Oryza sativa) is a staple food that feeds more than half the world population. As rice is highly sensitive to soil salinity, current trends in soil salinization threaten global food security. To better understand the mechanistic basis of salinity tolerance in rice, three contrasting rice cultivars—Reiziq (tolerant), Doongara (moderately tolerant), and Koshihikari (sensitive)—were examined and the differences in operation of key ion transporters mediating ionic homeostasis in these genotypes were evaluated. Tolerant varieties had reduced Na+ translocation from roots to shoots. Electrophysiological and quantitative reverse transcription PCR experiments showed that tolerant genotypes possessed 2-fold higher net Na+ efflux capacity in the root elongation zone. Interestingly, this efflux was only partially mediated by the plasma membrane Na+/H+ antiporter (OsSOS1), suggesting involvement of some other exclusion mechanisms. No significant difference in Na+ exclusion from the mature root zones was found between cultivars, and the transcriptional changes in the salt overly sensitive signaling pathway genes in the elongation zone were not correlated with the genetic variability in salinity tolerance amongst genotypes. The most important hallmark of differential salinity tolerance was in the ability of the plant to retain K+ in both root zones. This trait was conferred by at least three complementary mechanisms: (1) its superior ability to activate H+-ATPase pump operation, both at transcriptional and functional levels; (2) reduced sensitivity of K+ efflux channels to reactive oxygen species; and (3) smaller upregulation in OsGORK and higher upregulation of OsAKT1 in tolerant cultivars in response to salt stress. These traits should be targeted in breeding programs aimed to improve salinity tolerance in commercial rice cultivars.Rice ( Soil salinization is a major abiotic constraint for crop productivity worldwide . About 5Oryza sativa L.) is one of the world’s most important cereal crops accounting for over 20% of all calories in the human diet  that encodes plasma membrane (PM)-localized Na+/H+ exchanger  channels and explains the massive K+ loss reported upon acute salinity treatment from roots of several plant species, including Arabidopsis  technique] and molecular [quantitative reverse transcription PCR (qRT-PCR)] approach, we have explored the cell-type-specific mechanisms underlying differences in responses to salinity stress in these cultivars. Our data suggests that KO. sativa) cultivars, Koshihikari, Doongara, and Reiziq, were used in this study. Koshihikari is a japonica genotype, whereas Reiziq and Doongara are Australian commercial indica varieties. Seeds were obtained from the Western Sydney University and multiplied in a glasshouse at Tasmanian Institute of Agriculture facilities in Hobart. Seeds were germinated in deionized water in a growth chamber at 28°C in the dark for 2 days. Uniformly germinated seeds were then transferred to the hydroponics system and grown in International Rice Research Institute nutrient solution (Three different rice (solution  at 28°C/Root length and plant height were measured using a ruler. Fresh weight (FW) was determined using an electronic balance immediately after cutting. Fresh samples were then washed with deionized water and dried at 70°C to a constant weight (DW). Water content (WC) of each plant was determined as a percentage as WC = (FW − DW)/(FW) × 100.o), variable (Fv), and maximum (Fm) chlorophyll fluorescence characteristics were recorded. The photochemical efficiency of PSII was then calculated as the ratio of Fv/Fm.Leaf chlorophyll content was quantified using a SPAD-502 chlorophyll meter . Measurements were taken from the middle part of the first fully developed leaf. Chlorophyll fluorescence measurements were conducted on the same leaves as Soil–Plant Analyses Development (SPAD), using the OS-30p portable chlorophyll fluorometer . Leaves were dark-adapted for 20 min before the measurements. Initial . The Na+ translocation coefficient from roots to shoots was calculated as Na+ content in the root divided by Na+ content in the shoot.Dry rice root and shoot samples were ground into powder and digested with 98% HFive-day old rice seedlings were treated with 100 mM NaCl for 48 h, and then the primary roots were sampled and used for viability staining and anatomical analysis. The viability of root cells was determined by using the fluorescein diacetate (FDA)–propidium iodide (PI) double staining method as described by +, K+, and Na+ were measured using the noninvasive microelectrode MIFE technique  for 3 days. Plants with roots length 40–60 mm were used for ion flux measurements. For each treatment, net ion fluxes were measured from the elongation  was then administered, and transient ion flux responses were measured for another 30 min. H2O2 (10 mM) was selected as a physiologically relevant treatment . Net ion flux was first measured for 5 min in BSM solution, to record steady state initial flux values. The appropriate test solution , a known blocker of PM H+-ATPase; 1 mM amiloride, an inhibitor of the PM Na+/H+ exchanger activity; 20 mM TEA, a known blocker of K+-selective PM channels. All inhibitors were prepared in background BSM solution.About 30–60 min prior to 100 mM NaCl or 10 mM H−ΔΔCT method was used to determine the relative expression levels of the studied genes, related to SOS pathway, K+ channels and transporters, and PM H+-ATPase , encompassing the root cap, meristem, and elongation zone was excised with a scalpel, and the remaining root system beyond 0–10 mm from the root apex was considered the mature zone .Salinity stress resulted in a significant reduction in plant height, root length, tiller number, FW and DW of shoot and root, and WC in all cultivars Figure 1+ and K+ contents were determined in shoots and roots after 15 days of treatment with 50 and 100 mM NaCl  genotypic differences in cell viability were detected in the mature root zone  difference in net Na+ efflux was found between contrasting cultivars in mature root zone. Pharmacological experiments revealed that Na+ efflux in the elongation zone was significantly suppressed by amiloride, an inhibitor of PM Na+/H+ exchanger activity, and by sodium orthovanadate, a known blocker of PM H+-ATPase  salinity exposure significantly upregulated OsSOS1 expression, with OsSOS1 expression being several folds higher in root tips than in the mature zone . Salt-stimulated K+ efflux gradually recovered to the relative stable values as treatment time increased , a general potassium channel blocker  in elongation zone, with strongest induction in salt-tolerant cultivar Reiziq  application increased K+ effluxes in both root zones, with ∼2-fold stronger responses from the elongation zones than mature zones  and provided some insights into mechanistic basis of this phenomenon.+ efflux from roots , an inhibitor of Na+/H+ exchanger activity, and by sodium orthovanadate , an inhibitor of H+-ATPase activity (Arabidopsis (+ efflux was inhibited by amiloride (> 80% inhibition)  cultivars  . Also, Sibition) , while i in rice . ConsistArabidopsis, SOS1-mediates Na+/H+ exchange is controlled by a multifunctional protein kinase encoded by SOS2 , calling for higher emphasis on the functional studies.In  by SOS2 . However+ accumulation in the shoot . Amongst all root cells, the pericycle cells are considered to possess the greatest ability to sequester Na+ and Cl- in vacuoles  grant to SS. This work was supported by the Rice Industry Technology System of Henan Province (S2012-04-G02) grant to QZ. We thank Sunrice (Ricegrowers Limited) for kind supply of seeds of Australian rice varieties.