extfieldsofstudy: Medicine Chemistry

text:
Moringin Pretreatment Inhibits the Expression of Genes Involved in Mitophagy in the Stem Cell of the Human Periodontal Ligament

Moringin [4-(α-L-rhamnosyloxy) benzyl isothiocyanate] is an isothiocyanate extracted from Moringa oleifera seeds. It is an antioxidant known for several biological properties useful in the treatment of neurodegenerative diseases. Several neurodegenerative disorders such as Parkinson’s and Alzheimer’s diseases are linked to dysfunctional mitochondria due to the resulting increase of Reactive Oxygen Species (ROS). Stem cell-based therapeutic treatments in neurodegenerative diseases provide an alternative strategy aimed to replace the impaired tissue. In this study were investigated the deregulated genes involved in mitophagy in the human periodontal ligament stem cells pretreated with moringin. The RNA-seq study reveals the downregulation of PINK1, with a fold change (FC) of −0.56, such as the genes involved in the phagophore formation (MAP1LC3B FC: −0.73, GABARAP FC: −0.52, GABARAPL1 FC: −0.70, GABARAPL2 FC: −0.39). The moringin pretreatment downregulates the pro−apoptotic gene BAX (−0.66) and upregulates the anti-apoptotic genes BCL2L12 (FC: 1.35) and MCL1 (FC: 0.36). The downregulation of the most of the caspases (CASP1 FC: −1.43, CASP4 FC: −0.18, CASP6 FC: −1.34, CASP7 FC: −0.46, CASP8 FC: −0.65) implies the inactivation of the apoptotic process. Our results suggest that mitochondrial dysfunctions induced by oxidative stress can be inhibited by moringin pretreatment in human periodontal ligament stem cells (hPDLSCs).

Introduction
Isothiocyanates are a class of phytocompounds obtained by hydrolysis of glucosinolates. Isothiocyanates have generated great interest in the clinical field for their pharmacological properties, ranging from cancer prevention [1] to neuroprotective effects [2]. Moringin (Figure 1) is an isothiocyanate extracted from Moringa oleifera, also called "miracle tree" [3]. In particular, moringin is the bioactivate form of glucomoringin and it is renowned to promote healthy benefits at doses of 0.1-1 g/kg due to its anti-inflammatory, anti-fibrotic, antimicrobial, anti-hyperglycemic, antioxidant and anti-tumour properties [4]. Additionally, moringin mitigates the neurodegeneration process and promotes neuroprotective effects through the suppression of proinflammatory mediators, protection from oxidative damage and the reduction of apoptosis [5,6]. Our research group has already studied the effect of moringin treatment on stem cells [7]. In particular, human Periodontal Ligament Stem Cells (hPDLSCs) pretreated with moringin differentiate into neuronal cells and, consequently, they could support neurological therapies [8]. Indeed, mesenchymal stem cells are immunoprivileged cells useful in regenerative medicine [9]. hPDLSCs have a fibroblastic-like morphological shape and are characterized by several mesenchymal and stemness markers [10]. They can spontaneously diversify into osteogenic, chondrogenic and adipogenic lineages. Because of their origin from the neural crest, hPDLSCs have the innate potential to differentiate into neuronal cells and, consequently, they are good candidates in neurodegenerative disease studies [11][12][13][14][15][16]. Moreover, they can be easily isolated from the oral cavity and represent an ideal model of mesenchymal stem cells. Our group has already shown that hPDLSCs have a neuroprotective effect against autoimmune encephalomyelitis, an experimental model of multiple sclerosis [17]. The ethiopathogenesis of the neurodegenerative diseases seems to correlate with mitochondria dynamics, morphology, motility, activity and oxidative stress [18][19][20]. Mitochondrial homeostasis is mediated by fission and fusion processes [21]. In particular, fission is required to generate novel mitochondria and contribute to quality control, while fusion reduces cellular stress mixing the impaired mitochondria. The elimination of the damaged mitochondria is mediated by mitophagy, a selective type of autophagy that has recently drawn the scientific interest [22]. In mammalian cells, mitophagy is induced by different pathways such as the PINK1/Parkin-mediated pathway and Outer Mitochondrial Membrane (OMM) receptor-mediated pathway [23]. The PINK1/Parkin-mediated is the best characterized and PINK1 protein plays the pivotal role in this process [24]. Under normal conditions, PINK1 acts as stress sensor involved in the detection of mitochondrial quality. Changes in the expression of PINK1 appear related to neurodegenerative disorders such as Alzheimer's and Parkinson's diseases [23,25]. In addition, several genetic mutations of PINK1 and Parkin are implicated [26,27]. These mutations generate an accumulation of impaired mitochondria and aggregation of proteins that lead to neuronal decline [28]. The purpose of this study was the investigation of the mitophagic and apoptotic processes in hPDLSCs pretreated with moringin (hPDLSCs-MOR). In particular, the RNA-seq analysis was performed to evaluate changes in the expression profile of the hPDLSCs-MOR compared to untreated hPDLSCs (hPDLSCs-CTR). In particular, moringin is the bioactivate form of glucomoringin and it is renowned to promote healthy benefits at doses of 0.1-1 g/kg due to its anti-inflammatory, anti-fibrotic, antimicrobial, anti-hyperglycemic, antioxidant and anti-tumour properties [4]. Additionally, moringin mitigates the neurodegeneration process and promotes neuroprotective effects through the suppression of proinflammatory mediators, protection from oxidative damage and the reduction of apoptosis [5,6]. Our research group has already studied the effect of moringin treatment on stem cells [7]. In particular, human Periodontal Ligament Stem Cells (hPDLSCs) pretreated with moringin differentiate into neuronal cells and, consequently, they could support neurological therapies [8]. Indeed, mesenchymal stem cells are immunoprivileged cells useful in regenerative medicine [9]. hPDLSCs have a fibroblasticlike morphological shape and are characterized by several mesenchymal and stemness markers [10]. They can spontaneously diversify into osteogenic, chondrogenic and adipogenic lineages. Because of their origin from the neural crest, hPDLSCs have the innate potential to differentiate into neuronal cells and, consequently, they are good candidates in neurodegenerative disease studies [11][12][13][14][15][16]. Moreover, they can be easily isolated from the oral cavity and represent an ideal model of mesenchymal stem cells. Our group has already shown that hPDLSCs have a neuroprotective effect against autoimmune encephalomyelitis, an experimental model of multiple sclerosis [17]. The ethiopathogenesis of the neurodegenerative diseases seems to correlate with mitochondria dynamics, morphology, motility, activity and oxidative stress [18][19][20]. Mitochondrial homeostasis is mediated by fission and fusion processes [21]. In particular, fission is required to generate novel mitochondria and contribute to quality control, while fusion reduces cellular stress mixing the impaired mitochondria. The elimination of the damaged mitochondria is mediated by mitophagy, a selective type of autophagy that has recently drawn the scientific interest [22]. In mammalian cells, mitophagy is induced by different pathways such as the PINK1/Parkin-mediated pathway and Outer Mitochondrial Membrane (OMM) receptor-mediated pathway [23]. The PINK1/Parkin-mediated is the best characterized and PINK1 protein plays the pivotal role in this process [24]. Under normal conditions, PINK1 acts as stress sensor involved in the detection of mitochondrial quality. Changes in the expression of PINK1 appear related to neurodegenerative disorders such as Alzheimer's and Parkinson's diseases [23,25]. In addition, several genetic mutations of PINK1 and Parkin are implicated [26,27]. These mutations generate an accumulation of impaired mitochondria and aggregation of proteins that lead to neuronal decline [28]. The purpose of this study was the investigation of the mitophagic and apoptotic processes in hPDLSCs pretreated with moringin (hPDLSCs-MOR). In particular, the RNA-seq analysis was performed to evaluate changes in the expression profile of the hPDLSCs-MOR compared to untreated hPDLSCs (hPDLSCs-CTR). . SEM analysis and evaluation rate of hPDLSCs-MOR and hPDLSCs-CTR proliferation. The SEM analysis is shown for the untreated (A) and treated (C) cells. The immunofluorescence for actin confirmed that the morphology of hPDLSCs-MOR (D) was not modified compared to hPDLSCs-CTR (B). Green fluorescence: cytoskeleton actin. Blue fluorescence: cell nuclei. The proliferation rate of hPDLSCs-MOR results higher when compared to the hPDLSCs-CTR (E). MTT analysis is then confirmed by Trypan Blue exclusion test (F). * p < 0.05; ** p < 0.01.

Transcriptome Investigation of hPDLSCs-MOR and hPDLSCs-CTR
The transcriptome of hPDLSCs that received moringin treatment (0.5 µM for 48 h) and hPDLSCs that did not receive any treatment were explored by Next Generation Sequencing (NGS) analysis. An in-deep analysis of the global gene expression displayed that 4390 statistically relevant genes. Among them, 12 genes are exclusively expressed in hPDLSCs-MOR whereas 10 are exclusively included in the hPDLSCs-CTR. The remaining 4364 genes are in common between the two groups (Table S1). . SEM analysis and evaluation rate of hPDLSCs-MOR and hPDLSCs-CTR proliferation. The SEM analysis is shown for the untreated (A) and treated (C) cells. The immunofluorescence for actin confirmed that the morphology of hPDLSCs-MOR (D) was not modified compared to hPDLSCs-CTR (B). Green fluorescence: cytoskeleton actin. Blue fluorescence: cell nuclei. The proliferation rate of hPDLSCs-MOR results higher when compared to the hPDLSCs-CTR (E). MTT analysis is then confirmed by Trypan Blue exclusion test (F). * p < 0.05; ** p < 0.01.

Transcriptome Investigation of hPDLSCs-MOR and hPDLSCs-CTR
The transcriptome of hPDLSCs that received moringin treatment (0.5 µM for 48 h) and hPDLSCs that did not receive any treatment were explored by Next Generation Sequencing (NGS) analysis. An in-deep analysis of the global gene expression displayed that 4390 statistically relevant genes. Among them, 12 genes are exclusively expressed in hPDLSCs-MOR whereas 10 are exclusively included in the hPDLSCs-CTR. The remaining 4364 genes are in common between the two groups (Table S1).

