Quercus suber) is native to southwest Europe and northwest Africa where it plays a crucial environmental and economical role. To tackle the cork oak production and industrial challenges, advanced research is imperative but dependent on the availability of a sequenced genome. To address this, we produced the first draft version of the cork oak genome. We followed a de novo assembly strategy based on high-throughput sequence data, which generated a draft genome comprising 23,347 scaffolds and 953.3 Mb in size. A total of 79,752 genes and 83,814 transcripts were predicted, including 33,658 high-confidence genes. An InterPro signature assignment was detected for 69,218 transcripts, which represented 82.6% of the total. Validation studies demonstrated the genome assembly and annotation completeness and highlighted the usefulness of the draft genome for read mapping of high-throughput sequence data generated using different protocols. All data generated is available through the public databases where it was deposited, being therefore ready to use by the academic and industry communities working on cork oak and/or related species.Cork oak ( Quercus suber is an evergreen tree, commonly known as cork oak, which is native to the western Mediterranean Basin, especially southwest Europe, where it occurs in the coastal regions. Cork oak has the rare characteristic of producing a continuous and renewable cork layer, which has fine physical and chemical properties that make it highly profitable for industrial uses.The cork oak is the basis of an ecological system, known as “montado” that is unique in the world, contributing to the survival of many native species of fauna and flora and preventing desertification in vulnerable areas. Over the last few years, a decline in cork oak populations has been observed, eventually as a result of agriculture intensification, biotic stresses, fires and climate changes. If this trend is not stopped, serious negative impacts may arise at the ecosystem, social and economic levels.The availability of a fully sequenced and annotated genome is essential to support many of the studies needed to answer fundamental questions about cork oak biology and interactions with the environment, and about cork formation/production, as well as to develop the tools required to address the problems presently affecting this species. In fact, despite the economic relevance of cork production, no genetic breeding/selection schemes have so far been established in this species, thus compromising the development of genetically superior trees, able to produce high quality cork, displaying enhanced resistance to the (a)biotic stress factors they are exposed to and with potential to adapt to new forestry management practices that may be implemented in the future.The development of genetic markers in cork oak is an extremely important field, which will greatly benefit from the availability of a reference genome. For example, cork quality, perhaps the most important trait in cork oak production, cannot be properly evaluated before at least 40 years of growth, due to the specificities of the production cycle. This extremely long cycle represents a textbook scenario in which genetic markers associated with better cork quality can be highly beneficial. The same reasoning can be applied to other relevant traits, such as resistance to drought, diseases, pests, or developmental modifications important to face climate changes.1, which increased the amount of genomic resources available for cork oak and allowed transcriptomic analyses of several biological systems, such as the response to ectomycorrhizal symbiosis establishment2, response to drought3 and flower and fruit development5.A comprehensive characterization of the cork oak transcriptome was previously producedTo assist and enhance the development of the solutions needed for the future of cork oak production we have generated a draft version of the cork oak genome (Data Citation 1 and Data Citation 2).6. In the present study we used a combination of Paired-End (PE) and Mate-Pair (MP) libraries sequenced using the Illumina platform to generate a draft genome assembly with an estimated genome size of 953.3 Mb, which is a very close match to the previous estimate. The bioinformatics pipeline involved a de novo genome assembly step, followed by scaffolding, gap filling and removal of heterozygous regions. The cork oak draft genome is distributed over 23,344 scaffolds, even though the vast majority of the assembly is represented in a considerable smaller number of larger scaffolds .The genome size of cork oak, a diploid (2n=24) species, was estimated, using flow cytometry, to be 934 MbThe structural annotation of the genome yielded 79,752 genes, with complete open reading frames, and 83,814 transcripts. The number of transcripts with a valid functional annotation varied with the database used, and the maximum number was 69,218, when searching against InterPro signatures, which represented 82.6% of the total. Finally, using a validation approach based on the RNA-Seq data available for five cork oak tissues, a total of 33,658 predicted genes could be confirmed and classified as high confidence genes, since they presented assembled transcripts within the genome annotation coordinates.Quercus genus. In fact, hybridization between Quercus suber and Quercus Ilex, mainly Quercus ilex subsp. rotundifolia, another species widely distributed in the Mediterranean region, is fairly common, since both are found in mixed stands where they cohabit. Even though some of these hybrids maintain the ability to produce cork, only a pure cork oak tree can produce high quality cork. Thus, in order to select the tree to sequence, a total of 28 trees, derived from four different locations, were selected, based on historical records that demonstrated their ability to produce very high quality cork. These individuals fulfilled an estimated median age of more than 50 years, were at least 50 meters apart , and were not planted in rows, to ensure that they were naturally grown trees. Leaves were collected from these trees for DNA extraction. Then, all individuals were genotyped with 16 microsatellite markers, to estimate the degree of homozygosity, since a higher level is preferable for a genome sequencing project. The microsatellite markers used were selected from available literature, and included markers developed in Quercus mongolica7, Quercus petraea8, Quercus robur9, Quercus macrocarpa10, Quercus myrsinifolia11, Castanea sativa12 and two additional markers developed for cork oak at CEBAL and INIAV (published here). This information is summarized in Interspecific hybridization is a widespread phenomenon in plants, which also occurs among species of the Multiplex PCR amplification was conducted in a GeneAmp PCR system 2700 (Applied Biosystems) and PCR products were analyzed on an ABI Prism 310 capillary electrophoresis system (Applied Biosystems). Size alignment and quality control were analyzed on Genescan 3.7 (Applied Biosystems). The number of homozygous loci per tree ranged from four to nine, the latter being observed in three cork oaks located at Herdade dos Leitões . From these three individuals the HL8 cork oak was considered the most robust tree and selected for genome sequencing.Leaves collected from the HL8 cork oak tree were used for DNA extraction. To reduce contamination with chloroplast and mitochondrial DNA nuclei were first isolated using the CelLytic™ PN Isolation/Extraction Kit (Sigma) and nuclear DNA was then extracted using the innuPREP Plant DNA Kit .All Illumina DNA sequencing was performed at the Beijing Genomics Institute (BGI), and included a combination of PE and MP libraries (Data Citation 2), sequenced using different Illumina sequencers .13, which trimmed/removed the low quality reads, using as parameters minimum quality  and minimum length (80% of the read length). To remove reads derived from the chloroplast and mitochondrial genomes the high quality reads were mapped, with BWA using the BWA-mem algorithm14, against eight mitochondrial and eight chloroplast genomes. These genomes were selected from the species more closely related to cork oak for which these organelle genomes were available at the time this step was executed. The NCBI Reference Sequence numbers for each of these genomes are indicated in A total of 10,560,988,448 reads were generated. The raw reads were filtered by removing adapter sequences and reads containing undetermined nucleotides (N’s). The reads that remained in the dataset were further processed with SickleThe reads that did not map against any of the 16 genomes were kept and formed the set of Illumina DNA data that was used for all downstream analyses, which comprised 8,544,112,336 reads . A total of five tissues were sampled from the HL8 cork oak tree, in order to generate the RNA-Seq data (Data Citation 2) needed for the genome annotation stage . These tA total of 1,530,447,601 RNA-Seq reads were produced. These reads were further processed using a similar approach to the one described for the DNA sequence data, which included the application of the same procedures for the removal of reads with adapters or undetermined nucleotides and trimming of the reads. The number of reads kept amounted to 1,438,136,157, which represented 94% of the initial number of reads.15 with a k length of 23. The k-mer spectrum followed a binomial distribution expected for a heterozygous diploid genome  dividing all k-mers under the haploid peak, except the error k-mers , by the haploid coverage depth, (2) dividing all k-mers counted between the haploid coverage peak +1 and the diploid coverage peak by the diploid coverage depth, and (3) summing those values. The estimated cork oak genome size based on sequence data was 968.7 Mb, a value that slightly exceeds the genome size estimated by flow cytometry (934 Mb)6.A k-mer analysis of the PE libraries with read length of 100 bp was performed to characterize the genome sequence using Jellyfishd genome . The hom17 was also used to estimate genome size and heterozygosity, using the k-mer distribution determined with a k-mer size of 23, which provided estimates of 1.06 Gb and 1.62% for genome size and heterozygosity, respectively.Additionally, GenomeScope18, the assembler that was selected because it provided the best results  after testing several assembly tools. The k-mer sizes used in the assembly were 81 and 121, for the PE libraries with a read length of 100 bp and 150 bp, respectively. The best k-mer size was estimated with SGA preqc19.The Illumina reads were used to generate the genome assembly, a process that was performed in two stages, due to the different read length of the PE libraries. Hence, two independent genome assemblies were produced, one for each type of read length, using Ray20, in order to obtain a set of contigs common to both individual assemblies and identify the unique contigs from each assembly.The contigs that resulted from each assembly were integrated into a single assembly using GARM14, in order to differentiate the contigs representing valid genome regions from the erroneous contigs possibly derived from the assembly step. The mapping results were analyzed for coverage using BEDtools21. We considered as valid all the contigs which displayed a minimum length of 1,000 bp, at least 95% of the length covered with mapped reads, and that 95% of the regions with mapped reads had a minimum coverage of 50x. The contigs that passed these filters were added to the set of contigs common to both assemblies, which formed the assembly used to proceed with scaffolding of the genome. The total number of contigs that were used as the reference for the scaffolding step was 168,041, and comprised 939,042,321 bp of sequence  and subsequently used for scaffolding the genome using BESST22, using the mate-pair libraries in ascending