Cells within tumors vary in phenotype as a result of changes in gene expression caused by a variety of mechanisms, permitting cancers to evolve under selective pressures from immune and other homeostatic processes. Earlier, we traced apparent losses in heterozygosity (LOH) of spontaneous breast tumors from first generation (F1) intercrossed mice to atypical epigenetic modifications in the structure of DNA across the tumor genomes. Here, we describe a parallel pattern of LOH in gene expression, revealed through quantitation of parental alleles across a population of clonal tumors. We found variegated patterns of LOH, based on allelic ratio outliers in hundreds of genes, enriched in regulatory pathways typically co-opted by tumors. The frequency of outliers was correlated with transcriptional repression of a large set of homozygous genes. These findings suggest stochastic losses in gene expression across the genome of tumors generate phenotypic variation among cells, allowing clonal selection during tumor development. Sara Felts et al. present atypical losses of heterozygosity in genes expressed in breast tumors of first-generation intercrossed mice. They find evidence suggesting sporadic epigenetic errors causing dysregulated cellular homeostasis in tumors. However, as more minor genomic variants are cataloged with small associations with tumor development and disease outcomes7, there is a need to understand how these minor changes contribute in concert to cancer phenotypes. Our recent studies indicate that small differences in gene expression integrated across many genes can specify cellular phenotypes8. How tumor phenotypes are shaped by such integrative events needs further investigation. In vivo models of cancer often employ spontaneous or induced tumor formation in inbred mice. As humans are an outbred population characterized by genome-wide heterozygosity, important aspects of the evolution and behavior of human cancers can be modeled only using heterozygous animals.Key features of tumorigenesis include an accumulation of chromosomal, genetic, and epigenetic aberrations that result in losses of cellular control mechanisms and cancerous tissues with evolving phenotypes. Cancer genome studies provide robust data sets for understanding mutation burden important for patient stratification, the identification of targetable intervention strategies, and lending insight into cancer evolution12. In a previous study using an F1 model of spontaneous breast cancer, we found evidence of previously unappreciated widespread epigenetic modifications in tumor DNA marked by polymorphisms13. Tumorigenesis is initiated by oncogenic HER2 (rat neu) and ensues rapidly in this model14. As a test of whether stochastic epigenetic events had an impact on tumor gene expression, we used RNAseq to quantitate the balance  of germline-encoded, expressed single nucleotide variants (eSNVs) in tumor mRNA. Eighteen of twenty characterized tumors were dominated by homozygous X-chromosome inactivation, providing a strong indication of their clonal evolution. Within these tumor clones, we found unique, statistically significant variegated patterns of LOH (V-LOH) representing stochastic over- or under-representation of eSNVs. As these events were manifest in alleles integral to multiple regulatory pathways and each tumor had many such events, the data illustrate how various combinations of small quantitative changes can be selected to drive clonal evolution of tumors in the context of a driver mutation. Furthermore, a strong negative correlation between the frequency of outliers in the ratio of expressed alleles and the levels of expression for hundreds of genes in these tumors indicates that the underlying mechanism operates to decrease expression of genes whose normal functions are to maintain tissue homeostasis.Germline single nucleotide variants (SNVs) differentiating strains of laboratory mice can be used to track genetic changes at the level of genomic DNA, as well as at the level of tissue-specific transcriptomes16 share a common germline genetic origin, yet develop into genotypically diverse tumors over the course of 3–4 months13. Gene expression data obtained by RNAseq analysis of 20 independent late-stage tumors were aligned to the mouse reference genome ; FVB and BALB allele counts were measured separately for over 56,300 potential variants. Approximately 12,400 eSNVs met a threshold of 15 raw read counts and were considered further. All allelic ratios were converted to BALB/c-allele frequency (CAF) for ease of discussion and were visualized along each mouse chromosome (Chr1-Chr19 plus ChrX) as mean CAFs for individual eSNVs ] F1 spontaneous breast tumor samples. In this model, all oncogene-driven tumors18, the quantitative dominance of one set of parental alleles indicates that these mouse tumors were clonal, each being derived from a single breast epithelial cell marked early by inactivation of one parental ChrX. The second genomic region exhibiting strong LOH was the entirety of Chr4. eSNVs all along Chr4 were highly skewed toward FVB (P < 10−3), reflecting the selective loss of BALB/c alleles  with no apparent preference for either parental chromosome. The third loss of allelic information emerged in the eSNV expression pattern on a centromeric segment of Chr7 where 100% of the expressed alleles were FVB in origin (P < 10−6). As the BALB/c parent, whose Chr7 alleles were lost in these tumors, carries the neuT transgene and because we had observed genomic deletions associated with a transgene insertion in other contexts20, we considered that Chr7 of the BALB parent might be the location of the neuT insertion. We tested F1 breast tumor cells by in situ hybridization and found that a neuT probe co-localized with a marker for Chr7 on one of the sister chromosomes in cells from neuT+ but not neuT− littermates  in allelic gene expression occurred in three key genomic regions. First, the majority of tumors expressed ChrX-encoding genes from either the BALB or FVB parent  DNA to measure stochastic LOH at widely dispersed loci throughout the genomeZ-score  (std dev)−1) for the expression ratio for each tumor at every eSNV was calculated, and using a threshold Z-score of 2 a pattern of outliers was revealed  and below the mean allelic ratios (negative Z-score), and outliers were present in genes throughout the tumor genomes. Moreover, the pattern of observed outliers was variegated, much like that observed at the DNA level (V-LOH)13. Based on the expected distribution of outliers in 20 samples, the frequency of tumors with one or more outliers (~63%) should follow the Poisson distribution. We found, however, 87% (2634 of 3044) of expressed genes marked by eSNVs were represented in at least one tumor at an allelic frequency greater than 2 STD DEV away from the empirical mean of the tumor population (p < 10−6). This strong bias in favor of an excessive number of outliers indicates that, during tumor development, selection favors cells with these minor changes in gene expression.The analysis focused on testing whether, for each eSNV, an individual tumor had an aberrant ratio of expressed alleles compared to the other tumors in the cohort. We first corrected for DNA-based allelic imbalances by excluding genes on Chr4, ChrX and the centromeric end of Chr7 where all tumors had skewed allelic ratios. We found the CAF of the remaining eSNV  to be normally distributed, with an average mean CAF across these eSNV to be 0.511 ± 0.096. We then tested each eSNV for the occurrence of an allelic ratio outlier outside the distribution of allelic ratios described by the 20 tumors. To do this, an individual led Fig. . We foun21, Qiagen; Supplementary Table 24 of this same gene set identified a similar enrichment in pathways regulating cancer traits . A comparison of our gene set with that from a recent transcriptome-based search for new breast cancer susceptibility genes7 found fourteen exact matches . Furthermore, 98.6% of those correlations with P < 10−6 , and there was an overwhelming bias toward a negative correlation of gene expression with the observed frequency of outlier eSNV ratios in developed tumors. In a previous study of F1 tumors, we found evidence of epigenetic  processes capable of causing random distortions in allelic ratio measurements29. Our own analysis of data reported by Keane et al.29 found that the variation of allelic ratios expressed in those tissues was actually distributed more broadly than in the clonal tumors studied here, a finding consistent with their conclusion that allelic ratios of expressed genes are different in different tissues. We were unable to discern outlier patterns in the normal tissue data due to limited sample sizes. However, an IPA analysis of genes marked by potential outlier eSNV in the data sets derived from normal liver and normal hippocampus did not reveal significant enrichment of genes associated with cancer or antigen presentation, while an identically powered analysis of samples from our tumor cohort recapitulated the cancer profiles reported above . There thus exists a certain propensity for transcript variability in normal, homeostatic mechanisms that becomes enriched during tumorigenesis.The impact of cis- and transacting variability in epigenetic regulation in normal tissues has been suggested by transcriptome data from smaller studies of normal tissues from F1 heterozygotes8, we demonstrated that small changes in gene expression, when integrated across multiple genes, were descriptive of individual response phenotypes to subsequent immune activation. In a similar vein, this study reiterates this principle by demonstrating that small changes in gene expression among combinations of genes whose normal functions are to maintain homeostasis are selectively enriched and therefore must determine differential responses of expanding tumor clones to challenges and opportunities present within the microenvironment.In recent studies of human lymphocytes13. F1 hybrid mice  were from matings of neuT transgene positive BALB/c males with wildtype FVB/J females and were studied according to IACUC protocol #A47311-11. Animals were allowed to develop spontaneous breast cancer tumors , and monitored weekly for the appearance of palpable tumors at which point they were monitored daily. All animals developed multiple tumors (≥5 of 10 glands affected). When any tumor reached 100 mm2 (width × length), the mouse was killed using CO2 inhalation and one tumor removed post mortem and stored frozen at −80 C until processing for RNA.These animal studies were conducted using protocols approved by the Mayo Clinic Institutional Animal Care and Use Committee (IACUC). Hemizygous BALB/c-neuT mice were originally acquired from Dr. Guido Forni and were maintained by intercross with transgene negative BALB/c female as described previously30, read alignment using TopHat