin. These findings support the idea that endogenous Reelin modulates the migratory behavior of the GnRH system either directly and/or indirectly via OECs. We hypothesize that the lack of a gradient, over time, ceased movement of GnRH cells and OECs in explants, and that unknown endogenous cues not present in our explant model but present in vivo prior to E13.5 compensate for the disruption in Reelin signaling in vivo.
In sum, the data in this report indicate that the Reelin pathway can modulate, but is not essential, for the development of GnRH neurons and OECs, and that both of these cell types possess the necessary components for Reelin signaling through the canonical pathway and as such, are not appropriate models for identifying a non-conventional Reelin signaling pathway.

ACKNOWLEDGMENTS
We thank the DIRP NIH microarray core facility, Drs. A. Elkahloun and K. Johnson for labeling, Drs. K. Johnson and D. E. Donohue (CDNS, NINDS, NIH, Bethesda, MD, USA) for their help in analyzing the microarray data, Mr. H. Saadi (CDNS, NINDS) for sectioning of reeler embryos and Drs. A. Goffinet (Universite catholique de Louvain) and J. Herz (UT Southwestern) for their gift of antibodies. A special thanks to Dr. P. Phelps for brain tissue from reeler/Orleans mice as well as embryos.

SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fncel. 2018.00228/full#supplementary-material FIGURE S1 | Disabled 1 (Dab1) transcripts are preset in gonadotropin releasing hormone-1 (GnRH) cells and olfactory ensheathing cells (OECs). Top panel: band of the correct size (164 bps) for Dab1 was detected in cDNA produced from eight individual GnRH cells. Bottom panel: The purity of the OECs was confirmed by PCR for p75 (OEC positive, product size: 264 bps) and myelin basic protein (OEC negative, 282 bps). Like GnRH cells, OECs were positive for Dab1 transcript. Br = Brain (positive control), H 2 0 = water (negative control).
FIGURE S2 | Migration of GnRH neurons exposed to Reelin blocking antibody is rescued by co-incubation of Reelin. Three explants groups were run to validate that the CR-50 antibody was blocking the Reelin pathway. One group received G10 (an antibody with no known functional effects, control), one group received CR-50 (mutant-like) and the third group received CR-50 + Reelin (rescue). None of the treatments had an effect on the total number of GnRH cell in the periphery (GnRH: 229 ± 50 [mutant-like], 247 ± 89 [Control], 253 ± 85 [Rescue] N = 5 for each group). (A) The cumulative frequency of the distribution of GnRH cells in the periphery of explants treated with CR-50 and CR-50/Reelin is plotted. Exogenous Reelin blocked the effect of CR-50 (Kolmogorov-Smirnov (KS), p < 0.001), allowing GnRH cells to migrate further into the periphery (especially close to the endogenous reelin source which is at the tip of the nasal cartilage 0 on x-axis) (B). The cumulative frequency of the distribution of GnRH cells in the periphery of explants treated with G10 and CR-50/Reelin is plotted. No difference between the Control and Rescue groups was found (KS, p > 0.001).


s2fieldsofstudy: Biology Medicine

text:
Application of metabolomics to drug discovery and understanding the mechanisms of action of medicinal plants with anti-tuberculosis activity

Human tuberculosis (TB) is amongst the oldest and deadliest human bacterial diseases that pose major health, social and economic burden at a global level. Current regimens for TB treatment are lengthy, expensive and ineffective to emerging drug resistant strains. Thus, there is an urgent need for identification and development of novel TB drugs and drug regimens with comprehensive and specific mechanisms of action. Many medicinal plants are traditionally used for TB treatment. While some of their phytochemical composition has been elucidated, their mechanisms of action are not well understood. Insufficient knowledge on Mycobacterium tuberculosis (M.tb) biology and the complex nature of its infection limit the effectiveness of current screening-based methods used for TB drug discovery. Nonetheless, application of metabolomics tools within the ‘omics’ approaches, could provide an alternative method of elucidating the mechanism of action of medicinal plants. Metabolomics aims at high throughput detection, quantification and identification of metabolites in biological samples. Changes in the concentration of specific metabolites in a biological sample indicate changes in the metabolic pathways. In this paper review and discuss novel methods that involve application of metabolomics to drug discovery and the understanding of mechanisms of action of medicinal plants with anti-TB activity. Current knowledge on TB infection, anti-TB drugs and mechanisms of action are also included. We further highlight metabolism of M. tuberculosis and the potential drug targets, as well as current approaches in the development of anti-TB drugs.