order relative to their respective insert size. In order to reduce the number of gaps two rounds of gap closing were performed using SOAP de novo Gap Closer23,using in each round a different set of two PE libraries from all available insert sizes . Given the high heterozygosity of the cork oak genome, a final step targeting the removal of possible alternative heterozygous scaffolds was performed, using Redundans24, on an assembly that contained 44,287 scaffolds and comprised 1,000,388,124 bp. A total of 47.1 Mb of assembled sequence was removed, which represented a 4.7% decrease, while 20,943 scaffolds were discarded, a decrease of 47.3%. These results represent an approximate haplotype duplication level for the cork oak draft genome sequence.The mate-pair read dataset was mapped to the assembly using BWA with the BWA-mem algorithmThe final assembly of the cork oak draft genome contained 23,344 scaffolds greater than 1,000 bp, which represented an assembly length of 953.3 Mb . The per25 was employed to predict gene models in cork oak. To achieve best performance, species-specific parameters were generated by training Augustus with a subset of cork oak genes. To generate this subset, Maker26 was run with transcripts obtained from the five HL8 RNA-Seq tissues assembled with a Star27 and StringTie28 pipeline and protein sequences from Arabidopsis thaliana. The run with Maker was stopped when a reasonable number of genes had been predicted, which resulted in a total of 1,268 genes selected to train Augustus. In order to maximize annotation accuracy several sources of external hints were produced to enhance the Augustus gene predictions. Initially, a custom repeat library was generated with RepeatModeler29, which was then used to perform the repeat masking of the genome with RepeatMasker30. Augustus repeat hints were obtained based on the RepeatMasker output. In addition, protein hints were generated based on the genome coordinates where CEGMA31 conserved proteins mapped . Exonic and intronic hints were produced based on the STAR mappings of the HL8 RNA-Seq libraries from four tissues  using Augustus utilities . Finally, Augustus was run by adding the option to also report alternative transcripts when they were suggested by hints.AugustusThe number of genes annotated for the cork oak draft genome was 79,752. The final list included only the genes for which a start and stop codon was detected. Additionally, a total of 83,814 transcripts were identified, an indication of the presence of alternative splicing events in the cork oak transcriptome.32. Additionally, eggNOG-mapper33 was used to assign orthologies based on precomputed phylogenies of Viridiplantae organisms from the eggNOG database34. Last, InterProscan35 was used to detect protein domains, Gene Ontology terms and KEGG mappings from the InterPro database36.Functional annotation was performed for the set of 83,814 transcripts using different sources of information. Detection of homologies in the predicted gene models was performed using BLASTP against the databases NCBI-nr and Swiss-ProtThe percentage of functionally annotated transcripts varied from 55.6% to 82.5%, for the SwissProt and InterPro databases, respectively . The per16, and lower than the pedunculated oak genome, which appears to contain approximately 33% of repetitive elements37.The percentage of the genome covered by repeat elements was 11.96%, for a total of 113,971,428 bp of the 953.3 Mb contained in the assembled cork oak draft genome (934.2 Mb after excluding the regions with Ns). These results were obtained running RepeatMasker using the eudicotyledons subset of RepBase, and are summarized in The execution of this work involved using many software tools, whose versions, settings and parameters are described below.1) Sickle: minimum quality ; minimum length (80% of the read length); 2) BWA: version 0.7.15, default parameters; 3) Jellyfish: version 2.2.6, k-mer size of 23; 4) GenomeScope: parameters used were k-mer length 23; read length 80; maximum k-mer coverage 1000; 5) Ray: version 2.3.1, default parameters, k-mer size of 81 for the assembly with the PE100 reads, k-mer size of 121 for the assembly with the PE150 reads; 6) SGA: version 0.10.14, default parameters; 7) GARM: version 0.7.5, default parameters; 8) BEDtools: version 2.25.0; 9) BESST: version 2.2.5, default parameters; 10) Gap Closer: version 1.12, parameters used were -l 150, in configFile: asm_flags=4; PE100 lib: rd_len_cutoff=90, pair_num_cutoff=7, map_len=45; PE150 libs: rd_len_cutoff=140, pair_num_cutoff=10, map_len=50; 11) Redundans: version 0.13a, with default parameters for homology, --nogapclosing, --noscaffolding, --norearrangements; 12) Augustus: version 3.2.2, parameters:  Maker: version 3.0.0 beta, parameters: est=merged transcripts from stringtie assemblies for 5 tissues, protein=arabidopsis_proteins, rmlib=repeatModelerOutput, est2genome=1, protein2genome=1; run was stopped when 2,574 scaffolds had been scanned, which predicted 2,394 genes genes, subsequently used to train Augustus; 14) Star: version 2.5.2b, parameters: --outSAMattributes All --outFilterIntronMotifs RemoveNoncanonical --outWigType wiggle --outSAMtype BAM SortedByCoordinate --chimSegmentMin 20 --outReadsUnmapped Fastx --outFilterScoreMinOverLread 0.4 --outFilterMatchNminOverLread 0.4 --outFilterMatchNmin 60; 15) StringTie: version 1.3.1b; parameters for first transcriptome assemblies for maker: default parameters; merge (samples per tissue and reference per tissue: -F 0.5 -T 0.5 -i); transcriptome assemblies for annotation validation: -c 5 -j 1.5 -a 20 -m 400 -f 0.2; 16) RepeatModeler: RepeatModeler-open-1.0.8; 17) RepeatMasker: RepeatMasker-open-4.0.6 (with specific specific cork oak library generated with repeatModeler using the scaffolds>5000 bp); 18) CEGMA: version2.5; 19) Blastp: version 2.6.0; 20) NCBI-nr: we used the subset of green plant proteins extracted using taxonomic ID 33090, downloaded on 11th January 2017, for a total of 7,903,686 entries; 21) SwissProt: we used the database version downloaded on 15th March 2017, for a total 593,119 entries, including isoforms; 22) eggNOG mapper: version 0.12.7; 23) eggnog: version 4.5; 24) InterProscan: version 5.22-61.0, with parameters -goterms, --pathways and using an –iprlookup; 25) BUSCO: version 1.22; 26) GMAP: version 2017-06-20, parameters: -D, -S, -f; 27) LAST: version 914.This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank (Data Citation 1). Raw read files are available at NCBI Sequence Read Archive (Data Citation 2).31 and BUSCO38, tools that investigate the presence of highly conserved orthologous genes in a genome assembly, to perform a first evaluation of the quality of the cork oak draft genome. CEGMA contains a total of 248 core eukaryotic genes. BUSCO was run over the plant set, which contains a total of 956 orthologue groups, defining Arabidopsis as the model species for the gene prediction performed by Augustus within the BUSCO pipeline. The results showed that the vast majority of the core genes/orthologues was present in the genome assembly , and with complete matches , which evidenced a good quality of the draft genome, in particular of the gene content and transcriptome. The number of complete orthologs found to be duplicated using BUSCO was 130, which represented 13.6% of the total (14.3% of the complete orthologs), percentages that are lower than the ones determined for the valley16 and pedunculated37genomes .We used CEGMAPredicted gene models were evaluated by running Stringtie over the RNA-Seq libraries, using the Augustus annotation as the reference and more stringent settings for the identification of transcripts. High confidence tags were assigned to the reference genes for which there were assembled transcripts within the genome coordinates determined during the annotation step. A total of 33,658 predicted genes were confirmed as high confidence genes with the analysis of the RNA-Seq data, which represented 42.2% of the 79,752 predicted genes. It should be emphasized that this validation procedure was limited to the genes expressed in the five tissues for which RNA-Seq data was available. Hence, assuming that transcriptomic data derived from different cork oak tissues and organs will likely contain a distinct set of expressed genes, it is reasonable to assume that more genes would be validated, emphasizing the quality of the genome annotation produced.3, the transcriptomic analysis of male and female flowers4, the comparison of good and bad quality cork samples39, the response of oak roots to the establishment of ectomycorrhizal symbiosis2, the dynamics of cork oak somatic embryogenesis (unpublished results) and miRNA profiling in leaf and cork tissues40. Additionally, the RNA-Seq reads derived from the five cork oak tissues were also used, even though this read dataset was derived from samples collected in the tree (HL8) used for genome sequencing. The Illumina RNA-Seq reads were mapped to the draft genome using Star, while GMAP41 and BWA-mem were used to map the RNA-Seq 454 and small RNA-Seq reads, respectively. Since the Illumina reads were produced using a paired-end sequencing protocol, the number of mapped reads was defined as the reads that were mapped as proper pairs, following the information specific to each PE library, while for the 454 reads only the reads that mapped to a unique genome location were considered. The percentage of mapped reads ranged from 65.6% to 89.6%, for the SRR1012034 and SRX2239662datasets, respectively  and were derived from RNA-Seq (generated in the 454 and Illumina platforms) and small RNA-Seq experiments, including the response of cork oak roots to droughtectively .Quercus genus were released, which included pedunculated oak (Quercus robur)37 and valley oak (Quercus lobata)16. A comparison of the assembly metrics of the cork oak draft genome with these two genomes indicated a similar number of scaffolds longer than 2 kb , even though the total genome assembly included in these scaffolds differed between species . The N50 scaffold size of the cork oak assembly was 465.2 kb, while for pedunculated oak and valley oak the N50 values were 260 kb and 278.1 kb, respectively. Moreover, the heterozygosity estimates for cork oak (1.62%) and valley oak (1.25%) indicate that Quercus genomes indeed display high levels of heterozygosity. Additionally, the similarity level between the cork oak and the other two Quercus genomes, defined as the percentage of the cork oak genome that aligned with the other Quercus genomes, was determined aligning the valley and pedunculated oak genomes to the cork oak genome, using LAST42. The results showed that at the nucleotide level the similarity between the cork oak and valley and pedunculated oak was estimated to be 36.7 and 58.7%, respectively.Recently, the draft genome sequences for two other species of the Quercus species. BUSCO was run with the same parameters described before over the valley and pedunculated oak genomes, in order to obtain the set of orthologs identified within their genomes. For this analysis two sets of orthologs were used, which included the orthologs found in common between cork oak and valley oak and the ones found in common between cork oak and pedunculated oak. In both cases, only the orthologs defined as “Complete” and “Duplicated” were used. As the number of copies of the orthologs between the three species was different only the largest set for each ortholog identifier was considered for the analysis. The final set for the Quercus suber vs. Quercus lobata comparison contained a total of 880 orthologs, while the set of Quercus suber vs. Quercus robur contained a total of 890 orthologs. After aligning the valley and pedunculated oak orthologs against the ones from cork oak, the similarity at the nucleotide level was estimated to be 78.8% and 81.8%, respectively. Additionally, the orthologs in common between valley and pedunculated oaks (895) were also aligned using the valley oak as a reference, which resulted in an estimated similarity of 85.3%. Previously, the identity between the valley and pedunculated oak genomes was determined to be 93.1% .Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Rice is one of the most researched model plant, and has a genome structure most resembling that of the grass common ancestor after a grass common tetraploidization ∼100 million years ago. There has been a standing controversy whether there had been five or seven basic chromosomes, before the tetraploidization, which were tackled but could not be well solved for the lacking of a sequenced and assembled outgroup plant to have a conservative genome structure. Recently, the availability of pineapple genome, which has not been subjected to the grass-common tetraploidization, provides a precious opportunity to solve the above controversy and to research into genome changes of rice and other grasses. Here, we performed a comparative genomics analysis of pineapple and rice, and found solid evidence that grass-common ancestor had 2n = 2x = 14 basic chromosomes before the tetraploidization and duplicated to 2n = 4x = 28 after the event. Moreover, we proposed that enormous gene missing from duplicated regions in rice should be explained by an allotetraploid produced by prominently divergent parental lines, rather than gene losses after their divergence. This means that genome fractionation might have occurred before the formation of the allotetraploid grass ancestor. After the split of them, rice was subjected by the grass-common genome doubling, while pineapple has not been affected since. This provides precious opportunity to solve the above controversy over grass ancestral chromosome number, and evaluate the effectiveness of the fusion and fission model. Moreover, comparison of them would show divergent genomic changes of them after their split. Furthermore, it will help reconstruct a credible evolutionary history of grasses, a family with several important cereal crops.Fortunately, a recent genome sequencing effort deciphered the genome of pineapple  between every possible pair of chromosomes within rice and pineapple, and between them. By running ColinearScan . By characterizing homologous sequence similarities, measured by collinear gene number and sequence identity, we characterized the paralogy and orthology among them.To find gene collinearity, by running BLASTP among genes, we searched for potential anchors  between genes, we used the Nei-Gojobori method implemented in PAML package . In rice, 2243 genes were found in 54 blocks containing more than 10 genes; while in pineapple, there are 1863 collinear genes in 87 blocks. For the large blocks having more than 50 collinear genes, there are 11 and 9 blocks involving 1599 and 555 collinear genes in these two plants, respectively. The largest block in rice was located between chromosomes 2 and 4, contains 268 collinear genes; while the largest block in pineapple was located on chromosomes 1 and 15, containing 79 collinear genes. This shows rice have longer blocks than pineapple. As to colinear gene number, The intergenomic homology between rice and pineapple is much better than intragenomic homology. There are 438 intergenomic blocks, containing 12057 collinear genes, with total 4071 collinear genes from 50 blocks with block size >50 collinear genes. Here, we found that there are much more homologs resided on longer blocks between different genomes. A higher similarity between different genomes makes it valuable to perform intergenomic comparison to understand genome structure of a genome.By using ColinearScan, we inferred intragenomic homologous genes in collinearity within rice and pineapple, respectively, and intergenomic homologs between them. We counted collinear genes in blocks with different sizes, which was measured by collinear gene numbers in blocks . Considering the fact of a more ancient polyploidization, if no gene or DNA losses, we would find that an pineapple gene or a chromosomal region would have two best matched or orthologous rice genes or chromosomal regions, and two secondary or out paralogous genes or chromosomal regions. In that there have often been gene or DNA losses after polyploidization(s), the above 1:2 ratio may not hold for all collinear genes revealed above. Here, we managed to classify the intergenomic blocks by considering the gene and chromosomal segmental similarities, and complement DNA breakages to distinguish orthologous from out paralogous blocks. Gene similarity was measured by inferring synonymous nucleotide substitutions (Ks). Chromosomal segmental similarity was measured by using Ks median of genes in the collinear blocks, and collinear gene numbers. Often without much difficulty, we managed to distinguish the orthologs from out paralogous blocks . The Ks corresponding to the split of two plants are often 0.95–1.20; while those around 1.5 may be related to homologs produced by more ancient events. For example, pineapple chromosomes 1, 20, and 21 , have orthologous regions in Os02 and Os04; and Ac05, Ac06, and Ac19 have orthologous regions in Os02 and Os04 . Sometimes, based on Ks median, it was difficult to distinguish orthologous and out paralogous regions. At these few cases, we had to make a close check of the collinear genes shared by these blocks. For example, one end of Ac19 has prominent homology with Os02, Os04, and Os06 , and the corresponding Ks medians are also similar. A close check of the collinear genes showed that Ac19 had 107, 51, and 135 collinear genes with the three rice chromosomes, respectively, indicating that Os02 and Os06 are much more similar to Ac19 than Os04. As to the collinear gene content, we found Os02 shared 45 or 42.05% of their collinear genes with Os06, but only 5 or 4.7% of their collinear genes with Os04. This finding showed clearly that Os02 and Os06 are orthologous copies of Ac19, while Os04 is out paralogous one. Eventually, we divided the orthologous copies from the out paralogous ones in the whole comparison of two genomes (Supplementary Table 1).As noted above, rice and other grasses share a tetraploidization event after the split with pineapple, which has not been affected by polyploidization ever since. Therefore, there should be 1:2 orthologous gene ratio between pineapple and rice .To solve the standing controversy of basic chromosome number in ancestral grass genome before the grass-common tetraploidization, we considered the corresponding orthologous segments between pineapple and rice. Let us first illustrate the two models: fission model and fusion model understand the circumstance of a grass-common tetraploidization . That is, chromosome S1 may correspond to a full rice chromosome P1, and at the meantime, each of them are the direct addition of rice chromosomes P2 and P3. The content of S1 chromosome may exist in segments due to genomic rearrangements, therefore may exist in two or more extant chromosomes, S11 and S12, which may share common breakage points, linking them to make a complete S1 . Considering possibility of more ancient polyploidization, there might have more ancient duplicates in pineapple genome.For the fission model, considering that there was an chromosome S1, preserved in pineapple, it should have corresponded to P1 in the grass ancestor before the tetraploidy, and then duplicated to two after tetraploidization to get two chromosomes P1, one of which split into two to produce P2 and P3 . The S1 and S2 chromosomes might be affected by genomic rearrangements to have it gene content separated to exist in extant pineapple chromosomes. Alternatively, they could have more ancient duplicates in pineapple. Nonetheless, if the fusion model is right, for S1 and S2 were independent chromosomes, we anticipate that they correspond to different sets of grass chromosomes.For the fusion model, considering that there were two ancestral chromosomes preserved in pineapple, S1 and S2, they should have corresponded to P2 and P3 in grass common ancestor, which were duplicated to get two P2 and two P3, and later one P2 and one P3 merged to produce another grass chromosome P1 ; while Os06 has orthologous correspondence with Ac05, Ac06, Ac19, and Ac22 . This shows a perfect independent correspondence of Os04 and Os06 in pineapple genome, strongly supporting the fusion model.As to the grass chromosomes Os02, Os04, and Os06, we found that they followed a fusion model. Os02 has orthologous correspondence in Ac03, Ac05, Ac06, Ac19, Ac20, Ac21, and Ac22, showing that it was composed of DNA segments from different pineapple chromosomes. Os04 has orthologous correspondence in Ac03, Ac20, and Ac21 ; while Os10 has orthologous correspondence with Ac08 and Ac15 . This shows a perfect independent correspondence of Os07 and Os10 in pineapple genome, also strongly supporting the fusion model.As to the grass chromosomes Os03, Os07, and Os10, we found that they also follow a fusion model. Os03 has orthologous correspondence in Ac01, Ac07, Ac08, Ac09, Ac15, and Ac18. Os07 has orthologous correspondence in Ac01, Ac09, and Ac18 .Therefore, together with the previous analysis published , the pree-10 or smaller, about two thirds are bidirectional best hits, and only ∼5500 genes did not have hits in rice. These facts showed the likelihood of the existence of their rice orthologs, which might have been transposed to other chromosomal locations after their split, and also showed that only ∼1/5 (∼5500/18101) of the pineapple genes might be totally lost in rice and other grasses.Wide-spread gene losses occurred in rice after the split with pineapple. As to inferred gene collinearity, we found that 18101 or 75.76% of pineapple genes could not find their collinear orthologs at expected locations in rice, only a tiny fraction of 4.59% have both duplicate copies preserved, and 19.65% have only one alternative copy preserved in rice, showing a large-scale fractionation in rice and possibly other grasses. This may possibly be resulted from gene losses after the grass-common tetraploidization. However, we found in these 18101 genes, 69.6% of them have BLASTP hits at 1n = 7 basic chromosomes before the grass-common tetraploidization  after its split from grasses, making it have a relatively simple genome structure, and be a valuable reference to understand those of grasses.Rice is among the most researched model plants for its economic importance to human food nutrient . More andization . Recentlomosomes . That isBrassica napus (AACC) was sequenced, and it was inferred to form only ∼7500 years ago, with parental lines of B. rapa (AA) and B. oleracea  gene losses or translocations in the parental lines before their hybridization; (2) gene losses or translocations during the early days after tetraploidization; (3) gene losses or translocations during the following time much after tetraploidization. The first scenario describes just like that of the Brassica plants. The ormation . The secn = 7 or 2n = 2x = 14 proto-chromosome model, and solved the controversy. As to above discussion, the grass-common tetraploidization is in essence or mostly an amphibian one, resulting in an allotetraploidization. This conclusion is consistent with previous finding by considering of genomic plasticity (Here, we provided a solid evidence for the asticity . TherefoThe study was conceived by XW and JW. YP, JiaY, PS, RX, JigY, YuL, YiL, XM, NY, TL, and XL contributed to data collection and bioinformatics analysis. JW, XW, YP, and JiaY participated in preparing and writing the manuscript. ZW, LW, WG, XS, SS, TL, and DJ performed the analysis with constructive discussions. All authors contributed to revising the manuscript. All authors had read and approved the final manuscript.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.