v2 using Mouse Genome Build, mm10 and primary data FastQC. While the initial run yielded reads of up to 150 bases, all sample reads were trimmed to 100 bases for downstream analyses to enhance the quality of the data.RNA was isolated from small pieces (<50 mg wet weight) of frozen breast tumors which were thawed, weighed and immediately homogenized using a Tissue Miser (Fisher), miRNeasy kit followed by DNase treatment, and additional clean-up using RNeasy MinElute Cleanup . All further steps for RNAseq were conducted by the Mayo Clinic Gene Expression Core, including TruSeq library preparation and Paired-end (PE)150 analysis using HiSeq4000, 8 samples per lane (Illumina), data processing using MapRseq 2.0.0http://www.sanger.ac.uk/science/data/mouse-genomes-project). Unique variants specific to FVB and BALB were extracted and the read depth of both FVB alleles and BALB alleles were counted in each.bam file using SAMtools31. The FVB and BALB allele counts for 56,328 unique loci were then copied into an Excel spreadsheet (Microsoft Office 2010) for further analyses. To keep our focus on genes consistently expressed with good exon coverage across samples, we kept only eSNVs where the minor allele count was at least 15. We then calculated allelic ratios for BALB as [BALB allele counts] per [(FVB + BALB) allele counts]. FVB allele ratios were calculated in the same way, then converted to BALB allele ratios (FVB = 1-BALB). Thus, all eSNVs are considered using BALB/c-allelic frequencies (CAF). To compare CAF variability across tumor samples, we calculated Z-scores at each polymorphic locus using the mean and standard deviation (std dev) for the 20 tumors  std dev−1). CAF data for 50 eSNV were arbitrarily sampled and tested for normality using Prism 7 (GraphPad). In that test, 92% of the eSNV tested passed using the optional Shapiro Wilk test32; of note, all sampled eSNV passed this test when any individual tumor sample containing outliers were removed from the analysis during the characterization of V-LOH.Germline-encoded sequence variants of FVB/J and BALB/c mouse strains were downloaded from the Mouse Genome Project . As 20 tumors were evaluated, a random frequency of outlier measures was expected to be 1 in 20. Using the Poison distribution, an expectation of allelic-frequency measures among the 20 tumors for each eSNV-marked gene containing no outliers was estimated to be 0.3679. Using these expected values, the probability of the observed frequencies of eSNVs with no outliers was calculated using the binomial distribution.LOH was characterized in two ways: first as eSNV in which all tumors displayed allelic loss (mean CAF <0.25 or >0.75), and secondly as eSNV in which the allelic ratio for at least one tumor was more than 2 std dev away from the mean for that locus. After removal of eSNV found to display allelic loss , outliers were defined as having a 21 with the optional Benjamini-Hochberg correction for multiple hypothesis testing33. The online tools (http://geneontology.org/) of the Gene Ontology Consortium (GO24) were also employed to characterize the gene sets. All graphs were generated using Prism.eSNV containing outliers were pooled to compile a gene set for further characterization. Correlations of gene expression and outlier frequency in individual tumors were performed using the ANOVA option in Partek Genomics Suite 6.6. Random data sets were generated using Excel. Characterization and statistical analyses of derived gene sets were conducted using IPA32 test in Prism 7 (GraphPad). Much of the analysis focuses on the assumption of equal chance for each parental allele to be expressed for most genes in each of these F1 tumors. We examined these allelic ratios across thousands of eSNV, representing hundreds of expressed genes. It was our assumption that the expected distribution of outliers  should follow the Poisson distribution. To test for allelic ratio outliers irrespective of mean, CAF were converted to Z-scores; eSNV in which only 1 in 20 contained an outlier ratio were explored further using Students’ t-test and binomial distribution. Correlations of gene expression and outlier frequency in individual tumors were performed using the ANOVA option in Partek Genomics Suite 6.6. Characterization and statistical analyses of derived gene sets were conducted using Ingenuity Pathway Analysis (IPA21) with the optional Benjamini-Hochberg correction for multiple hypothesis testing33. As independent checks on the power of these pathway analyses, we generated random gene sets using Excel. Correlation tests used ANOVA option in Partek Genomics Suite 6.6.The data presented were derived from a single study of 20 independent F1 mouse tumors, 1 tumor from each of 20 mice bearing multiple tumors at the time of sampling. RNA from these tumors were processed and sequenced at the same time in order to minimize batch effects. The raw data are the result of the interrogation of thousands of genomic loci for each of the samples in a single data set which has been deposited in the GEO database. All data to prepare the Figures are reported in the main text or in Supplementary Informahttps://bacpacresources.org/) at Children’s Hospital Oakland Research Institute  and used to test the hypothesis that the transgene integration site was adjacent. The plasmid pSV2neuT plasmid was obtained from Addgene (http://www.addgene.org/) and was a gift from Bob Weinberg35.SKY karyotype and DNA fluorescence in situ hybridization (FISH) experiments were performed by the Mayo Clinic Cytogenetics Core using primary cultures derived from F1 mouse breast tumors and freshly prepared normal fibroblasts. Briefly, fresh tumors were mechanically dissociated and cultured in RPMI 10% FBS containing Penn-Strep for 24–48 h, any debris was removed, the media replaced, and adherent cells were cultured for 1–3 weeks until sufficiently confluent. Cells were stored in liquid nitrogen until they could be processed for SKY. Reagents for SKY were from Applied Spectral Imaging. SKY images were captured using an Axioplan 2 microscope (Zeiss) and GenASis software version 7.27 . FISH images were captured using an Axioplan 2 microscope (Zeiss) and CytoVision Imaging software version 7.4 (Leica). The mouse centromeric probe BAC RP23-209m4 located at 7qA2 was provided by the BACPAC Resource Center (Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary ItemsReporting SummarySupplementary Data


Amniote skin appendages such as feathers, hairs and scales, provide thermoregulation, physical protection and display different color patterns to attract a mate or frighten an adversary. A long-standing question is whether “reptile scale” and “avian leg scales” are of the same origin. Understanding the relation between avian feathers, avian scales and reptilian scales will enhance our understanding of skin appendage evolution. We compared the molecular and cellular profiles in chicken feather, chicken scales and alligator scales and found that chicken scutate scales are similar to chicken feathers in morphogenesis at the early placode stage. When we compared the expression of the recently identified feather-specific genes and scale-specific genes in these skin appendages, we found that at the molecular level alligator scales are significantly different from both chicken feathers and chicken scales. Furthermore, we identified a similarly diffuse putative stem cell niche in morphologically similar chicken and alligator scales. These putative stem cells participate in alligator scale regeneration. In contrast, avian feathers have a more condensed stem cell niche, which may be responsible for cycling. Thus, our results suggest that chicken and alligator scales formed independently through convergent evolution. Reptile scales, as found on alligators, have a flattened, overlapping appearance on dorsal regions, as well as on the belly and leg of the animal  were shown to exhibit a placode configuration, which suggests that hairs, feathers and scales of extant amniotes are modified from ancient reptilian scale placodes15. Comparative genomic studies identified conserved non-exonic elements that suggest exceptional regulatory innovation in the archosaur lineage. Also, the presence of feather development genes predates the appearance of feathers, signifying that avian dinosaur ancestors already had the non-keratin molecular toolkit necessary to make feathers16. Recently, through genomic and functional studies in embryonic chicken skin, we identified mesenchymal feather-associated genes and proposed that multiple regulatory modules were involved in scale to feather evolution. In addition, we found that candidate molecules also caused the formation of localized growth zones (LoGZ) and invaginations in alligator skin17.Molecular and cellular studies have been used to understand the evolution of amniote skin appendages in extant reptiles and birds. Shh and Bmp2 signaling has been found to form a functionally conserved developmental signaling module in archosaur epidermal appendage development18. Feathers and hairs are periodically replaced throughout the life of each animal19. The cycling relies on activation of stem cells in a supportive niche within their follicular structure21. In hair follicles, the stem cell compartment is localized within the bulge22, whereas in the feather follicle, stem cells are located within the collar bulge23 , the presence of a dermal papilla, as well as proximal - distal polaritye23 Fig. . In cont24 and feather follicles21. Since stem cells do not often enter the proliferative phase of the cell cycle, once labeled with experimentally detectable thymidine analogs, they are expected to retain the label for long periods of time and are named label retaining cells (LRCs). Further studies using EGFP-tagged histone 2B (H2BEGFP) and the K15 promoter were used to identify stem cell populations in hair follicles27 and proved that LRCs in the hair follicle are multipotential stem cells. However, the location of LRCs (or the putative stem cells) in both avian and reptilian scales remains unknown.Bromodeoxyuridine (BrdU or 5-BrdU) retention was successfully used to locate slow cycling cells in hairIn this paper, we examine similarities and differences in cell behavior and molecular profiles among chicken feathers, chicken scales and alligator scales during development and regeneration. We find putative stem cell configurations diffusely clustered in the hinge regions of alligator overlapping scales and chicken scutate scales. We then demonstrate whether these specified putative stem cells participate in scale regeneration. However, at the transcriptome level, we demonstrate alligator scales are significantly different from both chicken feathers and chicken scales, suggesting that they evolved independently.in situ hybridization (WMIH) of β-catenin to show the placode  and then underwent a horizontal expansion