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Saccharomyces yeasts is known to be associated with such segregation errors, but estimates of the importance of nondisjunction to postzygotic reproductive isolation have been hampered by difficulties in accurately measuring nondisjunction frequencies. Here, we use spore-autonomous fluorescent protein expression to quantify nondisjunction in both interspecific and intraspecific yeast hybrids. We show that segregation is near random in interspecific hybrids. The observed rates of nondisjunction can explain most of the sterility observed in interspecific hybrids through the failure of gametes to inherit at least one copy of each chromosome. Partially impairing the anti-recombination system by preventing expression of the RecQ helicase SGS1 during meiosis cuts nondisjunction frequencies in half. We further show that chromosome loss through nondisjunction can explain nearly all of the sterility observed in hybrids formed between two populations of a single species. The rate of meiotic nondisjunction of each homologous pair was negatively correlated with chromosome size in these intraspecific hybrids. Our results demonstrate that sequence divergence is not only associated with the sterility of hybrids formed between distantly related species but may also be a direct cause of reproductive isolation in incipient species.Genome-wide sequence divergence between populations can cause hybrid sterility through the action of the anti-recombination system, which rejects crossover repair of double strand breaks between nonidentical sequences. Because crossovers are necessary to ensure proper segregation of homologous chromosomes during meiosis, the reduced recombination rate in hybrids can result in high levels of nondisjunction and therefore low gamete viability. Hybrid sterility in interspecific crosses of  Saccharomyces yeasts, quantification of the frequency of these errors has been elusive until now. We use the recently developed technique of spore-autonomous fluorescent protein expression to quantify chromosome segregation errors in gametes produced by both interspecific and intraspecific yeast hybrids. By doing so, we show that segregation is near random in interspecific hybrids, accounting for nearly all of the observed gamete inviability. We also demonstrate that sequence divergence is associated with sterility of hybrids both between distantly related species and between different strains of the same species, providing a general mechanism for reproductive isolation in yeast.Different species are kept genetically separated from each other by reproductive isolation, which can result in the production of sterile hybrids. Despite the central role of reproductive isolation in evolutionary biology, the mechanisms underlying hybrid sterility remain controversial. We focus on one potential cause: genome-wide sequence divergence. Sequence divergence can lead to hybrid sterility by interfering with the proper segregation of chromosomes during gamete production, leading to most gametes lacking at least one essential chromosome. While hybrid sterility is known to be associated with mis-segregation in  Saccharomyces cerevisiae have established genome-wide sequence divergence as a cause of meiotic errors underlying hybrid sterility  between the estimated genetic divergence of the parental pairs and the resulting offspring viability […] indicating that general DNA sequence differences were not sufficient to explain the observed reproductive isolation’. In our reanalysis, we found a negative, albeit not statistically significant, correlation between hybrid spore viability and parental genetic divergence . However, two groups (indicated by red and yellow points) represent hybrids formed between parents with different chromosomal arrangements. The red points are hybrids formed between S288C and strains carrying a Chr VIII to XVI reciprocal translocation with an ECM34-SSUI breakpoint [S1-S3]. The region on Chr VIII involved in this translocation is near the telomere and contains no essential genes, and consequently this rearrangement should cause only 25% of hybrid spores to be inviable. The yellow points represent hybrids between S288C and strains containing a translocation between two large chromosomal regions  that each contains at least one essential gene [S1]. These two rearrangements are expected to cause a 50% reduction in spore viability in these hybrids. (B) Following the practice of Liti and colleagues [S4], we have corrected the observed spore viabilities for the effects of known chromosomal rearrangements in the groups represented by red and yellow points. The corrected spore viabilities are highly correlated with parental genetic divergence . Thus, after correcting for the effects of chromosomal rearrangements, parental genetic divergence was a clear predictor of hybrid spore viability even among closely related parents (slope = −1.25% viability per 0.1% genetic divergence). Hybrids represented by blue points contain no known rearrangements and were therefore not adjusted. Omitting these blue points from the analysis has no effect on our conclusion , nor does restricting the analysis to only the high-fertility collinear pairs represented by grey and blue points . (C) Spore viabilities of intraspecific S. cerevisiae hybrids were independently measured by [S2] as part of the ‘100 genomes’ project; each strain was crossed with S288C to generate intraspecific hybrids. Since not all major chromosomal rearrangements have been identified for these strains, we have restricted our analysis to hybrids formed between parents identified as having collinear genomes (hybrid spore viability >75%) in [S2]. Full genomes for strains that Strope and colleagues [S2] provided hybrid fitness measures for but no genomic sequences were obtained from Genbank . The genome sequence of strain YJM1628 was used for the isogenic strain YJM1615. Genetic distances were determined by aligning whole genomes using REALPHY [S5] and calculating % identity using Geneious 10.2.3 based on 8,813,278 aligned sites. Once again, hybrid spore viability was significantly correlated with parental genetic divergence . The negative relationship was very similar to that in (B) with a slope = −1.33% viability per 0.1% genetic divergence (grey line). The slope was only slightly changed by the omission of the homozygous S288C parent (−1.35% viability per 0.1% genetic divergence). Underlying data can be found in 16. The dotted red line represents the relationship between spore viability and genetic divergence reported by Liti and colleagues [S4]. Our method underestimates spore viabilities for intraspecific hybrids because Chr I has a higher than average rate of nondisjunction in these strains Click here for additional data file.S3 FigYKL050c and YKR005c. From genetic maps in S. cerevisiae strain S288C , we estimated the map distance of these two loci at approximately 35 cM, the approximate limit at which linkage can be calculated by tetrad analysis without empirically derived correction [S6]. We marked YKL050c with PYKL050c-GFP_URA3 and YKR005c with PDIT1-RFP_LEU2 in both S. cerevisiae strain Y55 and S. paradoxus strain N17 and scored tetrads produced by parental diploids and hybrid diploids as the PD (2 red spores and 2 green spores), the NPD (2 red/green [represented here as yellow] spores and 2 nonfluorescent spores), or as the T . Tetrads not matching any of these three categories were omitted from the analysis . Since nondisjunction is extremely rare in parental types, we calculated genetic distances in parents according to the standard equation [S6]: 100(T + 6NPD)/2(PD + NPD + T). In hybrids, the low number of Ts observed indicates that single crossover events are rare; the frequency of double crossover events must therefore be negligible, and all non-parental ditypes can be ascribed to nondisjunction events [S7]. We therefore estimated the genetic distance between these loci in hybrids as 100T/2(PD + T). Chr, chromosome; NPD, non-parental ditype; PD, parental ditype; T, tetratype.We investigated recombination between two loci located approximately 100 kb apart on Chr XI: (PDF)Click here for additional data file.S4 FigLEU2 and URA3 were knocked out using antibiotic resistance cassettes (in most cases either HYGMX or KANMX) in haploid S. cerevisiae strains Y55 and S288C and/or S. paradoxus strains  to allow selection for the integration of spore-autonomous fluorescent protein expression cassettes . Fluorescent constructs, excluding the promoters, were amplified from plasmids pSK691 (RFP_LEU2) and pSK726 (GFP_URA3) and placed under the control of the endogenous YKL050c or DIT1 promoter by replacing the appropriate ORF, generating strains with ykl050c::RFP_LEU2, ykl050c::GFP_URA3, and dit1::RFP_LEU2, dit1::GFP_URA3 genomic regions. The integrated fluorescent constructs plus the appropriate endogenous strain-specific promoters  were then amplified from genomic DNA and integrated at the desired site on each chromosome. GFP-marked haploids were mated to strains of the opposite mating type with RFP at the allelic position to generate parental or hybrid diploids, which were then sporulated to examine meiotic segregation. See (PDF)Click here for additional data file.S1 DatapCLB2_SGS1) parents and hybrids marked at each chromosome with the DIT1-promoted (‘DIT1 promoter’ sheet) or YKL050c-promoted (‘YKL050c promoter’ sheet) spore-autonomous fluorescent reporter. Tetrads with ‘other’ patterns of segregation were included in the total counts but not in the nondisjunction counts. The ‘100 genomes hybrids’ sheet includes hybrid spore viability data from [S2] and genetic distances from S288C calculated using REALPHY [S5].Data include numbers of tetrads scored as having correct segregation (different cells) or nondisjunction (same cells) for wild-type and anti-recombination–impaired ((XLSX)Click here for additional data file.S1 TextIncludes strain tables, primer tables, and supporting references.(PDF)Click here for additional data file.