Background
Infection with Mycobacterium tuberculosis (M.tb) results in tuberculosis (TB) [1], a pandemic disease [2] that mostly affects developing countries [3]. According to World Health Organization (WHO) 9.6 million new cases of the disease were reported in 2015 alone, with over 2 million deaths in the same year [4]. Tuberculosis either kills the infected individual or renders him/her incapable of assuming normal functions. Upon gaining entry into a new host, M.tb may result into an active infection or remain latent [5]. In the WHO global Tuberculosis report of 2014, it was estimated that one-third of world population is latently infected with TB [1,3]. Reactivation of latent TB in immune deficit people such as human immunodeficiency virus (HIV)-positive individuals [6] results into active TB [1,5,6]. Thus, TB is a leading killer in HIV-infected individuals [7,8]. A third of HIV death is caused by TB [9].
Mycobacterium tuberculosis is spread via infectious aerosols from infected person (Fig. 1).

Open Access
*Correspondence: ntuyiringire@std.must.ac.ug 1 Pharm-BioTechnology and Traditional Medicine Centre (PHARMBIOTRAC), Mbarara University of Science & Technology, P.O. Box, 1410 Mbarara, Uganda Full list of author information is available at the end of the article Bacilli in aerosols inhaled deep into lung alveoli lodge in terminal air spaces [1,6]. Infection starts when M.tb interacts with host target cells [10]. Target cells are mainly macrophages, dendritic cells and recruited neutrophils [5]. The bacteria are engulfed by phagocytosis, in attempt to destroy invading pathogens by the host cells. As an intracellular pathogen, living in a hostile environment of immune cells, M.tb evolved means to survive. Mycobacteria are able to block normal acidification of phagosomes by preventing phagolysosomal fusion [1,5,11].
This enables the M.tb bacilli to establish a niche where they replicate within inactivated macrophages. Such macrophages offer limited defense to the host [6].
Fortunately bacterial replication is slowed following recruitment of innate components when host effectors like reactive oxygen species (ROS) and nitrogen intermediates are released. Innate immunity is strengthened by cellular immunity activated through release of chemokines such as interferon-gamma, tumor necrosis factor, interleukins and tumor growth factors. Immune activation results in recruitment of lymphocytes and additional macrophages that assemble into a granuloma [1,2,7]. A granuloma is described as a multicellular structure that sequesters the infecting bacteria from the surrounding tissue, and further inhibits the rate of bacilli replication [1,12]. The interior of the granuloma has been described as necrotic (or "caseous") and hypoxic [2]. At this stage bacilli are thought to enter a quiescent (latent) state characterized by limited or no replication and phenotypic resistance to drugs [13]. In the granuloma, although actively replicating bacilli are found, nonreplicating persistent (NRP) (dormant) forms of M.tb can also be found. The latter are induced by the environmental conditions that are found in specific granuloma types, particularly those associated with hypoxia/anoxia, nutrient deprivation and nitric oxide production [14].
The NRP state of M.tb is characterized by the presence of non-dividing bacilli with low-metabolic state, a phenotype associated with resistance/tolerance to standard anti-TB agents [1,6,15]. Thus novel anti-TB drugs that can target and kill the bacilli inside the granuloma are likely to offer the best opportunity to reduce treatment length and eliminate relapse [1,16,17]. Resistance is also attributable to M.tb's unique obligate intracellular parasitic biology [1,6,18,19]. To cope with this hostile  [20]. Fortunately, metabolic reprogramming could be an opportunity for drug targeting as will be explained later.
Infection with TB is not limited to lungs only. Bacilli may be localized to different compartments/tissues of the host on spread through blood and lymphatics [5,11]. The outcome may either be active or latent infections with TB. Activation of cellular immunity may result into containment of over 90-95% of bacterial replication [1,5]. Therefore, the state of disease (active infection) and the latency are defined by the state of host immunity. In fact, during latent infection, a dynamic equilibrium between the bacilli and host immune responses is established. Any event that weakens cell mediated immunity may lead to; active bacterial replication, tissue damage and disease discussed in background [1,5]. TB pathogenesis has been extensively reviewed by Salvatore and Zhang [6]. In short, it includes: Primary tuberculosis, primary progressive TB, Secondary TB and extra pulmonary TB [5]. Most common sites of extra pulmonary disease are the pleura, secondary lymphatic tissues, genitourinary tract, skeletal system, gastrointestinal tract, meninges and central nervous system (CNS). The most important extra pulmonary TB include; central nervous system and miliary TB. Pathological characteristics of TB disease are not mediated by bacterial toxins. Instead they reflect host immune response to mycobacterial antigens [5,6].