process. Avian scales and reptilian scales display diffuse patterns of molecular expression and cell proliferation, when compared to that of avian feathers.feather-associated genes in developing feather dermis and 170 scale-associated genes through the examination of transcriptome data of developing chicken feathers and scales including both region-independent genes and development-dependent genes17. We examined whether alligator skin expresses the feather-associated genes or scale-associated genes to determine at the molecular level if alligator scales are closer to avian feathers or avian scales in development. We conducted RNA-seq using the dermis of Es19 (placode stage) and Es22 (bud stage) alligator dorsal and ventral overlapping scales. We compared these RNA-seq data with those of chicken feather- or scale-associated genes.Previous studies from our lab have identified 102 feather-associated genes17, 86 have been annotated in the alligator genome28  Fig.  and the H&E staining showed that the morphology of chicken scutate scales and alligator overlapping scales are similar. Both have an outer surface and an inner surface connected by the hinge region Fig. . We focuWe first sought to identify the configuration of stem cells and TA cells in adult chicken scutate scales by comparing their outer surface and hinge regions Fig. . After a29.In alligator overlapping scales, a short term 3-hour BrdU labeling identified proliferating cells randomly distributed in the epidermis of both the hinge and outer surface Fig.  but onlyCompared to chicken feathers, whose LRCs are clustered and localized in the collar bulge area during the growth phase  has been used as a hair follicle stem cell markerWe first examined the expression of K15 and K75 in chicken adult feathers. At resting stage, both K15 and K75 are expressed in feather epidermis. K15 is expressed in the basal layer Fig. , whereasWe further investigated the expression of K15 in alligator overlapping scales. K15 is expressed in the basal layers of the hinge Fig.  but fainWe conclude that the hinge of both chicken scutate scales and alligator overlapping scales are K15 positive. Whereas K75 is expressed in the supra-basal layer of the surface which is complementary to K15 Fig. . The hinTo test the regenerative abilities of avian and reptilian scales, a piece of skin (1 cm × 1 cm) (including both epidermis and dermis) covering several scales was surgically removed from the adult chicken leg and juvenile alligator body.When the wound was made in a chicken scutate scale forming region, after 16 weeks, the wound site was covered with scales. However, overlapping scales formed incompletely Fig. . In norm32) expression is undetectable  / 5-Chloro-2-deoxyuridine (CldU) double labeling method. We have used this method to study the LRCs and TA cells in alligator dental laminaple Fig. .Figure 6H&E staining showed that the wound was not re-epithelialized by 4 days after wounding Fig. . IdU bl. The hinWe further examined whether K15 is expressed within the wound site. K15 expression is restricted to the basal layer cells of the hinge region but not in the outer surface under normal conditions Fig. . However11. We performed a number of experiments to compare the properties of avian scales and reptilian scales to resolve this issue. On one hand, we showed that both chicken scutate scale and alligator overlapping scale development have diffuse β-Catenin and Shh expression and form a diffuse LoGZ, which is different from the observed focal pattern of gene expression and LoGZ formation in chicken feathers  Fig. . Only a 40. However, the cellular and molecular basis of scale regeneration remains elusive, either at the single scale level or the coordinated response to large skin wounds. Is a specific configuration of stem/TA cells required to form a sustainable regenerative unit containing self-renewing multipotent stem cells? Does the lack of a follicle structure in scales prevent their ability to regenerate? Can this ability be restored by reprogramming during tissue regeneration?Scales in squamates, in contrast to hairs and feathers, have limited regenerative abilities. We have shown this to be true in the green anole body and tail scales and green iguana41. This paper highlights that proliferation of these scales differs from those found in leidosaurians which show higher proliferation rates in the outer scale surface. In the chick the length of the G-phase of the proliferation cycle was found to increase in the inner surface region as embryonic development progressed42. Also, a lineage of basal cells was found aligned along the avian scale axis at day 10 which then showed polarized divisions at day 11 producing suprabasal cells43.Other labs have also explored properties of scales and found that crocodilian and turtle scales share similarities33, the stem cell niche for new tooth generation. In this animal model, the dental lamina is present in the lingual side of each functional and replacement tooth localized within a single tooth family unit. Our current scale regeneration result indicates that the diffuse putative stem cells in the hinge may be responsible for the observed limited regeneration abilities.Our result showed that both avian scutate and reptilian overlapping scales have limited regeneration abilities. They each could regenerate surface and hinge-like regions but did not produce regions that overlapped neighboring scales. In response to wound injury, these putative stem cells could move and accumulate within the wound site to take part in the healing and regeneration. However, unlike the hair and feather which have follicular structures with the localized stem cells within their specific niche, these putative scale stem cells are diffusely distributed which may be related to their weak regenerative ability. Another example of robust ectodermal organ regeneration is found in the alligator tooth. In alligator teeth, we also found focally localized LRCs in the “bulge” of the dental laminaOverall, our results demonstrate in embryonic development, the gene expression profile in alligator scale is very different from both chicken feathers and scales. The shape of the placode for chicken scutate scales is closer to that of the feather than alligator overlapping scales which suggests that avian scutate scales do not have the same origin as reptile overlapping scales. However, in adults, there is a diffuse putative stem cell niche localized within the hinge of both chicken and alligator scales suggesting these structures form through convergent evolution. In contrast, avian feathers have a more condensed stem cell niche which may be responsible for feather morphogenesis and cycling.44. All procedures were approved by the local Institutional Animal Care and Use Committee at the University of Southern California, and the Louisiana Department of Wildlife and Fisheries. The methods were carried out in accordance with the relevant guidelines and regulations.We used adult chickens and juvenile alligators (6-month to 1-year-old) for TA and LRC labeling. Alligator eggs were collected from the Rockefeller Wildlife Refuge in Louisiana. Eggs were transported to USC and incubated at 30 °C. Embryo staging was according toTo perform RNA-seq analysis on developing alligator overlapping scales, we collected skin from Es19 and Es22 alligator dorsal and ventral overlapping scales. Epidermis and dermis were separated by treating with 2×CMF solution. RNA from dermis was extracted using Trizol reagent (Invitrogen). 1–2 ug of total RNA from each sample was used to construct an RNA-seq library using TruSeq RNA sample preparation v2 kit (Illumina). Sequencing (50 cycles single read) was performed using Hi-seq2000 at the USC Epigenome Center. Replicate samples from each region and stage were prepared. The full datasets have been submitted to the NCBI Gene Expression Omnibus (GEO).17. For alligator skin RNA-seq analysis, the raw reads from embryonic alligator skin were aligned with the alligator genome28 using STAR 2.4.1d. Gene expression levels were estimated using CPM (count per million) with TMM normalization.RNA-seq analysis of chicken feathers and scutate scales are from45.For the 3 hour pulse labelling of juvenile alligators and adult chickens, BrdU was injected intraperitoneally at 50 mg per kg (body weight). Scales were collected 3 hours later. For label-retaining studies, animals were injected with BrdU twice a day for 1-week, and ‘chased’ (left to metabolize the BrdU in their system) for up to 8 weeks for chickens and 16 weeks for alligators. One chicken and one alligator were euthanized after 1 week’s BrdU labelling (1-week pulse). Four adult chickens and four juvenile alligators were used for our LRC study. For chicken feathers, contour feathers (in both growth and resting phases) on the dorsal region were used. BrdU staining was performed according toFor scale wounding and regeneration, chickens were anesthetized by an intramuscular injection of ketamine (50 mg/kg) and xylazine (5 mg/kg). Alligators were anesthetized by inhaling of 2% isoflurane. Biopsy areas  were initially drawn with a pen and this line was traced with a scalpel to about 1 mm in depth. The skin was lifted with forceps and excised with a scalpel. Animals were euthanized after 16 weeks regeneration. Paraffin sections were prepared for H&E staining and immunostaining. Two adult chickens and two juvenile alligators were used.IdU (Sigma) was used to label LRCs and CldU (Sigma) was used to label TA cells during skin regeneration. Animals were injected with IdU intraperitoneally (50 mg/kg) two times per day for 1 week followed by a 16-week chase period. A piece of ventral skin (1 cm wide and 1 cm long) was removed. Four days after wounding, animals were injected with IdU (50 mg/kg) for 3 h before being euthanized. Two alligators were used in this study.For IdU/CldU double staining, CldU was detected using a rat anti-BrdU antibody ; Biotinylated Goat anti Mouse IgG (Abcam) and Streptavidin (Jackson ImmunoResearch) were used as secondary and tertiary antibodies; AEC (Vectorlabs) was used to develop the red color. IdU was detected by mouse anti-BrdU ; Anti-mouse AP (Abcam) was used as secondary antibody; NBT/BCIP (Promega) was used to visualize positive staining (blue). For fluorescent IdU/CldU staining, sections were treated with 0.01 M Citrate buffer (pH 6.0) by microwaving for 6 min. Alexa Fluor anti–mouse-546 (A11030) and anti–rat-488 (A11006) from Invitrogen were used as secondary antibodies. DAPI was used to visualize the nuclei. Stained sections were imaged with an AxioImager (Zeiss).46 and alligator33 were used for whole mount in situ hybridization. To generate Alligator K15 and K75 RNA probes, PCR was performed using cDNA from Stage 24 alligator skin. Primers for K15, Sense: TGGCCTACCTSAASAAGAAC, antisense: GGTASGTGGCRATCTCCTG. Primers for K75, Sense: GAAGAKGAGATCAACAAGCG; Antisense: GCCAGCTTGACRTTCATCAG. Primers for PCR were designed based on K15 mRNA sequences from chicken (NM_001001312) and green anole , and K75 mRNA sequences from chicken (NM_001001314) and green anole (XM_003216971). PCR products were inserted to p-drive (Qiagen) to make antisense RNA probes. Whole mount and section in situ hybridization were performed according to procedures described in47. Diluted eosin was used for faint counter-staining. Immunostaining (Tenascin-C) was performed according to33. β-keratin antibody was from Dr. Roger Sawyer. Alexa Fluor anti–rabbit-488 (A11008) from Invitrogen was used as secondary antibodies.β-catenin and Shh RNA probes from chickenAll procedures were approved by the local Institutional Animal Care and Use Committee at the University of Southern California, and the Louisiana Department of Wildlife and Fisheries. The methods were carried out in accordance with the relevant guidelines and regulations.Supplementary data