Genes Pathway Distribution
The Reactome database collects a set of genes interaction pathways. It was used to analyse the genes differently expressed in our transcriptome. The investigation was focused on the 4364 genes in common between the transcription profiles of the hPDLSCs-CTR and hPDLSCs-MOR whereby the section "Analyze data". Reactome was able to recognize 2906 genes. In particular, it was investigated the "mitophagy" pathway. To achieve the state-of-the-art, genes were integrated from the reviews of the last five years using the keywords "mitophagy" and "pink1 mediated" or "parkin independent", "apoptosis" and "mitochondrial dynamics" and "neurodegeneration". In Table 1 are listed the 22 genes related to mitophagy. In Table 2 are listed the 11 genes linked to apoptosis and oxidative stress. Figure 3 displays a heatmap of the genes expressed between the hPDLSCs-CTR and hPDLSCs-MOR groups and their fold change. In addition, Figure 4 shows the distribution of the up-and down-regulated genes. The cellular localization of the genes is illustrated in Figure 5 while the Figure 6 shows the interaction of the genes in PINK1-mediated pathway. Table 1.
Genes expressed both in hPDLSCs-CTR and hPDLSCs-MOR involved in the mitophagy pathway. For each gene in the column Genes, the column hPDLSCs-CTR highlights the level of expression of the transcript in the not treated hPDLSCs while in the column hPDLSCs-MOR the level after the treatment. The Fold change is obtained by Log 2 (hPDLSCs-MOR/hPDLSCs-CTR). The Q-value was used to choose the level of significance (<0.05). The Protein column shows the name of the encoded protein by UniProt. The last column highlights the source from which the gene was chosen. All the values are rounded to the second decimal digit. For each gene in the column Genes, the column hPDLSCs-CTR highlights the level of expression of the transcript in the not treated hPDLSCs while in the column hPDLSCs-MOR the level after the treatment. The Fold change is obtained by Log 2 (hPDLSCs-MOR/hPDLSCs-CTR). The Q-value was used to choose the level of significance (<0.05). The Protein column shows the name of the encoded protein by UniProt. The last column highlights the source from which the gene was chosen. All the values are rounded to the second decimal digit.
Molecules 2019, 24, x FOR PEER REVIEW 6 of 19 Figure 3. Heatmap of genes included in the analysis. In hPDLSCs-CTR and in hPDLSCs-MOR the green color represents a lower expression while the red color a higher expression. In fold change, the green color represents a downregulation in hPDLSCs-MOR while red represents an upregulation.  In hPDLSCs-CTR and in hPDLSCs-MOR the green color represents a lower expression while the red color a higher expression. In fold change, the green color represents a downregulation in hPDLSCs-MOR while red represents an upregulation.

Moringin Treatment Modulated the Expression of SOD1, CASP1, Bax, Bcl2 and LC3A/B
In order to confirm the changes in the expression of the proteins, the immunofluorescence observations were performed under confocal laser scanning microscope. They showed a downregulation of SOD1, CASP1, Bax and LC3 in hPDLSCs-MOR when compared to the hPDLSCs-CTR. On the other hand, Bcl2 is upregulated in hPDLSCs-MOR compared to hPDLSCs-CTR and it shows an opposite behavior (Figure 7). To validate the expression of SOD1, Caspase 1, BCL2, BAX, and LC3A/B proteins the western blotting analysis was performed. In Figure 7, the expression of Caspase 1, SOD1, BAX and LC3A/B were decreased in MOR treated hPDLSCs, while in control sample, an upregulation of BCL2 was evident. In order to confirm the changes in the expression of the proteins, the immunofluorescence observations were performed under confocal laser scanning microscope. They showed a downregulation of SOD1, CASP1, Bax and LC3 in hPDLSCs-MOR when compared to the hPDLSCs-CTR. On the other hand, Bcl2 is upregulated in hPDLSCs-MOR compared to hPDLSCs-CTR and it shows an opposite behavior (Figure 7). To validate the expression of SOD1, Caspase 1, BCL2, BAX, and LC3A/B proteins the western blotting analysis was performed. In Figure  7, the expression of Caspase 1, SOD1, BAX and LC3A/B were decreased in MOR treated hPDLSCs, while in control sample, an upregulation of BCL2 was evident.

Mitochondrial Membrane Potential Evaluation
In order to determine the involvement of mitochondrial membrane potential (ΔΨm) in moringin treatment, hPDLSCs were loaded with the mitochondrial membrane potential probe JC1. The greater ΔΨm, the greater concentration of JC-1 aggregate forms after the mitochondrial uptake which have a red fluorescent emission signal, as opposed to the JC-1 monomer that in condition of low ΔΨm fluoresces green. Figure 8A shows a typical image recorded in which JC1 staining depicted mitochondria structures in hPDLSCs. Nor in control neither in moringin-treated cells we observe a homogenous red or green JC-1 signal from the mitochondria. Instead, the mitochondria were green and red, indicating distinct polarized areas. The proportions of green-and red-emitting mitochondria were not evenly distributed between hPDLSCs-CTR and hPDLSCs-MOR. The red-fluorescing, highly energized mitochondria were proportionally more prevalent in hPDLSCs-MOR (see figure 8A). Quantitative analysis of changes in ΔΨm ( Figure 8B), expressed as

Mitochondrial Membrane Potential Evaluation
In order to determine the involvement of mitochondrial membrane potential (∆Ψm) in moringin treatment, hPDLSCs were loaded with the mitochondrial membrane potential probe JC1. The greater ∆Ψm, the greater concentration of JC-1 aggregate forms after the mitochondrial uptake which have a red fluorescent emission signal, as opposed to the JC-1 monomer that in condition of low ∆Ψm fluoresces green. Figure 8A shows a typical image recorded in which JC1 staining depicted mitochondria structures in hPDLSCs. Nor in control neither in moringin-treated cells we observe a homogenous red or green JC-1 signal from the mitochondria. Instead, the mitochondria were green and red, indicating distinct polarized areas. The proportions of green-and red-emitting mitochondria were not evenly distributed between hPDLSCs-CTR and hPDLSCs-MOR. The red-fluorescing, highly energized mitochondria were proportionally more prevalent in hPDLSCs-MOR (see figure 8A). Quantitative analysis of changes in ∆Ψm ( Figure 8B), expressed as red/green JC1 fluorescence ratio, put in evidence that in hPDLSCs-MOR the proportion of red fluorescence is increased respect to the hPDLSCs-CTR.

Discussion
The progress of stem cell research is clarifying new features of their developmental processes, such as self-renewal, longevity and differentiation capacities in vitro. Oral stem cells are widely described in literature for their use in regenerative medicine, for their capacity to undergo multiple processes of differentiation and for their therapeutic potential in repairing damaged tissues [10]. In vitro hPDLSCs showed stable growth and proliferation rate in a culture environment [45][46][47]. Our research group has already conducted several studies on the effects of moringin in hPDLSCs at different concentrations (0.25 µM, 0.5 µM, 1 µM) [48]. Nevertheless, the dose of moringin influences the expression of genes involved in neural differentiation up to 0.5 µM. When the dose is higher (1 µM), the fold change of the genes is not conditioned [8]. Based on this consideration, the dose used in this analysis is 0.5 µM. In this transcriptional study on hPDLSCs, we focused on the effects on moringin in mitophagic and apoptotic processes. Indeed, in physiological condition, mitophagy is a check quality system that preserves healthy mitochondria and eliminates impaired ones [49]. In detail, the correct mitochondrial biogenesis and its maintenance are possible by two processes: fusion and fission. The correct balance between fusion and fission appears to be crucial in the biogenesis of the mitochondria and in their maintenance. Fusion can be stimulated by accumulation of DNA modifications as a consequence of oxidative stress and ROS production. MFN1 gene is involved in this process and it is downregulated in hPDLSCs-MOR. In particular, it encodes for the protein mitofusin-1 that, during the fusion, changes the morphology of the mitochondria [50]. Fission generates novel mitochondria and promotes the quality control necessary for the cell growth.
The analysis of our transcriptome shows that DNM1L, involved in fission, is upregulated in hPDLSCs-MOR. It encodes for the dynamin-1-like protein, a GTPase indispensable for the process [51]. Arduino et al. describes that the downregulation of DNM1L along with the upregulation of MFN1 hinds the sharing of mitochondria in dendritic spines [52]. The best characterized pathway of mitophagy is the PINK1-mediated. In healthy conditions, this pathway preserves the mitochondrial homeostasis and does not promote mitophagy ( Figure 6A). The gene PINK1 plays a crucial role in mitophagy as stress sensor. It encodes for the protein PTEN-induced kinase 1 and it is

