Piscirickettsia salmonis, which affects farmed rainbow trout, Oncorhynchus mykiss, in Chile using imputed genotypes at the sequence level and searched for candidate genes located in genomic regions associated with the trait. A total of 2130 rainbow trout were intraperitoneally challenged with P. salmonis under controlled conditions and genotyped using a 57K single nucleotide polymorphism (SNP) panel. Genotype imputation was performed in all the genotyped animals using WGS data from 102 individuals. A total of 488,979 imputed WGS variants were available in the 2130 individuals after quality control. GWAS revealed genome-wide significant quantitative trait loci (QTL) in Omy02, Omy03, Omy25, Omy26 and Omy27 for time to death and in Omy26 for binary survival. Twenty-four (24) candidate genes associated with P. salmonis resistance were identified, which were mainly related to phagocytosis, innate immune response, inflammation, oxidative response, lipid metabolism and apoptotic process. Our results provide further knowledge on the genetic variants and genes associated with resistance to intracellular bacterial infection in rainbow trout.Genome-wide association studies (GWAS) allow the identification of associations between genetic variants and important phenotypes in domestic animals, including disease-resistance traits. Whole Genome Sequencing (WGS) data can help increase the resolution and statistical power of association mapping. Here, we conduced GWAS to asses he facultative intracellular bacterium  P. salmonis, producing the greatest economic losses. In the first semester of 2022, 8.5% of the total mortality was attributed to SRS [P. salmonis infection have been based mainly on vaccines and antibiotic administration during disease outbreaks. However, both approaches show low effectiveness in controlling SRS in field conditions [P. salmonis infection [P. salmonis–host interaction [P. salmonis infection [In 2010, the world production of rainbow trout was 752,400 tons, and in 2020, it increased to 960,600 tons; a very rapid growth of almost 27% in ten years [d to SRS . Prophyleraction ,13,14,15nfection ,17,18. IP. salmonis infection has been demonstrated in Atlantic Salmon [With the advances in the genomics of aquatic organisms in recent years , it is nc Salmon , coho Sac Salmon  and rainc Salmon ,23.The GWAS aims to map quantitative trait loci (QTL) to understand the genetic architecture and find DNA variants associated with the trait of interest . This apFlavobacterium psychrophilum [Aeromonas salmonicida subsp. salmonicida [Vibrio anguillarum [Ichthyophthirius multifiliis [Flavobacterium columnare [Yersinia ruckeri [P. salmonis resistance have been found in rainbow trout and other salmonid species [P. salmonis infection was determined to be a polygenic trait in rainbow trout, Atlantic salmon and coho salmon, with few QTL explaining a moderate proportion of the genetic variance for the trait [Mapping of QTLs through GWAS has been used to identify genomic regions related to disease resistance in different fish species . GWAS forophilum ,47,48, Amonicida , Vibrio uillarum , Ichthyotifiliis , IPN virtifiliis , Flavobaolumnare  and Yers ruckeri , have be species ,23,55,56he trait ,23,55. AP. salmonis, juveniles from 102 families, weighing approximately 7.0 ± 1.5 g were individually tagged with a PIT-tag, kept for seven months in a single tank, and then transferred to the Aquainnovo Research Station . The fish had an acclimatization period of 20 days before the challenge. Random sampling was performed to check for Flavobacterium spp., Infectious Pancreatic Necrosis virus (IPNV), infectious salmon anemia virus (ISAV), and Renibacterium salmoninarum using qRT-PCR. Then, 2130 juveniles  were inoculated intraperitoneally (IP) with 0.2 mL of the P. salmonis strain LF-89 at a lethal dose of 50, as described by [The animals used in this study belonged to the 2011 year-class from the rainbow trout breeding nucleus owned by EFFIGEN S.A. . This breeding program has applied selection for different traits of interest  since 2001. For the experimental challenge against ribed by . After iResistance against SRS was evaluated using the time of death (TD) with values ranging from day 10 to day 32 and binary survival (BS), where 1 indicates that the fish died and 0 that it survived challenge.Affymetrix Axiom) developed by the National Center for Cool and Cold Water Aquaculture of the USDA Agricultural Research Service [p-value > Genomic DNA was extracted from the 2130 sampled fins using a commercial kit , following the manufacturer’s instructions. Individual genotypes were obtained using a commercial 57K SNP chip  was used to align re-sequencing data. This reference genome version is comprised of a final assembly length of ~2.33 Gbp, 1591 contigs with an N50 contig length of 9, 8 Mb, of which ~95% is anchored in 29 chromosomal sequences. The re-sequencing reads from each sample were mapped to the reference genome using the Burrows-Wheeler Aligner (BWA) analysis tool [https://www.broadinstitute.org/gatk/, accessed on 19 June 2021) was used for the SNP calling. The final VCF file consisted of 22.6 million non-redundant variants