Engraulis encrasicolus) transcriptome, a species with no available reference genome, using next generation sequencing technologies. A cDNA library was constructed from four tissues of ten fish individuals corresponding to three populations of E. encrasicolus, and Roche 454 GS FLX Titanium sequencing yielded 19,367 contigs. Additionally, the European anchovy genome was sequenced for the same ten individuals using an Illumina HiSeq2000. Using a computational pipeline for combining transcriptome and genome information, a total of 18,994 SNPs met the necessary minor allele frequency and depth filters. A series of further stringent filters were applied to identify those SNPs likely to succeed in genotyping assays, and for filtering of those in potential duplicated genome regions. A novel method for detecting potential intron-exon boundaries in areas of putative SNPs has also been applied in silico to improve genotyping success. In all, 2,317 filtered putative transcriptome SNPs suitable for genotyping primer design were identified. From those, a subset of 530 were selected, with the genotyping results showing the highest reported conversion and validation rates  reported to date for a non-model species. This study represents a promising strategy to discover genotypable SNPs in the exome of non-model organisms. The genomic resource generated for E. encrasicolus, both in terms of sequences and novel markers, will be informative for research into this species with applications including traceability studies, population genetic analyses and aquaculture.Increased throughput in sequencing technologies has facilitated the acquisition of detailed genomic information in non-model species. The focus of this research was to discover and validate SNPs derived from the European anchovy ( Engraulis encrasicolus, L.1758) is a small pelagic teleost with major economic and cultural importance. It has been the focus of numerous ecological and genetic studies and major research efforts have been conducted to understand population dynamics from an ecological point of view. Genetic studies have been focused on population genetic structure E. encrasicolus, and this issue has impeded progress in molecular marker development.European anchovy (Genetic markers are important for many applications The advance in the genetics fields of the European anchovy had reached a plateau until the development of the next generation sequencing (NGS) technologies, which has now made possible the implementation of SNPs as standard genetic markers in non-model species in silico those markers potentially close to intron-exon boundaries (IEBs), as the proximity of an intron is known to be the main cause of genotyping assay failure due to the inability to handle large PCR amplicons loci in the genome but invariant at the population or species level In recent years, next generation sequencing (NGS) technologies have emerged as a cost-effective way to very rapidly generate a large amount of valuable genomic information E. encrasicolus and discovered SNP markers based on the combination of transcriptome and genome information. Importantly, next generation sequences from the European anchovy genome were used to supplement the transcriptome, in a novel strategy designed to avoid IEBs as well as potential repeated regions during SNP genotyping primer design and SNP validation. This sequencing and computational pipeline, which does not require a prior application of genome assembly, has resulted in the highest conversion and validation rates reported to date in a non-model species. The overall objectives of this study are to improve current SNP discovery procedures from transcriptome sequences through a new and accurate IEB prediction pipeline which could be reproducible for another non-model species, and to validate new SNP markers to be applicable in prospective genetic studies in European anchovy.In the current study, we characterized the transcriptome of The flowchart in Ten individuals from three genetically divergent populations www.iucnredlist.org). For surveying in French territories permission was received from the French Ministère des Affaires Etrangères et Européennes (document no. 1233/DGM/ATT/ENT). Anchovies were collected following fishing without unnecessary suffering of the animals and following usual procedures: samples were obtained as part of faunal surveys with trawl nets; immediately after collection, anchovies are sorted from the bulk of the catch and a sample of 2 kg was selected at random, for which extracted tissues of 30 individuals were stored in ethanol or at −20°C. No experimentation with live animals was performed. No other ethical issues applied to the present research project.All surveys followed local regulations and guidelines for such research. For Spanish territories, no specific permission is needed for sampling aquatic fauna for scientific objectives, and the European anchovy is not considered a threatened species according to the International Union for Conservation of Nature and Natural Resources . All 454 sequence data have been submitted to the NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA193183.For transcriptome sequencing, total RNA from the ten sampled individuals and four tissues was extracted using Trizol Reagent (Invitrogen) and quantified with Agilent 2100 Bioanalyzer combined with Agilent RNA 6000 Nano chips  at the Gene Expression Unit (SGIker) at the University of the Basque Country (UPV/EHU). Isolated RNA  was combined in equimolar quantities into a single pool in an attempt to maximize the diversity of transcriptional units sampled and RNA was normalized by Evrogen (Russia) to prevent over-representation of the most common transcripts, using the DSN normalization method For the genome sequencing, total DNA from muscle tissue of ten individuals  was isolated using NucleoSpin® 96 Tissue Kit (Macherey-Nagel) according to the manufacturer's instructions and DNA quantity and purity were measured with a Nanodrop ND-1000. The Illumina TruSeq® DNA sample preparation kit was used to generate a barcoded genomic library for each individual. The ten barcoded genomic libraries were pooled and sequenced with a 2×100bp paired-end module on 4 lanes of a HiSeq2000 (Illumina). Standard post-processing was applied  and sequences were de-multiplexed based on the specific barcoding tags used for each individual. The genome sequencing and post-processing was carried out at the Laboratory of Biodiversity and Evolutionary Genomics at the Katholieke Universiteit Leuven (Belgium). All HiSeq2000 sequence data have been submitted to the NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA202430.E. encrasicolus mitochondrial genome (NCBI Accession Number: AP009137) to identify and isolate all mitochondrial transcripts using GS Reference Mapper v2.6  with custom parameters. Additionally, a local BLASTn search on the trimmed dataset was performed to identify ribosomal transcripts  and the SeqClean tool was used to screen out UniVec database contaminating sequences.To improve the 454 native base-calling error rate, PyroBayes The European anchovy genome raw reads were trimmed using clean_reads de novo Assembler v2.6 (454 Life Sciences) with the cDNA assembly option, by setting a minimum overlap length of 50 and a minimum overlap similarity of 95%. Reads were re-trimmed with the GS de novo Assembler trimming tool, including in the GS de novo Assembler exclude filter file the previously created file with names from cDNA reads corresponding to mitochondrial, ribosomal or contaminant sequences. The assembly quality was verified by visual examination of a random subset of contigs with the Tablet assembly and alignment visualization tool Transcriptome trimmed sequences were assembled using GS A transcriptome reference assembly was created by filtering transcriptome contigs for (1) homologous genes, (2) low-complexity regions and (3) duplicated regions. For the removal of homologous genes  and low-complexity regions, gDNA reads were aligned to the transcriptome reference using Bowtie2 Finally, Bowtie2 loci) due to sequencing errors, SNP discovery was performed using both T2T and G2T alignments. For SNP discovery and selection we developed a method for automatically extracting SNPs amenable to genotyping from the SAM output files of the T2T and G2T alignments, and the VCF  output of the SAMtools package With the aim of avoiding false SNPs (monomorphic loci), only T2T variants with a minimum variant count of 2 high quality (HQ) bases and a minimum site depth of 8 (HQ bases) were called as putative T2T SNPs (no depth filter was applied for a variant count of 3 HQ bases or more). In parallel, from the G2T SNPs only variants with a minimum variant count of 2 HQ bases and a minimum site depth of 20 (HQ bases) were called, but no limitation of depth was required for a variant count of more than 2 HQ bases. Finally, a maximum site depth threshold of 200 for each SNP was applied. This final step was performed also with the aim of avoiding duplicated regions due to homologous genes (PSVs or MSVs).For SNP discovery, the T2T and G2T alignments were processed with SAMtools bcftools and duplicated reads (arising from errors in the PCR step prior to sequencing) from each dataset (cDNA and gDNA trimmed reads) were removed with rmdup option. Putative SNP discovery was accomplished by filtering all T2T and G2T observed variants in order to reveal only biallelic SNPs (no indels or complex SNPs). To avoid false positives due to sequencing errors . The selection criteria for SNPs were based on an analysis of putative intron-exon boundaries (IEBs) within each contig and on the compatibility of flanking sequence with the Taqman® method. Initial efforts of IEB finding through a BLAST search of European anchovy contigs against other teleost genomes showed that most contigs had no significant matches to other teleost genomes. Therefore, a novel approach to IEB detection change points: locations where a number reads either start or terminate their local alignment at an internal position , which were also masked as Ns in every sequence. We