Discussion
The progress of stem cell research is clarifying new features of their developmental processes, such as self-renewal, longevity and differentiation capacities in vitro. Oral stem cells are widely described in literature for their use in regenerative medicine, for their capacity to undergo multiple processes of differentiation and for their therapeutic potential in repairing damaged tissues [10]. In vitro hPDLSCs showed stable growth and proliferation rate in a culture environment [45][46][47]. Our research group has already conducted several studies on the effects of moringin in hPDLSCs at different concentrations (0.25 µM, 0.5 µM, 1 µM) [48]. Nevertheless, the dose of moringin influences the expression of genes involved in neural differentiation up to 0.5 µM. When the dose is higher (1 µM), the fold change of the genes is not conditioned [8]. Based on this consideration, the dose used in this analysis is 0.5 µM. In this transcriptional study on hPDLSCs, we focused on the effects on moringin in mitophagic and apoptotic processes. Indeed, in physiological condition, mitophagy is a check quality system that preserves healthy mitochondria and eliminates impaired ones [49]. In detail, the correct mitochondrial biogenesis and its maintenance are possible by two processes: fusion and fission. The correct balance between fusion and fission appears to be crucial in the biogenesis of the mitochondria and in their maintenance. Fusion can be stimulated by accumulation of DNA modifications as a consequence of oxidative stress and ROS production. MFN1 gene is involved in this process and it is downregulated in hPDLSCs-MOR. In particular, it encodes for the protein mitofusin-1 that, during the fusion, changes the morphology of the mitochondria [50]. Fission generates novel mitochondria and promotes the quality control necessary for the cell growth. The analysis of our transcriptome shows that DNM1L, involved in fission, is upregulated in hPDLSCs-MOR. It encodes for the dynamin-1-like protein, a GTPase indispensable for the process [51]. Arduino et al. describes that the downregulation of DNM1L along with the upregulation of MFN1 hinds the sharing of mitochondria in dendritic spines [52]. The best characterized pathway of mitophagy is the PINK1-mediated. In healthy conditions, this pathway preserves the mitochondrial homeostasis and does not promote mitophagy ( Figure 6A). The gene PINK1 plays a crucial role in mitophagy as stress sensor. It encodes for the protein PTEN-induced kinase 1 and it is downregulated in hPDLSCs-MOR. Physiologically, PINK1 is firstly translocated into the OMM and later in the inner mitochondrial membrane. Next, PINK1 is cleaved by the Mitochondrial Processing Peptidase (MPP) enzyme [53] and it is finally degraded in the cytosol by the Ubiquitin-Proteasome System [54]. In hPDLSCs-MOR, PMPCB, the subunit b of MPP [55], is downregulated. The downregulation of PMPCB correlates with the lower expression of PINK1. The MPP recognizes several mitochondrial precursor proteins among which PINK1. In particular, the MPP cleaves the N-terminal of PINK1 thus the C-terminal can be released into the cytosol and finally degraded. Nevertheless, PINK1 can be held in the OMM when the mitochondrial membrane is depolarized due to environmental and stress factors. Consequently, the activity of MPP is obstructed and unhealthy mitochondrial condition is promoted (as illustrated in Figure 6B) [56]. Our results show 21 deregulated genes involved in the pathway of mitophagy among which 15 are downregulated (PINK1, TOMM7, SQSTM1, NBR1, TAX1BP1, MFN1, OPTN, UBB BCL2L13, BNIP3L MAP1LC3B, GABARAP, GABARAPL1, GABARAPL2, FKBP8) and 6 are upregulated (TOMM5, CALCOCO2, DNM1L, UBC, UBA52, RPS27A). PINK1 plays the pivotal role because its accumulation is the trigger of many neurodegenerative diseases, like Parkinson's [56,57]. In hPDLSCs-MOR are expressed two subunits of the TOM complex, TOMM7 is downregulated and TOMM5 is upregulated. Actually, the complex regulates the entrance of the nuclear encoded proteins into the mitochondria thus it achieves a very important activity [58]. In detail, TOMM7 holds PINK1 to the mitochondrial surface [59], while TOMM5 seems do not have a specific role in the TOM complex [60]. Furthermore, we also evaluated the mitochondrial membrane potential both in hPDLSCs-CTR and hPDLSCs-MOR. The polarization of mitochondrial surface can be studied by fluorescence analysis [61]. As shown in Figure 8, the JC-1 fluorescence highlights in hPDLSCs-MOR an improvement in the mitochondria membrane polarization.
Interestingly, our transcriptome expresses the UBB, UBC, UBA52 and RPS27A genes that encode for all the ubiquitins. In particular, the hPDLSCs-MOR shows the upregulation of UBC, UBA52, RPS27A and the downregulation of UBB. The ubiquitins play a very important role in molecular signalling. They target toxic and misfolded proteins and allow their degradation through the ubiquitin-proteasome system [62]. Moreover, the ubiquination process contributes to stem cells fate and the upregulation of the ubiquitins may also be correlated with the effect of moringin in stem cells differentiation [63]. When the ubiquitins are recruited in OMM, they are phosphorylated by PINK1 [64]. Consequently, the autophagy receptors are assembled to get started the mitochondrial digestion [65]. In hPDLSCs-MOR are expressed five autophagy receptors (SQSTM1, NBR1, OPTN, TAX1BP1, CALCOCO2) among which SQSTM1, NBR1, OPTN, TAX1BP1 are downregulated while CALCOCO2 is upregulated. Autophagy receptors are involved in oxidative stress and DNA damage response pathway [66]. Furthermore, they are active in axon homeostasis and promote the replacement of the inflammatory proteins [67]. The protein structure of the autophagy receptors is characterized by an ubiquitin domain that identifies the phosphorylated ubiquitins and by a LC3-interacting region (LIR) motif [68]. The LIR motif also characterizes the structure of receptors that take part in OMM receptor-mediated pathway. The OMM receptors can promote the mitophagy without mediation of ubiquitin or autophagy receptors [69]. The hPDLSCs-MOR show a downregulation of BNIP3L, BCL2L13 and FKBP8 genes that encode for OMM receptors [70]. Finally, the LIR motifs of autophagy and OMM receptors binds the phagophore, the precursor of the autophagosome [71]. The proteins of the LC3/GABARAP family entail the biogenesis and maturation of the autophagosome [72]. Interestingly, hPDLSCs-MOR show a downregulation of MAP1LC3B (LC3B), GABARAP, GABARAPL1 and GABARAPL2 genes which encode for proteins of LC3/GABARAP family. Thus, the downregulation of these genes may obstruct the phagophore formation. Furthermore, the expression of LC3A/B was evaluated by immunofluorescence that confirms the downregulation (Figure 7).
In addition, the overproduction of ROS and the oxidative stress can promote mitophagy [73] and lead to apoptosis [74]. In hPDLSCs-MOR six genes that encode for pro-apoptotic proteins are downregulated (CASP1, CASP4, CASP6, CASP7, CASP8, BAX) and one is upregulated (CASP2). In particular, CASP1 and CASP4 mediate the activation of inflammosome. CASP2 and CASP8 are involved in the initiatory effects of apoptosis while CASP6 and CASP7 are the effector caspases.
Moreover, BAX is a pro-apoptotic gene that handles the permeabilization of the OMM and it is downregulated. The overall downregulation of pro-apoptotic genes reduces of the apoptotic process [75]. Furthermore, the immunofluorescence analysis performed for CASP1 and BAX confirms their downregulation. In hPDLSCs-MOR the anti-apoptotic genes BCL2L12 and MCL1 are upregulated. They encode for pro-survival proteins of the Bcl-2 family that bind the pro-apoptotic proteins. This interaction prevents the release of cytochrome C in the cytosol and consequently the apoptosis [76,77]. BCL2 were analyzed with immunofluorescence that demonstrates its upregulation. The cytochrome C is encoded by CYCS gene that is upregulated in hPDLSCs-MOR. In addition, in hPDLSCs-MOR SOD1 is downregulated. It encodes for a protein of the superoxide dismutase family and its downregulation correlates with the reduction of the oxidative stress [78]. The immunofluorescence analysis also shows a downregulation for SOD1. Our group has already verified the inhibition of the apoptosis in hPDLSCs treated with moringin at the same concentration (0.5 µM) [48].

Purification of Moringin
The extraction of moringin by M. Oleifera (fam. Moringaceae) seeds (cake powder PKM2 provided by Indena India Pvt. Ltd.; Bangalore, India) was performed at the Bologna laboratory (CREA-AA; previously CIN) through established methods and the molecular structure was proved by nuclear magnetic resonance (NMR) spectroscopic analyses [83,84].

Ethic Statement
The study was performed in accordance with guidelines of the Helsinki declaration (2013). The protocol used for cell isolation and culture, was approved by the Ethical Committee at the Medical School, "G. d'Annunzio" University, Chieti, Italy (number 266/ April 17, 2014). Individuals recruited in the study signed the informative consent form prior tissue collection and all experiments were accomplished according to relevant guidelines and regulations.

Cell Culture and Moringin Pretreatment
Cells of periodontal tissue were collected scraping a third coronal root surface using Gracey's curette [85]. The three individuals enrolled in the study were healthy and in the absence of oral and systemic diseases. After collection, hPDLSCs were cultured using MSCGM-CD medium (mesenchymal stem cell growth medium chemically defined) (Lonza, Basel, Switzerland) and were maintained in an incubator at 37 • C in a humidified atmosphere of 5% CO 2 in air. The hPDLSCs were observed at the light microscopy DMIL and DM 2000 (Leica Microsytem, Milan, Italy) [86]. The cells were treated after reaching the 80% of confluence in a Petri dish. The cells at passage 2 were seeded for all experiments with a density of 300,000 cells/cm 2 . In order to accomplish transcriptomic analysis, hPDLSCs were treated with 0.5 µM of moringin (dissolved in 0.1% DMSO) for 48 h.
Morphological evaluation of treated and untreated hPDLSCs was performed using SEM. Samples were processed as previously reported by Gugliandolo et al. [87]. Finally, cells were mounted on alluminium stubs and gold-coated in an Emitech K550 (Emitech Ltd. Ashford, UK) sputter-coater before imaging by means Zeiss SEM EVO 50 (Zeiss, Jena, Germany). Cell were processed for immunofluorescence observations as following described. Samples were fixed with a solution of 4% paraformaldehyde diluted in 0.1M sodium phosphate buffer (PBS, Lonza). To highlight the cytoskeleton actin samples were incubated with the Alexa Fluor 488 phalloidin green fluorescence conjugate (1:400, Molecular Probes, Eugene, Oregon, USA) for 1 h. After washing cells were incubated with TOPRO (1:200, Molecular Probes) for 1 h at 37 • C [47] to mark cell nuclei. Samples were observed under Zeiss LSM800 confocal system (Zeiss, Jena, Germany) [88].
The viability rate of treated and untreated hPDLSCs were determined by means of the 3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazoliumbromide (MTT) method, at different time point (24, 48 and 72 h), as previously described [89]. Cell proliferation rate was also evaluated by the doubling time of Trypan blue harvested cells at 24, 48 and 72 h of culture and was calculated by using a software available online (http://www.doubling-time.com) [90]. All the experiments were performed in triplicate.

Statistical Analysis
All data were visualized by barplots. The statistical analysis was performed using ANOVA test with GraphPad Prism 6.0 software (GraphPad Software, La Jolla, CA, USA). The p-value threshold used to validate the hypothesis was p < 0.05.

Total RNA Extraction and cDNA Library Preparation
The Reliaprep RNA cell Miniprep System (Promega, Madison, WI, USA) was utilized for the extraction of the total RNA from all samples and each of them was treated with 0.1% of DMSO. In accordance with the TruSeq RNA Access library kit protocol (Illumina, San Diego, CA, USA), 40 ng of total RNA has been fragmented by using a thermal cycler to 94 • C for 8 min. The fragments obtained (>200 nt) have been used for the synthesis of the first strand cDNA by SuperScript II reverse transcriptase (Invitrogen, Carlsbad, CA, USA). Next, by using the Second Strand Marking Master Mix, a double strand cDNA has been synthesized by incubation at 16 • C for 1 h and then purified by AMPure XP beads to remove the reaction mix. Fragments adenylation at the 3' ends was performed to permit the ligation of the complementary adapters in order to avoid chimera generation. The samples identification and them preparation for flow cell hybridization was performed through the ligation of Adapter-Indexes to double strand cDNA fragments. After the purification by a clean-up step, a first PCR amplification was accomplished in accordance with the following program: denaturation at 98 • C for 30 s, 15 cycles composed of 98 • C for 10 s, 60 • C for 30 s, 72 • C for 30 s and extension at 72 • C for 5 min. To select and enrich specific regions of interest, a step of hybridization reaction was performed to mix exome capture probes with cDNA library. Later, in order to obtain a pool of different indexing libraries, by using 200 ng of each one, a hybridization reaction was accomplished in accordance with the following program: 95 • C for 10 min, an incubation consisting of 18 cycles of 1 min, starting at 94 • C and decreasing 2 • C per cycle and a final step of 58 • C for 90 min. For the sample pool purification streptavidin conjugated magnetic beads were utilized and, a second hybridization reaction, followed by another streptavidin purification, has been accomplished prior the final PCR amplification performed with the following program: 98 • C for 30 s, 10 cycles: 98 • C for 10 s, 60 • C for 30 s, and 72 • C for 30 s, and 72 • C for 5 min. A last clean-up was performed in order to obtain the final cDNA library purified. Lastly, the cDNA library has been qualitatively validated by Bioanalyzer instrument (Agilent High Sensitivity DNA kit, Richardson, TX, USA) and quantitatively by Real-Time PCR KAPA Library Quantification Kit-Illumina/ABI Prism®(Kapa Biosystems, Inc., Wilmington, MA, USA). After, the library has been denatured by 2 N NaOH and diluted in order to reach a final concentration of 12 pM. MiSeq Reagent Kit v3 has been used for sequencing on the Illumina MiSeq Instrument, by setting a single read.

Data Processing
The CASAVA software was used to produce the "FASTQ" format file. The alignment of the reads was achieved by means of "STAR" tool against the "homo sapiens UCSC hg19" reference genome. Cufflinks Assembly & DE package version 2.0.0 was used to perform the statistical evaluation of the genes obtained by the alignment. The normalization of the samples was calculated using the FPKM (fragment per kilobase of exon per million fragmented mapped) strategies: (1000 x read count)/(number of gene covered bases x number of mapped fragments in million).

Immunofluorescence Analysis
The hPDLSCs-CTR and hPDLSCs-MOR were processed as previously reported by Trubiani et al.  [91]. Samples were observed by means of a Zeiss LSM800 confocal system, connected to an inverted Zeiss Axiovert 200 microscope equipped with a Plan Neofluar oil-immersion objective. Images were collected using an argon laser beam with excitation lines at 488 nm and a helium-neon source at 543 and 633 nm. Post-acquisition image analyses were carried out with a Zeiss ZEN software.