NOTCH signaling pathways are enriched in post-treatment samples. These data suggest that ctDNA sequencing is promising to delineate genomic landscape, subclonal architecture, and genomic evolution of SCLC.Subclonal architecture and genomic evolution of small-cell lung cancer (SCLC) under treatment has not been well studied primarily due to lack of tumor specimens, particularly longitudinal samples acquired during treatment. SCLC is characterized by early hematogenous spread, which makes circulating cell-free tumor DNA (ctDNA) sequencing a promising modality for genomic profiling. Here, we perform targeted deep sequencing of 430 cancer genes on pre-treatment tumor biopsies, as well as on plasma samples collected prior to and during treatment from 22 SCLC patients. Similar subclonal architecture is observed between pre-treatment ctDNA and paired tumor DNA. Mean variant allele frequency of clonal mutations from pre-treatment ctDNA is associated with progression-free survival and overall survival. Pre- and post-treatment ctDNA mutational analysis demonstrate that mutations of DNA repair and  Small cell lung cancer (SCLC) may evolve under treatment. But tumor tissues are often not available to study evolution of SCLC. Here, the authors utilize circulating tumor DNA to investigate the genomic evolution and subclonal architecture of SCLC during therapy. Clinical advances in SCLC remain an unmet need, as the treatment paradigm has not significantly changed over the past several decades5.Small-cell lung cancer (SCLC) accounts for ~15% of newly diagnosed lung cancers. SCLC is a very aggressive malignancy characterized by rapid growth and early hematogenous spread. At initial diagnosis, about 1/3 of patients presented with limited-stage disease (LD) that can be treated with chemoradiation, while the remaining patients presented with extensive-stage disease (ED), which are usually treated with palliative chemotherapy. Although an initial response to chemotherapy and/or radiotherapy can be achieved in most patients, nearly all patients recur with resistant disease, and the 5-year overall survival (OS) is 5–10%Given the pattern of initial response to chemotherapy and/or radiotherapy and nearly invariable relapse, it has been speculated that treatment-naive SCLC harbors subclones of inherently refractory (resistant) cancer cells that give rise to the relapse in these patients. Therefore, delineating the subclonal architecture of SCLC and its molecular evolution during treatment by comparing genomic profiles of recurrent (and often chemo/radiation-resistant) SCLC tumors to paired treatment-naive tumors may provide new insight into the mechanisms underlying recurrence and therapeutic resistance and guide the development of novel treatment strategies.9. However, compared to many other solid tumors, there have been only a few studies investigating the genomic landscape of SCLC12. This is primarily due to the lack of adequate tumor tissues because the majority of SCLC patients are not treated with surgical resection. Moreover, because recurrent SCLC usually progresses relatively quickly, recurrence suspected on imaging is typically followed by immediate second-line treatment without biopsy. Therefore, our knowledge of the genomic landscape of recurrent SCLC is very limited. There is an urgent need for alternative approaches for genomic profiling of SCLC, particularly in patients under treatment.Over the past decade, comprehensive genome-wide profiling has substantially advanced our understanding of the genomic landscapes of various cancer types and led to the identification of novel predicative/prognostic biomarkers and therapeutic targets15 and detection of minimal residual disease17. Because SCLC has a rapid growth rate and is highly metastatic with early hematogenous spread, ctDNA may be readily detectable in SCLC patients. In addition, since ctDNA sequencing is noninvasive and “real-time”, it could be an ideal tool for investigating genomic evolution of SCLC over time, particularly during treatment. Herein, we report a study on deep sequencing of 430 cancer-related genes in 43 ctDNA samples collected prior to treatment and at different time points during treatment from 22 SCLC patients. We show that circulating tumor DNA sequencing is promising to delineate genomic landscape, subclonal architecture and investigate genomic evolution of small-cell lung cancer under therapy.Sequencing circulating cell-free tumor DNA (ctDNA)—fragmented DNA shed from tumors into circulating system—may be such an alternative. ctDNA sequencing was reported in many cancer types to have potentials in disease monitoring12. C > A transversions that are associated with smoking and C > T transitions that may be associated with aging18 were the dominant aberrations, accounting for 39.5% and 27.2% of the total somatic SNVs, respectively. Twenty-nine genes were found to be mutated in more than 10% of patients , including 4 patients with mutations in both alleles based on informative SNPs  of patients . Somatic mutations were identified in all eight tumors with an average of 13 mutations per sample   of the 69 shared mutations between ctDNA and paired tumor DNA moderately correlated to each other (Spearman 20 to infer cancer cell fraction (CCF) of each mutation in each ctDNA sample. Mutations were then clustered based on corresponding CCF, and subclonal architecture of ctDNA from SCLC was subsequently inferred20. The results demonstrated distinct subclonal architecture in different patients. Different numbers of mutation clusters were present in individual patients with a median of 11 (ranging from 2 to 26) clusters per patient. Of particular interest, the subclonal architecture derived from ctDNA demonstrated high similarity to those derived from paired tumor DNA in 6 of the 8 patients . These results suggest that ctDNA may be a reasonable alternative to tumor DNA for delineating the subclonal architecture of SCLC. On the other hand, ctDNA demonstrated more mutation clusters than tumor DNA  highlighting again the advantage of ctDNA over single biopsies in revealing the global genomic landscape of SCLC.The variable VAFs in different mutations from ctDNA implied variable clonality of different mutations. To explore the subclonal architecture of SCLC from ctDNA sequencing, we used PyClonents Fig.  and the r = 0.413, p = 0.056, Supplementary Fig. p = 0.002 for PFS and p = 0.012 for OS; Fig. p = 0.008 for PFS and HR = 4.7, p = 0.021 for OS; Supplementary Data Next, we assessed whether ctDNA is associated with patient characteristics. We did not find any association between the yield of DNA per 1 ml plasma sample, total mutation burden, mutations of particular genes, or VAF of certain mutations with patient age, gender, smoking status, cancer stage, recurrence status, or survival, although the small sample size limits the power to detect these associations. In view of the substantial variation in ctDNA subclonal architecture as mentioned above, we next investigated whether ctDNA subclonal architecture correlated with clinicopathological parameters. We utilized the average VAF of mutations from the major clones (the cluster with the greatest CCF) as a surrogate for overall ctDNA level. Varied ctDNA levels were observed among patients, with a median value of 0.18  and ctDNA level was moderately correlated with tumor burden (sum of the longest diameters of the target lesions on CT scans)  and is alive 17 months post resection without evidence of recurrence. In comparison, CA181, another patient with limited-stage disease (AJCC stage IIIA), had only three mutations detected in the tumor, all of which were also detected in the corresponding plasma sample. These results suggest that the sensitivity of ctDNA sequencing depends on the proportion of tumor-derived DNA in the plasma, which is influenced by factors such as tumor burden24 and subclonal architecture.Mutations from ctDNA are usually easier to detect in late-stage malignancies than early-stage diseases27 have reported on the potential clinical and biological impact of the genomic subclonal architecture of non-small-cell lung cancer. The genomic subclonal architecture of SCLC has not been well studied, primarily due to the lack of large resected tumor specimens for multiregion sequencing. In this study, we demonstrated very similar subclonal architecture between ctDNA and paired tumor in most patients, suggesting that ctDNA can be used not only to detect somatic mutations, but also to study subclonal architecture of SCLC. In addition, ctDNA levels as measured by the average VAF of clonal mutations, rather than any single gene mutation, were found to be associated with PFS and OS, suggesting that subclonal architecture may provide deeper prognostic information than any single gene mutation.Subclonal architecture may have profound impact on cancer biology and thus clinical outcomes of cancer patients. Recent studies24. On the other hand, tumor burden is also a well-known prognostic factor associated with patient survival across different cancer types31. Therefore, it is not unreasonable to hypothesize that ctDNA levels may simply reflect tumor burden that in turn is associated with survival. We did observe a moderate correlation  in, recurrent tumors compared to treatment-naive tumors are potential candidates for driving this broad therapeutic resistance. Due to the difficulties inherent in sampling tumor at progression, serial analysis of plasma DNA before and after treatment provides an opportunity to study the genomic evolution of SCLC during and after treatment. Using this approach, we identified many mutations that were enriched in the post-treatment samples and some of these genes, such as 41. Recently presented early phase clinical trials have shown encouraging activity of immune checkpoint blockade in treatment of SCLC patients44, and high TMB has been demonstrated to be