Mycobacterium tuberculosis virulence factors that trigger immune responses are related to the structure and metabolism of the bacterium. These include: mycobacterial cell surfaces that mediate the interactions with the host receptors, inhibition of phagosomal maturation and phagosome-lysosome fusion, mitigation of oxidative stress, mycobacterial secretion systems, and bacterial proteasome [2,3,5,6]. Those virulence factors along with the others that are discussed later are regarded as potential anti-TB drug targets.
Since its discovery in 1882 [1,8,9], efforts have been made to treat or prevent M.tb infection, but there are still gaps to be bridged for its effective treatment. The association of high TB burden with HIV and the increasing emergence of multidrug resistant (MDR), extended drug resistant (XDR), and total drug resistant (TDR)-TB have worsened the situation [11]. For instance, resistance to rifampin, a TB drug was reported to be associated with the reduction of the efficacy of some antiretroviral drugs [10]. McIlleron et al. [10] demonstrated cross-resistance between rifampin and most commonly used antiretroviral agents through induction of cytochrome-P450 enzymes in patients with HIV-TB co-infection. Rifampin is a promiscuous inducer of expression of a broad array of enzymes and drug-transporting molecules through its activation of a master transcriptional regulator, pregnane X receptor. Induction of cytochrome P-450 enzymes and P-glycoprotein by rifampin results in reduced concentrations of non-nucleoside reverse transcriptase inhibitors, particularly protease inhibitors. This potentially results in the loss of antiviral efficacy and development of viral resistance [7,10].
Tuberculosis was first treated with streptomycin over 73 years ago [12,13]. At present, so many new drugs have been introduced [21]. However, M.tb remains as one of the leading infectious diseases worldwide [9] mostly due to lack of effective anti-TB drugs. Most antimycobacterials are not very effective due to highly adaptable intracellular parasitic biology of M.tb as highlighted before.
Almost 80% of the population in developing countries rely on traditional medicine (TM) for their primary healthcare [22]. The reasons for the use of TM include its affordability and accessibility [22,23]. Unfortunately, the mechanism of action of most medicinal plants traditionally used for TB treatment is not well understood.
Most of the current anti TB drug targets and mechanism of action were identified through target structure-based radiolabeling and screening [18]. Target macromolecules and their role in M.tb metabolism can be identified through directed mutagenesis methods [18,24]. Macromolecule structures of targets can be elucidated by X-ray crystallography or NMR spectroscopy [6,18]. These methods however are expensive and time consuming usually requiring first knowing the active compound and then radiolabeling it. For medicinal plants extracts usually the active compound may not be known or even where they are know they may work in synergy to kill M.Tb. In such a case radiolabeling can difficult at the extract screening phase.
Metabolomics is a recent and emerging field within the 'omics' [25][26][27] science. It is defined as the study of the complete set of metabolites inside cells, tissues, organs and biological fluids. It represents a major and rapidly evolving component of systems biology; a new integrative approach to deciphering the complexity of biological systems [26,[28][29][30]. It uses advanced analytical techniques such as nuclear magnetic resonance (NMR), liquid chromatography mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) or Capillary electrophoresis-mass spectrometry (CE-MS) to comprehensively identify and quantify a large number of metabolites [25,31]. These techniques have shown great impact on classifying phenotypes, investigation of physiological status, diagnosing disease, measuring the response to treatment, and discovery of biomarkers [32]. The physiological effects of small molecules make the field of metabolomics critical to the aims and interests of many researchers in the pharmaceutical industry [24,[33][34][35][36]. Thus, metabolomics is increasingly finding applications that cover the full length of drug discovery and development pipeline i.e. from lead compound discovery to post-approval drug surveillance [36].
Though metabolomics has been used in studying M.tb metabolism in general [20], available literature does not indicate its application to understanding anti-TB drug mechanism of traditional medicinal plants. This review aims at exploring new available methodologies for understanding the mechanism of action of anti-TB traditional medicinal plants.