The cell proliferation marker, Ki67 and the immature neuron marker, doublecortin are both expressed in the major human neurogenic niche, the subependymal zone (SEZ), but expression progressively decreases across the adult lifespan (PMID: 27932973). In contrast, transcript levels of several mitogens  do not decline with age in the human SEZ, suggesting that other growth factors may contribute to the reduced neurogenic potential. While insulin like growth factor 1 (IGF1) regulates neurogenesis throughout aging in the mouse brain, the extent to which IGF1 and IGF family members change with age and relate to adult neurogenesis markers in the human SEZ has not yet been determined. We used quantitative polymerase chain reaction to examine gene expression of seven IGF family members  in the human SEZ across the adult lifespan . We found that only IGF1 expression significantly decreased with increasing age. IGFBP2 and IGFBP4 expression positively correlated with Ki67 mRNA. IGF1 expression positively correlated with doublecortin mRNA, whereas IGFBP2 expression negatively correlated with doublecortin mRNA. Our results suggest IGF family members are local regulators of neurogenesis and indicate that the age-related reduction in IGF1 mRNA may limit new neuron production by restricting neuronal differentiation in the human SEZ. The human brain retains the ability to generate new neurons throughout postnatal life with the largest reservoir of newly-born cells in the subependymal zone  . NeurogeIGF1 and 2 transcripts decrease in most regions of the rodent nervous system during postnatal life but levels remain elevated in adult neurogenic regions , 16. IGFIGF1 and IGFBPs are expressed in the peripheral blood and cerebrospinal fluid and show distinct age-related alterations -29. The Tissue from the anterior caudate of 50 healthy individuals was obtained from the Stanley Medical Research Institute and New South Wales Brain Tissue Resource Centre . Cases had no known history of psychiatric symptoms or substance abuse and showed no significant neuropathology on post-mortem examination. The brain cohort consisted of 9 females and 41 males, with an average age of 52 years , average pH of 6.59  and average post-mortem interval (PMI) of 29 hours . Demographic details of each individual have been described previously [x 60 µm sections interspersed with 10 x 14 µm sections. SEZ tissue was dissected from the caudate nucleus while frozen over dry ice from 60 µm thick sections, ~2 mm deep to the surface of the lateral ventricle. For each case, tissue was dissected from 3 sets of 3-4 adjacent 60 µm sections spaced ~1340 µm to give 10 sections per case .Fresh-frozen caudate tissue was sectioned on a Leica CM3050 S cryostat, taking 20 Total RNA was extracted for all cases using Trizol (Life Technologies). RNA quality and concentration were assessed with Agilent Technologies 2100 Bioanalyzer and Nanodrop ND-1000 spectrophotometer. The average RNA integrity number (RIN) was 7. cDNA was synthesized from 3 µg total RNA per case using SuperScript® First-Strand Synthesis kit and random hexamers (Life Technologies).mRNA levels were measured by TaqMan Gene Expression Assays  using an ABI Prism 7900HT fast real-time PCR system and a 384-well format. All measurements from each subject were performed in duplicate and relative quantities were determined from a seven-point standard curve of pooled cDNA. The no template controls did not produce a signal for any mRNA examined. Expression of two housekeeping genes, TATA-box binding protein (Hs00427620_m1) and ubiquitin C (Hs00824723_m1), was used to calculate the normalizing factor for gene expression (geometric mean), and neither of these mRNAs nor the geometric mean correlated significantly with age . Quantitative reverse transcription polymerase chain reaction data were captured with sequence detector software . SDS software plotted real-time fluorescence intensity and the threshold was set within the linear phase of the amplification profiles.t-tests or analysis of variance co-varying for brain cohort characteristics were used as appropriate to detect sex-related differences in gene expression, and sex did not show a significant effect on gene expression (p>0.05).Statistical analyses were performed using IBM SPSS Statistics Version 24 and GraphPad Prism Version 7.0 B. Results were considered as significant at an α level of p<0.05. Population outliers were defined as points lying outside of a 95% prediction interval from the linear regression line . Data were tested for normality using the Shapiro-Wilk test. Pearson’s product-moment correlations were used to investigate the relationships of brain cohort characteristics  to each other and target gene expression. Pearson’s product-moment or semi-partial correlations were used to analyze age-related changes in target gene expression and their relationships to markers of cell proliferation and neuronal differentiation. When semi-partial correlations were performed, the semi-partial correlation coefficient sr is reported. Independent Gene expression of seven out of ten IGF family members was reliably detected by quantitative polymerase chain reaction in the adult human SEZ from 21-103 years. Transcript levels of IGFBP1, IGF2 and IGF2 receptor were below the level of detection. IGF1 mRNA decreased significantly with age  , which wDetailed statistical data for the relationships between brain cohort characteristics and target gene expression are presented in This study provides the first molecular evidence for an age-related reduction in local IGF1 expression in the human SEZ from adulthood into aging and lends translational support for the fundamental work in rodents. Our results suggest that subependymal cells remain responsive to IGF1 as indicated by stable expression of cognate receptors IGF1R and INSR. We found that IGFBP2 and IGFBP4 may be important in the regulation of cell proliferation, while IGF1 may promote neuronal differentiation. This study only allows evaluation of transcriptional alterations in the human SEZ at one time point; however, it is of high value considering the pronounced interspecies differences in neurogenesis and migration between rodents and humans -11, 34.in silico modelling predicts that the number of stem cells is preserved until late adulthood [The role of IGF1 signalling during aging remains unclear in many aspects . Our resdulthood  and accodulthood , 7. Longdulthood . These oThe age-related decrease in IGF1 mRNA in the SEZ was not unexpected, although important to document in humans; however, it was unexpected that none of the other IGF family members declined with age. Since IGF1 was the only transcript to change significantly throughout adulthood, there may be an age-related alteration in control of gene expression specific to IGF1, such as methylation, histone modifications or loss of transcriptional activators. Age-related changes in local and peripheral neurogenesis-regulating factors may act in concert to decrease Ki67 and DCX expression in the SEZ across the human lifespan , 46. Ki6In summary, our results support that IGF family members impact the age-related decline in cell proliferation and neuronal differentiation markers in the human SEZ, though specific factors involved may depend on the stage of neurogenesis. We suggest that IGF family members only partially contribute to the complex local milieu available to regulate neurogenesis in the adult brain. This study is limited by the homogenate-based experimental approach and further cell-type specific analysis would shed light on whether proliferating cells and immature neurons maintain responsiveness to IGF1 during human aging. Gene expression studies also only provide clues as to whether protein levels may be altered and cannot ascertain if differences in brain protein levels may be of functional significance; however, several studies demonstrate that transcription changes in IGF family members significantly contribute to protein levels and to biological function -50. We s


Doxycycline (DXC) is a tetracycline antibiotic which has been proposed as a breast cancer radiosensitizer by specifically reducing the expression of the DNA repair enzyme DNA PK in breast cancer initiating cells. Since DXC presents favorable pharmacokinetics properties including the capacity to cross the blood-brain barrier, it has been hypothesized that it could radiosensitize brain tumors as well. We have investigated the radiosensitizing capacity of DXC towards human glioma initiating cells (GIC)-driven orthotopic glioblastomas (GB) in NOD/SCID mice that faithfully mimic the growth properties of the clinical tumors of origin. DXC at 10 mg/Kg body weight was subcutaneously delivered daily, 5 days/week for 4 weeks. At the same time, radiotherapeutic fractions of 0.25 Gy to the head were delivered every 3–4 days (twice/week) for 15 weeks. No survival advantage was observed in DXC-treated mice as compared to vehicle-treated mice by this radiosensitizing protocol. On the contrary, skin damage with hair loss and deep ulcers were observed after 4 weeks in DXC-treated mice leading to discontinuation of drug treatment. These results do not support the use of DXC as a radiosensitizer for brain tumors and indicate skin damage as an important side effect of DXC. Glioblastoma (GB-WHO grade IV) is the most common malignant brain tumor in adults; it is almost invariably lethal in 10–12 months. GB appears as infiltrating lesions on MRI or CT often characterized by central necrosis, perilesional edema and abnormal vasculature .Since its FDA-approval in 1967, doxycycline (DXC) has been used as a broad-spectrum antibiotic targeting bacterial ribosomes. In mammalian cells, DXC may function as an inhibitor of mitochondrial biogenesis by binding to the small subunit of the mitochondrial ribosome which shows a number of conserved properties and protein homologies with ancestor bacterial ribosomes . ClinicaMice were provided by the Breeding Unit of the Animal Facility at IRCCS Ospedale Policlinico S.Martino - Genova, Italy. Immunodeficient non-obese diabetic/severe combined immunodeficient (NOD-SCID) mice were housed under maximum barrier conditions, one animal per cage to facilitate health status inspection. Husbandry conditions included a 12 hours fluorescent light/dark cycle and ad libitum access to standard laboratory chow and water. The experimental unit was the single animal that was uniquely identified by a number marked on the tail and coded by ear punches. This mouse ID number was in the format XX.XX where the first two digits indicate the experiment and the second two digits indicate the animal. The ARRIVE  guidelines were followed throughout this report .One 100 mg tablet of DXC [Bassado ] was dissolved in 100 ml of 0.9%NaCl under stirring (f.c. 1 μg/μl). The solution was then filtered, divided into aliquots and frozen at –20°C. Each aliquot was thawed only once.The pure formulation of the drug was purchased from Sigma (doxycycline hyclate – cat D9891) and dissolved as above.PI3K/Akt pathway genes as well as the IDH1, TP53, H3F3A, PDGFRA, CDKN2A and EGFR status as previously described [TP53 sequence but they express the TP53 RNA at low levels [5 GIC were injected at a depth of 3 mm in correspondence of the left corpus striatum and the skin closed using metal staples . In order to avoid significant subpopulation selection during prolonged cell