Conclusions
The treatment of hPDLSCs with moringin downregulates most of the genes involved in mitophagy. Noteworthily, moringin downregulates both the trigger of the process (PINK1) and the genes that regulate the formation of phagophore (MAP1LC3B, GABARAP, GABARAPL1, GABARAPL2). Moreover, the genes involved in oxidative stress are not expressed except for SOD1 that is also downregulated. The immunofluorescence analysis supports the reduction of the expression of SOD1, CASP1, BAX and LC3A/B and the increase of the anti-apoptotic protein BCL2. In addition, our results confirm that moringin pretreatment is not cytotoxic. Consequently, its beneficial effects seem to improve hPDLSCs for possible uses in stem cell therapies. In particular, it could benefit more the disorders that have the oxidative stress as etiophatogenesis mechanisms, such as the neurodegenerative diseases.
Supplementary Materials: The following are available online, Table S1: Full list of differential expressed genes between hPDLSCs-CTR and hPDLSCs-MOR.


s2fieldsofstudy: Biology Psychology

text:
Changes in behavioral state account for the modulation of cerebellar learning by cannabinoid receptors

Attempts to identify cellular mechanisms underlying learning often include knocking out genes involved in candidate forms of synaptic plasticity and assessing subsequent effects on behavior. Within the cerebellum, multiple plasticity mechanisms have been proposed as cellular substrates of learning. For example, type 1-cannabinoid receptors (CB1Rs) mediate several forms of synaptic plasticity in the cerebellar cortex and have been implicated in cerebellum-dependent delay eyeblink conditioning in knockout experiments. However, recent work has shown that eyeblink conditioning is modulated by behavioral state, and global CB1KO mice are known to be hypoactive. We therefore asked to what extent altered locomotor activity vs. impaired CB1-dependent plasticity within the cerebellar cortex contribute to learning impairments in these mice. We find that eyeblink conditioning deficits in global CB1KOs can be fully accounted for by their hypoactivity. Impairments disappear when the level of locomotor activity is taken into account, and externally controlling running speed rescues learning. Moreover, both global and cerebellar granule cell-specific CB1KOs exhibit normal cerebellum-dependent locomotor adaptation. Our results suggest that the apparent effects of CB1R deletion on cerebellar learning are not due to direct effects on CB1-dependent plasticity, but rather, arise as a secondary consequence of hypoactivity. These findings highlight the importance of considering general changes in behavioral state as a powerful means through which individual genes contribute to complex behaviors, particularly in transgenic models.

Introduction
Knockout animals have been used extensively to investigate the role of specific receptors and signaling pathways in complex behaviors such as learning and memory (Crawley, 2008;Dubnau and Tully, 1998;Picciotto and Wickman, 1998). The sometimes conflicting findings from this approach are often attributed to its widely-acknowledged drawbacks, including limited cellular and temporal specificity (El-Brolosy and Stainier, 2017). Possible compensatory effects of chronic gene deletion, as well as both the level of cell-type specificity and multiple signaling targets within cells, all need to be taken into consideration.
The cerebellum has been used extensively as a model system for linking cellular plasticity and learning. Simple forms of learning, such as delay eyeblink conditioning, in which animals learn to close their eye in response to a neutral conditioned stimulus (CS) that is consistently paired with an puff of air to the eye, are known to take place within the cerebellum (Heiney et al., 2014;McCormick and Thompson, 1984). The well-described cerebellar circuit architecture places strong constraints on the sites and mechanisms for learning. Despite these advantages, however, results from gene knockout studies have contributed to controversies about how specific plasticity mechanisms, including long-term depression between parallel fibers (the axons of granule cells) and Purkinje cells, contribute to cerebellar learning (Ito, 1972;Aiba et al., 1994;Carey, 2011;De Zeeuw et al., 1998;Koekkoek et al., 2003;Schonewille et al., 2011).
Mutant mice lacking CB1 receptors display a variety of additional behavioral phenotypes. Notably, global CB1KOs are hypoactive, and spend less time running than their wildtype littermates (Chaouloff et al., 2011;Dubreucq et al., 2010;Zimmer et al., 1999). This hypoactivity has been attributed to the loss of CB1Rs from GABAergic neurons in the ventral tegmental area (Dubreucq et al., 2013). They also exhibit altered levels of anxiety and impaired spatial memory and extinction of fear conditioning (Degroot and Nomikos, 2004;Marsicano et al., 2002;Pamplona and Takahashi, 2006;Varvel and Lichtman, 2002).
We recently demonstrated that engaging in locomotor activity modulates learning of delay eyeblink conditioning (Albergaria et al., 2018). This raises the possibility that the hypoactivity of CB1KO mice could indirectly contribute to their apparent impairments in learning for this task. Here we investigate the relative contributions of decreased locomotor activity vs. altered CB1R-dependent synaptic plasticity to cerebellum-dependent learning in global and cell-type specific CB1KO mice. We conclude that the previously described effects of CB1R deletion on cerebellum-dependent learning arise as a secondary consequence of hypoactivity in CB1KOs, and not from direct effects on cerebellar plasticity. These findings highlight the importance of considering general changes in behavioral state as a potential mechanism through which individual genes contribute to complex behaviors in transgenic models.

CB1KOs are hypoactive and have apparent delays in eyeblink conditioning
We tested cerebellum-dependent delay eyeblink conditioning in global CB1KO mice and their wildtype littermates using a head-fixed apparatus with a freely rotating running wheel (Fig. 1A, B), as previously described (Albergaria et al., 2018;Chettih et al., 2011). Conditioning sessions included 100 trials in which a neutral visual conditioned stimulus (CS, a white LED) was paired with an air-puff unconditioned stimulus (US; a 50ms, 40psi air-puff directed at the eye). The CS preceded the US by 300ms and the two stimuli co-terminated. We measured the mouse's locomotor activity continuously with an infrared sensor placed underneath the wheel (Fig. 1A).
Consistent with a previous study that found impaired learning over seven acquisition sessions (Kishimoto and Kano, 2006), we found that CB1KOs displayed delayed learning, as measured by the percentage of trials that yielded a learned conditioned responses (CR; Fig. 1C). When compared to their littermate controls, CB1KO mice displayed significantly less locomotor activity on the selfpaced running wheel during training sessions (Fig. 1D, p < 0.05), consistent with the previously described hypoactivity of these mice (Chaouloff et al., 2011;Dubreucq et al., 2010;Zimmer et al., 1999).
Because of the recently described modulation of eyeblink conditioning by locomotor activity (Albergaria et al., 2018;Chettih et al., 2011), we asked whether the hypoactivity of CB1KOs could contribute to their delayed learning. We found that regardless of genotype, the more an animal ran on average, the earlier it learned ( Fig. 1E; controls: slope=-44.2, p < 0.05; CB1KO: slope = -66.5, p < 0.05). Over the course of 20 training sessions, however, we found that both genotypes eventually learned the task (Fig. 1F). Comparing acquisition curves of CB1KOs with littermate controls with comparable activity levels ( Fig. 1F, thick red and black lines) revealed no delays in learning in the CB1KOs, suggesting that locomotor activity is a stronger determinant of rate of learning than is genotype.

Trial-to-trial CR amplitudes are modulated by locomotor activity in CB1KOs
The amplitude and timing of conditioned responses were generally normal in CB1KOs ( Fig. 1G-I). As previously described in wildtype mice (Albergaria et al., 2018), trial-to-trial variation in the amplitude of conditioned responses was positively correlated with locomotor speed in both global CB1KOs and their littermate controls (Fig. 1G,H; CB1KO: n=2470 trials, N=11 animals, F (1,45.5) = 62.4, p < 0.0001; littermate controls: n=2639 trials, N=12 animals, F (1,73.7) = 35.9, p < 0.0001) (Fig. 1H). Interestingly, there was a tendency for CB1KOs to have larger CR amplitudes when compared to speed-matched trials from control mice ( Fig. 1H; F (1,41.03) = 26.4, p < 0.0001). This surprising result likely accounts for the finding (Fig. 1I) that overall, the average CR amplitudes for the two genotypes were not significantly different, despite the slower speed distributions in CB1KOs (histograms in Fig.  1H).

Externally controlling locomotor speed rescues learning deficits in CB1KOs
If the delayed eyeblink conditioning observed in CB1KOs is a consequence of their hypoactivity, then circumventing it by externally controlling running speed (Albergaria et al., 2018) should be sufficient to rescue learning. Indeed, replacing the self-paced running wheel with a motorized treadmill that equalized locomotor activity across mice completely eliminated the delayed learning of CB1KOs ( Fig.  2A, B). The average amplitude of eyelid responses was also not statistically different between the two genotypes under these conditions (Fig. 2C, D). Interestingly, in line with the results of Fig. 1H, when running speed was fixed, there was a trend for CB1KOs to learn faster ( Fig. 2A, B) and to have larger CR amplitudes (Fig. 2C, D) when compared to littermate controls.

Parallel fiber CB1 receptors are not required for delay eyeblink conditioning
The impaired eyeblink conditioning in global CB1KOs has been hypothesized (Carey et al., 2011;Kishimoto and Kano, 2006) to be due to the loss of CB1Rs from parallel fibers, where they mediate several forms of synaptic plasticity, including parallel fiber LTD (Brenowitz and Regehr, 2005;Carey et al., 2011;Kreitzer andRegehr, 2001a, 2001b;Safo and Regehr, 2005;Soler-Llavina and Sabatini, 2006). In contrast, our results so far suggest that the apparent effects of CB1R deletion on eyeblink conditioning may arise as a secondary consequence of the hypoactivity of global CB1KOs. However, they do not necessarily rule out a possible additional role for parallel fiber CB1Rs in eyeblink conditioning. To address this, we tested eyeblink conditioning in mice in which CB1Rs were selectively deleted from cerebellar granule cells (g6CB1KO, Fig. 3B), whose axons form parallel fibers (Carey et al., 2011;Fünfschilling and Reichardt, 2002).
Consistent with an extracerebellar source of decreased locomotor activity in global CB1KOs (Dubreucq et al., 2013), g6CB1KO mice were not hypoactive (Fig. 3C). Further, delay eyeblink conditioning was intact in granule-cell specific CB1 knockout mice compared to littermate controls ( Fig. 3A). Unlike global CB1KOs, neither the distribution of running speeds nor the speed-matched CR amplitudes differed between g6CB1KOs and controls ( Fig. 3D). There was also no difference in learning between the two groups on a motorized treadmill ( Fig. 3E-G).
Taken together, these results suggest that none of the differences in eyeblink conditioning in global CB1KO mice are due to the loss of CB1Rs from cerebellar parallel fibers.

CB1Rs are dispensable for locomotor learning
There are many similarities between learning mechanisms for eyeblink conditioning and other forms of cerebellum-dependent learning such as motor adaptation (Raymond et al., 1996). However, there are also important differences, particularly in the time course of learning, which could be particularly relevant in the context of CB1-mediated plasticity, which often acts on shorter time scales. We therefore investigated whether CB1Rs might play a role in locomotor adaptation on a split-belt treadmill, which is another form of cerebellum-dependent learning (Morton and Bastian 1996; Yanagihara et al. 1996;Darmohray et al., 2018). This also provided an opportunity to analyze the gait of CB1KO mice for any potential signs of cerebellar ataxia (Supp. Fig. 1B).
In locomotor adaptation on a split-belt treadmill, animals learn to regain overall gait symmetry in response to a perturbation that imposes unequal speeds on the two sides of the body (Fig. 4A). Learning is specific to measures of interlimb coordination, such as step-length (Fig. 4B, see Methods); whereas individual limb parameters, such as stride-length (Fig. 4B), change with the changes in belt speed but do not show learning (Reisman et al., 2005;Darmohray et al., 2018).
Finally, both locomotion and locomotor learning were indistinguishable from controls in granule cellspecific CB1KOs, who exhibited none of the gait alterations observed in global knockouts (Fig. 4E,F).
Thus, both global and cell-type specific CB1KO mice show normal cerebellum-dependent locomotor learning.