An elevated WHtR provided the best correlation with MetS in a prior study in a large Italian population. In order to assess the validity of this conclusion, a long-term follow-up study re-examined this population, also in order to detect possible associations with cardiovascular (CV) risk.The body variable associated with the diagnosis of Metabolic Syndrome (MetS) is an elevated waist circumference (WC), although a number of other variables have been suggested. Among these, an elevated waist-to-height ratio (WHtR), 2 had intermediate sensitivity and specificity regardless of gender. WC showed the highest odds ratio  for the prediction of CV occurrence.1,071 subjects with a complete follow-up of over 6 years were evaluated with a comparative assessment of the three anthropometric variables, namely WHtR, WC and body mass index (BMI). WHtR≥ 0.5 had the highest sensitivity for the identification of MetS, both in males and females (94.1% and 86.7% respectively). WHtR was of reduced specificity, occurring, yet less frequently , in patients without MetS. By contrast, enlarged WC occurred with a lower frequency in male patients who developed MetS (30.2%) whereas in females, frequency was higher than in males (69.3%). Finally, a BMI≥ 25 kg/mThe present study confirms WHtR as an excellent screening tool in identifying MetS carriers, but, different from reports in other countries, it shows a lower specificity in our population. There is, however, some variability in the diagnostic criteria, particularly relative to the body variable/s [The profile of Metabolic Syndrome (MetS), riable/s ; while oriable/s , and somriable/s  or hyperriable/s .The recommendation to measure WC rather than body mass index (BMI) has been based on the central role of abdominal obesity in MetS , but theThe follow-up of this patient series has given the opportunity to re-evaluate the reliability of these findings after a period of 6 years. Hence, this study attempted to either confirm or refute the predictive value of WHtR, compared to WC and BMI, on the occurrence of MetS. The possible association of these variables with the CV risk was also evaluated.ie 539 males (57.8 ± 12.8 years (y), mean ± standard deviation (SD) and 532 females . The study protocol of this observational study was approved by the Ethical Committee of the ASST Grande Ospedale Metropolitano Niguarda. A signed consent form was obtained from all of the participants. The study conforms to the Ethical guidelines of the 1975 Declaration of Helsinki. Vascular events at baseline and over a period of 6 years were recorded in the medical electronic records by a hospital physician. Pre-existing CV disease was defined in participants with a previous diagnosis of angina, acute myocardial infarction, stroke and peripheral arterial disease. All determinations were performed in the morning of the clinical examination.The baseline study population has been previously described. Briefly, 1,104 subjects were recruited among those attending the Dyslipidemia Center of the ASST Grande Ospedale Metropolitano Niguarda, Milan in 2008 . After 6et al. [Blood samples were taken after a 12 h overnight fast. Serum lipids, lipoproteins and glucose were determined by standard procedures as used in the institution , 15. Weiet al.  These Auet al.  further To allow a direct comparison with previous data, MetS was diagnosed according to the American Heart Association/National Heart, Lung and Blood Institute (AHA/NHLBI) guidelines , 19 as t2 and WHtR ≥ 0.5.Continuous variables are presented as means ± SD, and categorical variables as cases (n) or percentages (%). Prevalence of obesity-related risk factors in MetS are presented as percentage values and stratified by gender. Sensitivity and specificity for the identification of MetS, as the clustering of two or more coronary risk factors plus the anthropometric one, were investigated using each of the proposed indices, that is, an enlarged WC according to AHA/NHLBI criteria, BMI ≥ 25 kg/mComparisons between baseline and follow-up were performed using paired Student’s 2-tailed t-test. Receiver operating characteristic (ROC) curves for the clustering of two or more coronary risk factors were produced for each anthropometric variable; the values of the indices that maximized the Youden index (sensitivity + specificity– 1) were defined as optimal. Odds ratios and their 95% confidence intervals (CI), were calculated by univariate logistic regression analysis. All reported P values < 0.05 were considered as statistically significant. Statistical analyses were carried out using the SPSS statistical package .ie 20.8%, 12.4% and 2.8%, respectively). A small subgroup  had newly diagnosed MetS at the follow-up.The characteristics of study participants at baseline and after the 6-year follow-up are presented in 2, WHtR ≥ 0.5 and WC ≥ 88 cm for females and 102 cm for males) remained almost unchanged over the baseline data, with a WHtR ≥ 0.5 being overall more prevalent . A prevalence of all the three factors increased significantly from baseline in males, whereas in females only the presence of an enlarged WC was raised at follow-up (from 47.2% to 51.7%). Interestingly, altered anthropometric indices remained higher in males versus females, with the exception of WC, retaining an almost two-fold higher prevalence in females  in patients without MetS.Specificity and sensitivity of the proposed anthropometric variables in the identification of the clustering of two or more risk factors of MetS are shown in 2 had intermediate sensitivity and specificity regardless of gender  whereas in females, frequency was higher (sensitivity 69.3%), although below that of WHtR. Specificity was elevated in both genders , indicating that the vast majority of MetS carriers have enlarged WC, and this occurs seldom in non-carriers. Finally, a BMI ≥ 25 kg/mf gender .Of note, among to the 10.5% of subjects  with a newly diagnosis of MetS at follow-up, WHtR ≥ 0.5 showed the highest sensitivity  compared to BMI and WC.2).The ROC curves for a comparative assessment of sensitivity and specificity of the different anthropometric indices were, therefore, evaluated . Areas uAfter excluding subjects with CV events at baseline, a sub-analysis was performed to test the association between anthropometric indices and CV events. The evaluation of CV risk associated with MetS has become a standard goal in clinical practice. This goal includes the best choice of variables, both biochemical and body structure associated, allowing an optimal diagnosis of MetS, as well as providing a possible marker for CV risk prediction. Body variables associated with MetS have gained considerable interest, also in view of their simplicity of assessment and, in the case of WHtR, of the apparent uniformity of findings in different populations with marked differences in height . In the ie WHtR with a breakpoint of ≥ 0.5, consistent with values in a large number of populations, maintained these qualifications.A difference of 6–7 years between evaluations carries significant weight, since longitudinal analyses such as in the Uppsala County in Sweden, clearly indicated that a 5-year difference in age is associated with a significantly higher prevalence of MetS and, more so, of MetS components . In addiThe present study was also aimed at the re-evaluation of the predictive values of the three anthropometric indices assessed at baseline on the long-term development of MetS, as well as possibly, with CV events. Elevated WHtR was the index with the highest sensitivity, close to 100% for predicting the development of MetS; it had, however, a reduced specificity. Conversely, enlarged WC, particularly in males, had an elevated specificity, albeit with a lower sensitivity.ie occurring frequently in non-MetS carriers. This finding may possibly explain the relatively low CV predictivity. Instead, WC, with a lower sensitivity but a high specificity, provided the highest ORs when tested in terms of prediction of CV events  [et al. [The present report thus provides clear evidence of a significant prevalence of MetS in middle-aged individuals from an Italian population. A limitation is that findings may not be transferable to other populations: a full generalizability of results from this large series may be, of course, the geographical location and the Caucasian ethnicity. In addition, life habits were not fully evaluated, sk > 5%) , in a co [et al.  that bod [et al. , appeare [et al. .