culture, GIC samples frozen after no more than 30 days of culture were used for orthotopic tumor development.The primary GIC line COMI derived from an adult GB has been previously described –13  and control  groups: mice in the experimental group were subcutaneously (s.c.) infused in the dorsal region over the shoulders on weekdays (5 days per week) for 4 weeks (from d21 to d46 of tumor development) with 10 mg/Kg DXC in 0.9% NaCl. Animals in the control groups were s.c. infused with an equal volume of vehicle.RT of orthotopic GB was performed by an RS 2000 Biological Irradiator  whose “2 asphyxiation at the moribund status H1 that was the primary experimental outcome in this study.Animals were monitored daily in blind by experienced researchers assigning to each animal a health score  based on neurological symptoms including posture changes, arched back, tail weakness, diminished activity and skin turgor. Euthanasia was performed by CO2 asphyxiation and brains were cryopreserved. Coronal sections obtained at a cryostat microtome were fixed and stained with hematoxylin/eosin (H/E) or nestin.For histological analysis, animals were euthanized by COWe wished to investigate whether DXC may radiosensitize GIC-driven orthotopic tumors that faithfully mimic the growth patterns of clinical tumors. The GIC driven COMI tumor and its histological and growth features closely resembling the original patient’s tumor have been described . Briefly5 COMI serum-free grown GIC were stereotactically injected into the left corpus striatum of NOD/SCID mice. Staining with hematoxylin/eosin of brain tissue sections revealed 56 . DXC administration had to be discontinued at d49 of tumor development due to deep skin ulcers appearing on the back of DXC-treated mice, more frequently in proximity of the injection sites Fig.. SimilarAlbeit the presented results arise from a single experiment, they might indicate likely skin damage complications by DXC that were observed in all mice in the DXC-treated group as compared to none in the vehicle-treated group. No significant difference in overall survival was observed between DXC-treated and control mice under those conditions Fig. g. In theDamage has been occasionally described to different tissues of DXC-treated patients. A challenging case of a 13-year-old adolescent boy who acutely developed ulcerative plaques as well as systemic symptoms after being treated with DXC and isotretinoin for acne conglobata has been reported . After sGastric mucosal toxicity has been reported after oral use of DXC. Gastric mucosal erosions covered with adherent exudate have been repeatedly observed by esophagogastroduodenoscopy in patients assuming DXC orally , 19. MicParenteral administration of DXC may cause varying damages as well. Injectable DXC formulations  are intended for intravenous administration and, if infused intramuscularly, may cause muscle necrosis at the site of injection, more severe with the U.S. formulation .Using a faithful mouse model of human GB where orthotopic tumors were induced by primary GICs, we found no beneficial effect of subcutaneously administered DXC in terms of overall survival to animals treated with whole brain radiotherapy. For radiosensitization, the effect of the radiosensitizing drug has to be present during the DNA repair phase . This phWe have recently shown that adoption of a hyperfractionated irradiation scheme increases the radiosensitizing capacity of ATM inhibitors towards the COMI GIC-driven orthotopic tumor model . The radATM and DNA-PKcs are core factors of the DNA damage response (DDR), a common resistance mechanism in GIC , 25. TheUnlike previous in vitro studies reporting a radiosensitizing capacity of DXC towards breast tumor initiating cells, no beneficial effect was observed on survival of mice bearing orthotopic GIC-driven GB . On the Preliminary results show that in the absence of DXC, hyperfractionation of radiation  conferred significantly improved animal survival as compared to standard 2.5 Gy x 3 fractions. The mechanistic basis for this effect could reside in the inability of very low doses (such as 0.25 Gy) to trigger the DDR resistance mechanism, while creating sufficiently toxic levels of DNA damage to the tumor cells , 30. ThiIn conclusion, our results do not allow extending to the animal setting the radiosensitizing capacity of DXC previously observed towards in vitro cultured tumor initiating cells . Chronic


Anatomical atlases in standard coordinates are necessary for the interpretation and integration of research findings in a common spatial context. However, the two most-used mouse brain atlases, the Franklin-Paxinos (FP) and the common coordinate framework (CCF) from the Allen Institute for Brain Science, have accumulated inconsistencies in anatomical delineations and nomenclature, creating confusion among neuroscientists. To overcome these issues, we adopt here the FP labels into the CCF to merge the labels in the single atlas framework. We use cell type-specific transgenic mice and an MRI atlas to adjust and further segment our labels. Moreover, detailed segmentations are added to the dorsal striatum using cortico-striatal connectivity data. Lastly, we digitize our anatomical labels based on the Allen ontology, create a web-interface for visualization, and provide tools for comprehensive comparisons between the CCF and FP labels. Our open-source labels signify a key step towards a unified mouse brain atlas. Anatomical brain atlases elucidate the anatomical and functional organisation across species but different atlases have conflicting anatomical border and 3D coordinates. The authors integrated two atlases into a unified and highly segmented anatomical labelling system of the mouse brain. Whole brain anatomical atlases provide a spatial framework for examining, interpreting, and comparing experimental data from different studies. For the mouse, the most widely used animal model to understand the mammalian brain, a variety of printed and/or digital atlases exist with varying levels of segmentations in 2D or 3D images acquired from different imaging modalities 11. Among many, the Franklin-Paxinos atlas (FP)6 and the Allen Reference Atlas (ARA)8 are the two most commonly used brain atlases13. Both atlases are largely based on manual delineation by expert neuroanatomists using cytoarchitectonic features based on a variety of staining including Nissl and acetylcholine esterase antibody staining in 2D histological sections.Anatomical delineation of the brain is critical for elucidation of the anatomical and functional organization of the brain across species14. This reference brain marked a significant departure from classical neuroanatomy based on 2D sections and provides an excellent platform for the registration of 3D mouse brain imaging datasets collected from in vivo imaging  and emerging high-resolution whole-brain imaging modalities such as serial two-photon tomography and light sheet fluorescent microscopy18. More importantly, the Allen CCF facilitates the integration and sharing of scientific data from different studies in a common spatial context19. The accompanying anatomical labels have smooth delineation across all 3D planes, which enable easy views of 3D perspective of brain regions.In 2015, the Allen Institute for Brain Sciences released a 3D reference brain with 10 µm isotropic voxel resolution, called the Allen Common Coordinate Framework (Allen CCF)20. To make it worse, the labels in the Allen CCF released in 2017 (CCFv3) also introduced significant changes from its original ARA labels that were based on 2D Nissl stained sections. This has created confusion and misinterpretation of experimental results21. These issues motivated us to create a unified and highly segmented anatomical labeling system in the adult mouse brain based on the Allen CCF. We decided to use the FP labels for our initial anatomical labeling because it represents one of the most popular adult mouse brain atlases with detailed segmentations, and because a huge body of prior research is based on the FP labels18. Here, we adopt the FP labels into the Allen CCF by rigorous alignment using an MRI based atlas and cell type specific transgenic mice marking for distinct anatomical areas22. We also further segment labels where cell types could be distinguished within single anatomically defined regions. The resulting labels create a unique opportunity for comprehensive comparisons between the two most frequently used anatomical labels in a common space. Furthermore, we use topographically distinct cortico-striatal projection patterns to add segmentations to the dorsal striatum, which is unsegmented in the existing atlases.Unfortunately, significant discrepancies exist between the anatomical labels on the ARA and the FP labels. For example, these two atlases often have discordant anatomical borders and 3D coordinates as well as different names for the same structures14. Digitized labels combined with image registration can serve as a powerful tool to automatically quantify signal of interest across whole brain regions in a reference brain24. To facilitate its usage, our digital map data is freely available for viewing and downloading from our web-based atlas implementation at http://kimlab.io/brain-map/atlas/.Lastly, we digitize the anatomical labels based on the Allen ontology to facilitate integration of labels as a neuroinformatics toolWe used the FP labels drawn in 2D histological sections for our initial template segmentation. We first imported vector drawings of FP labels into the Allen CCF Fig. . Automat26. We first registered the MRI reference brain to the Allen CCF and transformed the MRI labels to fit in the CCF  atlas with the FP labels in most brain regionsCCF Fig. . AlthougCCF Fig. . The MRICCF Fig. .6. We utilized a similar approach using 14 different transgenic mouse lines that mark specific neuronal subtypes27 . We chose marker brains from different neuropeptides, neurotransmitters, transcription factors, G protein coupled receptors, calcium binding proteins, and a growth factor that highlight anatomical boundaries otherwise often not visible in the Allen CCF tissue autofluorescent background -Cre crossed with Cre dependent reporter mice expressing nuclear GFP (H2B-GFP) were useful for delineating structures in the thalamus, midbrain, and hindbrain -Cre crossed with H2B-GFP reporter mice have been useful for amygdala, hypothalamus, olfactory regions, and subcortical regions, such as the bed nucleus of the stria terminalis (BST) -Cre crossed with Cre dependent reporter mice expressing tdTomato (Ai14) highlighted selected brain regions including dorsal endopiriform nucleus (DEn), CA2 in the hippocampus, amygdala, and entorhinal regions  delineation. We used Ctgf-Cre for L6b, Ntsr1-Cre for L6, Rbp4-Cre for L5, and Cux2-Cre for L2/3  on 2D sections has been used to guide detailed delineation in anatomical regionsain Fig. 6,29. Somons Fig. 31. Lastl34. We have used this approach to further segment structures in the thalamus, hypothalamus, and hindbrain. Using PV-Cre and Cux2-Cre marker brains, the ventral posteromedial nucleus of the thalamus (VPM) was further segmented into dorsal and ventral parts   Fig. . We obsely) Fig. . We furt7. Although highly useful, this approach cannot be applied to the caudate putamen  without such features. Thus, the CP remains unsegmented in FP, ARA, and CCFv3 