ensured that target sites were not in regions of similarity with any other contig in the European anchovy transcriptome, as identified by a BLASTn search . Based on the genotyping technology requirements, we rejected any sequence with less than 30 available bases upstream or downstream of the target site (due to the start/end of the contig or due to a predicted IEB), to comply with the minimum requirement for primer design, and any target site without complete alignment conservation (no variation or sequencing Ns) within +/−5 bases.For each filter (T2T and G2T) all SNPs meeting the respective requirements described above were marked and the T2T and then the G2T SNPs sets were joined, resulting in only those SNPs discovered from both approaches. After the SNP discovery step, SNPs were position . Taking ® OpenArrayTM SNP genotyping assay construction, SNPs were divided by two selection criteria. A first SNP subset was selected based on homology to zebrafish (Danio rerio) exome. For this, conservation of gene structure between European anchovy and zebrafish was assumed, and each contig was assessed using a database of coding sequences for all transcripts from the Ensembl zebrafish genome (Zv9). Anchovy filtered transcriptome contigs were aligned at the protein level using BLASTx  to identify a possible unique orthologous zebrafish gene. SNPs were annotated in 4 categories: no homology (no homology found to zebrafish), cSNP (contig with homology and SNP in a coding region), ncSNP (contig with homology and SNP outside of coding region) and tSNP (ambigious cases where a contig has a homology to a coding region and another homology outside a coding region). All cSNPs were selected and further SNPs were randomly selected from the tSNPs list in order to have potentially coding SNPs, interesting for adaptative and evolutionary studies. For the second SNP subset, a preliminary Fst value was calculated for suggesting how well the SNPs may discriminate populations. In this case, individual genotypes for each called SNP were determined by inspecting the VCF output of bcftools. This genotype information was used to calculate a preliminary Fst value For TaqManE. encrasicolus from five different populations (Bay of Biscay (BIS1 and BIS2), Mediterranean Sea (MED), Atlantic (CAD), Canary Islands (CAN) and North Sea (NSE); ;  accordina (NSE); . DNA extTaqMan® OpenArray™ Genotyping System User Guide at the Sequencing and Genotyping Service (SGIker) of the University of the Basque Country (UPV/EHU). Scoring of individual genotypes was performed using TaqMan® Genotyper software v2.1 (Life Technologies). After default clustering was performed, data was viewed in the scatter plot and genotype calls were reviewed and manually adjusted for producing the final cluster assignments. Based on these assignments, SNPs were classified as no signal (no amplification), disperse , monomorphic , PSV/MSV , and polymorphic (MAF ≥0.01).A submission file with the sequences specifying the target SNPs was created and sent to the Applied Biosystems Assay Service for primer and probe design. Genomic DNA (66 ng per sample) was used as template at the required DNA starting concentration (22 ng/µl). Subsequent reactions for the amplification and detection of the SNPs were carried out following no signal and disperse SNPs were removed, the remaining SNPs were used for the calculation of the SNP conversion rate, while only polymorphic SNPs were used for the calculation of SNP validation rate and for further analysis of descriptive statistics.Once genetics package from R e), observed heterozygosity (Ho), and minor allele frequency (MAF) were calculated using GeneClass2 loci under natural selection. In this test, because multiple comparisons were involved, critical values for the test were adjusted with false discovery rate (FDR) procedure  For each polymorphic SNP, the genotyping percentage was calculated using Genepop v4.0 Transcriptome contigs were independently searched for microsatellite repeats and primers using the software QDD2 −6) to align the contigs to the manually curated protein database Uniprot/Swissprot We used BLASTx  resulting in 821,107 reads with a length average of 304 bp comprising 225 Mbp. These trimmed sequences were considered as high-quality (HQ) sequences (92.3% of raw reads). Only nucleotides with Phred “high quality bases” were considered (HQ Phred score >20) high-quality region. FastQC tools analyses showed that the trimming process substantially improved the genome sequence dataset, especially in terms of sequence quality, which is essential for the success of the SNP calling process.Genome sequencing contaminant removal, and length and quality trimming, yielded 1,364,994,151 HQ paired-end sequences , with read lengths ranging from 61 to 100 bp. The trimming results for each individual are detailed in de novo Assembler software assembled 657,778 reads (80.5% of trimmed reads) into 24,494 contigs with a N50 of 459, and an average coverage of 15.13. The assembled contigs had an average length of 498bp, comprising more than 180 Mbp. The longest contig was 3,336bp length and 10,095 contigs (41.2%) were longer than 500 bp. The N50 of these large contigs was 766. The total assembled reads length was 12,209,523bp. Reads that were not assembled constituted 143,537 singletons (17.6%), which were excluded for further analyses. The remaining 1.9% of reads was identified by GS de novo Assembler software as repeats, outliers, or reads too short for use in the assembly. The transcriptome reference was filtered for low-complexity regions, spurious contigs, chimeric contigs, and duplicated regions in the genome, yielding a total of 19,367 high-confident consensus sequences. From these, 10,402 contigs containing multimap reads were identified. In the T2T alignment, 593,122 reads successfully mapped, with a coverage of ∼15. For the G2T local alignment, 11,361,696 reads aligned one or more times, with a coverage of ∼78.GS A summary of SNP discovery and selection statistics is presented in TM SNPs genotyping system requirements were passed by 2,317 SNPs which appeared within 1,617 contigs. From these, 892 contigs (55.16%) showed homology to the D. rerio proteome: 195 cSNPs (21.86%), 423 ncSNPs (47.42%) and 274 tSNPs (30.72%); for the first SNP subset every cSNP was selected and it was completed with markers annotated as tSNPs. Regarding the SNP selection criteria based on preliminary Fst, the second SNP subset consisted on those SNPs – not coincident with those selected from the zebrafish homology criteria – with the highest Fst values (from 0.31 to 0.83) were selected. The two SNPs subsets led to a total of 530 SNPs for genotyping and validation.TaqMan® OpenArrayno signal, 30 disperse); which constitute a conversion rate of 91.3%. From those SNPs, 441 were polymorphic  resulting in a validation rate of 83.2%.The final set of selected and genotyped 530 SNPs is listed in loci meeting HWE and 11 SNPs in LD formed 5 haplotypes. Therefore a total of 420 independent markers meeting HWE were found to be potentially used in prospective studies. The expected heterozygosity (He) of these markers ranged from 0.012 to 0.495, while observed heterozygosity (Ho) fluctuated between 0.007 and 0.550, and MAF values ranged from 0.003 to 0.498. The distribution of SNPs frequencies over the range of MAF categories does not suggest an elevated non-random exclusion of SNPs with low MAF, adequately showing even representation over the entire MAF range  . The mosActinopterygii) except one amphibian , which matched only one microsatellite sequence . All detected microsatellite loci are listed in Approximately 10% of the detected repeats matched 49 sequences isolated from 12 different species, all of them fishes . These sequences were assessed for gene ontology terms , metabolic (15.9%) and biological regulation (11.1%). Most molecular functions found in this study were related to binding (16.7%) and catalytic activity (10.8%). Finally, regarding the cellular component gene ontology category, the most common components were the very general term cell (19.6%) and organelle (16.5%).The 19,367 transcriptome contigs reference were annotated using the Blast2GO tool against the zebrafish proteome. The BLASTx analysis resulted in 6,100 sequences with at least one BLASTx hit. This result indicated that 31.5% of AF range . The vasE. encrasicolus. Moreover, we report the highest SNP conversion and validation rates described to date for a non-model species, demonstrating a method for rapid and cost-effective SNP discovery in the exome of non-model organisms.This study presents one of the largest combined (transcriptome and genome) sequencing projects for a non-model species and is the most extensive genomic analysis performed on the ecologically and economically important The success on the recovery and validation rates of the SNP markers in the present study relies on the strategies adopted to (1) avoid ascertainment bias, (2) trim and quality filter the transcripts, (3) establish criteria for accurate SNP calling and (4) accurately identify duplicated regions and intron-exon boundaries.E. encrasicolus individuals from genetically distant populations Regarding ascertainment bias, it has been previously shown that the deviation towards detecting only SNPs with high or intermediate allele frequencies might be a problem as it influences the precision of estimates related to demographic parameters such as migration or Ne, which could lead to mistaken assumptions about demographic history of the species de novo assembly for SNP discovery. To deal with this issue, in the present study, a transcriptome rather than a genome assembly was chosen as the primary substrate for SNP discovery. The chosen strategy has the additional advantage that SNP markers derive directly from exonic regions of the genome, which are especially relevant for fisheries genetics, traceability, adaptative studies, conservation studies and aquaculture applications. For non-model species, high-throughput technologies are currently the most recommended since they generate a massive quantity of sequences