associated with superior response and prolonged survival in SCLC45. Therefore, ctDNA sequencing using a large gene panel, such as the one presented here, could be used to assess TMB for SCLC patients, particularly when biopsy is not clinically feasible or the quantity and/or quality of biopsy is not satisfactory.A significant obstacle to advancing translational SCLC research is the challenge in obtaining tumor material. In this study, we provide proof-of-concept evidence that ctDNA sequencing may be a reliable modality for detecting somatic mutations, depicting the subclonal architecture and investigating the genomic evolution of SCLC. To our knowledge, this is the first study that employed ctDNA sequencing in SCLC patients using a large gene panel capable of identifying sufficient mutations for subclonal analysis. Another foreseen advantage of a large gene panel is a more accurate estimation of tumor mutation burden (TMB), a potential biomarker for response to immune checkpoint blockade in many cancer typesPatients with SCLC diagnosed at Beijing Chest Hospital between 2014 and 2015 were enrolled. The study was approved by the institutional review board and all patients provided written informed consents. A volume of 10 ml of blood was collected within 2 weeks before treatment from each patient. Serial post-treatment blood sample collection was also planned before the 2nd, 3rd, 4th, and 5th cycle of chemotherapy and at disease progression for each patient. Tumor burden were calculated according to RECIST 1.1 (sum of the longest diameters of the target lesions on CT scans). In addition, pre-treatment formalin-fixed paraffin-embedded (FFPE) specimens obtained by biopsy or surgical resection were collected from eight patients with a median of 5-day interval (0 day to 10 days) between the collection of pre-treatment blood samples.g for 10 min, transferred to new microcentrifuge tubes, and centrifuged at 16,000× g for 10 min to remove remaining cell debris. Peripheral blood lymphocytes (PBLs) from the first centrifugation were used for the extraction of germline genomic DNA. PBL DNA was extracted using the DNeasy Blood & Tissue Kit . DNA was isolated from plasma using QIAamp Circulating Nucleic Acid Kit . Genomic DNA was extracted from FFPE samples using Maxwell® RSC DNA FFPE Kit . DNA concentration was measured using a Qubit fluorometer and the Qubit dsDNA HS (High Sensitivity) Assay Kit . The size distribution of plasma DNA was assessed using an Agilent 2100 BioAnalyzer and the DNA HS kit .Peripheral blood was collected in EDTA Vacutainer tubes  and processed within 4 h. Plasma was separated by centrifugation at 1600× Before library construction, 1 μg each of genomic DNA extracted from PBL and FFPE specimen was sheared to 300-bp fragments with a Covaris S2 ultrasonicator . A volume of 20–80 ng DNA from plasma were used for library construction. Indexed Illumina next-generation sequencing (NGS) libraries were prepared from PBL DNA, tumor DNA, and plasma DNA using the KAPA Library Preparation Kit .Target enrichment was performed with a custom SeqCap EZ Library . To explore the comprehensive genetic properties of SCLC, the capture probe was designed based on ~2.3 Mb genomic regions of 430 genes frequently mutated in SCLC and other common solid tumors. Capture hybridization was carried out according to the manufacturer’s protocol. Following hybrid selection, the captured DNA fragments were amplified and then pooled to generate several multiplex libraries.Sequencing was carried out using Illumina 2 × 75 bp paired-end reads on an Illumina HiSeq 3000 instrument according to the manufacturer’s recommendations using TruSeq PE Cluster Generation Kit v3 and the TruSeq SBS Kit v3 .46. MuTect2 (3.4–46-gbc02625)47 was employed to call somatic small insertions and deletions (InDels) and single nucleotide variants (SNVs). Contra (2.0.8) was used to detect copy number variations48 and an algorithm was used to identify LOH based on informative SNPs49. For structure variations (SV), baits were designed to capture selected exons and introns of RET, ALK, ROS1, and NTRK1 oncogenes based on previously reported SVs in these genes and an in-house algorithm was used to identified split-read and discordant read-pair to identify SVs. All final candidate variants were manually verified with the integrative genomics viewer browser.After removal of terminal adaptor sequences and low-quality data, reads were mapped to the reference human genome (hg19) and aligned using BWA (0.7.12-r1039)20. The copy number information of each SNV was used as input for PyClone analysis50 and the CCF was inferred and variants were clustered as previously described20. PyClone was run with 20,000 iterations and default parameters. For comparing CCF in matched tumor DNA and ctDNA, the parental_copy_number mode and a burn-in of 2000 were added. Variants located in the cluster with greatest mean CCF were defined as clonal, the rest were subclonal20.Pyclone was employed to infer the subclonal architecture of all DNA samples from plasma and available SCLC tumorsp < 0.05.Survival analysis was performed by multivariate Cox proportional hazards regression analysis and Kaplan–Meier survival analysis with log-rank test. IBM SPSS software (23.0) and GraphPad Prism (6.01) were used in statistical analysis. All tests were two-sided and considered statistically significant at All mutations reported in this study were provided in the supplementary information. De-identified patient clinical information was provided in the supplementary information. All other relevant data could be obtained from the corresponding authors of this study.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7


Brain activity and connectivity alter drastically during epileptic seizures. The brain networks shift from a balanced resting state to a hyperactive and hypersynchronous state. It is, however, less clear which mechanisms underlie the state transitions. By studying neural and glial activity in zebrafish models of epileptic seizures, we observe striking differences between these networks. During the preictal period, neurons display a small increase in synchronous activity only locally, while the gap-junction-coupled glial network was highly active and strongly synchronized across large distances. The transition from a preictal state to a generalized seizure leads to an abrupt increase in neural activity and connectivity, which is accompanied by a strong alteration in glia-neuron interactions and a massive increase in extracellular glutamate. Optogenetic activation of glia excites nearby neurons through the action of glutamate and gap junctions, emphasizing a potential role for glia-glia and glia-neuron connections in the generation of epileptic seizures. During epileptic seizures, neural activity across the brain switches into a hyperactive and hypersynchronized state. Here, the authors report on the role of glia-glia and glia-neuron interactions in mediating the changes that result in the ictal state in a zebrafish model of epilepsy. When the brain switches from a resting state into a seizure, dramatic transitions occur leading to abnormally synchronous brain activity. Profound changes in neural activity and connectivity were observed during seizure initiation and propagation2. High-frequency oscillations and breakdown of inhibition are often considered as the key triggers of ictal discharges8. Once the inhibition surrounding the epileptic focus breaks down, excessive neural activity spreads to highly connected hubs and propagates to the rest of the brain4. These hubs display increased synchrony and connectivity during seizures9 and play an important role in seizure propagation by acting as gatekeepers between seizure foci and the global brain network10. Focal low amplitude and high-frequency neural activity evolves into large amplitude and low-frequency activity, as localized seizures propagate13. Taken together, the initiation and propagation of seizures are often viewed as transitions between brain states. For example, epilepsy patients show a transition from a balanced baseline connectivity state into a temporary hypersynchronous connectivity state13. It is, however, unclear how such profound changes in neural activity and connectivity can occur rapidly during state transitions of generalized seizures14.Epilepsy is a group of neurological disorders characterized by recurrent seizures16. Astrocytes, the largest group of glial cells, regulate the availability of neurotransmitters and ions at the synaptic cleft, especially glutamate and potassium18. Moreover, astrocytes are highly connected through gap junctions, and form a functional syncytium, which effectively redistributes ions and neurotransmitters across large distances in the brain20. Interestingly, transgenic mice with a deficiency in the astrocytic gap junction coupling show spontaneous seizures21. Alterations of astrocytic coupling and clearance of potassium are linked with temporal lobe epilepsy, both in rodents and human patients23. Precisely how glia-neuron interactions are involved in initiation and propagation of epileptic seizures remains an open question with potential therapeutic applications.The important role of glia in modulating synaptic connectivity and neural excitability is now well accepted26. The brains of transparent zebrafish larvae combined with two-photon calcium imaging allowed us to monitor the activity and functional connectivity of thousands of individual neurons and glia, in vivo, with unprecedented spatiotemporal resolution, across several brain regions27. We show that during the preictal state, the neural synchrony increases only locally, while the gap junction coupled glial networks are activated and synchronized across large distances, independent of neural activity and synchrony. We also observe that glial activity bursts during preictal state correspond to dampening of neural activity. At the initiation of a generalized seizure, when a strong surge of both glial and neural activity occur, we observe a rapid transition in the functional connectivity of neural and glial networks, as well as a sudden increase in extracellular glutamate. Finally, we show that activation of glial networks leads to a strong increase in neural activity through the action of glutamate and gap junctions. Taken together, these interactions could underlie the rapid transition from a preictal state to a generalized seizure, which goes beyond neural connectivity rules. We propose that alterations in glia-neuron interactions underlie state transitions that lead to generalized epileptic seizures.To address this question, we characterized the state transitions of neural and glial activity and connectivity in pharmacological-induced seizure models of zebrafish larvaeTg(elavl3:GCaMP6s)28 zebrafish larvae expressing GCaMP6s in all neurons simultaneously30. Seizures were induced