atlases despite its prominent size and heterogeneous functions in the brain. Recent studies have shown that different parts of the CP receive topographically distinct cortical inputs37. To confirm previous observations, we downloaded 129 datasets with anterograde tracing using C57bl/6 mice covering the entire isocortical area from the Allen connectivity dataset24 and registered all of these brains to the Allen CCF , rostral , intermediate , caudal , and caudal extreme . The CP at each level was further subdivided by community and domain as sub-segmentations as originally proposed by Hintiryan et al.35. For example, the CPi, dm, dl represents the dorsolateral (dl) domain within the dorsomedial (dm) community in an intermediate level CP  in FP labels. Boundaries of the subdivisions were delineated by observing cell density differences between each division with SST-Cre expression  in the FP labels matches the anterior cingulate area, dorsal part (ACAd) in the CCFv3 labels  neurons are heavily enriched in ventral ZI15. Our FP-based labels segmented PV enriched ventral ZI separately from dorsal ZI while both the ARA and the CCFv3 labels have only one segmentation for ZI , the substantia innominata basal (SIB), and the extended amygdala (EA). In our marker brains, the VP and the EA are marked by cholinergic and somatostatin neurons, respectively  in the CCFv3 labels (but not in the ARA labels), which does not match with our border between the hypothalamus and the basal forebrain  to visualize and share our anatomical labels. The web visualization includes easy identification of anatomical labels in the background CCF. We recommend using Chrome as the browser to navigate our website. All vector drawing files, digitized labels, and associated files are freely available for download  or histological staining25. For example, Nissl stained 2D sections utilize rich cytoarchitectural information for neuroanatomists to finely delineate anatomical regions7. In contrast, atlases based on neuroimaging data provide a 3D perspective of the brain with relatively lower resolution and simpler segmentations compared to histology based atlases18. These atlases exist either in printed or online form . However, independently generated atlases with different nomenclature and boundaries can make it difficult to integrate data from different studies19. Significant effort has been made to standardize a rodent brain atlas as a key neuroinformatics tool to facilitate data exchange and to enhance reproducibility between different studies49. For example, the International Neuroinformatics Coordination Facility established digital atlas infrastructure for a common spatial framework such as the scalable brain atlas under FAIR (Findable Accessible Interoperable Reproducible) principles19. Recently, the Allen CCF, generated from iterative averaging of over 1000 different mouse brain samples, provides the highest resolution 3D digital atlas platform14. There has been significant encouragement by funding agencies  to use the CCF as a common anatomical framework for functional and anatomical studies to facilitate seamless exchange between results from different studies13. To further support this trend, computational tools are being developed to integrate individual datasets  in the standard atlas framework54. While the CCF provides an ideal atlas platform with high-resolution 3D images, its associated anatomical labels CCFv3 released in 2017 have been controversial due to fewer fine segmentations and significant changes in their anatomical borders from the original version. Moreover, inconsistencies in borders and nomenclature compared to the widely used FP labels make it difficult to compare findings from studies that use different atlases. Discrepancies between atlases can arise from variations in anatomical delineations by different neuroanatomists, different sample preparation, strains, and normal phenotypic variation. For instance, the ARA was based on a fresh-frozen brain while the FP atlas was based on a brain fixed with 4% paraformaldehyde7. While co-registration of Nissl stained sections from two different atlases may help to reduce the differences, different z sampling rates  and challenges caused by registering images from different signal content  make it difficult to merge two independent atlases in one spatial context. We resolved the issues by combining manual alignments and co-registration of MRI and marker brains.A reference atlas serves a critical role in understanding the spatial context of the brain22. These strategies enabled us to establish highly detailed FP based labels in the Allen CCF, which provided a unique opportunity to compare two atlases in the same spatial context. Our comparison revealed substantial discrepancies in anatomical demarcation of same brain regions. Our systematic comparison between the two atlases marks an important first step towards unified anatomical labels in a common atlas platform. As neuroscience research becomes increasingly collaborative, it is essential to have consistency in anatomical labels to specify regions of interest. By integrating FP based labels in the CCF, our labels can be used to facilitate the comparison of anatomical interpretations from past and future studies regardless of the atlas used.Our strategy was to establish the FP based anatomical labels in the Allen CCF. We used a series of steps to rigorously align the FP labels in the Allen CCF. We further generated finer segmentations based on marker brains that highlight specific anatomical regions otherwise not visible in the background57, similar approaches can be used to segment other brain regions with distinct projection patterns. Moreover, since this anatomical connectivity is related to functional interactions between neural circuitry, connectivity based anatomical segmentation can provide a unique opportunity to integrate functional circuits in anatomical maps.We also used cortico-striatal long-range connectivity to finely segment the dorsal striatum. Projectome-based atlasing provides an alternative way to segment brain regions that do not have distinct cytoarchitectonic features. Since brain-wide projectome data are becoming increasingly available in open source platforms41 as a neuroinformatics tool. Thus, our labels can be easily integrated into data processing pipelines to automatically quantify target signals throughout anatomical regions in the whole brain. We previously built such a pipeline to quantitatively map neural activity based on c-Fos induction, GABAergic cell subtypes, and long-range neural connectivity56. Moreover, mapping pipelines are increasingly available for high-resolution 3D image data and histological sections59. With image registration to the Allen CCF, our digitized labels can serve as an invaluable neuroinformatics tool to examine target signals in the FP based labels as well as the built-in CCFv3 labels.We digitized anatomical labels from vector drawings and organized our labels hierarchically according to the Allen ontology60. Resolving discrepancies observed between atlases is of growing importance to the future of neuroscience due to constantly changing parameters of neuroimaging and spatially resolved functional characterization. Rapid progress in large scale cell-type and connectivity mapping as well as in vivo neural recording will provide valuable information to refine anatomical borders, especially in areas with less clear cytoarchitectonic features, towards an increasingly rich, unified atlas of the mouse brain. The presented segmentations contain potential errors in boundaries of fine structures due to limitations of currently available marker brains and connectivity datasets. On-going and future efforts to generate large scale cell-type and connectivity database will no doubt facilitate the refining and even further segmentation of anatomical labels57. Moreover, potential sex differences in marker expression may exist, and need to be considered in future work. To facilitate such work, we are making all the data (including vector drawing for delineations) freely available to visualize and download via public data repository and our website. In addition, atlas template brains based on different imaging modalities  co-registered to the Allen CCF space will facilitate incorporation of neuroimaging data from the different imaging modalities. Such development can bring us closer to the ideal of allowing data from different imaging modalities to be seamlessly registered into a common atlas space in which detailed anatomical segmentations are available. We envision that similar approaches can be taken to integrate independently generated atlases within animal species including humans.Moving forward, by integrating the two most popular brain segmentations in the same 3D anatomical context, our atlas will help to build unified anatomical labels for the mouse brain15. Because we observed highly stereotypical expression in each marker brain, we used one representative brain per each marker line for our anatomical work. The complete list of the maker brain with their source is listed in Supplementary Data We have complied with all relevant ethical regulations for animal testing and research. All animal work has been approved by the Institutional Animal Care and Use Committee of Penn State University College of Medicine. We used the following transgenic mice to fluorescently label specific cell types (marker brains). For Cre drivers, we used OT-Cre (Jax: 024234), Avptm-Cre (Jax: 023530), and OTR-Cre . For Cre dependent reporter mice, we used Ai14 (Jax:007908). We crossed cell type specific Cre driver mice with Ai14 to create maker brains. We used both male and female mice at ~2–3 months old. All mice were group housed in 12/12 light/dark cycle  with access to food and water ad libitum. Other marker brains were downloaded from either publically available BICCN datasets or previously published databases as specified in Supplementary Data 115. We used custom-built algorithms to reconstruct the whole brain. Our imaged brains and downloaded marker brains were registered to the Allen CCF based on mutual information using the open source program (Elastix)61 based on affine and bspline parameters  pixel spacing. Previously, we assessed the warping accuracy using 13 unique 3D landmarks acquired from Waxholm space10 in 6 different mouse brains after warping each dataset onto the reference brain23. Warping accuracy was 65.0 ± 39.9 μm after 3D registration23, supporting the accuracy of our registration results.Transgenic mice were perfused using cardiac perfusion with 0.1 M phosphate buffer (PB) followed by 4% paraformaldehyde (PFA). Brains were post-fixed with 4% PFA at 4 °C overnight and transferred to 0.05 M PB until imaging. The fixed brain was embedded in oxidized 4% agarose and cross linked by 0.05 M sodium borohydride at 4 °C overnight. Oxidized 4% agarose was made by stirring 7 g agarose and 0.735 g sodium periodate in 350 mL of 0.05 M PB for 2.5 h at room temperature in a light-protected beaker under the fume hood. The agarose was filtered three times with distilled water followed by one wash with 0.05 M PB. The agarose was re-suspended in 175 mL of 0.05 M PB and was stored in 4 °C for brain embedding. In all, 0.05 M sodium borate buffer solution was made with 19 g Borax and 3 g of boric acid in 1 L of distilled water. With 100 mL of