Saliva is secreted from the salivary glands and has multiple functions, including mouth cleaning and protection, antibacterial effects and digestion. With the rapid advancement in salivaomics, saliva is well recognized as a pool of biological markers. Saliva, as a non-invasive and safe source, could be a substitute for blood in the diagnosis and prognosis of diseases. This review summarizes the latest advancements in saliva-related studies and addresses the potential value of saliva in the early diagnosis of oral diseases, such as dental caries and periodontal disease, as well as cancer, diabetes and other systemic disorders. Saliva biomarkers range from changes in the biochemical indices of DNA, RNA and proteins to the diversification of microbiota structures. This study integrates data reported in the recent literature and discusses the clinical significance and prospects for the application of saliva in the early diagnosis of diseases, translational medicine and precision medicine. Saliva has multiple functions as follows: first, it helps to clean the mouth by washing away bacteria or food residues and freshening the breath; second, salivary amylase, a form of amylase in the saliva of human beings, catalyses the hydrolysis of starch into maltose and sometimes glucose in the mouth; third, lysozymes and thiocyanate ions in the saliva are bactericidal, making saliva an important part of the nonspecific immune system of humans; and fourth, saliva is secretory and contains risk factors for some diseases by excreting or transmitting KI, Pb and Hg, and viruses such as rabies, polio and human immune deficiency virus (HIV). In the past, doctors have diagnosed diseases with the use of serum or urine tests, which are either painful or embarrassing, respectively. However, saliva is now considered a potential pool of biological markers that range from changes in biochemicals, DNA, RNA and proteins to the microbiota structure. It is relatively safe to collect saliva and minimizes the risk of virus spread. Hence, saliva provides a new, non-invasive and simple way to help in the diagnosis of disease, and it is expected to become a substitute for serum or urine tests in disease diagnosis.Saliva is a hypotonic solution of salivary acini, gingival crevicular fluid and oral mucosal exudates. Approximately 90% of saliva is secreted from the salivary glands and the major glands include the parotid glands, submandibular glands and sublingual glands. The salivary glands with high permeability are surrounded by abundant capillaries, blood and acini, and can exchange molecules. Thus, biomarkers in the blood circulation can infiltrate acini and eventually be secreted into the saliva. Saliva is colourless, odourless and has a relative density of 1.004–1.009 and a pH of 6.6–7.1. A normal person produces 600 mL of saliva per day. Saliva consists of 99% water and the remainder is organic molecules such as salivary amylase, mucopolysaccharide, mucin and lysozymes, and some inorganic matter such as Na+, Cl–, Ca2+, K+, HCO3–, H2PO4–, F–, I– and Mg2+ from the serum. In addition, saliva contains >700 microorganisms that are related to oral and systemic diseases.Saliva has a complex composition that includes urea, ammonia, uric acid, glucose, cholesterol, fatty acid, triglycerides, neutral lipid, glycolipid, amino acid, steroid hormones, mucin, amylase, lectin, glycoprotein, lysozyme, peroxidase and lactoferrin. It also contains high concentrations of Na1 built a professional Salivaomics Knowledge Base (SKB) that can systematically manage the data of research related to salivaomics. The SKB (http://www.hspp.ucla.edu/skb.swf) is currently the only website that is devoted to research on salivaomics and it has collected a large amount of information related to salivaomics, pharmacoproteomics, pharmacogenomics and similar fields. It has been well recognized that salivary biomarkers can be exploited for the early diagnosis of some oral and systemic diseases.Owing to the rapid progress made in salivary studies, researchers have proposed the concept of salivaomics. Salivaomics encompasses genomics, transcriptomics, proteomics, metabonomics and microRNA (miRNA) analysis. WongStreptococcus mutans and Lactobacillus in the saliva. Samaranayake2 used paraffin wax to stimulate the production of the salivary samples, which were then incubated in a selective growth media for up to 24 h. Saliva from populations with high caries activity contained >1 × 106 mL−1 of S. mutans and/or 1 × 105 mL−1 of Lactobacillus. Saliva from populations with low caries activity harboured <1 × 105 mL−1 of S. mutans and/or 1 × 104 mL−1 of Lactobacillus. The structures and functions of salivary bacteria have been studied as potential predictive markers for caries onset. Yang et al.3 analysed adult saliva microbiomes in 19 caries-active and 26 healthy human hosts by whole-genome-based deep sequencing and cross-validated 16S rRNA amplicon-based technologies. They observed an overabundance of the Prevotella genus in the caries microbiota compared with healthy ones. In addition, Prevotella species differed in caries-active and normal individuals, indicating the predictive role of Prevotella in the onset of dental caries. Another study from the same group established the first model, known as Microbial Indicators of Caries, to diagnose caries and predict potential caries onset for samples clinically considered healthy. The accuracy of the prediction using the Prevotella genus and microbiota is similar, validating the idea that the Prevotella genus is of great significance for the timely prediction of caries.4The prevalence of dental caries is positively correlated with the microbial load of Porphyromonas gingivalis is a ‘red complex' bacteria that is closely associated with periodontitis. Recently, some researchers developed an enzyme-linked immunosorbent assay-based P. gingivalis saliva kit to specifically detect this bacterium in saliva. The kit can detect both laboratory and clinical isolate strains of P. gingivalis at concentrations of 5 × 104 to 5 × 105 CFU·mL−1 and yields results within 90 s. Compared with real-time polymerase chain reaction technology, the P. gingivalis saliva kit is rapid and has a sensitivity of 92% and a specificity of 96%. Therefore, the P. gingivalis saliva kit is expected to be an easy and time-efficient chair-side diagnostic tool for the detection of P. gingivalis.5 A recent study detected the levels of adrenomedullin (AM) and nitric oxide (NO) in saliva and gingival crevicular fluid collected from patients with gingivitis, aggressive periodontitis and chronic periodontitis, and compared them with the healthy controls. Salivary AM and NO levels distinguished patients with aggressive periodontitis from other groups. In contrast, patients with chronic periodontitis, aggressive periodontitis and gingivitis showed increased levels of NO in the gingival crevicular fluid and the levels of AM were higher in patients with periodontitis compared with those with only gingivitis. These data indicate a functional linkage between NO and AM in periodontal disease, and AM and NO could be used as salivary diagnostic markers for periodontitis.6 Data from another study demonstrated that NO and its end metabolites in saliva are more valuable for the diagnosis of periodontal diseases than gingival crevicular fluid.7 Tobacco use is regarded as a major risk factor for periodontal diseases. A recent study showed positive correlations between salivary superoxide dismutase levels and clinical symptoms such as gingival index, pocket depth and clinical attachment loss in patients with chronic periodontitis. The study also demonstrated the potential of saliva as a more convenient and non-invasive way to diagnose patients with higher risks for precancerous lesions and conditions.8 Salivary macrophage inflammatory protein-1α, matrix metalloproteinase-8, interleukin (IL)-1β, IL-6, prostaglandin E2 and tumour necrosis factor (TNF)-α levels have shown potential for indicating gingivitis and periodontitis.9 In addition, salivary levels of Toll-like receptor-4, IL-18, uric acid, aspartate transaminase and procalcitonin in patients with periodontitis were higher than in healthy individuals, showing positive correlations with clinical measurements including probing depth, clinical attachment level and gingival index. Hence, these parameters might be useful in the diagnosis and prognosis of periodontal diseases.12 Recently, a new system that uses a new type of oral rinse to estimate the neutrophil abundance in saliva was developed to screen for the presence of periodontal diseases. This system provides valuable information from the bench to chairside.1314 The DNA of tumour-related viruses in saliva, such as HIV and human herpes virus (HHV), can also be associated with cancers of the oral cavity or other sites.15 Park et al.16 found that miR-125a and miR-200a levels were of great significance as follows: compared with healthy individuals, patients with oral squamous cell carcinoma (OSCC) have lower levels in their saliva, indicating that miRNAs in saliva have a potential application in oral cancer detection.The onset and development of malignancy are related to somatic mutations of tumour-specific DNA, which can be found in the saliva, plasma or other body fluids. These somatic mutations can be used as biomarkers to diagnose oral or other tumours. In saliva, tumour-specific DNA was positive in 100% of patients with oral tumours. However, only 47%–70% of patients with tumours in the other parts of the human body carry tumour-specific DNA in the saliva. In contrast, tumour-specific DNA was found in 80% of plasma samples from patients with oral tumours and in 86%–100% of patients with tumours in other sites. Based on these results, saliva has an overabundance of tumour-specific DNA from oral tumours. Thus, tumour-specific DNA in saliva has the potential to be applied to diagnose oral cancers.17 Further studies showed that hyaluronidase, IL-6 and IL-8 might be potential biomarkers for patients presenting with head and neck squamous cell carcinoma (HNSCC). Based on this study, they found that there was an overexpression of palate, lung, and nasal epithelium clone protein (PLUNC) and zinc-α-2-glycoprotein in the saliva of cancer patients. Both proteins might provide potential targets for a new analysis of HNSCC.18 Similar research showed that IL-4, IL-10, IL-13 and IL-1RA levels were increased in the saliva of patients with OSCC. Of note, the levels of IL-10 and IL-13 were significantly increased, whereas the level of IL-1RA was the highest in poorly differentiated OSCC lesions compared with well- and moderately differentiated OSCC lesions.19 A similar study found that the levels of TNF-α were higher in moderately and poorly differentiated tumours than in well-differentiated and stage IV tumours. Moreover, there was also a positive correlation between the histological grading of OSCC and TNF-α.20 In conclusion, salivary cytokines have been proven to be superior for detecting OSCC clinically and can be further investigated for use as biomarkers of histological grading and clinical staging for OSCC.Salivary proteins can also be used for cancer detection. It was reported that the increase in tumour antigen CA15-3 and antibodies for tumour protein markers c-erbB2, CA-125 and P53 in saliva can also be considered salivary biomarkers for cancers of the oral cavity and other sites.21With the development of high-throughput sequencing technology, researchers have realized the importance of microorganisms in the development of oral cancer. The species diversity and relative abundance of bacteria in the saliva of patients with oral tumours are greater than in healthy patients.22 Another research identified 19 genes  