Current anti-tuberculosis drugs and their mechanism of action
Presently, major antitubercular drugs include, Isoniazid, rifampin, pyrazinamide, ethambutol etc. [7]. Isoniazid was discovered in 1952 [37]. This drug followed streptomycin and paraaminosalicylic acid (PAS) which are usually referred to as the first clinical antibiotics. Isoniazid (INH; isonicotinic acid hydrazide), structurally known as pyridine-4-carboxy hydrazide has been the most commonly used anti-TB drug since its discovery in 1952 [13]. This drug was discovered by three different pharmaceutical companies, BAYER (Leverkusen, Germany); Hoffmann LaRoche (Nutley, NJ, USA) and ER Squibb & Sons (Princeton, NJ, USA) [37], and was shown to inhibit the synthesis of cell envelope of M.tb [38]. Considering the importance of the cell membrane in pathogenesis of M.tb, INH cured many patients and was dubbed "magic drug" [37]. Since 1952, INH has remained an essential drug in the fight against TB. Isoniazid is a prodrug that requires activation by M.tb catalase-peroxidase katG enzyme to form an INH-NAD complex. It inhibits nicotinamide adenine dinucleotide (NADH)-dependent enoyl-ACP reductase (encoded by inhA gene) of the fatty acid synthase type II system, a key player in the mycolic acid biosynthetic pathway of M.tb [39]. The inhibition of enoyl-ACP reductase (encoded by inhA gene) causes accumulation of long-chain fatty acids and cell death [38]. In clinical isolates, mutations in the katG and inhA genes have been shown to contribute approximately 70% and 80%, respectively, to isoniazid resistance in M.tb [38,40]. As a prodrug, its activity is importantly influenced by mutations in the katG enzyme. Thus, designing drugs that do not require the katG enzyme activation, but targeting inhA enzyme would be a reasonable alternative to bypass this Isoniazid-associated resistance mechanism. For instance, triclosan inhibits inhA enzyme [41], but it has not been successfully used as anti-TB drug because of its sub-optimal bioavailability [42]. Ethionamide (ETH, 2-ethylisonicotinamide), a derivative of isonicotinic acid, has been used as an anti-TB agent since 1956. The drug has the same mechanism of action as INH. As stated above, p-amino salicylic acid (PAS) was one of the first antibiotics to show anti-TB activity. However, its mechanism of action was never elucidated, though it is thought to act through inhibition of thymine synthesis [21].
Pyrazinamide (PZA) is an analog/derivative of nicotinamide [43]. This is also a prodrug that requires conversion to pyrazinoic acid by M.tb pyrazinamidase [encoded by the pyrazinamidase/nicotinamidase (pncA) gene] [37,44]. The cellular target of PZA was identified as ribosomal protein S1 (RpsA) [45,46]. As such, it was reported that over-expression of RpsA (wild-type RpsA) is implicated in PZA resistance in M.tb. Though the mechanism of action of PZA is not well understood, it is known that binding of activated pyrazinoic acid to RpsA interferes with its binding to the messenger RNA, thus compromising accurate protein synthesis. Some PZA-resistant M.tb strains without mutations in pncA have mutations in RpsA [47]. It was found that the RpsA enzyme is essential for translation [45], with its C terminus being involved in trans-translation [37,48] in M.tb. The authors reported that trans-translation in M.tb is dispensable during active growth conditions, and also required by some bacterial strains for survival under stress conditions and in disease progression. Thus, the inhibition of particular essential the trans-translation steps in M.tb may interfere with its survival under the dormancy state. Moreover, this could possibly explain the ability of diverse stress conditions such as starvation, acidic pH and hypoxia to increase PZA activity against M.tb [41,47,49,50].
Ethambutol (EMB), interferes with mycobacterial cell wall synthesis by inhibiting polymerization of arabinogalactan, an important cell wall component in M.tb [21,51,52]. It also interrupts utilization of arabinose donor by inhibiting either arabinosyltransferase enzymatic activity or formation of an arabinose acceptor in mycobacteria [53]. The embCAB operon is responsible for ethambutol resistance in M.tb [37,51,52]. It is worth noting that ethambutol and benzothiazinones block/inhibit the same pathway, although the steps are different.