present in the 102 rainbow trout re-sequenced individuals. The quality control of genotypes was performed with Plink v1.90 [p-value > Detailed information on the sequencing procedures is available in . Brieflynk v1.90  using th2 (Pearson’s squared correlation between observed and imputed genotypes) statistic, and SNPs with low imputation accuracy (lower than 0.8) were removed from the final set of imputable SNPs. Finally, the challenged animals genotyped with 57k SNPs were imputed using only validated SNPs with an accuracy above 0.8.For the validation of the imputation, five cross-validation sets were formed using only the 102 parents divided into 20% validation  and 80% reference  sets. The validation animals had only the 57K SNPs in common with the genotyped animals and the remaining SNPs were masked to perform genotype imputation. Reference animals had information at WGS level including only SNPs that passed by QC. FImpute v3.0  was usedmlma function. The linear mixed model applied was:p-value for each SNP were also estimated using the GCTA.GWAS was performed for two trait definitions: time to death (TD) and binary survival (BS) using a linear mixed animal model implemented in the Genome Complex Trait Analysis (GCTA) software. This software uses a Restricted Maximum Likelihood (REML) analysis to estimate the proportion of additive genetic variation captured from all SNPs  through enotypes , e is thFor TD and BS, heritability  and the chromosomal significance threshold (0.05/number of SNPs), which represent α values corrected by Bonferroni. A systematic approach to search for candidate genes was performed using the Multi-marker Analysis of Genomic Annotation (MAGMA) software, which uses a multiple regression model to test the joint effect of multiple markers of a gene and then tests the association of the gene with the trait [O. mykiss (GenBank Assembly Accession GCA_002163495.1) was used in the gene search, and Bonferroni correction was also applied to obtain the significance threshold of the candidate genes.The SNPs significantly associated with he trait . The Omyp > 0.05) phenotypic variation based on Kaplan–Meier survival analysis. For final weight (FW), values were: 177.5 (SD = 52.05); 167.3 (SD = 44.90) and 176.2 (SD = 58.62) grams for challenge tanks 1, 2 and 3, respectively ; 23.5 (SD = 7.82) and 22.73 (SD = 8.08) days for challenge tanks 1, 2, and 3, respectively. A Kaplan–Meier survival curve  was drawectively .p-value > A 57K SNP chip was used for genotyping 2130 challenged rainbow trout. Initial quality control (QC) of genotypes was applied with Affymetrix Axiom software according to default settings. Additional QC filters were sequentially applied, discarding 17.6% SNPs by call-rate, 2.7% SNPs by MAF and 17.14% by HWE, leaving a total of 31,275 SNPs available for downstream analyses. For the genotypes from WGS data, 22,649,022 SNPs from the 102 samples were obtained after SNP calling. The call-rate filter eliminated about 83.3% of the initial SNPs. MAF and HWE filters discarded 5.2% and 1% of SNPs, respectively, with a remaining list of 2,382,000 SNPs available for downstream analysis  and 0.25 (SD = 0.02), for TD and BS, respectively .P. salmonis measured as TD and BS was performed for each trait using the imputed WGS data . In the same chromosome, we found the pleckstrin homology domain-containing, family G (with RhoGef domain) member 6 (PLEKHG6), also known as myosin interacting guanine nucleotide exchange factor (MyoGEF) that participates in the regulation of apoptosis with the appearance of blisters and cytokinesis [Neoparamoeba perurans in Atlantic salmon.Host defense against infection by g others , In addiresponse . Severalresponse . This geresponse  with parresponse ,70. Ref.response  describeokinesis ,73, and okinesis . Ref. [7okinesis  identifiOmy13, we found the specific erythropoietin receptor (EPOR), a fundamental signaling agent in erythropoiesis [P. salmonis in salmonids, the EPOR could be a candidate gene involved in defense mechanisms. In a study developed by [P. salmonis in Atlantic salmon by a lack of sufficient amounts of iron in the head kidney, resulting in a decrease in the expression of erythrocyte-specific EPOR genes.In opoiesis ,77. The opoiesis ,79,80. Topoiesis , such asopoiesis . Due to loped by , the autN.perurans in Atlantic salmon.In Omy26, we found the N-acetylglucosamine-6-sulfatase-like (GNSL) gene, which is related to phagocytosis ; the CBFP. salmonis resistance in the same rainbow trout population. The leucine-rich repeat-containing protein 75A-like gene (LRRC75A), located on this same chromosome, belongs to the long non-coding RNAs (lncRNAs) that play multiple vital roles during inflammatory processes caused by pathogens [Omy27 [Aeromonas hydrophila, a Gram-negative bacterium, in Rohu carp (Labeo rohita) liver tissue [P. salmonis in rainbow trout [Omy27, participates in response to endoplasmic reticulum stress, organization of the cytoskeleton, renal osmoregulation, cell adhesion, and migration [Anguilla anguilla) [Omy27, is a regulator of cytokinesis that considerably increases stress-induced apoptosis and decreases the invasive capacity of