that were closely related to the pathological process of SS, which was characterized by functions such as induction of interferons, osmosis of lymphocytes and antigen presentation.23 Hu et al. successfully verified a panel of biomarkers that are elevated in patients with primary SS, including three mRNA biomarkers  and three protein biomarkers .24 These biomarkers from the proteome and transcriptome might provide a simple clinical tool for the diagnosis of primary SS in the early stages.Sjögren's syndrome (SS) is a chronic systemic autoimmune disease characterized by keratoconjunctivitis sicca and xerostomia. With further development of SS, the salivary flow rate is decreased and the salivary constituents change. There are also significant changes in the proteome and transcriptome in patients with SS. Thus, the levels of IL-4, IL-5 and cytokine clusters might help to accurately predict the diagnoses for patients with SS.25 However, the concentration of salivary melatonin decreased in patients with type 2 diabetes and patients with periodontitis. This indicates that salivary melatonin has an important role in the pathogenesis of diabetes and periodontal diseases, and might become a key biomarker in the diagnosis and treatment of these two diseases.26 Barnes et al.27 found 475 specific metabolites in the saliva of patients with periodontitis and/or diabetes. Levels of cellular energetic stress, purine degradation, glutathione metabolism, oxidized glutathione, cysteine glutathione disulphide, markers of oxidative stress, amino acids, a ω-3 fatty acid (docosapentaenoate) and ω-6 fatty acids (linoleate and arachidonate) signatures were significantly increased in patients who had gingivitis and periodontitis but not diabetes. In contrast, patients with diabetes had significantly higher levels of glucose and α-hydroxybutyrate, in addition to a significant change in the levels of carbohydrate, lipid and oxidative stress. Therefore, the metabolites might be useful for the diagnosis, treatment and prognostic assessment of periodontal diseases and diabetes. There was a significant correlation between both HbA1c and salivary glucose concentrations and patients with diabetes. Thus, this indicated that the blood glucose concentration could be monitored by the saliva in patients with diabetes mellitus.28Diabetes is a metabolic disease caused by insufficient insulin secretion, insulin action or insulin resistance, which leads to a glucose metabolism disorder. A positive correlation was found between α-2-macroglobulin and HbA1c, which demonstrated that levels of α-2-macroglobulin in the saliva could reflect the glycaemic control in patients with type 2 diabetes mellitus.et al.29 found that levels of salivary inflammatory cytokines including IL-1β, IL-6, TNF-α and prostaglandin E2 increased significantly in both atherosclerosis and periodontal diseases. These cytokines might be potential biomarkers for the diagnosis of periodontal disease and atherosclerosis.29 Miller et al.30 identified that the C-reactive protein (CRP) was the most predictive biomarker of acute myocardial infarction. Acute myocardial infarction was predicted by a combination of electrocardiogram and CRP levels with 80.0% sensitivity and 100% specificity. These data demonstrated the potential use of salivary biomarkers with electrocardiogram for the diagnosis of acute myocardial infarction. Moreover, the levels of α-2-HS-glycoprotein in saliva decreased in patients with CVD, which indicates that the peptidome might provide a potential way for the early diagnosis of patients with CVD.31Cardiovascular disease (CVD) is related to the circulatory system and includes atherosclerosis, myocardial infarction and coronary heart disease. Kosaka 32 Moreover, the dengue virus (DENV) RNA and non-structural protein 1 antigens are detectable from saliva, which might provide a more effective way to diagnose dengue.33 In the research of Nefzi et al.34 the saliva appears to be more sensitive than the blood in the detection of HHV-6 or human cytomegalovirus.Diagnostic tests for viral infections currently rely on salivary biomarkers, such as viral DNA and RNA, antigens and antibodies. At the proteomic level, there are saliva-based antibody tests to detect viruses, including hepatitis A virus, hepatitis B virus, hepatitis C virus, HIV-1, measles virus, rubella virus and vesicular stomatitis virus mumps virus, among others. The Raffaele Scientific Institute in Milan used a new salivary test named OraQuick hepatitis C virus rapid antibody test, to detect the hepatitis C virus in an easier and faster way.35 Therefore, it is important to diagnose and classify patients with pancreatic cancer at earlier stages, to give them timely treatment. In rodent models of pancreatic cancer, vesicles similar to exosomes can carry and transport tumour-specific biomarkers into the saliva.36 It was found that KRAS, MBD3L2, ACRV1 and DPM1 levels enabled the differentiation of patients with pancreatitis and healthy individuals.37 It was also found that hsa-miR-210 and let-7c were overexpressed in the saliva of patients with pancreatitis. In addition, significantly increased levels of hsa-miR-21, hsa-miR-23a, hsa-miR-23b, miR-29c and hsa-miR-216 were identified in the saliva of patients with pancreatic cancer; among them, hsa-miR-23a and hsa-miR23b were overexpressed in precursor lesions.38 Moreover, miR-3679-5p and miR-940 have an excellent ability to indicate pancreatic cancer, which enables curative surgery.39 The results of an ROC-plot AUC can distinguish patients with pancreatic cancer from chronic pancreatitis and healthy individuals with a 90.0% sensitivity and 95.0% specificity.40Pancreatic cancer has a low incidence rate but a high mortality rate. Worldwide, more than 200 000 patients with pancreatic cancer are registered annually and the disease results in the death of 98% of patients. It has been predicted that pancreatic cancer will become the second cause of death worldwide by 2030.41 There was an increase in 31 bacterial species and a decrease in 25 bacterial species in the saliva of pancreatic cancer patients. Moreover, two bacterial biomarkers, Neisseria elongata and Streptococcus mitis, have a high sensitivity and specificity for the diagnosis of patients with pancreatic cancer.42There are also correlations between periodontitis and pancreatic cancer onset. Patients with periodontitis had a 64% higher risk of pancreatic cancer.43 Zhang et al.44 found eight mRNA biomarkers and one protein biomarker that could be used to detect breast cancer with an 83% sensitivity and 97% specificity. In another study, it was found that the levels of vascular endothelial growth factor, epidermal growth factor (EGF) and carcinoembryonic antigen in the saliva were significantly increased in patients with breast cancer.45 The levels of CA15-3 and c-erB-2 were also found to be increased in the saliva, which has positive correlations with the serum of patients with breast cancer.46 Based on these studies, potential salivary biomarkers can be applied to the early diagnosis of breast cancer.Breast cancer is one of the most common cancers in females. ATP6AP1 is an ATPase that is expressed in normal tissues such as the brain marrow, blood, nerves and skin, and it is also correlated with several tumours such as head and neck carcinomas, lung tumours, adrenal tumours and various other cancers. Nevertheless, its prevalence in breast cancer is the greatest among these cancers. ATP6AP1 auto antibodies are spontaneously generated in patients and they can be detected in early stages. Thus, it is indicated that ATP6AP1 can contribute to the early detection of breast cancer.47 In addition, Xiao et al.48 found 16 candidate proteins that can discriminate lung cancer patients from healthy individuals and are effective biomarkers with a high sensitivity and specificity. This demonstrates that proteomic biomarkers can be established for the early detection and prognosis of lung cancer.Lung cancer has a high incidence rate. Mutations identified in the EGF receptor (EGFR) are the tumour-specific biomarkers for non-small cell lung carcinoma (NSCLC). A novel core technology known as electric field-induced release and measurement relies on a multiplexible electrochemical sensor that can detect EGFR mutations in bodily fluids was shown to be effective, accurate, rapid and cost-effective for the detection of EGFR mutations in the saliva of patients with NSCLC.49MiR-141 and miR-21 are two tumour biomarkers; the former is significantly elevated in patients with advanced-stage prostate cancer, whereas the latter is overexpressed in early-stage prostate cancer. It has been demonstrated that the expression of miR-21 and miR-141 in the saliva can be detected by nano-graphene oxide. This is expected to be a non- or minimally invasive approach to diagnose early-stage prostate cancer.Helicobacter pylori DNA can be detected to identify H. pylori infection.50 Significant correlations were found between salivary caffeine clearance and liver diseases. Thus, saliva can be used as an effective biochemical parameter for the diagnosis of chronic liver diseases (CLDs) and assessment of residual liver function in CLD.51 The saliva of patients with chronic renal failure presented significantly higher levels of NO. After haemodialysis treatment, the saliva showed significantly higher levels of immunoglobulin (Ig)A, IgG and CRP. Thus, this demonstrated that salivary levels of IgA, IgG, NO and CRP might have an important role in monitoring renal disease.52 Further studies found significant associations between fusion transcript levels in the saliva and bone marrow. Such associations might allow for the potential use of saliva to detect minimal residual disease in leukaemia.53 Based on recent studies, it has been proposed that salivary trehalose, which could be detected by some cell-based extended gate ion-sensitive field-effect transistor biosensors, might provide a sensitive and direct way to screen for Alzheimer's disease.54 Wilson's disease is a rare inherited disorder of copper metabolism and is characterized by hepatic, neurological and psychiatric symptoms. Researchers analysed the salivary proteome and peptidome of Wilson's disease patients and found an increase in α-defensins 2 and 4. Overall, the oxidative stress and inflammatory conditions reflected by the salivary proteome of patients with Wilson's disease might be key clues of disease exacerbation.55 Moreover, the level of salivary cortisol is a regular biomarker for psychology stress. Three potential biomarkers from saliva have been demonstrated to diagnose atopic dermatitis in its early stage.56In the saliva of patients with gastric ulcer and chronic gastritis, The advantages of saliva as a diagnostic fluid are that it is simple to collect, convenient to store, essentially non-invasive and contains high-quality DNA. Thus, we conclude that saliva is a perfect substitute for blood. The research in salivaomics has an important role in identifying biomarkers of diseases and potential drug targets. Salivaomics also has the potential to diagnose diseases in their early stages. However, research on saliva and its applications for the diagnosis of disease is still in its early stages and the progress of these studies is limited by the lack of efficient and useful methods and techniques. We need to develop systems of salivary molecular identification and standardize salivary evaluation. Systemic knowledge networks of salivaomics and precise biomarkers of diseases can contribute to a better understanding of the correlations between oral health and systemic health, which will promote the application of precision medicine by facilitating the individualization of precise, pain-free and convenient targeted therapy.