through application of pentylenetetrazole (PTZ), a widely used proconvulsant agent that is a GABAA receptor antagonist31  larvae under an epifluorescence microscope  zebrafish larvae across multiple brain regions with single cell resolution. We collected data from five anatomically defined brain areas: telencephalon, thalamus, optic tectum, cerebellum and brainstem )32 versus glutamatergic neurons (Tg(vglut2a:dsRED))33 across the brain  before the seizure. We found that the rearrangement of neural synchrony was not a gradual process that is progressively morphing the neural connectivity rules, but instead a rapid process that reshapes the functional connectivity histograms, within a few seconds nw7 zebrafish line expressing Gal4 under the astrocytic glial fibrillary acidic protein (GFAP) promoter36. In zebrafish, GFAP promoter was shown to label radial glial cells, which in addition to their neurogenic capacity35, serve the function of mammalian astrocytes38. We first demonstrated that GFAP:Gal4 expressing cells are primarily glia and not neurons. To this end, we crossed Tgnw7;Tg(UAS:GCaMP6s) fish with Tg(elavl3:jRCaMP1a)39 fish expressing the red-shifted calcium indicator jRCaMP1a in all neurons. Confocal images revealed exclusively GFAP expressing cells along the ventricular zones at the midline, whereas we observed more overlap with elavl3 positive neurons in other parenchymal zones nw7;Tg(UAS:GCaMP6s) zebrafish, where PTZ was added after a baseline period, as described earlier with Tg(elavl3:GCaMP6s) fish. We observed strong increase in glial calcium signals after the perfusion of PTZ s1020t;Tg(UAS:GCaMP6s) zebrafish expressing GCaMP6s in only thalamic neurons with GFAP:Gal4 plasmid at the one-cell stage. This approach allowed us to visualize the calcium signals of both thalamic neurons and sparsely labelled GFAP expressing glial cells along the ventricles  transgenic zebrafish43. We observed no change in extracellular glutamate levels near glial cells until the generalized seizures, when transient increase of extracellular glutamate occurred  in glial cells using Tgnw7;Tg(UAS:ChR2-mCherry) zebrafish line44. Optogenetic activation of glial cells upon blue light stimulation led to a sharp depolarization of glial membrane potential  appear to have hub-like properties with earlier recruitment of synchronous activity before generalized seizures. These findings highlight a potential role of non-cortical brain regions in the initiation and spreading of seizures. Interestingly, the first epilepsy with a proven monogenic etiology, autosomal dominant sleep-related hypermotor epilepsy (ADSHE), is accompanied by prominent alterations in these midbrain regions in human patients47. Future studies on the role of non-cortical brain regions in human epilepsies will likely provide a better understanding of underlying mechanisms.We provide evidence for several common physiological features between zebrafish and mammalian models of epilepsy. Previous studies suggest that the seizure propagation arises from profound alterations in local and global network connectivity12, our electrical LFP recordings in zebrafish brain suggest a reduction of high-frequency activity and an increase in low-frequency activity during seizure generation. We observed that the emerging hypersynchronous connectivity during the generalized seizure is independent of spatial constraints of baseline or preictal connectivity state in two very different pharmacological seizure models; PTZ and pilocarpine. Furthermore, functional connectivity patterns during generalized seizures exhibit no or little negative correlations that would be driven by inhibitory connections. All these drastic alterations in brain activity and connectivity during seizure propagation is in line with the general view of epileptic seizures as state transitions, where brain networks shift from a balanced state to a hypersynchronous or hyperconnected state45. Intriguingly, epileptic seizures in human patients are more likely to occur during transitions of sleep states which show profound alterations in brain synchrony and connectivity48. Similar to epileptic state transitions, higher frequency desynchronized activity during wakefulness and rapid eye movement (REM) sleep is replaced by low-frequency oscillations during slow wave sleep49. Moreover, thalamocortical and corticocortical pathways that are involved in sleep50 can facilitate long range connectivity and are proposed to play important roles in epileptic seizures10. Hence, investigating parallels between sleep state transitions and epileptic state transitions are promising avenues for better understanding of common underlying mechanisms.The abrupt transition from a preictal state to a generalized seizure was accompanied by a drastic reorganization of functional connectivity and synchrony across neurons. Similar to epilepsy patients and rodent models51. Interestingly, during the preictal phase we observed that glial activity bursts coincide with the dampening of neural bursts and is anticorrelated with neural activity. Yet, this picture changed completely during generalized seizures, which are characterized by an abrupt increase in correlated activity between glial and neural networks. Altogether, our results suggest that the initiation and spreading of generalized seizures can be mediated by a transient alteration of glia-neuron interactions.The rapid alterations of neural activity and connectivity that we observed during the transition from preictal to ictal period, was difficult to explain by only considering the rules of neural connectivity mediated by classical synaptic transmission. The abrupt and excessive increase in positive correlations that is not constrained by spatial distances between neurons encouraged us to investigate the properties of glial cells during seizure generation. Our results showed a direct involvement of glial networks during seizure generation in two different pharmacologically induced zebrafish seizure models and therefore are likely to have broad relevance. In both of these seizure models, we observed consistent behaviors of glial and neural networks. Already during the preictal phase, glial networks were highly active and synchronized across large distances, when compared to rather small and local increase of neural activity and synchrony. Strong synchronization of glial networks across large distances are likely due to gap junction coupling between glial networks, of which we provide direct evidence by neurobiotin coupling between glia cells and glia specific expression of connexin 43. These findings are in line with the idea that glial networks are strongly coupled and can act as a single unified entity with highly coordinated activation52, and several mutations in glia associated genes were linked with epilepsy53. Especially, gap junction coupling between astrocytes was shown to play important roles in redistribution of ions and neurotransmitters across large distances in the brain52. Consistent with protective functions of glia, we observed that preictal glial activity appears to dampen neural activity bursts. We propose that during this preictal state, large synchronous glial activity represents the homeostatic function of glia that can absorb excessive levels of glutamate and cations through the major glial excitatory amino acid transporter, EAAT2 54, and redistribute these substances across the gap junction coupled glial networks55. In accordance with this hypothesis, GLT1 knockout mice display spontaneous seizures56 and mutations in SLC1A2 gene are associated with epilepsy in human patients53. Therefore, we propose that preictal glial activity in our recordings reflects the protective function of glia against epileptic seizures. Interestingly, it was reported that gap junction coupling between astrocytes is diminished during epileptogenesis and results in a reduced homeostatic potential of astrocytes51. Moreover, deficiency in the astrocytic gap junction coupling was shown to cause temporal lobe epilepsy22.Astrocytes modulate synaptic transmission and neural excitability56. Extracellular glutamate anions accompanied by Na+ and H+ are exchanged with intracellular K+ ions58. Hence, the clearing of excessive glutamate in the preictal brain is a process heavily dependent on electrochemical gradients. It was reported that increased potassium concentrations around glial cells may reverse the uptake leading to a release of glutamate into the extracellular space59. It is likely that during preictal activity extracellular potassium levels reach a threshold, which prevents EAAT2 from functioning. The glial network may eventually collapse, triggering a massive release of glutamate from the strongly gap junction coupled glial network, initiating a generalized seizure. In fact, our optogenetic activation of small number of glial cells in zebrafish brain was effective in generating transient and strong excitation of nearby neurons through the action of glutamate and gap junctions, highlighting a potential way to excite neurons through gliotransmission18. In line with these ideas, we observed a large rise of extracellular glutamate near glial cells only during generalized seizures. Further experiments will be important for a better mechanistic understanding of the role of glial networks both in prevention and induction of epileptic seizures.What can lead to an abrupt alteration in glia-neuron interactions from preictal to ictal state? We propose that prolonged preictal activity fatigues the homeostatic function of glia. The cellular mechanisms for an eventual collapse of the homeostasis and the precise trigger of the generalized seizures are yet to be identified. A potential mechanism for such rapid alterations in glial function might be directly related to the way the major glial glutamate transporter, EAAT2, operates61. Both glial and neuronal connexins have been shown to contribute to high-frequency gamma oscillations63, which are proposed to be important for cognitive functions. Furthermore, impairment of astrocytic potassium handling may lead to enhanced network excitability and increase in high-frequency bands64. Interestingly, a reduction in gamma activity was reported in Alzheimer’s disease, while an increased activity was shown in epilepsy65. Moreover, an expanding number of studies propose diverse roles for glia-neuron interactions in brain functions such as regulating locomotion, motivation, memory formation, and sleep68. Given the large number of glial cells in the brain, their prominent role in regulating neural excitability and activity is not that surprising. Future studies on glia-neuron communication will shed further light on the role of glia-neuron interactions in the physiology and pathophysiology of brain circuits.While in this study we focused on epileptic seizures, glia-neuron interactions are likely to be involved in