tumor cells [Seriola dumerili) infected with the ectoparasite Neobenedenia girallae [P. salmonis, were also identified in Omy27: Tektin 1 (TEKT1) has been shown to be related to sperm quality, cytoskeleton organization, and motility [In Omy27, we found the cytoskeletal protein smoothelin-like protein 2 (SMTNL2) expressed in skeletal muscle, which is activated by c-Jun N-terminal kinase (JNK), in response to cellular environmental stress, including inflammatory stimuli and apoptosis ,89. Ref.athogens ,91; possathogens . Unconves [Omy27 . This ges [Omy27 . An incrr tissue . The larr tissue ,97 and ir tissue ,99. In tr tissue ,101. Thiow trout . The inoigration . Loss ofigration . The INPnguilla) . F-box-onguilla) , and hasnguilla) . The X-lor cells ,108. XAFor cells ,110, whior cells , and skior cells . The expgirallae . In raingirallae . The medgirallae . The actgirallae . Moleculmotility ,118; sodmotility .Omy03, we found the domain family protein 1 (TSC22D1) gene that participates as an early immune response molecule and that modulates the TGF-beta dependent signaling pathway [P. salmonis, a decrease in the expression of TSC22D, from the TSC22 family, was shown in the head kidney [Omy17, we found the striatin-interacting protein one homolog (STRIP1) gene, which plays an important role in the migration and metastasis of cancer cells [Omy26, we found a single gene called AP-2 complex subunit alpha-2 (AP2A2), involved in the activation of the leukocyte response, granulocytes, and myeloid cells involved in the immune response [For BS, three essential candidate genes were found flanking the significant SNPs. In  pathway , a tumor pathway ,122, and pathway . These g pathway . In a trd kidney . In Omy1er cells ,127 and er cells . In Omy2response  and provresponse .Omy26. Myotubularin-related protein 10 (MTMR10), which is associated with the protection of dendrites induced by oxidative stress or infection by pathogens and is present in several tissues was identified [Ictalurus punctatus) after the infection with Flavobacterium columnare [Epinephelus lanceolatus) infected with spotted knifejaw iridovirus (SKIV). Slingshot phosphatases (SSH) are critical in controlling actin dynamics [Genes associated with both TD and BS were found in entified . Nile tientified . In Omy2entified ,134. It entified . The expolumnare . Ref. [1olumnare  observeddynamics , especiadynamics ,140,141.P. salmonis resistance in rainbow trout, of which 14 have been identified in Omy27. Likewise, in [Omy27 for TD. [P. salmonis in farmed populations of salmonids, including rainbow trout. The authors identified nine candidate genes in Omy27 . These same genes were identified in our study for the TD phenotype. These candidate genes may be considered strong candidates to explain P. salmonis resistance variation in rainbow trout and are potentially relevant to test further genetics- or pharmacological-based strategies for controlling this disease in rainbow trout.We found 24 candidate genes that may be involved in wise, in , which u for TD.  used a gP. salmonis (SRS) has a complex polygenic inheritance architecture in rainbow trout. Here, we use imputed whole genome sequence data to refine the GWAS results for P. salmonis resistance in rainbow trout. We found 24 putative functional genes related to phagocytosis, innate immune response, inflammation, oxidative response, G-protein signaling pathway, lipid metabolism, and apoptotic processes, which may be related to variation in the host response against the bacterium, which is mainly found in a significant QTL found on chromosome 27. Further studies are needed to validate the results obtained in this study, especially related to candidate genes located on Omy27.Resistance to the infectious disease caused by


Most plant traits are governed by polygenes including both major and minor genes. Linkage mapping and positional cloning have contributed greatly to mapping genomic loci controlling important traits in crop species. However, they are low-throughput, time-consuming, and have low resolution due to which their efficiency in crop breeding is reduced. In this regard, the bulk segregant analysis sequencing (BSA-seq) and its related approaches, viz., quantitative trait locus (QTL)-seq, bulk segregant RNA-Seq (BSR)-seq, and MutMap, have emerged as efficient methods to identify the genomic loci/QTLs controlling specific traits at high resolution, accuracy, reduced time span, and in a high-throughput manner. These approaches combine BSA with next-generation sequencing (NGS) and enable the rapid identification of genetic loci for qualitative and quantitative assessments. Many previous studies have shown the successful identification of the genetic loci for different plant traits using BSA-seq and its related approaches, as discussed in the text with details. However, the efficiency and accuracy of the BSA-seq depend upon factors like sequencing depth and coverage, which enhance the sequencing cost. Recently, the rapid reduction in the cost of NGS together with the expected cost reduction of third-generation sequencing in the future has further increased the accuracy and commercial applicability of