Rifampicins (RIF) comprise a group of antibacterial drugs that include the derivatives: rifampicin, rifapentine, rifabutin and rifalazil [41]. These bind bacterial beta RNA polymerase (rpoB) subunit thus interfering with transcription [41]. Resistance to rifampicins in M.tb is conferred by mutations in the 81-bp region of the rpoB gene (encoding beta Ribonucleic Acid, RNA polymerase) [54,55]. Both rifampicin and isoniazid are essential and commonly used as first-line drugs for TB therapy [37]. Following introduction of rifampicin into clinical use, treatment of active TB was reduced from 9-12 to 6 months, while the duration for treatment of latent TB was reduced from 9 to 3 months [40]. It is important to note that rifampicins are among the few drugs that can kill the dormant (non-replicating) strains of M.tb. Rifampicin was developed by blind whole-cell screening in an extensive program of chemical modification of the rifamycins, the natural metabolites of Amycolatopsis mediterranei [56,57]. Since rpoB is an essential gene in M.tb and RNA polymerase is a proven target for antibacterial and anti-TB therapy, it would be interesting to search for new RNA polymerase inhibitors binding at sites different from that utilized by rifampicin [58].
Streptomycin (SM) SM is an aminocyclitol glycoside antibiotic. As mentioned in the background, it was the first drug to be used in treatment of TB in 1948. It binds to small 16S rRNA of the 30S subunit of the bacterial ribosome, interfering with the binding of formylmethionyl-tRNA to the 30S subunit. This leads to the interruption of the initiation of protein synthesis. Other aminoglycosides (kanamycin, amikacin and capreomycin) also inhibit protein synthesis of M.tb [21,59]. As for rifampicins, gaps exist in detecting the metabolites affected my streptomycin.
Fluoroquinolones (FQs) These include ciprofloxacin, ofloxacin, levofloxacin, and moxifloxacin. They have broad-spectrum anti-bacterial activity. Fluoroquinolones have excellent in vitro and in vivo activity against M.tb. They target M.tb DNA gyrase, a type II topoisomerase consisting of A and B subunits encoded by gyrA and gyrB genes, respectively. The DNA gyrase (gyrase) enzyme catalyzes formation of negative supercoils that aid DNA unwinding. Thus, FQs compromise bacterial DNA replication [21,60]. This results in abnormally localized, condensed chromosomes that blocks DNA replication and interrupts chromosomes segregation [61].
Cycloserine (CS) CS prevents tuberculosis bacteria from making peptidoglycans needed in the formation of bacterial cell wall. Cycloserine can inhibit the d-alanine racemase enzyme [62]. d-alanine racemase is an isomerase enzyme that catalyzes the conversion of l-alanine to d-alanine. The latter takes part in synthesis of peptidoglycan. Therefore, CS can indirectly inhibit the synthesis of peptidoglycan [43,63]. Recently, two anti TB drugs (Bedaquiline and Delamanid) were reported [18].
Bedaquiline (TMC-207) In December 2012, diarylquinoline was approved by FDA as part of combination therapy for treatment of adult patients affected by MDR-TB. It was in phase III of clinical development in 2015 [37] and in confirmatory phase III trials this year [64]. Bedaquiline is considered to be the first major drug approved by FDA for TB therapy in the last four decades. It came out of phenotypic screening of compounds against M.tb. The target of bedaquiline was identified through the whole-genome sequencing of M.tb and M. smegmatis spontaneous mutants that were resistant to diarylquinolines. The resistant mutants showed missense mutations in the atpE gene (encoding the c subunit of ATP synthase) [65]. It was later shown that Bedaquiline acts by inhibiting adenosine triphosphate (ATP) synthase and has activity against active and dormant M.tb strains [66]. Human mitochondrial ATP synthase is 20,000-fold less sensitive to diarylquinoline compared to that of the mycobacteria. For this reason ATPase has a high potential as a drug target against M.tb [65].
Delamanid was reported as a newer mycobacterial cell wall synthesis inhibitor [67]. It inhibits the synthesis of mycobacterial cell wall components, methoxymycolic acid and ketomycolic acid [67]. Delamanid is a pro-drug which is activated by the enzyme deazaflavin dependent nitroreductase (Rv3547) [67]. A reactive intermediate metabolite formed between delamanid and desnitro-imidazooxazole derivative, plays a vital role in the inhibition of mycolic acid production and this explains its mode of action. This drug has received conditional approval from European Medicines Agency (EMA) for treatment of MDR-TB [67]. Preclinical and clinical studies have shown that delamanid has high potency, very low risk for drugdrug interactions and better tolerability [67]. Eight drugs in phase 2-3 trials were reported as current drug pipeline for treatment of TB. They include Bedaquiline and delamanid for confirmatory trials. These two were approved as stated above. The remaining six are in trials. Two of the six (sutezolid and pretomanid) are new compounds. Others are members of rifampicins (rifapentine) and fluoroquinolones (levofloxacin and moxifloxacin) [64]. Table 1 illustrates some of the new drugs and their mechanism of action.