Discussion
Several lines of evidence presented here support the conclusion that the apparent effects of global CB1R deletion on cerebellar learning arise as a secondary consequence of the hypoactivity of global CB1KOs, rather than through direct effects on cerebellar plasticity. First, the delayed eyeblink conditioning in global CB1KOs was fully accounted for by differences in locomotor activity across animals (Fig. 1E,F). Second, learning impairments of CB1KO mice were rescued by externally controlling running speed. In fact, under conditions with comparable levels of locomotor activity, global CB1KOs exhibited improved performance relative to controls (e.g. Fig. 1F,H, Fig. 2). Third, global CB1KOs did not show signs of cerebellar ataxia (Supp. Fig. 1), and cerebellum-dependent locomotor learning was normal in these mice (Fig. 4E,F). Finally, granule cell-specific CB1KO mice exhibited none of the behavioral phenotypes observed in global knockouts (Figs. 3,4), suggesting that they do not depend on CB1Rs in parallel fibers.
Our findings suggest that parallel fiber CB1Rs are dispensable for cerebellum-dependent learning, but they do not rule out a possible role for LTD. While previous studies have demonstrated a role for CB1Rs in parallel fiber LTD (Carey et al., 2011;Safo and Regehr, 2005), this form of plasticity has been investigated in vitro with several different induction protocols (for review see Carey, 2011;Hansel et al., 2001). It is not known which of the resulting plasticity mechanisms are invoked in vivo during behavioral learning, and it is possible that not all forms of LTD are CB1-dependent. Moreover, there could be compensatory mechanisms in the knockouts that mask the behavioral consequences of the loss of CB1R-dependent LTD.
We show here that despite the strong constraints placed on the sites and mechanisms for delay eyeblink conditioning, CB1R deletion modulates this form of cerebellum-dependent learning via indirect, extracerebellar (Dubreucq et al., 2013) effects on behavioral state. By demonstrating that controlling locomotor activity levels can rescue apparent learning deficits in mutant mice, the current findings extend recent work showing that behavioral state is a powerful modulator of brain function (Ayaz et al., 2013;Bennett et al., 2013;Dadarlat and Stryker, 2017;Dipoppa et al., 2018;Erisken et al., 2014;Niell and Stryker, 2010;Pinto et al., 2013;Polack et al., 2013;Musall et al., 2018;Vinck et al., 2015), including capacity for learning (Albergaria et al., 2018). Further, they draw attention to the importance of considering altered levels of locomotor activity as an additional potential confound in gene knockout studies (Crawley, 2008;Eisener-Dorman et al., 2009;El-Brolosy and Stainier, 2017;Picciotto and Wickman, 1998).

Animals
All procedures were carried out in accordance with the European Union Directive 86/609/EEC and approved by the Champalimaud Centre for the Unknown Ethics Committee and the Portuguese Direcção Geral de Veterinária (Ref. No. 0421/000/000/2015). All procedures were performed in male and female mice approximately 10-14 weeks of age.

Global and conditional knockouts
Global CB1R knockout mice (CB1KO, CB1R -/-) (Zimmer et al., 1999) and their littermate controls (CB1R +/+) were obtained by crossing heterozygous breeding pairs (CB1R +/-). Gabra6Cre;CB1flox mice were generated by crossing mice (Gabra6Cre) in which Cre recombinase expression was driven by the promoter of the alpha6 subunit of the GABA A receptor and was specific to granule cells within the cerebellar cortex (Fünfschilling and Reichardt 2002), with mice (CB1flox, (Marsicano et al., 2003) carrying floxed alleles of the Cnr 1 gene that encodes the CB1R (Matsuda et al. 1990). The Gabra6Cre:CB1flox mouse line has been previously characterized and shown to lack parallel fiber LTD, as well as CB1R-mediated short-term forms of parallel-fiber-Purkinje cell plasticity (Carey et al., 2011). All lines were kept in a C57BL/6J background.

Surgical procedures
In all our surgeries, animals were anesthetized with isoflurane (4% induction and 0.5 -1% for maintenance), placed in a stereotaxic frame (David Kopf Instruments, Tujunga, CA) and a custom-cut metal head plate was glued to the skull with dental cement (Super Bond -C&B). After any surgical procedure, mice were monitored and allowed ~1-2 days of recovery.

Behavioral procedures Eyeblink conditioning
The experimental setup was based on previous work (Albergaria et al., 2018). For all behavioral experiments, mice were head-fixed but could walk freely on a Fast-Trac Activity Wheel (Bio-Serv) and habituated to the behavioral setup for at least 4 days prior to training. To externally control the speed of the treadmill, a DC motor with an encoder (Maxon) was used. For experiments on the motorized treadmill, mice were additionally habituated to walk at the target speed until they walked normally and displayed no external signs of distress. There was no difference across genotypes in the amount of habituation time.
Running speed was measured using an infra-red reflective sensor placed underneath the treadmill. Eyelid movements of the right eye were recorded using a high-speed monochromatic camera (Genie HM640, Dalsa) to monitor a 172 x 160 pixel region, at 900fps. Custom-written software using LabVIEW, together with a NI PCIE-8235 frame grabber and a NI-DAQmx board (National Instruments), was used to trigger and control all the hardware in a synchronized manner.
Acquisition sessions consisted of the presentation of 100 CS-US paired trials and 10 CS-only trials, which allow for the analysis of the kinematics of CRs without the masking effect that comes from the US-elicited reflex blink. The 110 trials were separated by a randomized inter trial interval (ITI) of 5-20s. In each trial, CS and US onsets were separated by a fixed interval (ISI) of 300ms and both stimuli co-terminated.
For all training experiments, the unconditioned stimulus (US) was an air-puff (40psi, 50ms) controlled by a Picospritzer (Parker) and delivered via a 27G needle positioned ~0.5cm away from the cornea of the right eye of the mouse. The direction of the air-puff was adjusted for each session of each mouse so that the unconditioned stimulus elicited a normal eye blink. The CS had a 350ms duration and was a white light LED positioned ~3cm directly in front of the mouse.

Eyeblink conditioning analysis
The video from each trial was analyzed offline with custom-written software using MATLAB (MathWorks). The distance between eyelids was calculated frame by frame by thresholding the grayscale image and extracting the minor axis of the ellipse that delineated the eye. Eyelid traces were normalized for each session, ranging from 1 (full blink) to 0 (eye fully open). Trials were classified as CRs if the eyelid closure reached at least 0.1 (in normalized pixel values) and occurred between 100ms after the time of CS onset and the onset of US. The average running speed for each animal was calculated by summing the average speed of each session (total distance run divided by session duration) and dividing by the total number of learning sessions, usually 20. Running speed for trial was calculated by dividing the distance run in the intertrial interval preceding the current trial by the elapsed time.

Locomotor coordination and learning
Locomotor coordination was assessed using our previously described LocoMouse setup, a tracking and analysis system for freely moving mice (Machado et al., 2015). Briefly, mice walked across a glass corridor, with a mirror placed at 45 deg below the mouse, so that a single high-speed camera recorded both bottom and side views. Individual trials consisted of single crossings of the corridor. Mice initiated trials by walking back and forth between two dark 'home' boxes on each end of the corridor. Tracking and gait analysis was performed offline. Tracking data was first broken down into strides using a simple peak detection algorithm (Machado et al., 2015). For the analyses shown in Supp. Fig 1A, we computed a range of locomotor parameters to compare global CB1KO mice to mutants (Purkinje cell degeneration, pcd and reeler) with cerebellar atrophy and consequent ataxia (Lalonde and Strazielle, 2007;Mariani et al., 1977;Mullen et al., 1976). The reported gait parameters have been previously described (Machado et al., 2015). Individual limb measures: stride length, cadence, stance and swing duration, swing velocity. Interlimb coordination: double support, step length, leftright (LR) and front-hind (FH) stance phasing, % time in 1 paw, 3 paw, non-diagonal 2 paw (other) supports. Body coordination: front paw base of support (BOS), maximum upward motion in a stride (peak z), maximum left-right (y) of front paws (max y front paw) and nose (max y nose).
Split-belt locomotor adaptation experiments and analyses were performed on a modified version of the LocoMouse setup (Machado et al., 2015) as previously described (Darmohray et al., 2018). Two motor-driven transparent treadmill belts independently imposed the walking speed on the two sides of the body. Split-belt locomotor adaptation experiments consisted of 'baseline' tied, split-belt, and 'washout' tied belt trials. All trials were one minute in duration, with brief periods in which the motors were off, in between trials.
For split-belt locomotor adaptation analyses (Fig. 4), we compared mutants and littermate controls on individual and interlimb coordination parameters. For individual limb analyses, we compared the two groups on their initial response to split-belt walking by assessing how stride length symmetry scaled on the first split-belt trial. To assess learning of new interlimb coordination parameters, we compared the aftereffects (first washout trialaverage baseline) of mutants and littermate controls.

Histology
To confirm CB1R expression in the different mouse lines, animals were perfused transcardially with 4% paraformaldehyde and their brains removed. Sagittal sections (50um thick) were cut in a Cryostat and stained with a polyclonal guinea pig antibody raised against the last 31 amino acids of the CB1R C-terminal (from Frontier Institute co., ltd) and DAPI. Sections were mounted on glass slides with Vectashield® mounting medium and imaged with a 10x objective.

Statistical analysis
All statistical analyses were performed using the Statistics toolbox in MATLAB. For the correlation between speed and CR amplitude ( Fig. 1H; Fig. 2D), we used a mixed model approach. We specified random slopes and intercepts models and included mouse/subject as a random covariate using the lme2 package (Bates, 2005). We report F tests (ANOVA) with Satterthwaite degrees of freedom correction; reported post-hoc analyses are ttests with Tukey corrections for multiple comparisons. For the correlation between speed and onset session (Fig. 1E), we used linear regression analysis. To compare the average distance between animals of each genotype ( Fig. 1D; Fig. 3C), and the average amplitudes of eyelid closure ( Fig. 1I; Fig. 2D; Fig. 3G) we used a Student's unpaired t-test. We used the same test to analyze stride length in early split trials and step length aftereffect of mutants and littermate controls (Fig. 4C-F). All t-tests were two-sided. Differences were considered significant at *p < 0.05, **p < 0.01 and ***p < 0.001. Mice were randomly assigned to specific experimental groups without bias and no animals were excluded. . Onset session was defined as the session in which the average CR amplitude exceeded 0.1. Each dot represents an animal. Lines are robust linear fits for all CB1 KO (red, N=11, slope = -66.5, *p = 0.02) and controls (gray, N=12, slope = -44.2, *p = 0.04). F. Average of %CR learning curves of CB1 KO (red line, N=11) compared with wildtype littermates with comparable average walking speed (solid black line, N=6) or with increased locomotor activity (dashed black line, N=6). Error bars indicate SEM. G. Average eyelid traces of slow (≤ 0.1m/s, dashed line) and fast (> 0.1m/s, solid line) trials for one representative animal of each genotype (black, controls; red, CB1KO), from the session in which that animal's learning crossed a threshold of 50% CRs, plus the following session. Blue horizontal line represents the time LED CS is on and black horizontal line represents a 50ms airpuff US. Shadows indicate SEM. H. Trial-to-trial correlation between CR amplitude and walking speed. Histograms indicate the relative % of trials (averaged across animals) from each genotype that fell in each speed bin. CR amplitudes for all trials from the same sessions as in (G) are plotted with lines representing averages across control (black) and global knockout (red) animals; shadows indicate SEM. There was a significant positive relationship for both controls (n=2639 trials, N=12 animals, F(1,73.7) = 35.9, ***p = 7.1e-08) and global knockouts (n=2470 trials, N=11 animals, F(1,45.5) = 62.9, ***p = 4.0e-10). I. Mean CR amplitudes from same sessions in (G). There was no significant difference in the average amplitude of control (black, N=12) vs. global knockout (red, N=11) animals (p = 0.44). Dots represent individual animals. Box indicates median and 25th-75th percentiles, whiskers extend to the most extreme data points.