Terlipressin is safely used for acute variceal bleeding. However, side effects, such as hyponatremia, although very rare, can occur. We investigated the development of hyponatremia in cirrhotic patients who had acute variceal bleeding treated with terlipressin and the identification of the risk factors associated with the development of hyponatremia.  This retrospective, case-control study investigated 88 cirrhotic patients who developed hyponatremia and 176 controls that did not develop hyponatremia and were matched in terms of age and gender during the same period following terlipressin administration. P < 0.05).  The overall change in serum sodium concentration and the mean lowest serum sodium concentration were 3.44 ± 9.55 and 132.44 ± 8.78 mEq/L during treatment, respectively. Multivariate analysis revealed that baseline serum sodium was an independent positive predictor, and the presence of baseline serum creatinine, HBV, DM, creatinine, and shock on admission was independent negative predictors of hyponatremia ( The presence of HBV, DM, the baseline serum sodium, shock on admission, and especially baseline creatinine may be predictive of the development of hyponatremia after terlipressin treatment. Therefore, physicians conduct vigilant monitoring associated with severe hyponatremia when cirrhotic patients with preserved renal function are treated with terlipressin for variceal bleeding. The mortality from variceal bleeding in the patients with cirrhosis has decreased from approximately 50% to 15–20% due to the development of endoscopic treatments and the early administration of portal pressure-reducing drugs –6 and isThus, we investigated the development of hyponatremia in cirrhotic patients with and without HCC who had acute variceal bleeding that was treated with the standard doses of terlipressin and attempted to identify the risk factors associated with the development of hyponatremia.3) or ultrasonography or computed tomography features suggesting cirrhosis, including liver surface irregularities and nodularities [A total of 88 cirrhotic patients with or without HCC who developed hyponatremia following the administration of terlipressin  for acute variceal bleeding were retrospectively enrolled from the Hallym University Sacred Heart Hospital, Korea, from January 2007 to March 2012. For the purpose of comparison, 176 cirrhotic patients with or without HCC who had not developed hyponatremia and who matched the patients who developed hyponatremia in age and gender were selected during the same period. The diagnosis of cirrhosis was based on clinical evidence of portal hypertension . All data are expressed as the means ± the SD or the medians with the ranges. The continuous data were analyzed with Student's n = 264) are summarized in P = 0.011; and 47.9% versus 19.3%; P = 0.029). The Child-Pugh score and the Model for End-Stage Liver Disease (MELD) score were used to assess decreased liver function, and both were significantly higher in the control group than in the case group . The serum levels of creatinine were significantly higher in the control group compared with the case group . The baseline sodium levels were 137.8 ± 4.5 mmol/L in the case group and 134.1 ± 7.1 mmol/L in the control group (P < 0.001). The lowest sodium levels after terlipressin treatment were 125.9 ± 6.8 mmol/L in the case group and 137.2 ± 7.8 mmol/L in the control group (P < 0.001). There was statistically insignificant difference between the groups in terms of age, sex, alcohol consumption, hepatitis C virus (HCV) antibody positivity, concomitant complications of cirrhosis, HCC including stages of Barcelona Clinic Liver Cancer (BCLC), hypertension, hemoglobin level, the amount of blood transfusion, cumulative dose of terlipressin, and the amount of free water hydration.The baseline characteristics of all of the patients  exhibited mild decreases (between 5 and 10 mmol/L), 20 patients (22.7%) exhibited moderate decreases (between 10 and 15 mmol/L), and 21 patients (23.8%) exhibited severe decreases in serum sodium (greater than 15 mmol/L). The mean baseline serum sodium concentrations in the groups of mild, moderate, and severe decreases were 137.28, 136.96, and 139.9 mmol/L, respectively. The mean lowest serum sodium concentrations in the groups of mild, moderate, and severe decreases were 129.98, 124.25, and 118.57 mmol/L, respectively. The majority of patients who developed hyponatremia exhibited recoveries toward baseline serum sodium concentration after the end of treatment. The recovery time of the serum sodium concentration was 2.5 ± 1.8 days after the end of treatment. The patients who developed hyponatremia had higher baseline serum sodium levels than the patients in the other groups. There were inverse associations between the changes in the baseline serum bilirubin, MELD score, and Child-Pugh score and the changes in the serum sodium levels. The patient who developed severe hyponatremia exhibited the lower serum bilirubin levels, lower MELD scores, and lower Child-Pugh scores relative to the patients who developed mild hyponatremia, who had the highest serum bilirubin levels, the highest MELD scores, and the highest Child-Pugh scores. There were no significant differences between groups with respect to the previous histories of ascites and hepatic encephalopathy, baseline hemoglobin, baseline serum albumin, international normalized ratio, serum creatinine, blood transfusion amount, cumulative terlipressin dose, or the amount of free water hydration.One patient developed mental changes with seizure coincidence with a marked reduction in serum sodium levels from 135 to 114 mmol/L within the first 48 hours of terlipressin treatment. This patient did not exhibit any abnormalities on brain computed tomography or electroencephalogram. The serum sodium levels and mental status of this patient did not improve despite the administration of 3% hypertonic saline. However, this patient successfully recovered the serum sodium concentration and mental status within 36 hours after terlipressin withdrawal.P < 0.05). Other clinical factors, such as age, gender, alcohol consumption, infectivity of HCV, ascites, hepatic encephalopathy, spontaneous bacterial peritonitis, HCC, hypertension, cumulative dose of terlipressin, baseline hemoglobin level, and the amount of free water hydration, were not significantly associated with the development of hyponatremia during terlipressin treatment. In the multivariate analysis, baseline normal or near-normal serum sodium and creatinine levels were independent positive predictors of the development of hyponatremia. The presence of HBV, DM, and shock on admission was independent negative predictors of the development of hyponatremia (P < 0.05). The strongest predictor of the development of hyponatremia following terlipressin treatment was the baseline serum creatinine level  positivity, DM, baseline serum creatinine level, baseline serum sodium level, baseline Child-Pugh score, and shock on admission were the candidate variables that were identified for the multivariate analysis (P < 0.001). In the univariate analysis, HBV infection, alcohol, hepatic encephalopathy, HCC, baseline serum sodium level, serum bilirubin, serum albumin, international normalized ratio (INR), baseline serum creatinine level, baseline Child-Pugh score, baseline MELD score, shock on admission, the amount of blood transfusion, and the development of hyponatremia after terlipressin treatment were the candidate variables that were identified for the multivariate analysis (P < 0.05). Other clinical factors, such as age, gender, HCV infection, ascites, spontaneous bacterial peritonitis, hypertension, DM, hemoglobin level, and the cumulative dose of terlipressin, were not found to be significantly associated with in-hospital mortality. In the multivariate analysis, the development of hyponatremia after terlipressin treatment , HCC , and baseline serum bilirubin  was found to be independent predictors of in-hospital mortality  died during hospitalization. The in-hospital mortality rate was significantly lower in the group that developed hyponatremia than in the control group . Terlipressin is an effective drug for variceal bleeding and hepatorenal syndrome in cirrhosis because it vasoconstricts the splanchnic circulation via the V1 receptors [P = 0.05). HBV infection can cause HBV-associated glomerulonephritis, which is the most common extrahepatic disorder of chronic HBV infection due to immune complex-mediated injuries [P = 0.03). Diabetic renal injury, which is one of the most important chronic renal diseases in the world, may also exhibit protective effects against the development of hyponatremia for the same reason this effect is present in HBV infection. Shock on admission was negatively associated with the development of hyponatremia . Shock has been identified as a common cause of acute kidney injury [Recent studies have demonstrated that terlipressin treatment for variceal bleeding results in the development of hyponatremia in 35–67% of cirrhotic patients , 10. Howeceptors , but vaseceptors , 21. It eceptors . Therefoinjuries , 24. Recinjuries  and to pinjuries . The prey injury . UltimatP = 0.005), HCC , and baseline serum bilirubin level  was associated with in-hospital mortality. As expected, liver function at admission was a risk factor for in-hospital mortality. Indeed, because other studies have not enrolled patients with HCC, these studies were unable to describe the role of HCC in mortality or the occurrence of hyponatremia. However, in clinical settings, because HCC with and without portal invasion is the main cause of variceal bleeding, the present study was able to provide data regarding terlipressin treatment in HCC patients with variceal bleeding. The patients who developed hyponatremia after terlipressin treatment tended to exhibit lower in-hospital mortality, which may have been associated with the relatively preserved liver and renal functions of these patients who have been described above.In the present study, the development of hyponatremia following terlipressin treatment (OR, 0.19; 95% CI, 0.07–0.61; One of the 21 patients with severe hyponatremia developed seizures. This patient exhibited rapid recovery of the serum sodium level and the neurologic abnormalities without sequelae after the withdrawal of terlipressin administration. However, the serum sodium levels and mental status of the patient did not improve before the withdrawal of terlipressin administration despite the administration of 3% hypertonic saline. There have been several reports of cases of hyponatremia with seizure that have rapidly recovered nearly normal sodium levels within a day of the withdrawal of terlipressin , 8, 12. The present study has several limitations. First, enrolled patients were heterogeneous because we did not exclude patients with HCC. However, the presence or absence of HCC was not associated with the development of hyponatremia, and this finding might suggest that HCC was not related to the development of hyponatremia after terlipressin administration. Second, this was a retrospective matched case-control design study. Thus, future large-scale prospective studies are needed. Despite these limitations, to our knowledge, the present study is the first to report an association between baseline renal function and hyponatremia following terlipressin therapy in the patients with cirrhosis and other factors that influence renal function.In conclusion, terlipressin treatment is commonly applied in cases of variceal bleeding but may often cause hyponatremia. The presence of HBV, DM, the baseline serum sodium level, shock on admission, and particularly baseline renal function may be predictive of the development of hyponatremia after terlipressin treatment. The role of renal dysfunction and baseline hyponatremia as potential protective factors against terlipressin-induced hyponatremia should be further developed. Although the development of hyponatremia after terlipressin treatment is rapidly reversible and is not associated with in-hospital mortality, physicians closely monitor electrolytes daily to prevent the possible neurological complications associated with severe hyponatremia when cirrhotic patients with relatively preserved liver function and renal conditions who exhibit variceal bleeding are treated with terlipressin.