this sodium borate buffer solution, the cross-linking solution sodium borohydride was made by adding 0.2 g of sodium borohydrate at room temperature in a light-protected beaker under the fume hood. This can be stored at room temperature for 7 days. For brain embedding, oxidized agarose was heated in the microwave and poured into a custom-built mold with centered brain sample when the agarose cooled down to 65 °C. After solidification at room temperature, the block was trimmed and placed in 50 mL Falcon tube with sodium borhydride for cross-linking at 4 °C overnight. The block was glued to a glass slide with magnetic bars underneath, which was placed in a special chamber with 900 mL of 0.05 M PB for STPT imaging. We used Tissuecyte 1000 (Tissuevision) to perform serial two-photon tomography imaging. We used a 970 nm wavelength laser and acquired a series of images  at 1 μm X-Y resolution in every 50 μm z section6. We also used the 4th version to incorporate the latest updated labels. We used a vector drawing tool (Adobe Illustrator) for our labeling work. We downloaded the Allen CCF and associated labels from the Allen Institute for Brain Sciences API (http://help.brain-map.org/display/mousebrain/API), and generated coronal slices (10 μm isotropic) using Image-Stacks-Reslices in FIJI (NIH)62. This produced 1320 Z coronal slices. Then, we selected one coronal slice in every 10 slices from Z95 to Z1315 using Image-Stacks-Tools-Make Substack in FIJI, generating 123 coronal images with 100 μm z spacing. We chose 100 μm z spacing to facilitate comparisons between the ARA and the CCFv3 because the ARA was created in coronal sections evenly spaced at 100 μm intervals38. We identified matching z planes between the FP atlas and the CCF using distinct anatomical landmarks . Anterior-posterior (A/P) Bregma coordinates of z sections were primarily based on ARA7 while cross-referencing to the FP atlas6. To aid our label alignment in 3D, we downloaded MRI labels from different brain regions from a publically available database (https://imaging.org.au/AMBMC/AMBMC). We combined labels from different brain regions to reconstruct the MRI labels using FIJI (NIH)62. Then, we registered the MRI atlas with the FP based labels to the CCF using Elastix. The MRI labels were particularly useful to align boundaries in cortical areas in 3D. We loaded cell type specific labeling from different transgenic mice and MRI labels as separate layers on the Illustrator, and used the information to further adjust anatomical delineations. To accommodate the FP labels (mostly 120 μm z spacing) in 100 μm z spacing, we used the 5th section of every 6 FP labels twice in the initial alignment and used the MRI atlas and marker brains to further modify the labels across the 3D plane. Once the FP labels were imported in the matching plane of the CCF on Adobe Illustrator, we used linear translation to stretch the FP labels to fit the CCF roughly. Then, we performed finer alignment manually based on specific landmarks of the brain with distinct contrast . We used shade from levels of background autofluorescence and texture from fine myelinated tracks in the Allen CCF to determine anatomical borders. The shade feature was useful for delineating subregions in the isocortex, the hippocampus, the hindbrain, and the cerebellum; the texture feature was useful in the ventral striatum and the medulla. One or two slides before and after in each 2D section were used to ensure the contiguity of 3D labels. In selected areas , boundaries were removed entirely and re-drawn based on key features of the CCF and distinct cell populations. In caudal areas, we often used 2–3 different FP planes to create hybrid labels to fit the CCF background as well as cell type specific features of the selected plane. The primary alignment of each label was performed by U.C., followed by a second and independent inspection by Y.K.We originally obtained vector drawings of Nissl 2D section from Paxinos and Franklin’s the Mouse Brain in Stereotaxic Coordinates, 3rd editionhttp://help.brain-map.org/display/mouseconnectivity/API). All downloaded datasets were registered to our modified CCF with 100 um z spacing using Elastix. After image registration, we removed the autofluorescent background of each sample using binary thresholding (FIJI). We clustered projection datasets into 10 groups based on their cortical injection sites and averaged projection signals in the same group using FIJI. Then, we imported the projection data into Illustrator as separate layers and used them to further segment the CP. To import segmentation dataset from Mouse Connectome Project , we used the cortico-striatal map  to add different projection data to the ARA. This website provides a way to overlay up to 10 different projection data sets in 12 different anterior/posterior positions. A set of cortico-striatal projection to distinct CP subregions was selected based on clustering data from Hintiryan et al.35, and was overlaid in the Allen CCF background for segmentations using Adobe Illustrator. Five different anterior/poster CP levels were determined by overlap, separation, and lack of projection from specific cortico-striatal projections36. CPi,dm,dl and CPi,dm,d were combined with CPi,dm,dt due to a high degree of in their projection patterns35.We downloaded 129 datasets with anterograde virus injection in different cortical areas from C57bl/6 mouse line using Allen connectivity database  to calculate overlap between atlases. The Dice Similarity Coefficient (DSC) was computed for each individual anatomical region including areas in different hierarchical ontology orders across atlases. DSCs were derived from individual absolute volumes (a region in Atlas A and a matched region in Atlas B) as well as the overlap of these volumes as Boolean data using overlap as true positive , false positive , and false negative  between each pair of atlases as listed in Supplementary Data Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Transparent Peer Review FileReporting Summary


Ecological speciation can sometimes rapidly generate reproductively isolated populations coexisting in sympatry, but the origin of genetic variation permitting this is rarely known. We previously explored the genomics of very recent ecological speciation into lake and stream ecotypes in stickleback from Lake Constance. Here, we reconstruct the origin of alleles underlying ecological speciation by combining demographic modelling on genome-wide single nucleotide polymorphisms, phenotypic data and mitochondrial sequence data in the wider European biogeographical context. We find that parallel differentiation between lake and stream ecotypes across replicate lake-stream ecotones resulted from recent secondary contact and admixture between old East and West European lineages. Unexpectedly, West European alleles that introgressed across the hybrid zone at the western end of the lake, were recruited to genomic islands of differentiation between ecotypes at the eastern end of the lake. Our results highlight an overlooked outcome of secondary contact: ecological speciation facilitated by admixture variation. Ecological speciation can proceed rapidly, but the origin of genetic variation facilitating it has remained elusive. Here, the authors show that secondary contact and introgression between deeply diverged lineages of stickleback fish facilitated rapid ecological speciation into lake and stream ecotypes in Lake Constance. However, the origin of genetic variants underlying rapid speciation and reproductive isolation has remained unknown in all but a few cases8, e.g., whether loci that contribute to reducing gene flow between populations are derived from de novo mutation, from standing genetic variation or from admixture variation acquired through introgression between divergent lineages. Understanding the process of speciation and its constraints requires knowledge on the origin of alleles under ecological, sexual or incompatibility selection. In turn, understanding the origin of alleles requires reconstructing the history of populations undergoing speciation.Contemporary speciation studies have shown that speciation can sometimes be surprisingly fast even in the face of gene flow, allowing for sympatric divergence or persistence of incipient species. Many of these cases involve divergent natural selection or habitat-dependent sexual selectionGasterosteus aculeatus complex) of Lake Constance, Central Europe3. Lake- and stream-adapted ecotypes differ in predator defense and feeding morphology, ecology, nuptial coloration, migration behavior, and life history: lake stickleback grow larger than stream stickleback12, are covered with larger or more lateral bony plates and possess longer spines as protection from predators14, have longer gill rakers and jaws adapted to feeding on zooplankton instead of benthic invertebrates13, migrate to lower reaches of streams to breed in contrast to resident stream stickleback3 and start breeding 1 year later and die older than stream stickleback12. In a South-Eastern tributary of Lake Constance, both ecotypes breed in sympatry and they maintain phenotypic10 and genomic differentiation despite ongoing gene flow3. Sympatric breeding of stickleback ecotypes is very rare and occurs here at a surprisingly early stage of speciation, given that historical records document the presence of stickleback in the Lake Constance catchment for the past 150 years only, and their prior absence from the basin18. The genomic architecture of this case is characterized by an undifferentiated genomic background interspersed by strong differentiation across multiple chromosomes, especially in low recombination regions and inversions enriched with quantitative trait loci (QTL) for divergent traits19. The lack of reduced diversity within genomic islands and of genomic background differentiation led us to hypothesize that ecotypes likely diverged in situ in Lake Constance, either from selection on standing genetic variation or admixture variation. From Lake Constance data alone, however, we were unable to distinguish these alternative origins of alleles.We recently documented a case of contemporary ecological speciation in threespine stickleback  primary divergence in situ, in which lake and stream ecotypes have recently diverged from standing genetic variation in a single lineage that colonized Lake Constance; (2) ecological vicariance (outlined above); (3) secondary contact, in which ecotypes correspond to West and East European lineages that have diverged in allopatry, recently met and sorted between lake and stream habitats with exchange of genes at the lake-stream boundaries; and (4) hybrid origin, in which one of the ecotypes has recently evolved through hybridization between divergent West and East European lineages or sorting of admixture variation following introgression from one divergent lineage into the other lineage.Here, we re-evaluate the population history of Lake Constance stickleback and investigate the origin of genetic variants underlying contemporary ecological speciation and early persistence in sympatry. We place all previously studied lake and stream populations from the Lake Constance catchment in a wider European phylogeographic context, using new and published genome-wide single nucleotide polymorphism (SNP), mitochondrial, microsatellite and plate morph data25 and