Fig. 2. Speed-dependent modulation of eyeblink conditioning on a motorized treadmill A.
Average %CR learning curves of controls (black, N=5) and global CB1 knockout (red, N=5) mice, while running at a fixed speed (0.12m/s) on a motorized treadmill. Error bars indicate SEM. B. Onset session of learning for control and global CB1 knockout mice (black and red, respectively) running on a motorized treadmill, superimposed on the self-paced treadmill data from Fig. 1E. C. Average eyelid traces of individual trials for one representative control (black) and global knockout (red) animal. Trials included from the session in which each individual animal crossed a threshold of 50% CR plus the following session. Shadows indicate SEM. D. Mean CR amplitudes from same sessions in (C). There was no significant difference in the average amplitude (p = 0.45) of CB1 CTL (black, N=5) and CB1 KO (red, N=5). Dots represent individual animals. Box indicates median and 25th-75th percentiles, whiskers extend to the most extreme data points. Fig. 3. Eyeblink conditioning performance is intact on granule cell-specific CB1 knockout mice A. Average %CR learning curves of granule cell-specific CB1 knockout mice (G6 KO, cyan, N=8) and littermate controls (G6 CTL, blue, N=9). Error bars indicate SEM. B. Sagittal sections showing the selective elimination of CB1R (red) from cerebellar granule cells in G6 KOs and DAPI staining the granule cell layer (blue). Images of the cerebellum of a representative control (upper panel) and granule-cell conditional CB1 knockout (bottom panel). C. Average running distance from G6 CTL (blue, N=9) and G6 KO (cyan, N=8) mice on a self-paced treadmill across 20 learning sessions. The difference in average distance between groups was not significant. Box indicates median and 25th-75th percentiles, whiskers extend to the most extreme data points. D. Trial-to-trial correlation between CR amplitude and walking speed. CR amplitudes for all trials from the session in which each individual animal crossed a threshold of 50% CR plus the following session. Lines are averages across G6 CTL (blue) and G6 KO (cyan) animals; shadows indicate SEM. There was a significant positive relationship for both controls (n=1960 trials, N=9 animals, F(1,51.1) = 20.1, ***p = 4.2e-05) and G6 KO (n=1760 trials, N=8 animals, F(1,47.2) = 32.6, ***p = 7.2e-07). Histograms represent the number of trials in each bin for each group, normalized for total number of trials from the learning sessions of each animal. E. Average %CR learning curves of G6 KO (cyan, N=4) and G6 CTL (blue, N=4), while running at a fixed speed (0.12m/s) on a motorized treadmill. Error bars indicate SEM. F. Average eyelid traces of individual trials for one representative control (blue) and granule cell-selective CB1 knockout (cyan) animal. Trials included from the session in which each individual animal crossed a threshold of 50% CR plus the following session, on the motorized treadmill. Shadows indicate SEM. G. Mean CR amplitudes from same sessions in (E). There was no significant difference in the average amplitude (p = 0.87) of G6 CTL (blue) vs. G6 KO (cyan) animals. Dots represent individual animals. Box indicates median and 25th-75th percentiles, whiskers extend to the most extreme data points.


The standardization of the report for urine cell counting—A converting factor for Sysmex UF‐1000i

Background Multicenter laboratory may apply both automated flow cytometer and microscopy for urinalysis. Automated flow cytometer such as Sysmex UF‐1000i evaluates particles with native urine without centrifugation and reports as “counts per μL.” Microscopic examination recommended as the reference method for urine sediment analysis reports results as “counts per HPF (or μL).” Moreover, some results from flow cytometer are needed to be checked visually under microscopy. Therefore, it is worth to establish the consistency of the results from these two methods. Methods Urine specimens from 412 patients were examined with Sysmex UF‐1000i and manual microscopy using FAST‐READ disposable counting chambers. White blood cell (WBC) and red blood cell (RBC) counting results from UF‐1000i after transferred with the converting factor (0.297) we estimated were compared with that from microscopic examination. Method comparison was performed using Passing‐Bablok analysis. Results After transferred with the converting factor (0.297), cell counting results from UF‐1000i showed a good correlation with that derived by the reference method (R 2 was 0.868 for RBCs (P < 0.001), 0.882 for WBCs (P < 0.001)). Passing‐Bablok analysis showed no systematic difference (intercept estimate, −1 [95%CI, −7 to 3] and slightly proportional (slope estimate, 1.2 [95%CI, 1.0 to 1.7]) bias between concentrations of cells measured by manual microscopy and Sysmex UF‐1000i using the converting factor. Conclusion The converting factor (0.297) helps to transfer “counts per μL (non‐centrifugal urine)” to “counts per μL (equal to centrifugal urine),” and to keep the urine particle analysis results of Sysmex UF‐1000i consistent with the results from the reference method.

brings confusion to clinicians when they are not used to the units of UF series. 3 The microscopic urine sediment analysis is recommended as the reference method for the urine cell analysis. 4,5 The sample showing abnormalities or no coincident between the flow cytometer and the dipstick was re-examined under the microscopy using cell chambers (KOVA or FAST-READ etc). The guideline GP-163A 4

approved by Clinical and Laboratory Standards
Institute (CLSI) mentioned that the urinalysis results should be reported in the same reporting format and using the same reference intervals, and emphasized that the cell counts from manual microscopy are not suitable for comparison between laboratories. However, Chinese expert consensus 5 required that results from different methods-based analyzers should be reported with their own reference interval and proposed that the urine sediment results should be reported as "counts per HPF (centrifugal urine)." In summary, the agreement has not been reached of these guidelines on the unit for reporting urine sediment results. The various reporting forms for the same test may bring confusion to clinicians who evaluate the treatment effect for patients with kidney disease on the basis of urine sediment results.
We adopted a converting factor to make the results from UF-1000i comparable to the results from recommended urine sediment examined procedure. After that, we testified this converting factor by an experiment.

| Specimens
A total of 412 fresh urine specimens were obtained from inpatients of Guangdong Provincial Hospital of Chinese Medicine during a period of 3 weeks. The samples were collected in polypropylene containers. The analysis was performed on the UF-1000i (Sysmex Corporation, Kobe, Japan) and then by FAST-READ disposable counting chambers (Immune Systems Ltd, Paignton, UK). This whole process was finished within 1 hour of receipt.

| Centrifugation efficiency estimation
The percentage of particles remained in supernatant or lost by discard could only be estimated. Fifty-five urine specimens were randomly selected for centrifugation efficiency estimation. After analyzed on the UF-1000i for the first time, samples were centrifuged for 5 minutes at 400 g. The supernatant was absorbed into a new tube (leaving about 200 μL residue) and mixed by reversing the tubes for eight times and then measured by UF-1000i again.
The centrifugation efficiency was calculated by the equation below:

| Adjustment of the UF-1000i converting factor
According to the recommended procedure of urine sediment analysis, 4 the sediment samples were examined without staining, and the RBC and WBC enumeration results were reported as average particle counts per high-power field (HPF, ie, 1 field at 400× magnification, 10× 0bjective).

| Urine particle analysis with Sysmex UF-1000i analyzer and microscopy
The urine samples was centrifuged at 400 g for 5 minutes, then the

| Statistical analysis
The SPSS statistical 20.0 software (IBM, Armonk, NY, USA) was used to analyze the EF results and to do regression analysis between the results from UF-1000i and chamber methods. EF data were presented as X50% (X25%, X75%). Passing-Bablok analysis was also performed to make comparison between UF-1000i and chamber methods (after conversion).

| Efficiency of centrifugation
The efficiency of centrifugation is presented in Figure 1. Around the concentration of the reference limit, the efficiency factor of RBC (EFR) was 77.8% (10%, 99%), and the efficiency factor of WBC (EFW) was 88.9% (29%, 100%). The EF was relatively high at the high concentration of cells. The centrifuge efficiency should be carefully considered.  Table 1.

| Comparison results between FAST-READ chambers and UF-1000i after conversion
The 95% confidence range for the intercept of the regression lines contains 0, which means there is no systematic difference between UF-1000i (using the converting factor) and chamber counting. The slopes of the estimated linear functions, at around 1.2, are very close to 1.0, indicating a good correlation between two methods.

| D ISCUSS I ON
Sysmex UF-1000i (TOA Medical Electronics, Kobe, Japan) flow cytometer makes urine particle counting more precise and effective.
Several guidelines recommended that negative results of WBC or RBC from urine strips can be reported without checking by manual microscopy, and optimizing workflow also helps to decrease the microscopic examination of the samples with positive results of urine strips. 1   The reference procedure of the International Society of Laboratory Hematology (ISLH) recommended using native urine for the quantitative urine particle analysis. 8 However, if the amount of the clinically significant elements was too small in the urine, they may be missed.
Although we can use the same unit (cells/μL) to evaluate the cells in both centrifugal and non-centrifugal urine, the results of the same sample on these two conditions and clinical significance are obviously different. Centrifugation and discarding steps are the main error source of manual microscopy examination. Although the efficiency of centrifugation can be increased significantly by tripling the centrifugation, there is an increase in the particle destruction rate at the same time. Therefore, the particles in the supernatant do not mean that the number of particles in the sediment is decreased. Taken this into account, it is not surprised that comparison experiments showed that the UF series urine flow cytometer (Sysmex) may detect more RBCs and WBCs than microscopic examination did. 9 This finding brought confusion to clinicians in hospital which has multicenter laboratories applying both urinary sediment microscopic examination and flow cytometry analyzer.
Simultaneously utilizing the UF-1000i analyzer, FAST-READ chamber in our laboratory has already confused clinicians in our hospital.
The converting factor (0.297) we introduced here makes the original counts per μL (non-centrifugal urine) transferred to "counts per μL (equal to centrifugal urine)," and enables the urine particle analysis results of Sysmex UF-1000i correlated with that from chamber counting. The intercept of the regression lines found with the Passing-Bablok analysis is a negative value, which means that the UF-1000i method (after conversion) may have bias when the concentration of WBC or RBC is <1 cells/µL. The slopes of those regression lines, which are really close to 1, showed after conversion UF-1000i counting is comparable to the reference method.
According to Hannemann-Pohl et al, 6 the WBC count in native urine measured by UF series analyzer is 3.15 times of that derived from urine sediment analysis, which is very close to our converting factor (1/0.297 = 3.37).
Since the concentration of urine particles needs to be continuously monitored, it is better to keep the same reporting form for urine particle result. Thus, using the same reference intervals in a certain laboratory is more convenient.