and, specifically, the main advantage of the 454-FLX system is the production of longer reads than other sequencing systems, which helps the de novo assembly step. Additionally, performing suitable bioinformatic analyses such as base-call accuracy improvement, trimming, and the assembly itself (as well as its posterior filtering), is also important. This study has carefully taken into account all these issues. First, regarding the reduction of the error rate of base calls and improvement of accuracy in quality scores, PyroBayes algorithm produced a significant improvement in terms of trimming on quality results (data not shown). This correction may have led to a significant reduction in false SNP calls and may have facilitated the assembly cellular component Gene Ontology terms from the annotation step. Indeed, the beginning and the end of genome reads had very low quality values prior to the trimming process. Such a feature is common to all high-throughput sequencing methods  is a result of the carefully quality controlled assembly, since the sequence for primer and probe design was correctly constructed in at least 91.3% of the cases. The assembly issue remains challenging mainly due to two factors: first, sequencing errors can lead to mismatching sequences between reads that came from the same location in the genome/transcriptome It is well known that one of the major challenges for SNP discovery studies in non-model organisms is to achieve a high quality hods see , each utmonomorphic markers represented just 7.5% of the putative SNPs (40/530). Compared to other de novo transcriptome sequencing and SNP discovery studies  are too divergent from Engraulis encrasicolus for significant BLAST matches at the nucleotide level. For Atlantic cod transcriptome SNP discovery no signal, which means that only the 3.0% of the putative markers have not amplified. The new approach for IEB detection described in this study provides successful detection of IEB within transcriptome assemblies while bypassing the need to construct an assembled genome, a complicated and time-consuming task.The key to achieving high SNP conversion rates from transcriptome data is the identification of IEBs and their avoidance in primer and probe sequences for posterior marker validation Most studies using high-throughput sequencing technologies for discovering SNPs from non-model fish transcriptomes have obtained conversion rates between 43% and 79%, and validation rates between 12% and 55% , which aEngraulis encrasicolus, a non-model organism . In all, these percentages reflect the accuracy and effectiveness of the described strategy.Moreover, based on the validation rates obtained on the 530 tested SNPs subset, the extrapolation of the 83.2% of validation rate obtained on the 530 tested SNPs to the whole 2,317 putative SNPs discovered, would yield around 2,000 SNP validated markers in the exonic regions of E. encrasicolus. The technique is based on a single half-plate run on a 454 GS FLX (Roche) sequencing instrument using titanium chemistry for transcriptome sequencing, and two lanes of a HiSeq2000 (Illumina) instrument for genome sequencing. This approach can be used for rapid, comparatively low-cost SNP discovery and high conversion and validation rates in any non-model organism. While the cost of the method described here is comparable to traditional alternatives for SNP discovery, the approach has the added benefit of detecting a large number of reliable SNPs in non-model organisms. As these loci are derived directly from transcribed sequences, gene function annotation of the discovered markers is possible and markers under selection are expected. The value of SNPs under selection for fisheries management is that these are more informative than neutral ones when aiming for population/origin assignment of individuals. Therefore these SNPs could be informative in studies on adaptation, origin assignment or aquaculture.The SNP discovery pipeline described in this paper has identified over 19,000 putative SNPs in Regarding European anchovy, the target non-model species of this study, the 441 validated SNPs may be useful in prospective genetic studies for understanding and estimating effective population size and detecting significant reductions in population size or population bottlenecks in populations which have suffered a loss of genetic variability. Since markers linked with genes influencing fitness might generally provide a good indicator of levels of adaptive variation within populations and their potential to respond to changing environmental conditions Figure S1FastQC tool generated quality plot for CAD-1 individual genome sequences before (left) and after (right) contaminants removal, and length and quality trimming. In the plot X axis represents position in the read (bp) from 0 to 100, and Y axis represents quality values in Phred+33 scale (from 0 to 40).(PDF)Click here for additional data file.Figure S2Minor allele frequencies (MAF) values obtained from 435 independent validated markers (in H-W equilibrium or not).(PDF)Click here for additional data file.Figure S3Level 2 gene ontology terms, divided in the three categories, and the percentage of Engraulis encrasicolus genes for each term.(PDF)Click here for additional data file.Table S1530 genotyped SNPs sequences and their descriptive statistics. For each marker following information is provided: NCBI Submitter SNP (ss) accession numbers, reference and alternative alleles, flanking sequence, category, genotyping percentage, HWE, linkage disequilibrium (LD), expected and observed heterozygosities , minor allele frequency (MAF) and natural selection state.(XLSX)Click here for additional data file.Table S2Best microsatellite markers detected in the European anchovy transcriptome.(XLSX)Click here for additional data file.


Equine guttural pouch tympany (GPT) is a hereditary condition affecting foals in their first months of life. Complex segregation analyses in Arabian and German warmblood horses showed the involvement of a major gene as very likely. Genome-wide linkage and association analyses including a high density marker set of single nucleotide polymorphisms (SNPs) were performed to map the genomic region harbouring the potential major gene for GPT. A total of 85 Arabian and 373 German warmblood horses were genotyped on the Illumina equine SNP50 beadchip. Non-parametric multipoint linkage analyses showed genome-wide significance on horse chromosomes (ECA) 3 for German warmblood at 16–26 Mb and 34–55 Mb and for Arabian on ECA15 at 64–65 Mb. Genome-wide association analyses confirmed the linked regions for both breeds. In Arabian, genome-wide association was detected at 64 Mb within the region with the highest linkage peak on ECA15. For German warmblood, signals for genome-wide association were close to the peak region of linkage at 52 Mb on ECA3. The odds ratio for the SNP with the highest genome-wide association was 0.12 for the Arabian. In conclusion, the refinement of the regions with the Illumina equine SNP50 beadchip is an important step to unravel the responsible mutations for GPT. Among all mammalian species, horses developed the largest guttural pouch. Other mammalian families possessing a diverticulum of the auditory tube include Tapiridae, Rhinocerotidae, Hydracoidea, some species of microchiopterans and Heteromys anomales.The guttural pouches are air filled The equine guttural pouches may have an important function during heavy exercise for brain cooling The majority of cases for GPT had been reported for Arabian, English thoroughbreds, Standardbreds, Appaloosas, Quarter, warmblood, American saddle and Paint horse A genome-wide mapping strategy using highly dense single nucleotide polymorphism (SNP) sets for linkage and association analyses may be the most appropriate approach to identify the target genomic regions and trait-associated SNPs. This study shows the effectiveness of using Illumina equine SNP50 beadchip with about 55,000 evenly spaced SNPs for both, linkage and association analyses, when inheritance and pedigrees are complex.The multipoint non-parametric linkage analyses using 4473 SNPs with an average distance of 500 kb revealed the highest peak on ECA15 for Arabian with genome-wide significance  and the For the German warmblood horses using 14,378 SNPs with an average spacing of 125 kb, the highest peak was located on ECA3  and in a10P-value of 6.26 on ECA15 for the SNP BIEC2-314665 at 64.17 Mb  kinship matrix and the fixed effects of sex and inbreeding as far as possible  of the highly associated SNP BIEC2-314665 was 0.18 (G) for the GPT-affected Arabian and 0.65 (G) for controls. The genotype distribution in GPT-affected Arabian was 0.64 for A/A, 0.36 for A/G and 0.00 for G/G. In controls, the genotype distribution was 0.20 for A/A, 0.30 for A/G and 0.50 for G/G  revealed for the Arabian a significant hit at a −log64.17 Mb . The P-vpossible . The SNP for G/G . The odd66–67 Mb .10P = 5.4, P-value<0.05 after correcting for multiple testing using the Benjamini-Hochberg procedure) on ECA3, located within intron 1 of ARHGAP24 (2 = 0.03 and LOD = 2.42 (D’ = 0.5). The LD of BIEC2-780830 to the neighbouring SNPs was r2 = 0.02 and 0.04, respectively.The GWAS for German warmblood showed unlike the highest peak of linkage an association at 51.86 Mb . The tetratricopeptide repeat (TPR) motif is a protein-protein interaction module found in multiple copies in a number of functionally different proteins that facilitates specific interactions with a partner protein(s). Most TPR-containing proteins are associated with multiprotein complexes, and there is extensive evidence indicating that TPR motifs are important to the functioning of chaperone, cell-cycle, transcription, and protein transport complexes BIRC6 (64.18–64.40 Mb). It is essential for cell viability and inhibits apoptosis by facilitating the degradation of apoptotic proteins.A positional candidate gene on ECA15 is For the German warmblood horses, a very high linkage signal was evident at 41–43 Mb on ECA3. The linkage peak region did not exactly fit with the associated SNP but was