these approaches in crop improvement programs. This review article provides an overview of BSA-seq and its related approaches in crop breeding together with their merits and challenges in trait mapping. Identification and dissection of genetic loci determining a particular trait is a regular process in genetics. Most of the complex quantitative traits are regulated by multiple loci distributed across the genome of a species. So to precisely detect the specific genetic elements linked with the trait of interest, one has to link all the loci with that trait . A quant2 by selecting individuals with contrasting phenotypes; for example, tall and short plants, tolerant and susceptible plants, etc. , which classifies segregants according to their marker genotypes, the BSA pools segregants according to their phenotypes. When the former compares trait values of different classes, the latter compares marker allele frequencies in different classes . Althougts, etc. . The keyts, etc. . BSA wasts, etc. . Both mats, etc. . BSA-seqts, etc.  and speets, etc. . It can ts, etc. . For BSAts, etc. , which cts, etc. . HoweverFor the fast-track identification of QTLs linked with a particular trait of interest, a mapping population has to be constructed from a cross between parents encompassing contrasting attributes . From thde novo genome assembly. A standard reference genome of a species is used for this purpose; however, owing to the fact that a single reference genome could not cover all the diversities present within a species, a pangenome concept has emerged to resolve this issue. Therefore, it would be more advantageous to sequence the genome of at least one parent and use it for aligning the reads of two bulks (http://broadinstitute.github.io/picard), Sambamba , restriction site-associated DNA sequencing (RAD-seq), etc. The outcome of the SNP calling depends on the sequencing strategy used. Whatever the sequencing strategy used, the downstream analysis of the sequenced reads, in the fastQ/fasta format, involves aligning them with a reference genome or a wo bulks . Read alwo bulks , Bowtie2wo bulks , and Minwo bulks . The reswo bulks . After rSambamba , etc., aSambamba  performsSambamba . A myriaSambamba , GATK HaSambamba , PlatypuSambamba , SAMtoolSambamba , TASSEL Sambamba , etc. StSambamba . ChoosinSambamba . In JVC,Sambamba . Errors Sambamba . In addiWhen the principle of BSA-seq for the mapping of QTLs is simple, a myriad of statistical methods have been developed to analyze BSA-seq data . A more 1 = frequency of an allele in pool-1, and f2 = frequency of an allele in pool-2. If an allele is linked with a trait, thenBlock regression mapping (BRM) is another robust approach developed to analyze BSA-seq data comprehensively. This method was developed by 1, and f2 curves, followed by block regression. To calculate the significance levels, the method relies on the fact that ∆f approximately follows a normal distribution under the central limit theorem. Therefore, the significance level of the ∆f is calculated using a two-tailed test. If it is significant, the alternative hypothesis is accepted, i.e., there is QTL present in this peak. Then, the confidence interval is derived as the region between the left and right intersection points of a horizontal line  with the curve. This region represents the 95% confidence interval of the QTL ; then, the SNP index is calculated using the formulaA simpler and widely accepted method known as QTL-seq was developed by QTL-seq estimates the contribution of each parent to the variation. If SNP index = 0, there is no variation and all SNPs are the same as reference. If SNP index = 1, all SNPs belong to either parent and if SNP index = 0.5, each parent contributes equally to the variation. Generally, only the SNPs with SNP index > 0.3 in either bulk are retained for downstream analysis. A sliding window is then applied to visualize the graphs based on the SNP index. After that, the ∆ SNP index is calculated for all genomic regions, and the regions exhibiting a higher ∆ SNP index than the background genome represent the regions associated with the trait of interest. These regions correspond to peaks or the valleys of the SNP index plot . DependiSeveral approaches have also been developed that rely on the hidden Markov model (HMM) concept to link SNPs with the phenotype. The HMM is used to explain or derive the probabilistic characteristic of any random process. It is used to describe the observed events that depend on hidden events. HMMs capture the hidden information from observed sequential events. In HMM, the system being modeled is assumed to be a Markov process with unknown parameters, and the observed parameters are used to determine the hidden parameters. The latter is used for further analysis . The EXPThe quantitative trait gene sequencing (QTG-Seq) method was developed by 3 resistant lines to the stem rust pathogen (strain Ug99 F3) and 11 susceptible homozygous lines, Ae. umbellulata, with two nucleotide-binding and leucine-rich repeat (NLR) genes as the potential candidate genes (glossy3 (gl3) gene of maize . The fundamental principles of BSR-Seq would remain the same as