ACK N OWLED G M ENT
We would like to thank Dr. Hongjun Zhu from Sysmex Corporation (Shanghai) for her assistance and guidance in this research.


extfieldsofstudy: Biology Medicine

text:
Association of single nucleotide polymorphism at long non-coding RNA 8138.1 with duration of fertility in egg-laying hens

A previous genome-wide transcriptional analysis identified long non-coding RNA 8138.1 (lncRNA8138.1) as a candidate gene related to hen duration of the fertility (DF) trait. LncRNA8138.1 gene response to growth factor and reproductive system development suggests it has a vital role in reproduction. In this study, we investigated the lncRNA8138.1 gene sequence in a population of egg-laying hens. The sequence analysis of the lncRNA8138.1 gene containing about 1.6 k nucleotides (nt) was observed with four single nucleotide polymorphisms (SNPs) and 7 nt indel including r.4937159A > G; r.4937219T > C; r.4937258G > C; r.4937318C > G and g.4937319_4937325delinsTGTGTGG. Next, the genomic DNAs from laying hen populations were subjected to polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP) to detect a region of 457 bp carrying lncRNA8138.1 r.4937159A > G substitution. Further inspection of the region containing r.4937159A > G mutation revealed three genotypes viz., AA, AG, and GG were observed with respective frequencies of 0.106, 0.607, and 0.287 in laying hen population 1 (P1) (n = 1, 042) and respective frequencies of 0.176, 0.708, and 0.116 in laying hen population 2 (P2) (n = 826). Moreover, to further examining the frequencies of r.4937159A > G genotypes in P1 and P2, and their additive and dominance effects; r.4937159A > G locus was significantly associated with DF-trait in both P1 and P2 (EN: the number of eggs, FN: the number of fertile eggs after a single AI), and DN (the number of days post-insemination until last fertile egg). In testing for additive and dominance effects, additive effect was significant (P < 0.05) in both P1 and P2 for DF-trait, and the dominance effect was significant (P < 0.05) for EN and FN traits, suggesting that r.4937159A > G polymorphism is a potential biomarker for DF-trait. However, the identified novel r.4937159A > G mutation and others require further investigation to confirm phenotypic causality and potential genetic relationships with reproductive traits. Overall, our findings suggest the significance of genetic variation in long non-coding RNAs may assist in future breeding programs to improve selection for prolonged DF-trait.

INTRODUCTION
Identification of genes and their association with economic traits in farm animals could assist in the genetic selection of breeding stock. In poultry production, duration of fertility (DF) is one of the most important traits defined by the number of day's post-fertilisation when viable eggs are produced. In the past decade, scientific advances have fueled progress in poultry industry to identify several proteins and enzyme-coding genes that are associated with period of fertility in laying birds, including, carbonic anhydrase (Holm, Ridderstråle & Knutsson, 1996), avidin and avidin-related protein-2 (AVR2) (Daryabari et al., 2014) aquaporins (Bakst, 2011;Zaniboni & Bakst, 2004), alkaline phosphatase (Bakst & Akuffo, 2007), the progesterone receptor (Yoshimura, Koike & Okamoto, 2000), the transforming growth factor-β (TGF-β) and its receptors (Das et al., 2006). These genes mostly affect immune system activity and metabolic processes implicated in maintaining sperm storage at the time of fertilization (Bakst, Wishart & Brillard, 1994). Hens often have prolonged DF, large and fertile egg production while some hens have short DF, with small and infertile eggs during the reproductive season. Both genetic as well as non-genetic causes have been described for DF trait in hens (Bakst, 2011;Holt & Fazeli, 2016;Sasanami et al., 2013).
The nuclei of avian cells composed of distinct molecules and transcriptome analysis indicate a major portion of the genome termed the non-coding RNAs (ncRNAs) which are generally classified into short and long non-coding RNAs (lncRNAs) depending on the length of the RNAs (Brosnan & Voinnet, 2009;Wang et al., 2017). Long non-coding RNAs (lncRNAs) have emerged as important molecules which underlie developmental and reproductive processes. LncRNAs required for gonadogenesis (Mulvey et al., 2014;Roeszler et al., 2012), sex determination (Zhang et al., 2010), sex hormone responses (Li et al., 2013;Yang et al., 2013), meiosis (Shichino, Yamashita & Yamamoto, 2014), spermatogenesis (Anguera et al., 2011;Arun et al., 2012;Wen et al., 2016), andOogenesis (Jenny et al., 2006) have been identified in different organisms. Given the diverse roles, lncRNAs play in essential biological processes; however, the role of lncRNAs in regulating DF-trait has not been extensively studied, especially in egg-laying hens. Although large-scale RNAsequencing data have revealed a large number of lncRNAs linked to special phenotypes in chickens (Adetula et al., 2018;Liu et al., 2017), none of these lncRNAs has been screened in a population of chickens for analyses of significant trait-associated single-nucleotide polymorphisms (SNPs). Currently, SNPs in lncRNA genes are one of the most common genetic variants of concern (Minotti et al., 2018); SNPs in lncRNA genes have been anticipated to contain some risk variants. For instance, emerging evidence has indicated the important role of genetic variants of lncRNAs in diseases (Jin et al., 2011;Tarighi et al., 2018) and chicken performance traits (Ren et al., 2017).
In 2018, a study applied a genome-guided strategy to reconstruct the uterovaginal junction transcriptome of egg-laying hens with long-and short-DF; and sought to uncover key lncRNA genes related to DF-trait by RNA-sequencing technology (Adetula et al., 2018). This study identified lncRNA8138.1 gene function in the biological processes that can affect response to growth factor, reproductive system development, positive regulation of cell differentiation, developmental process involved in reproduction, regulation of osteoblast differentiation, and carbohydrate binding. Importantly, lncRNA8138.1 gene was significantly differentially expressed between two groups (long-and short-DF hens), and its differential expression was confirmed by quantitative real-time polymerase chain reaction, suggesting that lncRNA8138.1 is vital for animal reproduction. To date, the chicken lncRNA8138.1 gene DNA polymorphisms are largely unexplored. Therefore, in this study, the SNP variants in lncRNA8138.1 gene identified and the relationship between lncRNA8138.1 mutation and DF-trait evaluated in large egg-laying hen populations. Our discovery provides a basis for further research about the underlying molecular markers that may help to improve the reproductive performance of hens.

Ethics statement
The protocols for all animal experiments were approved by the Scientific Ethic Committee of Huazhong Agricultural University with approval number HZAUCH-2018-005.

Egg-laying hens and duration of fertility trait
Commercial Jing-Hong laying hen populations were utilised in this study. Laying hen population 1 (P1) was obtained from the poultry farm of Huadu Yukou Poultry Industry, Co. Ltd, Beijing, China and population 2 (P2) from Jingzhou Yukou Poultry Industry, Co Ltd, Jingzhou, China. Briefly, the egg-laying hen populations were from two elite breeding lines, line 1 (white egg) and line 2 (brown egg). All hens were kept under standard conditions from 25 weeks aiming to study their duration of fertility. Hens were inseminated once with 2×10 8 pooled sperms ejaculates collected from viable rooster flocks. Eggs were collected and marked daily from day 2-20 after artificial insemination (AI); all hens completed a reproductive phase in three replicates and lasted 60 days. The number of egg per hen over the period was recorded, and the fertilized eggs were examined by candling on day 10 of incubation (dead embryos were considered as fertile). The reproductive history of all hens was recorded daily based on DF-trait: EN (number of eggs), FN (the number of fertile eggs after a single AI), and DN (the number of days post-insemination until last fertile egg). At the end of the reproductive season, a total of 1,868 hens had a record of EN, FN, and DN respectively.

DNA extraction and quality assessment
For DNA experiments, genomic DNA was isolated from 0.5 ml blood obtained from the egg-laying hen populations (P1, n = 1,042 and P2, n = 826) by phenol-chloroform extraction method (Sambrook & Russell, 2006). The quality of genomic DNA samples was rated using Nanodrop 2000 Spectrophotometer (Thermo Scientific TM ND2000USCAN; Waltham, MA USA). DNA samples were diluted to a working concentration of 50 ng/µL and stored at 20 • C for further analysis.

Primer design, PCR amplification, and SNP detection
LncRNA8138.1 is a double-exon and a single-intron gene at chromosome 18: (4936949-4937389, 4937470-4938607 bp) (GenBank accession number: MK336169). Primer pairs (AACCTGAGCTTTCAACAGAC & CCCAACTGCTCCAACATTAG) were designed using Primer Premier Software version 5.0 for amplification of lncRNA8138.1. Assays were performed by PCR in DNA samples of 60 hens selected randomly to construct a DNA pool with equal DNA concentration of 50 ng/µl for each hen. PCR amplifications were performed in a 25 µl volume containing 50 ng pooled DNA, 2.5 µl of 10× PCR buffer, 5 mM of dNTPs, 10 pmol of forward and reverse primer, 0.625U Taq DNA polymerase (Takara Biomedical Technology Co., Ltd. Beijing, China) and ddH2O. The reactions were performed under the following conditions: an initial pre-denaturing at 95 • C for 5 min; 35 cycles of 95 • C (20 s), 55 • C-60 • C (20 s) 72 • C (20 s); and a terminal incubation step at 72 • C for 5min. The PCR products were sequenced directly with Applied Biosystems 3730xl DNA Analyzer (Applied Biosystems R , Waltham, MA USA), and SNP discovery was conducted by the Seqman program of DNASTAR 7.1.0 software (DNASTAR, Inc., Madison, WI, USA).

PCR amplification and polymorphism genotyping
The region containing the lncRNA8138.1 polymorphism was amplified in a single PCR system with the necessary reagents, and the reaction conditions were identical to those described above. Genotyping of PCR products was performed by PCR-RFLP assay in both P1 and P2 individuals using the primer pairs (AGTCACAGACCAGTAGTTTT & CCTCTAAAA TCTTAGCAGCA). The PCR-RFLP condition for amplifying was pre-denaturing at 95 • C for 3 min, and 32 cycles of denaturation at 95 • C for 30 s, annealing at 64 • C for 30 s, extension at 72 • C for 20 s, and elongation at 72 • C for 7 min (Adeyinka et al., 2017). Next, PCR-RFLP products were digested with an enzyme (Hind III ) and electrophoresed using 3% agarose gel.

Mutational analysis and structure prediction
Predicting the common secondary structure of lncRNA is an important step towards understanding the function (Blythe, Fox & Bond, 2016). Here, we experimentally predict the secondary structure of lncRNA8138.1 r.4937159A>G polymorphism in chicken, which may be used to upgrade sequence/structure alignments for other species. DNA samples of hens were selected randomly to construct a DNA pool with equal DNA concentration of 50 ng/µl for 60 individuals having AA and GG genotypes. PCR amplification reactions were performed in a 20 µl volume containing 50 ng pooled DNA, 2.5 µl of 10× PCR buffer, 5 mM of dNTPs, 10 pmol of forward and reverse primer, 0.625U Taq DNA polymerase (Takara Biomedical Technology Co., Ltd. Beijing, China) and ddH2O. The reactions were conducted under the following conditions: an initial pre-denaturing at 95 • C for 5 min; 35 cycles of 95 • C (20 s), 55 • C-60 • C (20 s) 72 • C (20 s); and a terminal incubation step at 72 • C for 5min. The PCR products were sequenced with ABI3730xl DNA analyzer (Applied Biosystems R , Waltham, MA, USA). The resulting nucleotides from the genome of AA and GG genotypes were used to predict the common secondary structure of lncRNA8138.1 in chicken with the use of web-based structure prediction programs known as Dynalign (http://rna.urmc.rochester.edu/RNAstructureWeb/Servers/dynalign/dynalign.html) (Reuter & Mathews, 2010).