displaced proximally. A likely reason for this result could be that the associated region is smaller than in the Arabian horse due to the different warmblood breeds included and therefore, the SNP density is too low for identification of a highly associated region. The data set of German warmblood horses contained horses of different breeds like Hanoverian, Oldenburg, Trakehner, Holstein and others. A low MAF of the associated SNP and a low LD with adjacent SNPs may indicate a small GPT-associated region which is not well covered by informative SNPs. A further reason for missing coincidence of the highly linked region with the associated SNP could be that only very few animals possess a larger LD block harbouring the GPT-causing gene and in the other animals recombinations in preceding generations and intermixing with different warmblood breeds lead to a very small LD block with the GPT-causing gene. In this situation, the LD block is hardly detected because common SNPs are not associated with GPT and only rare SNP alleles may show significant associations. However, we can not preclude that a small LD block in association with GPT may be detected at another location within the linked region when the SNP density is increased. Within the highly linked region, it is hard to detect a clear candidate gene as most of the genes annotated in EquCab2.0 are novel non-coding pseudogenes or a retrotransposed pseudogene or a novel protein coding gene.tuba auditiva is the reason for the tympany, there are different ways of interfering by various genes. A putative candidate gene on ECA3 influencing cartilage formation is IBSP, the integrin-binding sialoprotein (50.04–50.05 Mb). The cartilage is an important reinforcing structure for the tuba auditiva and at the medial side for the flap at the orifice. Eustachian tube dysfunction also plays an important role in the otitis media in men SLC39A8, solute carrier family 39 (zinc transporter), member 8 (37.58–37.62) on ECA3 and SLC30A6, solute carrier family 30 (zinc transporter), member 6 (64.54–64.58) on ECA15 could possibly be involved. Both genes belong to a subfamily of proteins (GO:0071577) that show structural characteristics of zinc transporters. Zinc is an essential cofactor for hundreds of enzymes. It is involved in protein, nucleic acid, carbohydrate, and lipid metabolism, as well as in the control of gene transcription, growth, development, and differentiation Assuming that a failure of the mechanism of the flap at the orifice and the All animal work has been conducted according to the national and international guidelines for animal welfare. The EDTA-blood sampling at the University of Veterinary Medicine Hannover was approved by the Lower Saxony state veterinary office Niedersächsisches Landesamt für Verbraucherschutz und Lebensmittelsicherheit, Oldenburg, Germany (registration number 509c-42502-01A60).Clinical data and EDTA-blood samples of GPT-affected foals were collected in the Clinic for Horses of the University of Veterinary Medicine Hannover. All cases of GPT were clinically and endoscopically examined and surgically treated. We could not observe substantial differences in the type of signs for GPT between fillies and colts as well as between the two breeds. A total of 458 samples including 70 GPT-affected and 388 unaffected horses were available for the genome-wide association study. The data included 19 GPT-affected Arabian and 29 GPT-affected German warmblood fillies as well as 14 GPT-affected Arabian and eight GPT-affected German warmblood colts . UnaffecFor genotyping we isolated genomic DNA from EDTA blood samples or hair roots using standard methods with RBC (Red Blood Cell) lysis buffer and SE (sodium EDTA) buffer. The DNA concentration of the samples was adjusted to 50 ng/µl. DNA concentration was determined using the Nanodrop ND-1000 .Genotyping was performed using the Illumina equine SNP50 beadchip including 54,602 SNPs (single nucleotide polymorphisms) using standard procedures as recommended by the manufacturer. Data were analyzed using the genotyping module version 3.2.32 of the BeadStudio program (Illumina). With the help of BeadStudio software a cluster file was generated.http://pngu.mgh.harvard.edu/purcell/plink/Purcell et al. 2007) and SAS/Genetics version 9.3 . The genotyping rate per animal was set >0.98. The threshold for Hardy-Weinberg equilibrium was set to P = 10−7.A total of 49,797 SNPs with a minor allele frequency (MAF) of >0.05, a call rate of >90% and an average MAF of 0.25 were left for the analysis of the Arabian. For German warmblood horses, 43,440 SNPs were available under the same restrictions. Data quality control was done using PLINK version 1.07 (genome-wide  =  (1–Pchromosome-wide)1/r, where r is the length of the respective horse chromosome in Mb  divided by the total equine genome length .Whole genome scans were performed using multipoint non-parametric linkage analyses for each of both breeds separately. Linkage analyses were done using the software MERLIN software . Linkage between GPT and SNPs was estimated through the proportion of alleles identical-by-descent (IBD) for affected horses. The NPL (non parametric linkage) pairs-statistics by Whittemore and Halpern IS) were estimated using equidistantly distributed SNPs at a pairwise linkage disequilibrium (r2) <0.2. This pruned set of SNPs was generated jointly for both breeds using a window size of 100 SNPs, a shift by five SNPs at each step and an r2 <0.2. The number of SNPs contained of this subset was 4,473. The Q-matrix was determined using STRUCTURE, version 2.3.3 10P-values were constructed to control for population stratification and to visualize significant SNP genotype effects. P-values from MLM analyses were corrected using the Benjamini-Hochberg procedure 10P-values, we estimated the size of haplotypes for GPT-affected horses using LD and haplotype block analysis by Haploview, version 4.2 For the genome-wide association study, a case-control association analysis was performed using 49,797 SNPs for Arabian and 43,440 SNPs for German warmblood horses. A mixed linear model (MLM) was employed for the genome-wide association analysis in order to control for a complex population structure, sex and inbreeding of the horses. MLM analyses were performed using TASSEL , version 2.1, a software appropriate for association mapping of complex traits in diverse samples. The Q-matrix to explain population structure, the identity-by-state (IBS) kinship matrix and the degree of inbreeding Click here for additional data file.Figure S210P-values from the multipoint non-parametric linkage analysis for the German warmblood horses.Manhattan-plot of the −log The highest peak is located at 34–55 Mb on ECA3 and the next highest peaks on ECA1, 18 and 30. Genome-wide significant linkage could be detected in all these peak regions.(DOC)Click here for additional data file.Figure S3P-values from the genome-wide association analysis for the Arabian. Distribution of –log10P-values in the region of 60–67 Mb on ECA15. The lower panel shows the genes depicted by black boxes below the x-axis located in the region of interest. The SNP with the strongest association (BIEC2-314665) is located in between TTC27 and BIRC6.(DOC)Click here for additional data file.Figure S4Linkage disequilibria (LD) for the SNP alleles at 60–67 Mb on ECA15 for Arabian. The LD display presents Hedrige’s multiallelic D, which represent the degree of LD between two blocks. Red fields display LOD≥2 (D’ = 1), shades of red show the same LOD with D’<1. White and blue fields display LOD<2 with D’<1 and D’ = 1, respectively. The highly associated SNP BIEC2-314665 at 64 Mb with r2 = 0.3 is flanked by an aggregation of SNPs forming an LD block. Further 15 LD blocks can be detected at 62–63 Mb, 65 Mb and 66–67 Mb.(DOC)Click here for additional data file.Figure S5P-values from the genome-wide association analysis for German warmblood horses. Distribution of –log10P-values in the region of 40–53 Mb on ECA3. The lower panel shows the genes depicted by black boxes below the x-axis located in the region of interest. The SNP showing the strongest association (BIEC2-780830) is highlighted. BIEC2-780830 is located within intron 1 of ARHGAP24.(DOC)Click here for additional data file.Figure S6Linkage disequilibria (LD) for the SNP alleles at 45–52 Mb on ECA3 for German warmblood horses. The LD display presents Hedrige’s multiallelic D, which represent the degree of LD between two blocks. Red fields display LOD≥2 (D’ = 1), shades of red show the same LOD with D’<1. White and blue fields display LOD<2 with D’<1 and D’ = 1, respectively. The highly associated SNP BIEC2-780830 at 52 Mb shows an LD block of 82.4 Kb with r2 = 0.029 and LOD = 2.42 (D’ = 0.5).(DOC)Click here for additional data file.Figure S7Pedigrees of the five Arabian and five German warmblood families used in multipoint linkage analyses.(DOC)Click here for additional data file.Table S1Results from the genome-wide linkage analysis (ECA15). Multipoint chromosome-wide significant Zmeans and LOD scores, their chromosome-wide P-values  and positions in Mb for all Arabian horses. Genome-wide significant P-values<0.05 at 64–65 are in bold.(DOC)Click here for additional data file.Table S2Results from the genome-wide linkage analysis (ECA3). Multipoint chromosome-wide significant Zmeans and LOD scores, their chromosome-wide P-values  and positions in Mb for all German warmblood horses. Genome-wide significant P-values<0.05 at 34–55 Mb are in bold.(DOC)Click here for additional data file.Table S3Survey on all animals genotyped for the genome-wide association study and by foals affected by guttural pouch tympany and unaffected animals in total and by breed and sex.(DOC)Click here for additional data file.Table S4Arabian and German warmblood horses used for the multipoint linkage analysis. Number of animals used to construct pedigrees and number of genotyped samples using the Illumina equine SNP50 beadchip by families and their affection status, number of affected foals and their distribution by sex and family and in total.(DOC)Click here for additional data file.