that of the traditional BSA-seq, with the difference that only the transcribed genome is used as a data source. BSR-Seq has been applied for the elucidation of important genomic regions and SNPs associated with different traits in both plants and animals. For example, te genes . Moreoveof maize . In addiof maize . BSR-seqof maize . Throughof maize . The colpulation . Throughpulation .1 and F2 populations. With the aid of a marker system like SNPs, the mutant phenotype is screened for segregation in the filial generations and thus mapped on the genome. The key to these approaches is the utilization of traditional BSA to generate SNP data. They exploit the power of NGS technologies to map inherited traits across any plant species where the generation of an F2 mapping populations is possible. SHOREmap was developed by Arabidopsis. The authors first generated mutant lines and crossed it with distant parent to dilute the distribution pattern of non-causal SNPs present throughout the genome. Next, they created a bulk of 500 mutant individuals, and their DNA was pooled and sequenced. SNPs were identified between the parent and mutant. The basic idea of this technique is that among the progenies of mutant x parent, those with the mutant phenotype are assumed to have SNP distribution similar to mutants at the loci controlling the mutant phenotype, whereas other loci have a random distribution. NGM mapping was developed by Arabidopsis. NGM is similar to SHOREmap except that it utilizes less mutant lines (10) to create a bulk, without compromising the power to detect causal mutation. Like SHOREmap and NGM, the MutMap technique, which was developed by 2 progenies  no need of large mapping populations, 2) no need of genetic linkage maps, 3) no need of natural variation in the population, 4) time-saving and labor-effective, and 5) direct identification of casual SNPs. Key disadvantages include 1) availability of a reference genome, 2) artificial mutagenesis required to develop mutant lines, 3) maintenance of mutant lines, and 4) not applicable if a phenotype cannot be scored . The mutations caused by these agents lead to altered phenotypes through the generation of SNPs, indels, or segmental breaks . The pherogenies . MutMap,rogenies . The SNPtMap-Gap , b. Heree scored .Fusarium wilt, caused by the fungus Fusarium oxysporum f. sp. niveum (Fon) leads to significant losses in yield. Using BSA-seq coupled to QTL mapping, the genomic region controlling resistance to this fungus was fine-mapped to around the 315-kb region (Prs locus and identification of the candidate resistance gene for PRSV-W were elucidated (OsSAP16), was enhanced under drought stress, implying that OsSAP16 is the strong candidate gene (Glyma.13 g249400 showed significantly higher expression in soybean plants with greater plant height; therefore, it can be a strong candidate gene for this trait (A myriad of studies on diverse species have proven the applicability of BSA-seq in mapping QTLs for different traits of agronomic importance. The details of some important studies on important crop plants are presented in b region . In ordeb region . So undeb region . In addiucidated . The reducidated . Furtherucidated . Among tate gene . These rTilapia fishes (In addition to its importance in crop plants, BSA-seq is also widely used in other species as well like yeast , Tilapiaa fishes , etc. Usa fishes  successf2 generations. Thus, medicinal plant breeders can get a general idea about the nature of the progenies in the context of a specific trait by integrating BSA with NGS technologies. This information can then be used to develop trait-specific homogeneous lines through selfing of the selected lines. Practically, this approach is feasible for only those medicinal plants that have less generation time and flower early in life. Some examples of such medicinal plants that are best suited for which reference genome is available and hence BSA-seq can serve their breeding purpose effectively may include stevia, tea, and tulsi. For longer generation time in medicinal plants, especially tree species, the creation of F2 generation is almost impossible.Medicinal plants are less explored at the genomic level as compared to staple crop plants. Even the breeding programs for the genetic improvement of medicinal plants are at the pioneering stage, and the development of trait-specific homogeneous lines is far away from reality. However, BSA-seq can greatly speed up and facilitate their breeding programs by making the use of F2 generations to precisely identify trait-specific genomic regions/QTLs, thereby saving much time. However, this is achieved at the cost of additional capital investment for deep sequencing. Therefore, there is a trade-off between time and capital investment in using BSA-seq. With the rapid advancement of NGS technologies and a steep decrease in the cost of sequencing, it is expected that in near future, the sequencing depth would not be a matter of concern while estimating the overall cost of BSA-seq.BSA-seq and its related approaches have the potential to quickly identify the trait-specific genomic regions/QTLs in a high-throughput manner. It takes the advantage of traditional BSA in integration with rapidly evolving NGS technologies. The most admirable attribute of this approach is that it takes only F