uncover an unexpected outcome of secondary contact: repeated ecological speciation beyond the contact zone. Our results imply that caution should be taken when inferring modes and times of speciation from population genome sequence data in the absence of a thoroughly sampled phylogeographic context.Our analyses reveal admixture variation as genetic source of ecotype differentiation and thus contemporary ecological speciation, derived from hybridization between two divergent lineages from at least two European watersheds. We find that stream ecotypes in Lake Constance show a gradient of admixture between divergent European stickleback lineages: near the zone of secondary contact, stream ecotypes show a 50:50 hybrid origin, while far from the zone of contact, novel stream ecotypes evolved in situ from predominantly one genomic background, aided by sorting of introgressed alleles. Our analyses provide evidence for the hypothesis that admixture variation can be an important facilitator of rapid ecological speciation and adaptive radiation26  sequencing confirm the position of Lake Constance stickleback among the central European lineages  haplotypes in the Lake Constance catchment Fig. , all of 26 Fig. . All fiv 29 Fig. . Phylogeges Fig. . Among ts31 Fig. . In Lakes31 Fig. . Thus, mImportantly, contributions are not equally distributed among populations and ecotypes in the Lake Constance catchment Fig. . Lake anSbfI-RAD sequencing clusters the stream population NID with West European lineages rather than with East European lineages, with very high bootstrap support . When Constance and East European populations are treated as sister lineages in a phylogenetic tree, all Lake Constance populations show a significant excess of derived allele sharing with West European populations from the Rhine and the upper Rhone , Fig. , AGS1, F32. We compared models on three hierarchical levels of complexity  and stream  populations, and an NsiI-RAD dataset14 including Constance lake (ROM) and stream  populations (see Methods).We compared different modes of stickleback ecotype divergence in Lake Constance using a coalescent demographic modeling framework, and taking advantage of the West and East European sister lineages as discussed above. We tested the fit of multiple neutral demographic models to the observed site-frequency spectra (SFS) computed from sites in high recombination rate regions in order to minimize effects of selection on the SFSity Fig. . This ality Fig.  and minoity Fig.  contribuity Fig. . We optiWe infer West and East European lineages to have split ~3665 : 3636–4877) generations ago, while the upper Rhone and Rhine populations split 1710 1710–2239) generations ago according to our best 3-population model  generations for S2, 1276 (1015–1446) generations for NID, 2819 (1626–2819) generations for BOH and 1065 (888–1232) generations for GRA Fig. . Effecti473 Fig. , while e473 Fig. .In summary, demographic modeling suggests that West and East European lineages diverged ~4000 generations ago, translating to ~8000 years assuming 2 years average age of reproduction as generation time. After several thousand generations of isolation, these West and East European lineages met in the Lake Constance system, perhaps at lake-stream boundaries North and West of the lake. Stream stickleback there are of hybrid origin, having received most of their genome from a West or East European lineage depending on the population. Introgression across the secondary contact zone contributed some West European alleles to the lake ecotype and also to stream ecotypes in South-Eastern tributaries.Gasterosteus aculeatus), while tributaries North and West of the lake are of hybrid origin with the ‘classical freshwater’-type, low-plated stickleback from West European streams (Gasterosteus gymnurus). Even though stream ecotypes differ in admixture proportions and plate morph distributions  and one with parapatric reproduction (S2). These genomic islands are thus candidate loci for reproductive isolation or ecological adaptation to either the lake or the stream habitat due to their persistence in sympatry, parallel allele frequency shifts, habitat associations and their enrichment with QTL controlling adaptive traits divergent between ecotypes3. Before identifying the origin of alleles in genomic islands, we aim to confirm that these genomic islands represent regions that resist gene flow between ecotypes due to either divergent selection or reproductive isolation, rather than representing regions that diverged due to background selection35.We seek the answer to this question by identifying the origin of alleles in genomic islands of differentiation between lake and stream ecotypes breeding in sympatry. In a previous studyFST) across the genome will arise as by-product of background selection in the absence of gene flow41. Background selection removes proportionately more linked variation in low recombination regions, leading to negative correlations of differentiation (FST) with recombination rate, absolute divergence (dXY) and diversity levels (π), and correlated genome-wide differentiation across populations and taxa41. Allopatric stickleback populations from different European watersheds lack gene flow, but we find no correlation of FST with recombination rate or absolute divergence, even though differentiation landscapes are weakly correlated and FST and diversity show negative correlations in some populations  between allopatric European stickleback populations, even in the absence of gene flow.A rich literature has shown that heterogeneous differentiation  = 0.50, FST (S1 vs. AGS1) = 0.46, FST (S2 vs. AGS1) = 0.48, Fig. FST (L1 vs. PLS1) = 0.31, FST (L1 vs. PLS1) = 0.30, FST (S2 vs. PLS1) = 0.33), consistent with a largely East European genomic background of both ecotypes. In genomic islands of ecotype differentiation, however, stream stickleback are significantly less differentiated from West European stickleback  = 0.38, two-sided permutation test p < 0.01, FST  = 0.32, p < 0.01) than genome-wide, in contrast to the lake ecotype for which both genomic islands and genomic background show similar levels of differentiation from West European stickleback  = 0.55, Fig. If admixture variation facilitated ecotype divergence in South-Eastern tributaries of Lake Constance, we expect differential sorting of West- and East-derived alleles in genomic islands of differentiation between lake and stream ecotypes. We thus compared genome-wide differentiation and differentiation in genomic islands between Constance lake/stream stickleback and West European populations Fig. , 11. GenHere we have demonstrated that secondary contact between divergent lineages and the re-assortment of introgressed alleles into ecotypes underlie recent ecological speciation across lake-stream habitat boundaries in Lake Constance. Our analysis reveals an unexpected outcome of secondary contact between old allopatric lineages: rapid in situ ecological speciation outside the secondary contact zone fueled by introgression of admixture variation beyond the hybrid zone. It also explains and reconciles contrasting conclusions of previous studies regarding the origin, age and mode of ecotype divergence in Lake Constance. Finally, our results raise interesting questions about the evolutionary potential arising from recombining old alleles into new combinations and how distinctive the new combinations might be from parental combinations.14. Our analyses of mitochondrial, microsatellite, and genome-wide SNP data in a larger phylogeographic context using an array of different methods such as phylogenetic reconstruction, demographic modeling, D-statistics, hybrid index, and cluster analysis clearly reject an origin from a single lineage and identifie more than one source of origin. The colonization of the Lake Constance catchment by multiple lineages is consistent with historical accounts reporting introductions of stickleback into streams of North-Western Lake Constance between 1920 and 1940, in which the introduction of both fully plated and low-plated individuals was documented17. This is additional to historical accounts of introductions from unknown sources into streams South of Lake Constance in the late 19th century16. Consistent with an earlier mitochondrial phylogeography22 and the biogeographic context of a highly plated freshwater phenotype42, our analyses based on genome-wide SNPs identified East Europe as the major source of origin. The same lineage is also found in the nearby upper Danube drainage12 and it is one of the central European lineages28 as opposed to the highly divergent Black Sea or Mediterranean lineages. Such a central European rather than Black Sea origin is also in agreement with the historical ichthyologic record, which documents the absence of stickleback from the upper Danube until the early 20th century18 and thus rather a more recent colonization of the upper Danube and the Lake Constance basin from Northern catchments aided by human introductions instead of a natural colonization from the Black Sea.Analyzing the incipient radiation of Lake Constance stickleback in a European-wide biogeographic and phylogeographic context revealed contributions of at least two old central European lineages of freshwater stickleback that had evolved in isolation for several thousand generations before coming into secondary contact. Secondary contact has not been invoked previously in this system and explains the old, early Holocene  divergence times that had been estimated between Lake Constance ecotypes by applying a primary divergence model representing an ecological vicariance scenario17 and ecotype divergence time estimates hundreds of generations ago. Our parameter estimates are still associated with high uncertainty, as reflected by their wide confidence intervals in each model and variation for the same parameters between different datasets , outcomes varied from persistence with leaky reproductive isolation in steep tension zones48, through classical ‘mosaic hybrid zone’ patterns where each species occurs in habitat patches rich in the habitats they are best adapted to49, to situations resembling a hybrid swarm in which allelic combinations are sorted between parental species habitats on very small spatial scale, reminiscent of ecological speciation from a hybrid population50. In Lake Constance stickleback, we document a similar continuum of outcomes where secondary contact and environmental adaptation interact in diverse ways. Outcomes range from partial collapse at the zone of contact of the old lineages at habitat boundaries  to the assembly of alleles into new combinations in islands of genomic differentiation (S1 and S2 stream populations) that persist in sympatrically breeding ecotypes (S1 stream population versus lake ecotype).Secondary contact between old lineages can result in a complex mosaic of evolutionary outcomes. In classical work on contact zones between the West and East European species of firebelly toads  of one of the two old lineages. Our finding that genomic islands of sympatric ecotype divergence are derived from introgression of West European alleles suggests that introgression has likely facilitated or promoted early sympatric coexistence of ecotypes. The new stream ecotype populations carry a new combination of West and East European-derived alleles distinct from both original