Mycobacterium tuberculosis metabolism and metabolic related potential drug targets
In bacteria, metabolism is commonly used to describe the full set of complex and interconnected chemical transformations that enable individual cells to survive and replicate [68]. It includes pathways that enable the bacteria to respond to the dynamic environments and maintain structural integrity in the face of fluctuating nutrient availability. As such, in an obligate intracellular pathogen such as M.tb whose entire life cycle is driven in the context of human infection, metabolism underpins both physiology and pathogenesis [6,68]. Research on M.tb metabolism was mostly done through experimental mycobacteriology. It involved use of genetic tools to disrupt enzymatic and regulatory functions. This has provided critical insights into the metabolic pathways that are essential for mycobacterial survival and pathogenesis [6,20,69]. Despite the efforts to understand TB metabolism and its use to identify potential drugs targets, it remains extremely challenging to determine the precise metabolic status of the bacilli during different stages of the infection cycle in vivo. Moreover, infecting populations of M.tb bacteria are located in discrete cellular microenvironments in a single infected host [5,6,20,70].
Rather than investigating metabolic function directly and at the level of complete biological system during TB disease, investigations of mycobacterial metabolism have concentrated primarily on elucidating either the metabolic capacity of the bacillus in vitro in defined growth media and under specific environmental conditions or in experimental models of infection [6]. Metabolic related TB potential drug targets were mostly identified using directed mutagenesis techniques and metabolomics. Metabolomics is promising in elucidating other potential drug targets. Metabolomics analyses based on fate of intermediates of central carbon metabolism [6,20,69,71] has revealed central carbon metabolism anti-TB targets. It has been shown that the main difference in metabolism between M.tb and other bacteria is the phenomenon of ''compartmentalized co-catabolism of carbon substrates'' [20].
Many bacteria use catabolite repression as a regulatory mechanism to maximize growth by consuming individual carbon substrates in a preferred sequence. Thus growth follows diauxic kinetics defined by phases of growth determined by available substrates. Surprisingly, untargeted metabolite profiling of M.tb growing on 13 C-labeled carbon substrates revealed that M.tb could catabolize multiple carbon sources simultaneously to achieve enhanced monophasic growth. Moreover, when co-catabolizing multiple carbon sources, M.tb differentially catabolized each carbon source through the glycolytic, pentose phosphate, and/or tricarboxylic acid (TCA) pathways to distinct metabolic fates [20]. Besides the phenomenon of compartmentalization co-catabolism of carbon substrates, M.tb was found to reprogram some metabolic pathways to adapt to granulomas. To establish infection, inside the granuloma, M.tb reprograms its metabolism to support both growth and survival, keeping a balance between catabolism, anabolism and energy supply [72]. In a study to explore the metabolic reprogramming of two M.tb knockout mutants (pfkA-and icl-mutants), lacking key enzymes (Phosphofructokinase, PFKA and Isocitrate lyase, ICL) of central carbon metabolism, hypoxia increases glucose consumption. Interestingly, M.tb pfkA-mutant showed a detrimental growth effect derived from accumulation of toxic sugar phosphate intermediates (glucose-6-phosphate and fructose-6-phosphate) and tricarboxylic acid cycle also decreased. Metabolic reprogramming of the icl-mutant (icl1 & icl2) is a probable indicator of the importance of methylmalonyl pathway for detoxification of propionyl-CoA at high fatty acid consumption rates. Elevated levels of fatty acid uptake and hypoxia leads to a drop in TCA cycle intermediate accumulation thus possibly limiting redox imbalance [72].
There are many potential drugs targets in M.tb metabolism (Fig. 2).

Current approaches in TB drug discovery
A global threat posed by TB clearly demonstrates the need to search for new therapeutic regimens with improved outcomes including shorter duration of treatment and sustained efficacy. The following sections discuss the main approaches to achieve the TB drug discovery, production and development.