Among them, PUFAs synthesized through the PUFA synthase pathway typically have few byproducts and require fewer reduction equivalents. In the past 2 decades, numerous studies have been carried out to identify, analyze and engineer PUFA synthases from eukaryotes. These studies showed both similarities and differences between the eukaryotic PUFA synthase pathways and those well studied in prokaryotes. For example, eukaryotic PUFA synthases contain the same domain types as those in prokaryotic PUFA synthases, but the number and arrangement of several domains are different; the basic functions of same-type domains are similar, but the properties and catalytic activities of these domains are somewhat different. To further utilize the PUFA synthase pathway in microbial cell factories and improve the productivity of PUFAs, many challenges still need to be addressed, such as incompletely elucidated PUFA synthesis mechanisms and the difficult genetic manipulation of eukaryotic hosts. In this review, we provide an updated introduction to the eukaryotic PUFA synthase pathway, summarize the functions of domains and propose the possible mechanisms of the PUFA synthesis process, and then provide future research directions to further elucidate and engineer the eukaryotic PUFA synthase pathway for the maximal benefits of humans.Polyunsaturated fatty acids (PUFAs) are important nutrients that play important roles in human health. In eukaryotes, PUFAs can be  In addition to helping microorganisms adapt to low-temperature environments, PUFAs are also beneficial to human health . For exagh foods .Mortierella alpina is the only source for dietary arachidonic acid  certificated by FDA and European Commission and already have been used in the food industry  in these species can protect PUFAs from oxidation  (PUFA synthase is a multi-subunit enzyme complex and contains domains similar to FAS or PKS (polyketide synthase) . Therefo18:2 ω6) , arachid18:2 ω6) , eicosap18:2 ω6) , docosat18:2 ω6) , docosap18:2 ω6) , docosah18:2 ω6)  and tetr18:2 ω6) , and so 18:2 ω6) , in thisSchizochytrium sp. ATCC 20888 (Schizochytrium sp. S31), and three ORFs discovered from the Schizochytrium cDNA library exhibit homology to the PUFA synthase of Shewanella. Hauvermale et al.  domain, malonyl-CoA: ACP transacylase (MAT) domain, tandem acyl carrier protein (ACP) domains and ketoacyl-ACP reductase (KR) domain, and PKS-like dehydratase (DHPKS) domain. It is KSB, chain length factor (CLF) domain, acyltransferase (AT) domain and enoyl-ACP reductase (ER) domain in subunit B, and two tandem FabA-like dehydratase (DHFabA) domains and ERC domain in subunit C. The domain organization of each subunit shows little difference among different eukaryotic species.All eukaryotic PUFA synthases that have been identified thus far are composed of three subunits, subunit A, subunit B and subunit C, which are encoded by ectively . From thapo-ACP into holo-ACP with a 4′-phosphopantetheine (Ppant) arm   to transform ant) arm . Then, t E. coli . It was  E. coli . Each ACy PPTase , but thium SR21)  and Aurap. SD116 . Previouchytrium .A, and the other is KSB at the N-terminus of subunit B, which is adjacent to a CLF domain. The KS domain catalyzes the Claisen condensation reaction of the extender unit and acyl-ACP, leading to a two-carbon extension in the carbon chain (A and KSB of Thraustochytrium sp. strain ATCC 26185 could complement the defective phenotypes of both ketoacyl-ACP synthase I (FabB) and ketoacyl-ACP synthase II (FabF) mutants in E. coli, suggesting their ketoacyl-ACP synthase activities (B of Aurantiochytrium sp. OH4 could change the major product from DHA (EPA/DHA = 0) to EPA (EPA/DHA=1.07) in E. coli (There are two ketoacyl-ACP synthase (KS) domains in eukaryotic PUFA synthase. One is at the N-terminus of subunit A, named KSon chain . It has tivities . In proktivities . It is l E. coli . HoweverSchizochytrium sp. ATCC MYA-1381 (S. limacinum SR21) affected growth, decreased the content of PUFAs and increased the content of SFAs, and the proportion of C22 PUFA decreased by 57.51%  activity, but the AT domain could not  domain in subunit A and the acyltransferase (AT) domain in subunit B. The MAT domain catalyzes the binding of the malonyl group and ACP, and the AT domain has thioesterase activity and catalyzes the release of free fatty acids from long-chain acyl-ACPs. Complementation tests showed that the MAT domain of ould not . In vitrcyl-CoAs . The AT  DHA-CoA . These rene fadD . The del. HX-308 .3R)-hydroxyacyl-ACP in the presence of NADPH  from E. coli and are named DHFabA. All three domains can complement the defective phenotype of the E. coli fabA temperature-sensitive mutant, suggesting that they can function as β-hydroxyacyl-ACP dehydratases  affected cell growth, and the proportion of PUFAs was decreased, while the proportion of saturated fatty acids (SFAs) was increased  and long chain  substrates, and the KS-CLF didomain is responsible for the elongation of medium chain (C10) and