many other types of state transitions within the brain networks. Glia-neuron interactions are linked to a variety of neurological diseases. Increased expression of glial connexin 43 was shown in humans and animal models of neurodegenerative disorders like Alzheimer’s disease and Parkinson’s diseaseDetails of all key reagents and resources used in this article are included in Supplementary Tables 2 and 700 µS, at a 14:10 h light/dark cycle to simulate optimal natural breeding conditions. Fish received a normal diet of dry food  two times per day and Artemia nauplii once a day . Larvae were maintained in egg water  from fertilization to 3 dpf and in artificial fish water  from 3 to 5 dpf. The animal facilities and maintenance of the zebrafish, Danio rerio, were approved by the Norwegian Food Safety Authority (NFSA) and Belgian government. All experimental procedures performed on zebrafish larvae up to 5 days post fertilization were in accordance with the directive 2010/63/EU of the European Parliament and the Council of the European Union and the Norwegian Food Safety Authorities. Experimental procedures performed on zebrafish larvae older than 5 dpf were further approved by the Ethical Committee of KULeuven in Belgium (P088-2014) and Norwegian Food Safety Authority.Fish maintenance: fish were kept in 3.5-liter tanks at a density of 15–20 fish per tank in a Techniplast Zebtech Multilinking system at constant conditions: 28 °C, pH 7, 6.0 ppm OTg(elavl3:GCaMP6s)28, Tg(elavl3:jRCaMP1a)39, Tg(UAS:ChR2-mCherry)69, Tg(gad1:GFP)32, Tg(vglut2a:dsRED)33, Ets1020t70, Tg(UAS:GCaMP6s)71, Tg(GFAP:iGluSnFR)43. Tgnw7 transgenic animals were generated in our lab upon coinjection of tol2 transposase mRNA and GFAP:Gal4 plasmid obtained from the Ohshima lab35. The Tgnw7 expression pattern was identified in three independent founders.For experiments, the following fish lines were used: iGluSnFr imaging was performed together with LFP recordings of seizure activity on 7 days old Tg(elavl3:GCaMP6s) (n = 5) and 5 days old Tg(GFAP:iGluSnFR) zebrafish (n = 7). Zebrafish larvae were first anesthetized with 0.02% MS222 and paralyzed by α-bungarotoxin injection. They were embedded in 1–1.5% low melting point agarose  in a recording chamber . The layer of LMP agarose covering the dorsal side of the forebrain was carefully removed, so it was accessible for electrode placement. The fish were placed under the epifluorescence microscope . AFW was constantly perfused during the experiments. For electrophysiological recordings, a borosilicate glass patch clamp pipette (9–15 MOhms) loaded with teleost artificial cerebrospinal fluid72  was inserted into the optic tectum. Electrical recordings were performed in current clamp mode with a high impedance amplifier and band pass filtered at 0.1–1000 Hz, at a sampling rate of 10 KHz . Calcium signals were recorded using an EMCCD camera (Hamamatsu Photonics) at sampling rate of 25 Hz. iGluSnFR signals were recorded using an Allied Vision Manta Camera. After electrode placement the larvae were left 10 min for stabilization to ensure fish were fully awake and recovered from the anesthesia, MS222. Baseline activity was recorded for 10 min, then 20 mM pentylenetetrazole (PTZ) dissolved in AFW was delivered via perfusion system until the end of the experiment. Duration of the total recording was 90 min. Data acquisition of both signals  was performed with a custom code written in MATLAB (Mathworks).Combined LFP experiments together with epifluorescence calcium and iGluSnFR imaging: Simultaneous epifluorescence calcium imaging or Tgnw7;Tg(elavl3:jRCaMP1a) and Tg(gad1:GFP);Tg(vglut2a:dsRED) zebrafish larvae were anesthetized with 0.02% MS222 and embedded in 1.5–2% LMP agarose. Anatomical Z-scans were acquired using a Zeiss Examiner Z1 confocal microscope with a ×20 plan NA 0.8 objective, using ×10 average for each plane.Confocal anatomical imaging: For confocal anatomical imaging, 5 days old k-gluconate, 10 Na-gluconate, 10 HEPES, 10 Na2+-Phospho-Creatine, 4 NaCl, 4 ATP-Mg and 0.3 Na3+-GTP. Electrical signals were recorded by MultiClamp 700B amplifier at sampling rate of 10 KHz. All recordings and data analyses were performed using custom codes written in MATLAB. Activation of channelrhodopsin2 was done by flashing a 480 nm LED light, through the optical path of the Olympus BX51 microscope for a duration of 1 sec. For morphological reconstructions, neurobiotin  was added to the intracellular solution. Brains were fixed overnight in 4% PFA at 4 °C, and afterwards incubated first with PBS and later with 5% streptavidin (Vectorlabs) in 0,5% PBSTx. Brains were imaged using a Zeiss Examiner Z1 confocal microscope. To block ionotropic glutamate receptors, we used 5 μM NBQX (Tocris) and 25 μM D-AP5 (Tocris). To block gap junctions, we used 100 μM carbenoxolone (Tocris).Combined optogenetical stimulation and electrophysiological recordings: For combined optogenetical stimulation and electrophysiological recordings, 4–5-weeks-old zebrafish were anesthetized in ice cold water and sacrificed by decapitation. Their brains were dissected out in cooled and oxygenated ACSF. For intracellular recordings, borosilicate glass capillaries of 9–15 MOhms were filled with intracellular solution which contained (in mM): 130 Tg(elavl3:GCaMP6s) , 5 days old Tg(elavl3:GCaMP6s) , 5 days old Ets1020t;Tg(UAS:GCaMP6s) (n = 4) and 5 days old Tgnw7;Tg(UAS:GCaMP6s) zebrafish larvae . Animals were paralyzed with α-bungarotoxin  and embedded in 1.5–2% LMP agarose in a recording chamber . The recordings were performed in two-photon microscopes (from Thorlabs Inc and Scientifica Inc) using a ×16 water immersion objective . A mode-locked Ti:Sapphire laser (MaiTai Spectra-Physics) tuned to 920 nm was used for excitation. Either single plane or volumetric recording (6 planes with a Piezo) were obtained. Acquisition rates were for the recordings of Tg(elavl3:GCaMP6s), and Ets1020t;Tg(UAS:GCaMP6s) fish: 40 Hz for a single plane of 1408 × 384 pixels or 3 Hz for a volume of 1536 × 900 pixels × 6 planes. For Tgnw7;Tg(UAS:GCaMP6s) fish the acquisition rate was 24 Hz for a single plane of 1536 × 650 pixels. After a 3 min baseline period, a solution of 20 mM PTZ diluted in AFW was added through a perfusion system in order to induce epileptic activity. Total duration of these recordings were 60–70 min. For pilocarpine-induced seizures, a 60 mM solution of pilocarpine diluted in AFW was applied at the beginning of the recordings, which lasted for 120–240 min. Data analysis was done with a custom code written in MATLAB (Mathworks) as described in the following section.Two-photon calcium imaging: Two-photon calcium imaging was performed on 7 days old 30 that correct for occasional drift in the XY dimension, based on hierarchical model-based motion estimation. Individual neurons were automatically detected using a pattern recognition algorithm adapted from Ohki et al.73, which identify neurons by using a correlation based approach comparing the GCaMP6s labeled neurons with torus or ring shaped neuronal templates74. Once all neurons were segmented they were individually tracked over the length of the whole recording to assure that the same neuron was properly captured. In case a neuron was lost during the tracking because of Z-drift, it was discarded from further analysis. Later, all automatically detected neurons are confirmed by visual inspection. Once the pixels belonging to each neuron were identified, the average of those pixels per image was calculated providing the complete time course of each individual neuron over time. Sparsely labelled glial cells were manually identified for Fig. 74. All traces from neurons and glia were resampled to a final rate of 4 Hz (using decimate function in MATLAB). Neural and glial activity was studied in windows of 3 min: baseline (3 min prior to drug delivery), preictal period (3 min preceding the seizure) and seizure. In each interval a cell was considered active if its change in fluorescence was greater than three times the standard deviation from the baseline period. Generalized seizure onset was manually annotated as the time point with sudden increase of activity recruiting the majority of the cells in all brain regions. The activity of each cell was calculated using the area under the curve by trapezoidal numerical integration method .Two-photon microscopy images were aligned using an adapted algorithmP < 0.05 was considered as statistically significant.Statistical analysis was done using MATLAB. Wilcoxon ranksum test was used for non-paired analysis and Wilcoxon signed rank test for paired analysis. Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Supplementary Movie 4


Here, we carried out a meta-analysis to clarify polymorphisms between ApaI (C/A) (rs7975232), BsmI (G/A) (rs1544410), FokI (C/T) (rs10735810), TaqI (T/C) (rs731236) and Tru9I (G/A) (rs757343) in the VDR gene and PCOS susceptibility based on relative lager sample size.Polycystic ovary syndrome (PCOS) is a common endocrine disorder in reproductive-age women. Multiple susceptible gene as well as environmental factors and their interaction each other are contributed to the PCOS risk. Several case-control studies have researched the associations of the VDR gene variates and PCOS susceptibility published before 31th, May 2018. Crude odds ratios (ORs) and its 95% confidence intervals (95% CIs) in different comparisons were used to detected the strength of the association. All the statistical analyses of the present meta-analysis were performed by STATA version 12.0 software.English database of PubMed and Embase, and Chinese database of Wanfang and China National Knowledge Infrastructure (CNKI) databases were retrivaled for the relationship between VDR ApaI (rs7975232) polymorphism and PCOS susceptibility  was found in the overall population. After stratified by ethnicity, we showed that there is a significant association between VDR ApaI (rs7975232) polymorphism and susceptibility to PCOS in the Asian  population, but this association was not found in the Caucasian population. Additionally, a significant relationship between VDR BsmI (rs1544410) variates with PCOS susceptibility in the Asian  population, but this association was not found in the Caucasian population. We didn’t find any association between VDR FokI (rs2228570), VDR TaqI (rs731236), VDR Tru9I (rs757343) and PCOS susceptibility in the overall and the subgroup populations.Totally, 3587  participants from 13 studies were included which met our inclusion criteria. A statistically significant association between VDR ApaI (rs7975232) and VDR BsmI (rs1544410) polymorphisms are correlated with susceptibility to PCOS in the Asian population and VDR TaqI (rs731236), VDR FokI (rs2228570), VDR Tru9I (rs757343) did not reveal a relationship with the PCOS susceptibility.Our findings demonstrated that  A common endocrine syndrome, polycystic ovary