Danio rerio) model system is increasingly being used to study the brain and chemical neurotoxicity. In this review, the major neurotransmitter systems, including glutamate, GABA, dopamine, norepinephrine, serotonin, acetylcholine, histamine, and glutamate are surveyed and pathways of synthesis, transport, metabolism, and action are examined. Differences between human and zebrafish neurochemical pathways are highlighted. We also review techniques for evaluating neurological function, including the measurement of neurotransmitter levels, assessment of gene expression through transcriptomic analysis, and the recording of neurobehavior. Finally examples of chemical toxicity studies evaluating alterations in neurotransmitter systems in the zebrafish model are reviewed.Neurotransmission is the basis of neuronal communication and is critical for normal brain development, behavior, learning, and memory. Exposure to drugs and chemicals can alter neurotransmission, often through unknown pathways and mechanisms. The zebrafish ( Neurotransmission is the basis of neuronal communication and is critical for normal brain development, behavior, learning and memory, and even maintenance of life. The nervous system is exceedingly complex, and many enzymes, transporters, and receptors all work in concert to maintain these functions. Neurotransmission can be altered by exposure to drugs, pharmaceuticals, chemotherapeutic agents, radiation, food additives, and environmental toxicants including pesticides and heavy metals  research model is increasingly being used in neurotoxicity studies  and glutathione ) is a specific developmental marker of the raphe serotonergic nuclei . TPH is Acetylcholine (ACh) is the major neurotransmitter in the parasympathetic nervous system and is the neurotransmitter at neuromuscular junctions . AdditioACh is formed from acetyl-CoA and choline via choline acetyltransferase (ChAT). Two SLC family transport systems, a high affinity  and a low affinity , concentrate choline in terminals to provide a reserve for ACh synthesis. SLC44 is present ubiquitously throughout the body, but SLC5A7 is only found in cholinergic nerve termini . The ratq/11 family proteins to increase phospholipase C. M2 and M4 receptors act to decrease adenylyl cyclase through Gi/Go receptor activation. mAChR are expressed throughout the brain, but are not uniformly distributed  and muscarinic receptors (mAChR). The receptors were respectively named after nicotine and muscarine, their drug agonists. nAChRs are a member of the cys-loop family of ligand-gated ion channel receptors and are formed by combinations of receptor subunits ,157. In by Brown ).SLC5A7 (slc5a7a and slc5a7b), although slc5a7b has not been well described. The gene that produces ChAT has two paralogs in zebrafish, chata and chatb. slc18a3a and slc18a3b are two paralogs of SLC18A3 (VAChT). There is only one paralog of ACHE (ache). Currently, there are 12 putative genes encoding nAChR subunits in zebrafish and 10 putative paralogs of mAChR [The important genes in the cholinergic system are outlined in of mAChR ,160,161.Cholinergic neurons have been identified through immunohistochemical staining against choline-acetyltransferase (ChAT) ,162. In Histamine is a signaling molecule present in many tissues, serving functions in the stomach, skin, and immune systems. Histamine also has a role in neurotransmission . Within The structure of histamine, 2-(4-imidazolyl)ethylamine, is similar to 5-HT, NE, and EP, but histamine has an imidazole nucleus and therefore has tautomeric properties that may be associated with receptor affinity. In mammals, mast cells of bone marrow origin reside in perivascular spaces, choroid plexus, and meninges and can produce significant amounts of histamine within the brain although the only neurons that produce histamine are located within hypothalamic tuberomamilary neurons within the posterior hypothalamus . Zebrafil-histidine decarboxylase (HDC). The rate of biosynthesis is controlled by the availability of l-histidine and the rate limiting enzyme, HDC. Once formed, histamine is transported into vesicles by SLC18A2 (VMAT2) [Histamine is synthesized by  (VMAT2) . Most hi (VMAT2) .N-methyltransferase (HNMT), which methylates histamine and forms tele-methylhistamine (t-MH) and is then further metabolized by MAO  to tele-methylimidazole acetic acid (t-MIAA). The methylation metabolism pathway is more common in vertebrate brains and HMT is widely distributed throughout the brain [The metabolism of histamine can occur either by diamine oxidase , which oxidizes histamine to imidazole acetic acid (IAA), or by histamine he brain ,169.q and stimulate phospholipase C. H2 receptors may couple with Gq or Gs but act through the stimulation of adenylyl cyclase. H3 receptors are linked to Gi/o and inhibit adenylyl cyclase. H3 receptors may also activate MAPK, Akt/GSK-3β, and phospholipase A2 pathways [i/o proteins to inhibit adenylyl cyclase [Mammals have four histamine receptors that are found in the brain, H1, H2, H3, and H4. H1 and H2 are considered excitatory while H3 is inhibitory and often acts as an autoreceptor. H4 is the most recently discovered but does appear to localize to the brain . Histamipathways ,175,176. cyclase .HDC (l-histidine decarboxylase), AOC1 (diamine oxidase), and HNMT (histamine N-methyltransferase)  . To dateand hrh3 . In zebrand hrh3 ,178. TheGlycine is an amino acid that serves as a signaling molecule and neurotransmitter in the brainstem and spinal cord. Glycine is the simplest amino acid, with only a hydrogen for its side chain. Glycine, typically considered an inhibitory neurotransmitter, is involved with interneuron differentiation in neurodevelopment, mediation of spinal reflexes, and reflex behaviors such as breathing ,180.Glycine can be formed through the conversion of serine to glycine with either glycine dehydrogenase (GLDC), also known as glycine decarboxylase (GDC) or glycine-cleavage system, or serine hydroxylmethyltransferase (SHMT). Glycine is transported into synaptic vesicles via the vesicular inhibitory amino acid transporter, SLC32A1 ) .Glycine is transported out of the synaptic cleft by glycine transporters. Two glycine transporters, SLC6A9 (GLYT1) and SLC6A5 (GLYT2), have been identified in mammals . SLC6A9 1–α4) and the β gene group has only one member . Glycinnd Smart . Interesnd Smart .slc6A9) immunohistochemistry. In the developing zebrafish, glycine producing cells are limited to the hindbrain and spinal cord, and appear as early as 20 h post fertilization (hpf) [As outlined in on (hpf) ,188. In on (hpf) ,187.Other substances can also act as neurotransmitters in the brain, including purines, peptides, nitric oxide, and endocannabinoids ,190. PleThe neurotransmitter systems can serve as targets of chemical toxicity. The individual enzymes, transporters, and receptors may be altered by chemical toxicants through changes in gene expression or changes in activity of the enzyme or receptor. Multiple methods can be used to evaluate neurotransmitter systems in zebrafish, from the measuring of neurotransmitter levels in the brain, to evaluation of gene expression, to the functional testing of behavioral assays. These methods are reviewed below.In the evaluation of chemical toxicants, measuring neurotransmitter levels can provide information on the functional alterations in the brain resulting from chemical treatment. The quantity of neurotransmitters can be measured in the zebrafish brain. Sallinen et al.  and ChatExperimental neurotransmitter results may be comparable within one laboratory, but not comparable across multiple laboratories due to differences in experimental methods and equipment used. Furthermore, one difficulty in comparing neurotransmitter levels across studies and laboratories is the lack of reference intervals and the tendency to normalize neurotransmitter values to controls rather than reporting absolute values, which limits comparison. For example, Pan et al.  reportedOther methods for the measurement of neurotransmitters include an analytical method described by Tufi et al.  of hydroChemical toxicants can affect the expression of genes, and evaluation of these changes can provide information on pathways that may be altered due to chemical exposure. Quantitative PCR (qPCR) can be used to evaluate alterations in gene expression ; howeverMicroarray studies have been used to evaluate gene expression after chemical exposure in zebrafish ,201,202.The significance of apparent alterations in gene expression and changes in neurotransmitter levels is uncertain without having differences in phenotype. Zebrafish are an accepted model of neurobehavior ,214,215 The visualization of neurotransmitters has been important for evaluating the location, relative quantity, and pathologic alterations of neurotransmitters and neurotransmitter pathway components. A multitude of techniques, including immunohistochemistry, in situ hybridization, immunofluorescence, calcium indicators, selective fluorescent reporters ,225,226 The advantages of the zebrafish model system allow for high throughput screening of pharmacologic agents which can help identify neuropathways, mechanisms of toxicity, possible therapeutic drugs, and help classify zebrafish behavior Zebrafish have been used as a model organism to study the effect of multiple classes of chemicals on neurodevelopment and neural function. Classes of chemicals examined include drugs, especially ethanol, pesticides, and metals. The significant methods and findings from the literature are summarized below and in hdc (histidine decarboxylase), th1, and th2 at higher treatment levels, no alterations in the dopaminergic and histaminergic systems according to in situ hybridization and immunohistochemistry, and a decrease in dopamine levels, as measured by HPLC. These results suggest that ethanol has the ability to alter the cholinergic, dopaminergic, and histaminergic neurotransmitter systems and that neurobehavior can be a sensitive measure of altered neurotransmitter systems.The effects of alcohol have been extensively studied in zebrafish. Rico et al.  found thThe effects of alcohol on zebrafish behavior appear to have an inverted U shaped dose-response, with increased locomotor activity and shoaling behavior at lower doses and decreased locomotor activity and shoaling behavior at higher doses, as well as either increased or decreased measures of anxiety ,241,242.Zenki et al.  found thNicotine is often used to study nicotinic cholinergic receptors, but nicotine also is a drug known for anxiolytic effects. Levin et al.  and BencPesticides represent a broad group of chemicals that include herbicides, insecticides, fungicides, and rodenticides. Some pesticides are important environmental toxicants while others are more acutely toxic to humans .The effects of organophosphate pesticides on brain acetylcholinesterase are well known, however, exposure to organophosphates can also affect other brain neurotransmitter systems. Eddins et al.  studied Atrazine is a commonly used herbicide in the Midwestern United States that often contaminates drinking water supplies. Wirbisky et al.  found degad1 while adult zebrafish with 28 day exposure had down regulation of gad1, gabrr1, and gabbr2 [Semicarbazide is a contaminant formed from the breakdown of azodicarbonamide, a chemical used to treat flour. Semicarbazide derivatives have also been used as herbicides. Adult zebrafish exposed to semicarbazide for 96 h had increased expression of d gabbr2 . Yu et ad gabbr2  suggest glra4 at 24 and 48 hpf, gad1 at 24–96 hpf, and slc17a6a and slc17a6b (VGLUT2) at 48 hpf.Strychnine is commonly used as rodent bait. Roy et al.  found thgad2, gad1b, slc6a1 (gat1), slc32a1 (vgat), gabbr1, and gabbr1a as well as altering GABA levels during development. Furthermore, Lee and Freeman [Some metals, such as lead and mercury are important environmental toxicants ,255,256. Freeman  found thIn addition, studies have also started to evaluate the impact of mercury exposure on neurotransmitter systems. The toxicity of mercury depends on its chemical form. Methylmercury is associated with Minamata disease and nervous system impairment. Cambier et al.  fed adulThe neurotransmitter systems are highly conserved between zebrafish and mammals, making the zebrafish model a powerful tool for the study of mechanisms of chemical neurotoxicity. Although there are some differences in neurochemistry, the pathways of neurotransmitter synthesis, metabolism, and action are highly conserved across species. Although some genes have multiple paralogs in zebrafish, further research into these paralogs may help identify new functions of genes in humans. Furthermore, further research into neurotransmitter receptors may provide more information about basic neurological systems and connectivity.The nervous system is an important target of chemical toxicants. Neurotransmitter levels, gene expression, and neurobehavior can be evaluated after chemical treatment to discover toxicant based changes in the nervous system. New technologies provide more sensitive and powerful methods; however, the integration of these methods along with other techniques, such as imaging and activity assays, will be necessary to determine the mechanisms and pathogenesis of chemical toxicant related alterations. Overall, relatively few studies have focused on alterations of neurochemical systems in zebrafish, but the completed studies support the utility and application of zebrafish in neurochemical toxicology.