very long chain C20 substrates (3R)-hydroxyacyl-ACP (3R)-hydroxyacyl-ACP is dehydrated by the DH domain to form an α, β-trans double bond, but the difference between DHPKS and DHFabA leads to distinct subsequent reactions. When the trans double bond is formed under the dehydration of DHPKS, the next reaction will be the reduction by the ER domain and form an α, β-saturated bond. When the trans double bond is formed under the dehydration of DHFabA, it will be further catalyzed to form a cis double bond by 2,2- or 2,3-isomerization  domain exists in the PUFA synthase of myxobacteria  Eukaryotic PUFA synthases have been mainly identified in thraustochytrids, and the major products are all DHA, which limits their applications and hinders the functional comparative research on eukaryotic PUFA synthases; 2) The structure and function of each domain of eukaryotic PUFA synthases have not been fully revealed, which makes it difficult to elucidate the biosynthetic logic of PUFAs and brings difficulties to the subsequent optimization and modification of eukaryotic PUFA synthases; 3) The natural eukaryotic PUFA synthases discovered thus far are difficult to achieve mass production of specific PUFAs, and the modification of PUFA synthases is an effective way to achieve this goal, but compared with other iterative PKS synthases, there are few studies on the engineering of PUFA synthases; 4) The genetic transformation of eukaryotic microbes, such as protists, with the PUFA synthase pathway is still difficult, and the lack of efficient transformation methods and genome manipulation tools increases the complexity of researching the PUFA synthase mechanism Different from those in eukaryotes, prokaryotic PUFA synthases have been found in many marine bacteria and even myxobacteria, and the main PUFA products produced by these PUFA synthases are more diverse. E. coli will be an efficient way to identify novel PUFA synthases. The discovery and identification of distinct PUFA synthases in more eukaryotic species is of great value for both mechanistic research and engineering of eukaryotic PUFA synthases.In addition to the diverse PUFA profiles, the domain organizations between some prokaryotic PUFA synthases are also quite different , which pin vivo, in vitro and structural analysis.As mentioned above, functional research on each domain can help elucidate the biosynthesis process of PUFAs; for example, the study of the MAT domain can reveal the initiation of PUFA synthesis, the study of the KS, KR, DH, and ER domains can reveal the iterative process of PUFA synthesis, and the study of the AT domain helps us understand the release of PUFAs. Since AT-like, KS, ACP, DH, and ER domains have multiple copies in eukaryotic PUFA synthases, it is necessary to determine whether there are functional differences between homologous domains. Referring to the existing research of PUFA synthase and other multidomain enzymes, the functional research of eukaryotic PUFA synthase domains can be expanded through E. coli expressing eukaryotic PUFA synthase  technology  to the main EPA (EPA/DHA=1.07) in E. coli (M. marina DHA synthase completely lost the DHA production capacity and produced trace amounts of EPA in E. coli (With the increased level of structural elucidation of eukaryotic PUFA synthases, it gradually becomes possible to engineer PUFA synthase by random mutagenesis and site-directed mutagenesis. Utilizing random mutagenesis and screening, Hayashi et al. demonstrated that the amino acid mutation of F65L, F230L and I231T, located in the KS E. coli . In addi E. coli . For ins E. coli . There a E. coli . TherefoAt present, the genetic manipulation of eukaryotes that possess PUFA synthases is still challenging, which makes it difficult to modify and apply the eukaryotic PUFA synthase pathway. Expanding the molecular biology techniques of these eukaryotes may start from at least two aspects: developing efficient genetic transformation methods and establishing efficient genetic manipulation tools.Agrobacterium-mediated transformation (Until now, there has been no genetic transformation method that is universally applicable for PUFA-producing eukaryotes. In thraustochytrids, electroporation is the most common strategy for transformation , and parormation  have alsormation  and cellormation , should ormation  can be uA. limacinum OUC88, which can reduce the number of different resistance genes needed in multistep genetic manipulation. In addition, most studies utilize homologous recombination for the gene integration of thraustochytrids, but the efficiency is low, and random integration still exists (Aurantiochytrium sp (Additionally, other gene elements, such as promoters and terminators, for gene manipulation of PUFA-producing eukaryotes are limited. Rau et al. introduced some endogenous and exogenous promoters and terminators used in thraustochytrids . To perfl exists . To achitrium sp . Other gtrium sp , these tE. coli, Brassica napus (Arabidopsis, and corresponding PUFAs have been detected in product (Lactococcus lactis, Myxococcus xanthus, Pseudomonas putida and Yarrowia lipolytica are used as chassis for the heterologous expression