To analyze two phenotype characteristics – eye and hair color – using single-nucleotide polymorphisms (SNPs) and evaluate their prediction accuracy in Slovenian population.OCA2 – rs1667394, rs7170989, rs1800407, rs7495174; HERC2 – rs1129038, rs12913832; MC1R – rs1805005, rs1805008; TYR – rs1393350; SLC45A2 – rs16891982, rs26722; SLC24A5 – rs1426654) were used for the development of a single multiplex assay. The single multiplex assay was based on SNaPshot chemistry and capillary electrophoresis. In order to evaluate the accuracy of the prediction of eye and hair color, we used the logistic regression model and the Bayesian network model, and compared the parameters of both.Twelve SNPs (P < 0.0001), while the other four SNPs  showed additional association for color prediction.The new single multiplex assay displayed high levels of genotyping sensitivity with complete profiles generated from as little as 62 pg of DNA. Based on a prior evaluation of all SNPs in a single multiplex, we focused on the five most statistically significant in our population in order to investigate the predictive value. The two prediction models performed reliably without prior ancestry information, and revealed very good accuracy for both eye and hair color. Both models determined the highest predictive value for rs12913832 (We developed a sensitive and reliable single multiplex genotyping assay. More samples from different populations should be analyzed before this assay could be used as one of the supplemental tools in tracing unknown individuals in more complicated crime investigations. HERC2, OCA2, MC1R, SLC24A5, SLC45A2, TYR, and ASIP . To be able to predict eye and hair color based solely on biological material left behind at a crime scene or obtained from dismembered missing persons, or even of disaster victims, is one of the major expectations from the routine forensic work in the near future . Howeverand ASIP -16 genes of OCA2 ,12. As fC1R gene ,17. On tto black ,19.In order to correctly predict human eye and hair color from genetic data for the Slovenian population, we compared two alternative prediction models that are nowadays used most often in this field of forensics – the Bayesian network model and the logistic regression model. These models were developed and compared on the basis of the informative SNPs selected from our single multiplex assay.® Box kit .The research included 105 unrelated Slovenian volunteers, 70 male and 35 female donors. Buccal swabs of the adult volunteers were collected in 2008 using a SAFEThe eye and hair color of each volunteer were defined according to descriptions provided by the volunteers and through our observer grading. In the small number of elderly volunteers and volunteers with dyed hair, we used self-assessment for establishing natural hair color phenotypes. We then categorized each volunteer's eye and hair color into three groups. Eye color was defined as blue (44.7%), intermediate (25.7%), and brown (29.6%). For classification into blue and brown eye color, the eye must have been clearly composed of one color, regardless of its intensity. However, the classification into intermediate eye color included all other individuals with green or hazel eye colors, or those with two or more pigments within the iris (peripapillary rings or different colored spots). The hair color was defined as blond (5.7%), dark blond/light brown (41.0%), and dark brown/black (52.4%).Red hair color was not included because our research group included only one red-haired person due to the small number of red-haired persons in the Slovenian population. A demonstration of prediction accuracy for red hair color based on only one person’s data are not statistically acceptable, so we excluded this person and limited the study to the three hair color groups. The study was approved by the National Medical Ethics Committee in Slovenia. Before donating a sample, all the volunteers signed written consents for the use of their DNA solely for scientific research.DNA was extracted from all the samples using Chelex extraction . Isolate2, 0.1-1.7 µM of each primer, and 1-5U AmpliTaq Gold DNA polymerase (Applied Biosystems). The following cycle program was used: denaturation at 95°C for 10 minutes followed by 35 cycles of 95°C for 30 seconds, 60°C for 30 seconds, and 72°C for 30 seconds, followed by final incubation for 70 minutes at 72°C. PCRs were performed in a Perkin-Elmer 9600 thermal cycler (Applied Biosystems).Twelve of the SNPs ,6-8,21  that werAfter PCR amplification, the excess PCR primers and ddNTPs were removed by addition of freshly prepared mix of shrimp alkaline phosphatase (1U/µL)  and Exonuclease I (20U/µL) (ABGene) to 5 µL PCR products and incubation at 37°C for 1 hour followed by incubation at 75°C for 15 minutes.Ten single base extension (SBE) primers were collected from previous reports and two were self-designed  following standard protocol , with a For each SNP locus, we determined the average peak height ratio for heterozygotes and homozygotes, and the standard deviation of the peak height was also provided. From some of the randomly selected electropherograms, the peak height of the highest background peak in each dye window was collected and an arbitrary maximum background level determined . The background levels were used to calculate an arbitrary signal/noise ratio for homozygote allele calls. For heterozygote allele calls, the peak height ratio was calculated by dividing the peak height of the lower molecular weight allele by the peak height of the higher molecular weight allele for all SNPs. The right allele calls were determined for all 105 genotypes of twelve SNPs.Validation of the single multiplex was conducted on samples of two individuals. Multiplex PCR performance was assessed by analyses of dilution series of genomic DNA , amplifying with 1-5 U AmpliTaq Gold DNA polymerase (Applied Biosystems) in the reaction mix. To ensure the consistency of genotyping, two of the samples with all dilution series were analyzed in duplicates and additionally the homozygote and heterozygote peak height ranges were noted for each locus.P values were corrected using the Bonferroni correction for multiple testing (P > 0.0045) for the final determination of SNPs in linkage disequilibrium.In order to evaluate allele frequencies, locus-by-locus molecular variance (AMOVA), the Hardy-Weinberg equilibrium (HWE), and linkage disequilibrium (LD), we used Arlequin, version 3.1 . The P va priori conditional probabilities of colors were entered as having the same value , because we rarely have prior knowledge on the ancestry of the identified subject. For worldwide distribution, a threshold of 0.7 predicted eye or hair color probability was used for categorization. When the probability values were under this fixed threshold, the eye or hair color was predicted as undefined. This cut-off was based on the receiver operating characteristics (ROC) curve, or area under the receiver operating characteristic curve (AUC), derived from previous studies. AUC is the integral of ROC curves, which ranges from 0.5, representing a total lack of prediction, to 1.0, representing perfect prediction . This low frequency of red hair color was expected for the general Slovenian population and additionally confirmed that red haired individuals were more prevalent in the Baltic region and Northern Europe populations .P values ranged from 0.1889 to 1.0000) for all the 11 analyzed SNPs (SNP rs1426654 was monomorphic).The linkage disequilibrium (LD) between all the SNPs was calculated. As had been previously indicated , we estaP < 0.05) for the group for which the analysis was made. The multinomial/binary logistic regression model and the Bayesian network model  or in linkage disequilibrium with other SNPs . With the elimination of these four SNPs, we first used the eight remaining SNPs  to simultaneously test for the effect on a dichotomous dependent variable with binary logistic regression . Based on the results we selected the five SNPs  that were statistically significant , which means that all blue-eyed individuals were predicted correctly (24/24). The sensitivity was also high for intermediate (100%) eye color and relatively high for brown eye color (71%) . The higThe AUC for eye color defined as light vs dark was calculated for both colors to equal 0.985. The sensitivity in this categorization of eye color was also high and even reached 100% for dark eye color and 88% for light eye color. All the described results were mostly summarized only for the multinomial logistic regression model. However, most values based on the Bayesian model variant were slightly lower, but still in the same range .The highest value for AUC, 0.913, was obtained for blond hair color and even for dark brown/black hair color, 0.832, when using the logistic model for hair color categorized into three groups. As indicated for eye color prediction, for hair color the AUC values were also slightly higher when using the logistic model in comparison to the Bayesian network model. Sensitivity (88%) and also specificity (95%) were highest for dark brown/black hair color and they could not be extended for the other two hair color groups, presumably due to their small size sample in the model verification set of 24 Slovenian volunteers.The AUC for hair color, defined as light vs dark in the logistic model, was calculated for both colors to equal 0.929. The sensitivity was only highest for dark hair color (100%) when using the logistic model and was much lower for light hair color for the same reason .Predicting eye and hair color type separately using a multinomial logistic model yielded slightly higher accuracy compared to the Bayesian model. In any event, for both models the lowest AUC values were observed for intermediate eye color and for dark blond/light brown hair color. There is a possibility that the problem lies in imprecise color classification, which reflects uncertainties in distinguishing within intermediate eye color  and within different shades of hair color, which may be influenced by age-dependent hair color change during the entire lifetime of each individual .HERC2 showed the strongest association with both eye and hair color.Eye and hair color represent human traits that have a potential to be predicted from genetic material with high reliability and used in expansive forensic investigations. In the present study, based on a new single multiplex of twelve SNPs, we confirmed the effect of five statistically most significant SNPs for eye and hair color in both statistical models. Specifically rs12913832 in To illustrate the predictive performance of our single multiplex we focused on the five most statistically significant SNPs . The other seven SNPs were not included, as being either monomorphic for our population or in linkage disequilibrium with other SNPs, or as just not being sufficiently statistically significant for prediction accuracy for Slovenian samples. For our study population, the only SNP being monomorphic was SNP rs1426654, as all volunteers were homozygote for the derived allele A/A. Our data were therefore inconclusive regarding the hypothesis of Velenzuela et al  that rs1For genotyping accuracy of 105 Slovenian samples, this multiplex assay is for now designed to work optimally at 1 ng of template DNA, but its sensitivity has risen up to 62 pg by increasing the amount of AmpliTaq Gold DNA polymerase in reaction (5U). Our results on sensitivity and reliability of the multiplex could even be improved by increasing the injection and time voltage in capillary electrophoresis and also to use the polymer POP-7 instead of POP-4, due to its better mobility in the capillary of the ABI Prism 3130 Genetic Analyzer.The single multiplex assay presented was a robust and sensitive DNA tool regarding amplification and regular determination of the homozygosity/heterozygosity for each SNP included. On the basis of these facts, the single multiplex assay could be suitable for the use in forensic casework due to its more efficient use of template DNA. Furthermore, even markers not significantly associated with a trait in a temporary study population can still independently contribute to the trait prediction in the enlarged Slovenian or even European population. After all, the growing number of known sequence variants that underlie the differences in human pigmentation may provide new SNPs with specific markers that we could include in our assay for a more accurate prediction of eye and hair color.