East and West European lineages 22. Accessions are given in Supplementary Table 22 and from populations across Europe58, resulting in a combined dataset of 254 individuals from the Lake Constance catchment, each sequenced for 253 overlapping base pairs of the mitochondrial control region 21, featuring lake and stream populations from Lake Constance, populations from upper Danube, the upper Rhone catchment (Lake Geneva) and the River Rhine  with 10 replicates for each K. We identified the best number of genetic clusters supported by the data using the Evanno method63.We combined data for four microsatellite markers in common between two previous studies64, in which individuals were scored as low, partially or fully plated morph, based on the presence of 0–3 lateral plates posterior the pelvic girdle (low plated), more than three later plates but with a gap of at least two plates to the caudal peduncle  or the presence of a continuous series of lateral plates up to the caudal peduncle (fully plated)21. We embedded this lateral plate morph data in the context of historical phenotype distributions65.We combined lateral plate morph data from lake and stream populations around Lake Constance, from the upper Rhone drainage (Lake Geneva), the Rhine and the upper Danube66 restriction-site-associated (RAD) DNA sequence data for East (Vistula) and West  European stickleback populations  data due to non-overlapping restriction sites. The NsiI dataset includes three tributary populations from North and West of Lake Constance and adjacent lake sites, including two sites overlapping with the SbfI dataset , three individuals each for European lineages and two each for North American outgroups  model with optimized substitution rates and a gamma model of rate heterogeneity. We further applied an ascertainment bias correction to account for the fact that we only used polymorphic SNP positions. Significance was assessed using 100 bootstrap replicates.For phylogenetic analysis, we used subsets of the ups Fig. , by seleSbfI subset containing all individuals from populations L1, L2, ROM, S1, S2, NID, PLS1, AGS1, and FRS4 . We reduced the dataset to 3030 biallelic SNPs with a minor allele frequency >5%, <5% missing genotypes and no linkage disequilibrium exceeding r2 = 0.95 within 100 kbp windows. To avoid effects of allelic dropout due to a high proportion of PCR duplicates in some populations , we randomly sampled one allele at each genotype to mimic the same level of allelic dropout for each sample, using a custom bash script 3. We then ran Admixture v1.3.074 for haploid data with otherwise default settings assuming 2–10 clusters (K), followed by cross-validation to identify the number of clusters with the lowest cross-validation error.To infer population structuring, we used another SbfI dataset to test for excess allele sharing between Lake Constance stickleback populations and European sister lineages, resulting in a dataset of 23,277 SNPs. We added the alleles of three different outgroups, Japan Sea stickleback Gasterosteus nipponicus, Black-spotted stickleback G. wheatlandi and Ninespine stickleback Pungitius pungitius to this dataset, inferred from previously published whole-genome data75 . We aligned all three outgroups to the improved threespine stickleback reference assembly68 using Stampy v1.0.2276 with default settings except for adding the option ‘--substitutionrate’ to add the expected divergence from the reference genome as estimated by Yoshida et al.75. We used Freebayes v1.1.0-3-g961e5f3-dirty77 to call outgroup genotypes for the 23,277 SNP positions in our SbfI dataset for genotypes with minimal depth 6 and phred-scaled site and genotype quality 20, then merged variants with the SbfI dataset and removed any multiallelic sites or indels using Bcftools. We then computed the D-statistic using Admixtools v4.178 for different quartets featuring each of the three outgroup taxa .We obtained rough estimates for admixture proportions of Lake Constance populations derived from West and East European lineages by computing the hybrid index with the R-package Sbf1 and Nsi1 datasets into multidimensional site-frequency spectra (SFS). The Sbf1 data subsets included one lake population (L2), one South-Eastern tributary stream (S2) population, one Western tributary stream (NID) population, the East European lineage (PLS1), and two West European lineages from the Rhine (AGS1) and the upper Rhone (FRS4), depending on the hierarchical level tested  and three Northern and Western tributary stream populations , with NID overlapping between NsiI and SbfI datasets. We excluded individuals with a strong allele imbalance in heterozygous genotypes, i.e., a minor/major read number imbalance strongly deviating from 1:1 . In each population, we identified loci out of Hardy–Weinberg equilibrium using Vcftools and discarded sites with evidence of heterozygote excess plus/minus a buffer of 100 bp left and right of each site from the dataset in order to eliminate putative duplicated regions in the genome and removed the sum of all sites. Then, we removed sites in low recombination rate regions of the genome (<1.5 cM Mbp−1) to avoid biases due to linked selection32. We estimated local recombination rates based on the FTC cross recombination map68 by cubic spline smoothing of the genetic on the physical map for each chromosome with a spline parameter of 0.7 and calculating first derivatives (= recombination rates) for positions of interest3. Next, we randomly subsampled a fixed number of genotypes from each population  to a dataset without missing data, discarding sites with too few genotypes, with a custom python script . We further reduced the SNP portion of the datasets to unlinked SNPs by removing sites with r2 > 0.95 within 200 bp distance. After this, we added again N monomorphic sites proportional to the retained number of SNPs post linkage-pruning by randomizing the order of monomorphic sites and retaining the first N sites. Finally, we converted the resulting linkage-pruned datasets without missing data into multidimensional SFS using a custom python script , folded them with another custom python script  and converted them to joint 2D-SFS with a custom R script . We also generated 100 non-parametric block-bootstrap replicates of the observed multidimensional SFS by randomly resampling blocks of 10,000 sequenced sites adjacent to each other on a chromosome to the observed number of such blocks as implemented in vcf2sfs.py.For demographic modeling, we converted subsets of both 81 and a hierarchical modeling approach to reconstruct the demographic history of Lake Constance stickleback by comparing the fit of different demographic models to observed SFS and to estimate parameters for the best-fitting models. First, we optimized three population models , Rhine (AGS1), and upper Rhone (FRS4). For each model, we maximized the likelihood from 100 random starting parameter combinations and minimal 10 to maximal 50 ECM cycles with a stopping criterion of 0.00181. The expected minor allele (folded) SFS for each model and parameter combination was approximated with 100,000 coalescent simulations. We used a mutation rate of 1.7e−8 in all simulations82. The likelihood and parameter estimates of each model were obtained from the run with the highest likelihood among 100 optimizations. To identify the best-fitting model, the likelihoods of models were compared using the Akaike information criterion (AIC)81. We estimated confidence intervals for parameters of the best model from non-parametric block-bootstrap replicates of the observed 3D-SFS: we performed 10 parameter optimizations with each bootstrap replicate, starting from the best parameters inferred from the observed data, and the parameter estimates from the run with the highest likelihood for each bootstrap sample was used to compute 95% confidence intervals using empirical percentiles. Furthermore, we assessed whether other models than the best model explained the data similarly well or not by computing likelihood distributions from 100 SFS simulated under each model given the maximum-likelihood parameters inferred from the observed data and comparing the overlap of these likelihood distributions  and two South-Eastern tributary populations , one East European (PLS1) and two West European populations . We filtered the SbfI-data subset for each pairwise comparison to include only biallelic SNPs with at least three sequenced genotypes per population using Bcftools. We estimated pairwise F-statistics for each SNP by performing locus-by-locus AMOVAs in Arlequin v3.5.23. SNP-level F-statistics were averaged over non-overlapping windows containing at least 2500 sequenced bases without splitting RAD loci or over sliding windows of 1 Mbp size with 200 kbp step size. Non-overlapping windows were on average 344 kb (73–1564 kb) wide. We computed an average recombination rate for each window by computing the mean over 10 recombination rate estimates at equally spaced positions across each non-overlapping window. We computed weighted average F-statistics by averaging variance components83 and calculating the ratio of averages84. We used permutation tests to assess, whether differentiation in windows overlapping with genomic islands of differentiation persisting among sympatric populations identified in an earlier study3 differed from the genome-wide distribution of differentiation. For this, we permuted the position of genomic islands 10,000 times across the genome, generated a null distribution for the mean and computed empirical quantiles for the observed mean differentiation in genomic islands.We used a subset of the 3 rather represent genomic regions resisting gene flow between ecotypes or regions that diverged due to background selection in the absence of gene flow, using a similar subset of the Sbf1 dataset as above with one South European (SOR) and one West European population (CHA) added, as well as the full NsiI dataset. In non-overlapping windows containing at least 2500 SbfI-RAD sequenced sites, we computed differentiation (weighted average FST) for all pairwise comparisons as outlined above. In addition, we estimated nucleotide diversity (π) within population and pairwise absolute divergence (dXY) for the same non-overlapping windows. For the latter two statistics, we subsampled SbfI- or NsiI-data subsets of population pairs to N genotypes per population corresponding to 75% of a population’s individuals in order to get a dataset without missing data, discarding sites with too few genotypes, with the custom script sampleKgenotypesPerPop.py. We summarized the subsampled VCF files into 2D-SFS for each non-overlapping window with the custom script vcf2sfs.py and computed both π and dXY with custom scripts , respectively. Pearson’s correlation coefficient (r) between statistics was computed in R for windows without missing values, for pairwise statistics as FST or dXY using statistics from non-overlapping population pairs to avoid autocorrelation. All data analysis was performed on the servers of the Genetic Diversity Centre (GDC), the Ubelix computer cluster, University of Bern and the Euler cluster, ETH Zurich, Switzerland.We formally tested whether parallel genomic islands of differentiation between Lake Constance ecotypes identified previouslyFurther information on research design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data