Anti-TB drugs Suggested/confirmed function/mechanism of action References
Pretomanid (PA-824) 1. Potentially act on mycolic acid biosynthetic pathway through depletion of ketoymycolates and accumulation of hydroxymycolates on replicating bacteria [94] 2. Des-nitroimidazole derivative from pretomanid metabolism was responsible for generation of reactive nitrogen species and ATP depletion, which would explain its activity under anaerobic conditions [95] Delamanid (OPC-67683) Inhibits synthesis of mycobacterial cell wall components, methoxymycolic acid and ketomycolic acid [67] Bedaquiline Inhibits ATP synthase [96] Rifapentine Inhibits transcription by interacting with mycobacterial RNA polymerase. It does not inhibit the mammalian enzyme [97] Linezolid It is an oxazolidinone. It is a candidate for MDR TB. It inhibits the initiation of the protein synthesis [98] Sutezolid It is an oxazolidinone, an analog of linezolid. It inhibits the protein synthesis [99] Levofloxacin and Moxifloxacin These are members of fluoroquinolones. They inhibit DNA replication by binding bacterial gyrase [60,100] Target structure-based high-throughput screening The first step in drug discovery is the identification of initial chemical leads with reasonable affinity (K D of 10 μM or less) towards the target and novel structures (patentable) that have drug-like characteristics (Lipinski's Rule of Five) [73], synthetically achievable and malleable [24]. This is primarily accomplished by using a high-throughput screen of a chemical library composed of hundreds of thousands to millions of compounds [24,74]. The chemical leads are then evolved to high-affinity ligands (K D ≤ nM) through an iterative process involving structure-based drug design, traditional medicinal chemistry techniques, and multiple activity assays. This process may include whole cell-based assays to measure cell viability when the desired activity of a chemical lead is cell death (i.e., infectious disease and cancer) [18,24,37]. While this process is generally very efficient and leads to the emergence of high affinity ligands, there is a fundamental difference between a true drug lead and a tight-binding ligand [18,37]. It is wellknown that an increase in affinity can be achieved by increasing the size and hydrophobicity of a compound, which are generally detrimental to drug-like characteristics [24]. Despite significant effort is being expended on M.tb target based biochemical screens [18,75], no TB drug or drug candidate has emerged yet. Problems associated with target-based screens include their propensity to identify hits with potency against the target but without inhibition of bacterial growth. The target inhibition may not translate into bacterial killing for a variety of reasons including poor penetration and efflux [18]. In addition, the lack of efficacy and toxicity issues, which include off-target activity, are the primary reasons for failure of potential drugs in the clinic [24]. Metabolomics is thought to offer solutions that may provide new directions to overturn this limitation.

Metabolomics approaches and its application to medicinal plants with anti-tuberculosis activity
Metabolomics, a high-throughput analysis of metabolites is a recent and emerging type of omics biotechnology. It can be defined as the study of the complete set of small molecules of cells, tissues, organs and biological fluids [29,76,77]. Metabolomics is thus considered to be a systematic identification and quantitation of all metabolites in a given organism or biological sample using a range of analytical tools to scale the enormously large data generated. Further, metabolomics depends on molecular detection systems, statistics and bioinformatics (Fig. 3). It represents a major and rapidly evolving component of a systems biology [29,76,77], which is an integrative computational analysis and modeling of an organism and its well-being [28,29,78,79]. With metabolomics, (i) metabolic profile, (ii) metabolic fingerprint and (iii) metabolic footprinting of an organism can be determined [31,80]. As stated above, metabolomics can overcome the challenges related to drug discovery and development.
Because the metabolome captures the state of the cell and is a direct measure of protein activity, any observed changes in the metabolome as a result of a drug treatment would provide information on the drug's activity and selectivity [5,30,69,70]. The in vivo activity of a chemical lead can then be ascertained by metabolomics. Three samples are needed: (i) untreated wild-type cells representing a negative control, (ii) a knockout mutant cell line, where a gene encoding a target protein has been genetically inactivated, serving as a positive control and (iii) treated wild-type cells as test sample. The metabolome of the mutant and treated wild-type cells would show similar profile [24,81]. Metabolomics may hence be exploited to identify potential novel anti-M.tb candidates and their targets from medicinal plants and elucidate the mechanisms thereof as well (Fig. 4). The complete exercise of drug discovery, development and production is tedious and involves multiple steps. The whole process from the drug screening, through candidate validation, to clinical testing and eventual clinical adoption would lucidly require understanding the biochemical system of the disease itself a systems, pharmacological properties (i.e. absorption, distribution, metabolism, excretion, and toxicity-ADMET) of the therapeutic agents and their functional effects on the human body, both on-target and off-target actions [31,82]. Despite the fact that metabolomics is increasingly being adopted in drug discovery programs, there are currently no report on the use of the approach to unravel the action mechanisms of medicinal plants with anti-mycobacterial activities; though these have been extensively documented [17,18,37,[83][84][85][86][87][88]. There is a need to establish a suitable metabolomics protocol to reveal the mechanisms of action of plant-derived antimycobacterial agents. The protocol would presumably offer an advantage to identify both TB targets and the chemical lead simultaneously. Protocols for NMR analysis of bacterial metabolome were thoroughly reviewed in the past [89]. However, there are no reports on extension of the approach in the attempt to understand the action mechanisms anti-mycobacterial activity from medicinal plants. On the other hand, other analytical techniques such as chromatographic mass spectrometry (MS) may be used to generate metabolomics data as an alternative to NMR spectroscopy.