Citrobacter rodentium and followed disease progression. We found that ω-6 PUFA enriched the microbiota with Enterobacteriaceae, Segmented Filamentous Bacteria and Clostridia spp., all known to induce inflammation. During infection-induced colitis, ω-6 PUFA fed mice had exacerbated intestinal damage, immune cell infiltration, prostaglandin E2 expression and C. rodentium translocation across the intestinal mucosae. Addition of ω-3 PUFA on a high ω-6 PUFA diet, reversed inflammatory-inducing microbial blooms and enriched beneficial microbes like Lactobacillus and Bifidobacteria, reduced immune cell infiltration and impaired cytokine/chemokine induction during infection. While, ω-3 PUFA supplementation protected against severe colitis, these mice suffered greater mortality associated with sepsis-related serum factors such as LPS binding protein, IL-15 and TNF-α. These mice also demonstrated decreased expression of intestinal alkaline phosphatase and an inability to dephosphorylate LPS. Thus, the colonic microbiota is altered differentially through varying PUFA composition, conferring altered susceptibility to colitis. Overall, ω-6 PUFA enriches pro-inflammatory microbes and augments colitis; but prevents infection-induced systemic inflammation. In contrast, ω-3 PUFA supplementation reverses the effects of the ω-6 PUFA diet but impairs infection-induced responses resulting in sepsis. We conclude that as an anti-inflammatory agent, ω-3 PUFA supplementation during infection may prove detrimental when host inflammatory responses are critical for survival.Clinically, excessive ω-6 polyunsaturated fatty acid (PUFA) and inadequate ω-3 PUFA have been associated with enhanced risks for developing ulcerative colitis. In rodent models, ω-3 PUFAs have been shown to either attenuate or exacerbate colitis in different studies. We hypothesized that a high ω-6: ω-3 PUFA ratio would increase colitis susceptibility through the microbe-immunity nexus. To address this, we fed post-weaned mice diets rich in ω-6 PUFA (corn oil) and diets supplemented with ω-3 PUFA (corn oil+fish oil) for 5 weeks. We evaluated the intestinal microbiota, induced colitis with  The effects of nutrition on health are extremely complex. In the gastrointestinal (GI) tract, dietary antigens interact with both the microbiota and the host mucosal surface which serves as a gatekeeper to systemic homeostasis. Certain dietary choices alter the ecology of the microbiota , of which the intake has risen by 54% over the last three decades in Canada Citrobacter rodentium and followed disease progression. ω-6 PUFA enriched pro-inflammatory gut microbiota and exacerbated intestinal damage and inflammation during infection. Supplementing the ω-6 PUFA diets with ω-3 PUFA reversed these pro-inflammatory blooms and instead enriched anti-inflammatory microbes corresponding with less intestinal damage. However, ω-3 PUFA supplementation also reduced the presence of intestinal alkaline phosphatase (IAP) expressing cells resulting in the inability to detoxify LPS. The fish oil supplemented diet resulted in increased mortality during infection due to sepsis evident by the presence of serum LPS binding protein (LBP), IL-15 and TNF-α. We conclude that excess dietary ω-6 PUFA enriches pro-inflammatory microbes and augments colitis. In contrast, fish oil supplementation reverses the effects of an ω-6 PUFA rich diet but may prove detrimental to the host during infection when inflammatory responses are critical for survival.In this study, we hypothesized that excess dietary ω-6 PUFA would increase colitis susceptibility through the microbe-immunity nexus. C57BL/6 mice were fed high-fat diets (20% w/w) that varied in PUFA composition: corn oil (high ω-6 PUFA diet); corn oil+fish oil (ω-3 PUFA supplemented diet) and a low-fat diet composed of ω-6 PUFA (5% w/w corn oil) as a control. We evaluated the intestinal microbiota, induced colitis with the enteric bacterial pathogen Clostridia spp. compared to the low ω-6 PUFA control, the ω-3 PUFA supplemented group had reduced abundance compared to the ω-6 PUFA-rich diet , and were observed in the submucosal region (600× magnification) of the colons from mice fed ω-6 PUFA-rich diets. In contrast, the mice fed ω-3 PUFA supplemented diets were able to contain the pathogen at the top of the crypts and bacteria were not observed in the submucosal region. This suggested that the pathogen was more systemic in the mice fed ω-6 PUFA rich diets but that fish oil supplementation prevents this bacterial translocation. To confirm this, we examined the spleen and mesenteric lymph nodes (MLN) for C. rodentium colony forming units (CFU). We found an increase in CFU in the tissues from mice fed ω-6 PUFA rich diets, which was reduced following ω-3 PUFA supplementation , a clinical marker of sepsis nfection . In contnfection . Despiteex vivo LPS dephosphorylation activity of colons from mice fed PUFA diets similar to Goldberg et al (2008) C. rodentium infection, the ω-3 PUFA supplemented diet resulted in an impaired ability to dephosphorylate LPS during infection , a key endogenous mucosal defense factor inducible by microbiota nfection . OverallAn increase in IBD incidence has been associated with increased dietary intake of ω-6 PUFA in humans C. rodentium infection, these mice suffered severe colitis. Similarly, ω-6 PUFA diets increase the susceptibility for TNBS-induced enteritis We found that diets rich in ω-6 PUFA produced microbiota with increased numbers of potential pathobionts including bacteria from the family Enterobacteriaceae. This is similar to IBD patients Lactobacillus spp. and Bifidobacteria spp., both of which are associated with milder DSS and C. rodentium induced colitis Lactobacillus spp. and Bifidobacteria spp., since both are known to increase barrier function C. rodentium-induced inflammation promotes colonic damage in C57BL/6 mice C. rodentium clearance C. rodentium infection. Similarly, another study demonstrated that mice fed high levels of ω-3 PUFAs had impaired immune function and could not produce a response against Helicobacter hepaticus-induced infection Remarkably, diets supplemented with ω-3 PUFA prevented the bloom of Enterobacteriaceae, as well as the translocation of bacteria into the submucosal region and instead promoted the enrichment of C. rodentium infection. While the lack of colonic inflammatory responses likely played a role, a contributing factor was the inability to detoxify LPS through the lack of dephosphorylation activity in response to C. rodentium infection. In addition, there was a lack of IAP+ submucosal cells infiltrating into the colons of these mice. IAP prevents sepsis C. rodentium itself induces barrier dysfunction In following with this, the mice fed ω-3 PUFA supplemented high-fat diets suffered increased mortality during In conclusion, we found that excessive consumption of dietary ω-6 PUFA increases colitis through the microbe-immunity nexus. We also found that clinically recommended amounts of ω-3 PUFA supplementation reverses the effects of ω-6 PUFA rich diets. While this may be beneficial in the context of chronic colitis, it is detrimental in the context of an acute gut infection. Together, these data reveal that the colonic microbiota can be altered through exogenous dietary PUFA intake and varying ratios of dietary ω-6: ω-3 PUFA can alter susceptibility to infection-induced colitis by modulating inflammatory and cytoprotective responses.C57BL/6 mice  were maintained at the Center for Disease Modeling at the University of British Columbia (UBC), Vancouver, BC. They were bred in house and caged in a temperature-controlled (22±2°C) room with a 12 hr light/dark cycle and fed irradiated food and tap water under specific pathogen-free conditions.The protocols used were approved by the UBC’s Animal Care Committee and in direct accordance with guidelines drafted by the Canadian Council on the Use of Laboratory Animals.ad libitum.Female offspring were weaned at 4 weeks of age onto isocaloric, isonitrogenous high-fat diets with 40% energy or chow diets with 9% energy from fats. High-fat diets contained 20% w/w of various oils prepared by blending dietary oils  to a rodC. rodentium DBS100 were cultured overnight as previously reported Mice were euthanized by cervical dislocation, the distal colon removed (stool was removed) and immersed in RNA-later® (Qiagen). Portions of the colon were either stored at −20°C for real time qPCR analysis, homogenized and stored at −80°C for