syndrome (PCOS), is characterized by long-term absence of ovulation and high androgen, which is the most common causes of menstrual disorders and infertility in women during reproductive years , 2. The IR and hyperinsulinemia are frequent metabolic abnormalities in the PCOS, evidence suggests that vitamin D levels may be linked to hormonal and metabolic disorders . As a stVDR gene is located on chromosome 12cenq12 and contains 14 exons. Several single nucleotide polymorphisms (SNPs) in the VDR gene have been reported, such as ApaI in intron 8 (C/A) (rs7975232), BsmI in intron 8 (G/A) (rs1544410), FokI in exon 2 (C/T) (rs10735810), TaqI in exon 9 (T/C) (rs731236) and Tru9I in intron 8 (G/A) (rs757343). It has been shown that VDR polymorphisms  may contribute to the PCOS susceptibility, although the findings are as yet inconclusive [VDR gene polymorphism with incident PCOS outcomes, but only six studies were included for analysis [VDR gene polymorphisms and PCOS susceptibility.The nclusive . A previanalysis . CurrentPubMed, EMBASE, Chinese Wanfang, China National Knowledge Infrastructure (CNKI) and other databases were searched. The retrieval period is from establishment to May 31, 2018. The search terms and keywords are as follows: “vitamin D receptor or VDR”, “polymorphisms or variants”, and “polycystic ovarian syndrome or PCOS”. References to retrieved papers were also manually searched for other potential studies not included in the database.VDR gene with PCOS; (2) case-control study design; (3) genotype distributions were available for both cases and controls to calculate an OR and its 95%CI; (4) The diagnosis of PCOS is based on the Rotterdam criteria and the National Institute of Child Health and Human Development criteria [If the study met the following criteria, it was included in the meta-analysis: (1) study on the association of criteria , 29. ExcThe quality of the study was assessed using the Newcastle-Ottawa scale. The scale is composed of three aspects: selection, comparability and exposure, with a maximum score of 9 . A totalTwo researchers independently and carefully extracted the available data from each eligible study. Information on all eligible studies is as follows: (1) surname of the fist author; (2) publication year; (3) country of origin; (4) the ethnicity of population; (5) sample size of cases and controls. Our research team addressed the differences through discussion.VDR gene polymorphisms and PCOS risk were calculated using odds ratios (ORs) and its 95% confience interval (95%CI). All analyses used the allelic, recessive, and dominant genetic models. The chi-square test based on Q statistics was used to analyze the inter-study heterogeneity, which was considered to be significant when p value < 0.10 [2 test. When I2 was less than 50%, heterogeneity was acceptable, and the fixed effect model of mantel-haenszel method was adopted. Otherwise, the random effect model based on DerSimonian and Laird methods is adopted.The effect sizes of the association between the e < 0.10 . HeterogP-value of two-sided < 0.05 was considered statistically significant.We used Begg funnel plot and Egger’s linear regression test to assess potential publication bias. The Egger’s linear regression test examines the asymmetry of funnel plot measured on the natural log scale . One-wayAs Fig. ApaI rs7975232 (G > T) polymorphism was examined in 8 case-control studies [BsmI rs1544410 (A > G) variant was examined in 8 studies [Taq1 rs731236 (T > C) variant was examined in 10 studies [FokI rs2228570 (C > T) variant was examined in 7 studies [VDR Tru9I rs757343 with PCOS susceptibilitywas examined in 3 studies  variant and PCOS susceptibility are shown in Table VDR gene ApaI rs7975232 (A > C) polymorphism with PCOS susceptibility in the allelic , recessive  and dominant  genetic models in the overall populations. Population subgroup analysis showed that there was a significant correlation between VDR ApaI rs7975232 (A > C) polymorphism and PCOS susceptibility in the Asian population   and PCOS susceptibility. Significant heterogeneity was found in some comparisons and results from the random-effects model using the DerSimonian-Laird method were used. We did not find a correlation between VDR BsmI rs1544410 (A > G) polymorphism and PCOS susceptibility in all genetic models of the general population. Subgroup analysis by ethnicity revealed a significant association between polymorphism of VDR BsmI rs1544410 (A > G) and susceptibility to polycystic ovary syndrome in the Asian population   polymorphisms and PCOS susceptibility. Significant heterogeneity was found in most comparisons, and random effects model results on DerSimonian-Laird method were used. We found no correlation between variation and PCOS susceptibility in the general population and in subgroups by ethnicity.In Table VDR FokI rs2228570 (C > T) variant with PCOS susceptibility was found in the overall population and sub-groups by ethnicity.Table VDR Tru9I rs757343 (G > A) variation and PCOS susceptibility. The subjects of all included studies were conducted in the Asian populations. The heterogeneity test demonstrated no significant heterogeneity exist in all studies and fixed effects model results on Mantel-Haenszel method were used. There is no association between the VDR Tru9I rs757343 (G > A) polymorphism and PCOS susceptibility was found in the Asian population.In Table Except for the polymorphism of VDR FokI rs2228570 (C > T) and the risk comparison of PCOS in all white genetic models, as these comparisons included only two studies, the Begg and Egger trials were carried out in all comparisons. Begg’s funnel plots were performed in all comparisons showed the shape was symmetrical, and the Egger’s linear regression analysis further indicated that there was no publication bias in the meta analysis Table , Fig. 3.Sensitive analysis was conducted to estimate if our results were substantially affected by the presence of any individual. Our results suggest that no single study has a significant effect on the merger effect Fig. .Fig. 4SeVDR gene variants are associated with serum insulin levels in women with PCOS [VDR-mediated insulin resistance by regulating the expression of target genes [VDR gene involved in insulin signaling pathway is considered as an important candidate gene for PCOS [Genetic factors have become increasingly important in the progression of PCOS. Previous studies have shown that ith PCOS . Serum 2et genes . VDR genfor PCOS . Howeverfor PCOS , 36. Subfor PCOS .The metafor PCOS –39.VDR polymorphisms and PCOS susceptibility from available databases. The results included a total of 13 articles based on 1922 PCOS patients and 1665 controls, showed that VDR ApaI (rs7975232) and VDR BsmI (rs1544410) polymorphisms are associated with PCOS susceptibility and VDR TaqI (rs731236), VDR FokI (rs2228570), VDR Tru9I (rs757343) did’t reveal a relationship with the PCOS susceptibility. The results were in accordance with previous studies and might provide a new biomarker in the etiology of PCOS [VDR polymorphisms and PCOS risk. Subgroup analysis by ethnicity showed that VDR ApaI (rs7975232) and VDR BsmI (rs1544410) polymorphisms were significantly correlated with PCOS susceptibility in the Asian population but not in the Caucasian population. The reason for this finding may be genetic disparities between the ethnic groups. Due to the process of natural selection, different groups might have some differences in the functional variants [In this meta-analysis, we summarized the existing data on the associations of  of PCOS , 18, 20.variants .VDR gene polymorphism may play a role in the pathogenesis of PCOS by affecting the insulin signaling pathway [VDR gene would explain the observed association. In addition, VDR gene polymorphism may play a role in the pathogenesis of PCOS by affecting the PTH-vitamin D axis [VDR gene polymorphism is associated with serum PTH and 25 (OH) D levels, and vitamin D-VDR complex inhibits the secretion and synthesis of PTH [Accordingly,  pathway . Howevern D axis . Consists of PTH . Simsek s of PTH . Next, hs of PTH .VDR gene polymorphism in TaqI (rs731236) for T allele was significant association with PCOS and didn’t find any association between VDR ApaI (rs7975232), VDR BsmI (rs1544410), VDR FokI (rs2228570), VDR Tru9I (rs757343) and PCOS susceptibility in the all included studies. Such inconsistent results may be due to different number of studies included in the meta-analysis, different sample sizes and different statistical abilities. In their study, they conducted a meta-analysis and included 5 (9 studies in the present meta) studies on VDR ApaI rs7975232 (G > T), 4 (8 studies in the present meta) studies on VDR BsmI rs1544410 (A > G), 6 (10 studies in the present meta) studies on VDR TaqI rs731236 (T > C), 5 (7 studies in the present meta) studies on VDR FokI rs2228570 (C > T) and didn’t include VDR Tru9I rs757343 (G > A) studies (3 in the present meta) for analysis the association with PCOS susceptibility. In addition, we conducted a subgroup analysis by ethnicity among Asian and Caucasian populations, which was not included in their meta-analysis.The heterogeneity was observed in some comparisons, but partially it was resolved by subgroup analysis based on ethnicity. Our research found that VDR can act as an influencing factor on PCOS. These SNP mutations can be used as risk factors to evaluate PCOS.The results of this meta-analysis were different from those of Han et al. ‘s previous meta-analysis , which sAlthough the present meta-analysis has the advantage of a relatively large sample size for a combined result, several limitations should be addressed in interpreting our results. Firstly, we included relevant articles published only in English and Chinese so that potential language bias may exist in this study. Second, most of the studies were conducted in Asian populations, and the small number of studies in the Caucasian subgroup analysis may have resulted in insufficient statistical ability to detect subtle relationships. Third, age, gender, genetic variation, environmental factors exposure and other risk factors may have an impact on the incidence of PCOS, but this study only considered gene polymorphism. The effects of gene-gene and gene-environment interaction on the occurrence and development of the disease need to be further studied.VDR ApaI (rs7975232) and VDR BsmI (rs1544410) polymorphisms are associated with PCOS susceptibility in the Asian population and VDR TaqI (rs731236), VDR FokI (rs2228570), VDR Tru9I (rs757343) did’t reveal a relationship with the PCOS susceptibility. These results might not be generalized to other ethnic populations. Further studies with more sample size and including other confounding factors are still needed in the future for a definitive conclusion.In summary, current meta-analysis provided statistical evidence that