In context the of metabolomics application in discovery of new TB drugs, existing protocols can be modified and a variety of procedures might be adopted. For instance (Fig. 4), extracts from the medicinal plants can be screened for activity by adding them into the M.tb culture. The level of microorganism growth inhibition would be attributed to the metabolites present in the extracts. To achieve this, there is a need to empirically determine a sub lethal dose from the minimum inhibitory concentration (MIC) of the plants extracts. The dose required here is that translating into a significant change in the metabolome of bacterial cells, but does not reach the lethal concentration. In addition, the metabolic fingerprints of the bacterial culture would reveal the metabolic pathways affected by treatment with the plant extracts. As metabolic pathways are performed by genes products (enzymes), the macromolecules that are compromised by the plant extracts would be identified as well. These macromolecules would be considered as potential target of active compounds present in the extracts that would eventually be developed into new drugs. On the other hand, the metabolomics techniques can be used to identify the active molecules from the plants extracts. Those active molecules are the leads compounds for the drug development. With bioinformatics and other omics technologies such as genomics and transcriptomics, the relationship between identified metabolite or its intermediate products and genes responsible for its production may be elucidated [31,37,90,91]. Thereafter, a largescale production of the metabolite drug candidate would be envisaged using biotechnology possible applications. Although dramatic improvements in chemotherapy for TB have been achieved through careful studies of drug regimens, there is still a need for new agents exhibiting higher activity. Anti-mycobacterial drugs used at present in therapy for TB were obtained by either blind screening or chemical modification of active compounds [37]. Other approaches based on the knowledge of the biochemistry of the mycobacterial cell should be tried. Certain constituents of the cell, such as mycolic acids, arabinogalactan, peptidoglycan and mycobactin, may represent specific targets for new anti-TB drugs [37,92]. Many compounds that inhibit specific steps in either arabinogalactan or mycolic acid biosynthesis have been discovered [93]. Novel efficacious and safe anti-TB drugs are currently needed so as: to shorten the duration of TB therapy; treat MDR, XDR and totally drug resistant (TDR) TB strains; and latent TB; act synergistically with other co-administered anti-TB drugs; and, finally, to be safely co-administered with anti-HIV agents [37].
Collectively, there is the possibility of applying metabolomics to identify the macromolecules associated with microbe virulence and their inhibitors. This would likely The metabolites that make difference between the treated and untreated cultures will allow to elucidate the metabolic targets of medicinal plants contribute in the development of new pharmaceuticals with a high selectivity towards their specific targets. In this regard, a well-established protocol would apply and adapt available metabolomics analytical and data analysis techniques and tools to TB drug discovery, development and production. Similarly, this approach could be further extended to other burden human diseases including other infectious diseases and various cancers.

Conclusion and future perspectives
Human tuberculosis (TB) is one of the oldest and deadliest human bacterial diseases. It is still affecting and posing major health, social and economic burden at the global level. However, most affected people are mainly in low and middle income countries. TB exists in both active and latent, with one-third of the world population being latently infected. Anti-TB drugs with specific mechanisms of action were discovered. Despite this, the length, the cost, the emergence of Mycobacterium tuberculosis resistant strains and post-treatment relapse pose serious threats the disease elimination and there is an urgent need of approaches to develop new and more effective news drugs. Target structure-based highthroughput screening for anti-TB drugs leads has been used before. The lack of complete knowledge of M.tb biology and the complex M.tb pathogenesis have led to the failure. The metabolomics approach is likely to provide new insights into the discovery through understanding their mechanism of action and eventual development of anti TB drugs especially from medicinal plants. To render this feasible, the mechanisms action of the plant-derived antimycobacterial agents should be clearly understood. Metabolomics protocol has to be established in such way as to identify the metabolites affected by antimycobacterial drug leads from the plant extract screened that will guide elucidation of their targets and the mechanisms of their actions.