bacterial DNA extraction or immersed in 10% neutral buffered formalin (Fisher) for histological analyses and immunofluorescence.http://support.meta.moleculardevices.com/docs/mm%20bag.pdf). For immunofluorescence and TUNEL staining, paraffin-embedded tissue sections were deparaffinized and antigen retrieval of rehydrated tissues was performed using a 1 mg/mL trypsin (Sigma) followed by incubation with either: rabbit polyclonal antibody-1 raised against myeloperoxidase (Thermo Scientific) to examine neutrophils; rat monoclonal antibody raised against F4/80 (CedarLane) to examine macrophages; rabbit polyclonal antibody raised against prostaglandin (PGE)2 (Abcam); and rabbit polyclonal antibody raised against IAP (Abcam) followed by either goat anti-rabbit IgG AlexaFluor-conjugated 594-red antibody (Invitrogen); goat anti-rabbit IgG 488-conjugated antibody (Rockland) or goat anti-rat IgG DiLyteFluor 488-labelled antibody (Ana Spec Inc). Tissue sections were mounted using fluoroshield with DAPI (Sigma) and viewed on an Olympus IX81 fluorescent microscope. Negative controls using secondary rat or rabbit antibodies only were examined and found negative. For inflammatory cell counts, positive cells were quantified by two blinded observers under fluorescence from a stitched image using MetaMorph® software. Cell death was measured using a FragEL DNA Fragmentation Detection Kit  as per manufacturer’s instructions. The mean number of TUNEL-positive cells for each group was the total number of TUNEL-positive cells counted in 10 longitudinally sectioned crypts, imaged and quantified as above.Colonic histopathology was assessed using a scoring system previously described CGGCTACCACATCCAAGGAA, R; GCTGGAATTACCGCGGCT), Relm-β , IL-23 , TNF-α, IFN-γ, and IL-22 primers previously described Total RNA was purified using Qiagen RNEasy kits (Qiagen) according to the manufacturer’s instructions. cDNA was synthesized with iScript cDNA Synthesis Kit (Bio-Rad). Quantitative PCR reactions were performed as described previously in situ hybridization was performed using a FITC-labeled γ-Proteobacteria probe as previously described 4. Sections were imaged as described above. For systemic pathogen counts, the spleen and mesenteric lymph nodes were homogenized in PBS, serially diluted and plated on McConkey agar plates which were incubated for 24–48 hr at 37°C.To examine the mucosal associated intestinal microbiota, colon pieces were washed, homogenized and bacterial genomic DNA was extracted using a DNA stool minikit (Qiagen) according to the manufacturer’s instructions and quantified as above using 50 ng/µl of bacterial DNA. Primers are described previously Blood was collected from mice via cardiac puncture, spun at 1600 g for 5 min at 4°C, to separate the serum. The serum was then analyzed for TNF-α using a commercially available ELISA kit (Biolegend) according to the manufacturer. A standard curve was established between 0 and 1,000 pg/ml and all samples were assayed in duplicates. LPS binding protein and IL-15 was measured from sera serviced by Evetechnologies .et al. (2008) Escherichia coli 055:B5 LPS (Sigma L2880) was then added to 15 µL lysate and left for 2 hours at room temperature. 40 µL of a solution composed of 0.01% malachite green (Sigma), 16% sulfuric acid (Fisher), 1.5% ammonium molybdate (Sigma) and 0.18% Tween-20 (Sigma) similar to Baykov etal. (1988) To measure LPS- dephosphorylating activity, a protocol was produced from the work described by Goldberg P<0.05 was considered significant.The results are expressed as the mean +/− standard error of the mean where for uninfected mice: n = 12 for the high ω-6 PUFA group, n = 13 for the low ω-6 PUFA and high ω-6 PUFA+fish oil supplemented groups and for infected mice: n = 10 for the low and high ω-6 PUFA groups and n = 8 for the high ω-6 PUFA+fish oil supplemented groups). Significance for survival data was determined using the log-rank test. For qPCR data, minitab was used to test for normalcy and then one-way ANOVA with Tukey post hoc test was performed for parametric data and Krustal-Wallis with Dunns post-hoc test for non-parametric data. For weight loss, histological scoring, TUNEL+ cells, bacterial and host cell numbers, serum analysis and LPS- dephosphorylating assays data were analyzed via one-way ANOVA with Tukey post-hoc tests. All analyses were performed using GraphPad Prism 5 where


Bos taurus indicus).Feed intake plays an important economic role in beef cattle, and is related with feed efficiency, weight gain and carcass traits. However, the phenotypes collected for dry matter intake and feed efficiency are scarce when compared with other measures such as weight gain and carcass traits. The use of genomic information can improve the power of inference of studies on these measures, identifying genomic regions that affect these phenotypes. This work performed the genome-wide association study (GWAS) for dry matter intake (DMI) and residual feed intake (RFI) of 720 Nellore cattle , that influences these phenotypes are confirmed in this study.This study showed the first GWAS of DMI to identify genomic regions associated with feed intake and efficiency in Nellore cattle. Some genes and QTLs previously described for DMI and RFI, in other subspecies ( Feed intake plays an important economic role in cattle growth and may represent the greatest costs in beef cattle, both in beef cattle finishing systems and calves production . Feed inAlthough genetic breeding foci primarily on growth and reproductive traits, other variables must be taken into account because increased weight gain rates may lead to a concomitant increase in the adult size generating higher maintenance costs of animals . Thus, tIn recent decades, many efforts have been made to better balance the relationship of weight gain with feed intake in beef cattle; however, some traits are less effective to minimize the negative response of increased adult animal size, such as gross feeding efficiency ,5. In thBos taurus indicus), these difficulties are even greater, given the few animals with phenotype available for DMI and RFI, and the scarce knowledge about genetic parameters of these traits in Nellore. However, the phenotypic variability of RFI in Nellore show a standard deviation ranging between 0.31 and 0.69 kg/DM per day [Bos taurus taurus (often referred as taurine) animals and crossbreed [Both DMI as RFI in cattle have enough variability and heritability to respond to genetic selection ,7,8; how per day ,11, is sossbreed ,3,6,7.The use of genomic information can be a strategy to improve the selection of phenotypes such as RFI and DMI, if the marker effects are estimated accurately. Genome-wide association study (GWAS) allowed to identify subsets of markers that explain an important portion of the variation of these characteristics ,12,13. TSeveral studies have reported the viability of using the information from single nucleotide polymorphism (SNP) to identify regions of the genome that affect phenotypes of interest, aiming at improving breeding schemes for weight gain, reproduction and carcass traits in beef cattle -16. AddiBos taurus indicus), especially Nellore [However, the vast majority of GWAS has been performed in animals of the taurine subspecies. Also, the first beadchip of thousands of SNPs were developed based on this subspecies, which causes several SNPs, described as being polymorphic in taurines, to be non-informative in zebu cattle , respectively. The mean, additive genetic variances and residual variances were 8.76 ± 1.96, 0.29 and 0.42, respectively to DMI and 0.00 ± 0.89, 0.20 and 0.33, respectively to RFI. However, four samples were considered outliers in the DMI distribution and removed from the HD dataset and HDimp dataset, whereas two samples were considered outliers for RFI. Regarding the criteria for exclusion of samples and SNPs, the results of the quality control of 50 k and HD are shown in Table After the quality control criteria, the final datasets were: 672 samples in 50 k with 28,231 SNPs and 365 samples in HD with 436,588 SNPs.The number of SNPs imputed with over 95% accuracy was 290,620 and the number of remaining samples was 672 for the dataset HDimp. The median imputation accuracy was 97.2% with an average of 94.6%. Only those genotypes imputed with over 95% accuracy were used in the GWAS.The deflation/inflation factor (λ) calculated for all association analyses was lower than 1.1 , this tool enabled a much more robust association study due to