Statistical Analysis
Descriptive statistics for the DF-trait were calculated using an online based descriptive statistics calculator. The SNP genetic parameters including homozygosity (Ho), heterozygosity (He; Ho + He = 1) and polymorphism information content (PIC) were estimated according to methods developed by (Nei & Roychoudhury, 1974). PIC is an indicator of polymorphism that can be used to estimate genetic diversity. According to PIC values, the genetic variations were classified as high PIC (>0.5), medium (0.25 < PIC < 0.5) and low (PIC < 0.25) (Botstein et al., 1980). Genotypes were tested for Hardy-Weinberg equilibrium using the Chi-squared tests (R function HWE.chisq) implemented in the R environment (v 3.0.2) (Pouresmaeili et al., 2013). The statistical significance level was set at P <0.05. The relationship between lncRNA8138.1 polymorphism and DF-trait in P1 and P2 were estimated based on the general linear model procedure of SAS v. 9.2 (SAS Institute Inc., Cary, NC, USA) (Littell, Stroup & Freund, 2002). Briefly, the following linear model was used: where: Y ijklm represents the dependent variable (DF-trait), µ is the overall population means, B i is the fixed effect of ith breed (i = 1), G j is the fixed effect of jth genotype (j = AA, AG, and GG), A k = is the fixed effect of kth age (k = 1), L l is the fixed effect of lth location associated with the DF-trait, e ijklm is the random error. Differences in means were considered significant at P <0.05 and the SAS PROC t -test (SAS v. 9.2; SAS Institute Inc., Cary, NC, USA) was used to estimate the differences in the LS Means between genotypes. Additive and dominance effects were estimated using REG procedure in (SAS v. 9.2 (SAS Institute Inc., Cary, NC, USA)). Statistically significant differences in the investigated populations for additive and dominance effects were estimated as a t -test for LS Means.

Descriptive statistics of laying hens duration of fertility
During the reproductive season, DF-trait was defined by the EN (number of eggs), FN (the number of fertile eggs after a single AI) and DN (the number of days post-insemination until last fertile egg). The descriptive statistics of the recorded DF-trait in P1 (n = 1,042) are shown in Fig. 1A. In the P1, the median EN, FN and DN were 14.67, 9.33 and 11.33 respectively and interquartile range (IQR) for the EN, FN, and DN were (13.33-15.67 eggs), (7.67-10.67 eggs) and DN, (10.67-11.67 days) respectively (Fig. 1A). Figure 1B illustrates the descriptive statistics of the recorded DF trait in the P2 (n = 826); the median EN, FN and DN were 15.33, 9.33 and 10.33, respectively. The IQR for the EN, FN and DN were (14.00-20.00 eggs), (7.33-11.17 eggs), and (8.33-12.33 days) respectively (Fig. 1B). In general, DF-trait showed high individual variability in both P1 and P2, even when we observed the same and optimal multiple insemination conditions.

LncRNA8138.1 genetic polymorphisms
In this study, approximately 1.6 kb nucleotides of lncRNA8138.1 were analysed. The analyses revealed a set of four possible SNP candidates and 7 nt indel in egg-laying hens that were detected through sequencing, including SNP r.4937159A>G; r.4937219T>C; r.4937258G>C; r.4937318C>G and g.4937319_4937325delinsTGTGTGG (Fig. 2). Several previous studies have reported that variants associated with lncRNA cannot be undervalued (Bhartiya & Scaria, 2016;Hrdlickova et al., 2014). Therefore, r.4937159A>G was detected by PCR-RFLP; the relationship with chicken DF-trait was further investigated in large egg-laying hen populations.

Common secondary structures of lncRNA8138.1 mutation in chicken
To determine the common secondary structures of lncRNA8138.1 in chicken, the mutational profile of lncRNA8138.1 gene was utilized for the structure prediction. This is done over individuals having AA and GG genotypes separately, and the results showed that individuals with AA genotype differed in secondary structure by 2-point mutations when compared with the individuals with GG genotype. The 2-point mutations represent the AG_deletion in the AA individuals (Fig. 3A) and AG_insertion in the GG individuals (Fig. 3B); however, a consistent pattern of similarity was noticed in both AA and GG genotypes secondary structures (Fig. 3).

Analyses of associations between lncRNA8138.1 nucleotide variation and DF trait
Associations between lncRNA8138.1 r.4937159A>G polymorphism and DF-trait were investigated in the egg-laying hen populations. For all DF-trait (EN, FN and DN), LS Means and standard error of mean (SEM) values were tabulated for the genotype effect (Table 2). In the P1, association analysis confirmed that SNP r.4937159A>G was significantly related to DF-trait (EN: P < 0.0001, FN: P < 0.0001, and DN: P ≤ 0.05; Table 2). In the P2, a significant association was observed for the EN (P < 0.0001) and FN, (P < 0.01); Table  2). Besides, additive and dominance effects were significant for all DF-trait except DN in the P1 (P < 0.05, Table 2), whereas in the P2, additive effects were significant for EN and FN traits (P < 0.01, Table 2). Additionally, comparison of lncRNA8138.1 r.4937159A>G genotypes revealed significant differences between genotypes, i.e., (GG vs AA, GG vs AG, and AA vs AG) respectively (Table 2).

DISCUSSION
Previously, Adetula et al. (2018) identified a novel lncRNA8138.1 gene is closely related to DF-trait in egg-laying hens and several studies have also explored the role of lncRNAs in reproductive biology (Taylor et al., 2015) and their findings suggest that lncRNA gene has an essential role in reproduction. However, there are no previous reports of chicken lncRNA8138.1 genetic variant and its relationship with DF-trait. The relationship between lncRNA8138.1 gene variants and DF-trait in large egg-laying hen populations required further analysis. First, we measured the expression profiles of the chicken lncRNA8138.1 gene in our previous study, and the results demonstrated that lncRNA8138.1 was differentially expressed between long-and short-DF hens (Adetula et al., 2018). Given the numerous lncRNAs that are expressed in the reproductive system of human and mouse (Wichman et al., 2017), we postulated that lncRNA8138.1 gene might play critical roles in reproduction and fertility. Previous investigation indicated that lncRNA genetic variants play a central role in various biological processes (Wapinski & Chang, 2011), for instance SNP rs920778 in lncRNA HOTAIR, contributes to the risk of gastric cancer (Pan et al., 2016) and lncRNA SNP rs11655237 confers susceptibility to pancreatic cancer (Zheng et al., 2016). Mutations in lncRNA GAS8-AS1 were associated with papillary thyroid carcinoma (Chen et al., 2016). A study of whole-genome mutational landscape of liver cancer also discovered mutations in lncRNA NEAT1 and MALAT1 (Fujimoto et al., 2016). These studies have suggested an important role of nucleotide polymorphisms in lncRNAs.
Next, we explore the genetic variation of lncRNA8138.1 gene in egg-laying hen populations. Generally, mutations are one of the forces of evolution because they fuel the variability in populations and thus enable evolutionary change (Loewe & Hill, 2010).

Notes.
Data are summarized as means ± SEM. For all DF-traits, we examined multiple inseminations (up to about 60 wks. of age); (n = 1,042; P1) and (n = 826; P2). DF (duration of fertility), a EN (number of eggs); b FN (the number of fertile eggs after a single AI); c DN (the number of days post-insemination until last fertile egg). Genotypes comparisons p-values (GG vs AA, GG vs AG, and AA vs AG) were obtained from the genome of egg-laying hen populations.
Just like other genetic variations, SNPs can be directly detected by simple PCR amplification and agarose gel electrophoresis, making them convenient and practical. Therefore, SNP variants in the lncRNA8138.1 gene were identified, and their associations with DF-trait investigated in a large commercial population of egg-laying hens. Five novel mutations including r.4937159A>G, r.4937219T>C, r.4937258G>C, r.4937318C>G and g.4937319_4937325delinsTGTGTGG were identified in lncRNA8138.1 gene, and a single PCR system was used to detect SNP r.4937159A>G. Three genotypes (AA, AG, and GG) were identified and statistical analysis indicates that SNP r.4937159A>G genotypes were not in HWE (P < 0.05) in laying hen populations, because of the lower number of observed AA and GG genotypes. Another one possible reason for this is rapid and effective selection, which could affect the allelic balance of the SNP locus (Crowley et al., 2015;Dunn et al., 2011). Differences between genotypes indicate that (GG vs AA) and (GG vs AG) genotypes were significantly different whereas no significant difference between AA vs AG genotypes for the DF-trait in the P1. The GG carriers had prolonged-DF in both P1 and P2 when compared to AA and AG genotypes ( Table 2), suggesting that GG may be a favourable genotype for DF-trait in the egg-laying hens.
Advances in structural biology have made in the understanding of the molecular mechanism behind lncRNA function (Blythe, Fox & Bond, 2016;Novikova et al., 2013). To further understand lncRNA8138.1 r.4937159A>G mutation structure in chicken; we predicted a common secondary structure between AA and GG carriers by RNA structure software version 6.1 and a 2-point mutation (AG_insertion) was found in GG carriers, and 2-point mutation (AG_deletion) in AA carriers were noted in the loop (Fig. 3). The 2-point mutations that exist in the AA and GG individuals can be used as a fingerprint to find the structure in thousands of other species.
Furthermore, the analysis of association between r.4937159A>G polymorphism and DF-trait investigated in a total of 1868 individuals of egg-laying hens shows significant correlation with DF-phenotypes; however, it can be considered that this locus is indeed correlated with the tested trait, especially in large population which improves the credibility of the test (P < 0.0001, P ≤ 0.05). The association analysis based on the large laying hen populations revealed r.4937159A>G polymorphism was strongly associated with DF-trait in hens, which was consistent with our previous investigations, suggesting that there are genetic variations that might impact lncRNA8138.1 expression and function in egg-laying hens. Additionally, the fact that additive effect was significant for EN, FN and DN in egg-laying hens, indicating that lncRNA8138.1 gene plays a vital role in regulating DF-trait.

CONCLUSIONS
The biological role of lncRNA8138.1 gene in regulating DF-trait also held while examining the presence of nucleotide variations. In the present study, four candidate SNPs and 7 nt indel were identified in the lncRNA8138.1 gene; one of SNP (r.4937159A>G) was detected and found to be significantly associated with DF-trait (EN, FN and DN) in large egg-laying hen populations; indicating the emerging role of variants in long non-coding RNA might benefit the selection of birds for the marker-assisted breeding program. However, these findings extend our previous research on differential effects of lncRNA8138.1 gene in egg-laying hens and invite future studies to investigate the lncRNA8138.1 mutations in laying hen populations to enabling a better understanding of the genetic mechanism regulating DF-trait.