the considerable increase in the number of samples (672 vs 365).The benefits of using imputation were observed in this study because, in the dataset HDimp, we found regions significantly associated with phenotypes, which was not noted in the dataset HD even though both had a very close Bonferroni threshold. Although the number of SNPs of the dataset of imputed animals is much lower than that of the dataset HD ; however, it is in the region of QTL #4365 described previously for DMI. Other studies found SNP associated with DMI in the BTA14 in beef cattle [PLAG1, RDHE2 and CHCHD7 that notably influence the stature of various species [CCDC171 and surrounding other genes; moreover, it lies next to the QTL #4425, which is a genomic region that seems to affect RFI and DMI.Regarding the location of SNPs related to DMI, the SNP rs109784719 (BTA14) is at 27.4 kb of the single gene  involved with RFI surround this SNP and SNP rs41660853, associated with RFI. This SNP linked to RFI is located near gene l in RFI , corrobol in RFI . Three ol in RFI ,23,29.The ionic transport system can account for more than 10% of all ruminant energy expenditure ; therefoNPY, Gene ID 504216) positioned at 1.4 Mb of distance and the leptin  at 19 Mb from the SNP and inserted in this QTL. The neuropeptides and hormones that control appetite, energy expenditure and metabolism of fat and glucose have a relationship to each other, which seems to be mediated by leptin [AGRP, MCH, orexines and noradrenalines, or they can even have a reducing effect on appetite such as leptin, POMC, CART, CRH, α-MSH and serotonin [ZNF804B that is a form of protein “zinc finger”, characterized by coordination and stabilization of one or more zinc ions in several ionic exchange processes [Finally, SNP rs134003539 is in a locus described as important, for both DMI and RFI ,29,36,37y leptin . These cerotonin . In addiIn general, the GWAS presents itself as an interesting tool to identify genomic regions that can influence these phenotypes. Another potential advantage is the creation of less dense panels designed for a breed or subspecies of interest containing SNPs directed to a certain characteristic. This could reduce costs of a possible genetic selection with the use of genomic data, improving the accuracy of estimates of genetic value in animals.The identification of regions associated with DMI and RFI may elucidate loci that influence these variables and highlight possible important physiological mechanisms. In this study, some promising regions were identified, with important effect on DMI and RFI. However, the strict significance level adopted and the number of genotyped animals may have contributed to reducing the power of the present study, given that important regions may not have been identified, which suggests the need for further studies aimed at overcoming such restrictions.The current study showed the results of GWAS in high and medium-density panels to identify genomic regions associated with feed intake and efficiency. It is believed to be the first study of this kind conducted in Nellore cattle to these traits. The results suggest that RFI and DMI are influenced by loci previously described and these results obtained in zebu cattle are supported by the literature.The study was conducted from a compilation of 11 experiments of feed efficiency and intake conducted in Brazil from 2007 to 2011. One experiment was performed in the South , eight iDuring the experiments, the MBW of each animal was obtained by periodic weighing without fasting and ADG was calculated as the slope of the linear regression of weights compared to the testing days. To calculate the RFI, we considered the regression equation residue of DMI on ADG and MBW ; howeverRFI and DMI were tested for data normality (Shapiro-Wilk) and homoscedasticity of model residues (Breusch-Pagan). We also performed the control of data outliers , in which the records outside the mean range ± 3 standard deviations, classified as possible measurement error, were excluded from the analysis.Blood was collected from all animals evaluated by puncturing the jugular. We used tubes containing K2 EDTA as anticoagulant. The samples were stored at 4°C for late DNA extraction by NaCl precipitation . After eGenotyping was performed in two types of DNA beadchip: the Illumina® BovineHD BeadChip  , and the-5. This quality control was performed in R v2.15.2 software using scripts developed for this purpose [For GWAS, SNPs were subjected to a quality control in which only autosomal SNPs with known genomic coordinate were considered. Samples with Call rate (IDCR) lower than 90% were removed from the study. Markers were removed if they presented minor allele frequency (MAF) ≤ 0.02, Call rate (SNPCR) ≤ 0.95 and P-value for Fisher’s exact test for Hardy-Weinberg Equilibrium (HWE) ≤ 1 x 10 purpose  and the  purpose .-6, IDCR ≤ 0.90.In order to increase sample size, an imputation analysis was performed to combine the available information of animals genotyped with BovineSNP50 and BovineHD. The HD panel information was used to verify the imputation efficiency from the 50 k Panel to the HD. The quality control of SNPs was performed again, which excluded SNPs located in non-autosomal chromosomes, SNPCR ≤ 0.97, HWE ≤ 1 × 10Afterwards, 290 animals randomly sampled (genotyped in high-density panel) were considered as population reference for the imputation analysis, while the remaining animals of the panel were included in a validation set. Except for the markers in common between the two technologies, the genotyped animals in the validation BovineHD had their genotypes masked and imputed, which allowed to simulate the imputation of 50 k to HD. These analyses were performed using the FImpute 2.2 software , using HThe association analysis was based on a variance-components method, namely Grammar-Gamma . This meThe first step in the association analysis was to use the variance-covariance matrix as genomic relationship matrix, to correct for relatedness and substructure and Grammar-Gamma factor calculation. To ensure the reliability of the estimates, we calculated the inflation/deflation factor (λ) for the correction of the GC .10 were plotted, corrected for λ and the Bonferroni correction (α = 0.05/number of SNPs) was considered as significance threshold. All the procedures described in this item were carried out in R v2.15.2, using the GenABEL v1.7-6 package [The second step consisted of associating the phenotype with the genotypes without the variance-covariance matrix and the estimates of SNPs effects adjusted by the Grammar-Gamma factor . The mod package .The SNPs that surpassed the threshold of the Bonferroni adjustment were described and their allelic substitution effects were reported. The proportion of the phenotypic variance explained by the SNPs was also estimated as:2 is the square of the allele substitution effect and S2 is the total variance of phenotype.Where p and q are the allele frequencies of the analyzed SNP, βAdditionally, they were investigated for their genomic location . The exploration of the region searched for genes with known functions located at the maximum 100 kb distant from the SNPs in Ensembl genes 72 using the assembly UMD v3.1 . The seaJBSF coordinated and designed the study. MHAS, YTU, RCG, JFG, HF, SLL, PRL, TGL and JBSF contributed in study design and obtaining phenotypes. YTU, MHAS and HHRN helped in data analysis and imputation. MHAS, PAA, RCG and HF did the extraction and standardization of DNA samples from the animals. LLC and RAB performed the genotyping of animals. MHAS performed data analyzes, phenotype collection and preparation of the manuscript. JBSF, YTU, MHAS, RCG, GAOJ, JFG FVM HF and interpreted results and edited the manuscript. All authors approved the final version of the manuscript.Quantile-quantile plot for the test statistics used in the association analysis for DMI (50K).Click here for fileQuantile-quantile plot for the test statistics used in the association analysis for DMI (HD).Click here for fileQuantile-quantile plot for the test statistics used in the association analysis for DMI (HDimp).Click here for fileQuantile-quantile plot for the test statistics used in the association analysis for RFI (50K).Click here for fileQuantile-quantile plot for the test statistics used in the association analysis for RFI (HD).Click here for fileQuantile-quantile plot for the test statistics used in the association analysis for RFI (HDimp).Click here for file