of prokaryotic PUFA synthase. (Although the native hosts of eukaryotic PUFA synthase have high industrial value, their genetic engineering remains a challenge. Therefore, the heterologous expression of eukaryotic PUFA synthase in an appropriate host is also an option for PUFA production. At present, eukaryotic PUFA synthases have been expressed in ca napus  and Arab product . In addiynthase. .Thraustochytrium PUFA synthase in E. coli and found that the organization of three genes as one operon produced higher DHA and DPA than the expression of genes in three plasmids (. Gemperlein et al. applied many strategies such as gene optimization and promoter engineering to better express myxobacterial PUFA synthases in Pseudomonas putida (Y. lipolytica (Heterologous expression of PUFA synthase and the production of PUFAs can be improved by using synthetic biology approaches such as sequence design, promoter selection and engineering, and operon design. Meesapyodsuk et al. expressed plasmids . Gemperls putida  and Y. lpolytica , which ifadE gene when expressing PUFA synthase from Schizochytrium in E. coli. (In addition, many metabolic engineering strategies can also be used to improve the PUFA production, for example, optimization of the precursor supply and reduction of PUFA catabolism. The key precursor of PUFA synthesis is malonyl-CoA, improving the synthesis of malonyl-CoA and weakening competitive pathways such as the FAS pathway can make more malonyl-CoA direct to the PUFA synthase pathway. β-oxidation of fatty acids will lead to a decrease in the production of major PUFA. Some researchers chose to knock out the E. coli. . These mE. coli. .via the eukaryotic PUFA synthase pathway. In addition, we discuss the similarities and differences between the eukaryotic PUFA synthase pathway and the prokaryotic PUFA synthase pathway. Finally, we propose several new strategies that can be used to expand the research on the eukaryotic PUFA synthase pathway, such as identifying more eukaryotic PUFA synthases, deepening the research on the synthesis mechanism, modifying the eukaryotic PUFA synthase and expanding the molecular biology toolbox of PUFA-producing eukaryotes. This article provides a good reference for further investigation and engineering of the PUFA synthase pathway in eukaryotes and utilization of eukaryotic PUFA products for the maximal benefits of human beings.Some eukaryotic microalgae can accumulate a large amount of PUFAs and are gradually used for the industrial production of PUFAs. Compared with the desaturase/elongase pathway, the PUFA synthase pathway is simple and efficient, gradually attracting more attention from their potential application. However, the biosynthesis of PUFAs involves the cooperation of multiple domains of PUFA synthase, and the synthesis mechanism is complex and less clear. In this article, we first summarize the identification of PUFA synthases in eukaryotes and then review the functional studies of each domain and propose a possible process for the synthesis of PUFAs


Staphylococcus aureus (MRSA) could transform outbreak investigations, but its clinical introduction is hampered by the lack of automated data analysis tools to rapidly and accurately define transmission based on sequence relatedness. We aimed to evaluate a fully automated bioinformatics system for MRSA genome analysis versus a bespoke researcher-led manual informatics pipeline. We analyzed 781 MRSA genomes from 777 consecutive patients identified over a 9-month period in a clinical microbiology laboratory in the United Kingdom. Outputs were bacterial species identification, detection of mec genes, assignment to sequence types (STs), identification of pairwise relatedness using a definition of ≤25 single nucleotide polymorphisms (SNPs) apart, and use of genetic relatedness to identify clusters. There was full concordance between the two analysis methods for species identification, detection of mec genes, and ST assignment. A total of 3,311 isolate pairs ≤25 SNPs apart were identified by at least one method. These had a median (range) SNP difference between the two methods of 1.2 SNPs (0 to 22 SNPs), with most isolate pairs (87%) varying by ≤2 SNPs. This similarity increased when the research pipeline was modified to use a clonal-complex-specific reference . Both pipelines clustered 338 isolates/334 patients into 66 unique clusters based on genetic relatedness. We conclude that the automated transmission detection tool worked at least as well as a researcher-led manual analysis and indicates how such tools could support the rapid use of MRSA genomic epidemiology in infection control practice.Genomic epidemiology of methicillin-resistant IMPORTANCE It has been clearly established that genome sequencing of MRSA improves the accuracy of health care-associated outbreak investigations, including the confirmation and exclusion of outbreaks and identification of patients involved. This could lead to more targeted infection control actions but its use in clinical practice is prevented by several barriers, one of which is the availability of genome analysis tools that do not depend on specialist knowledge to analyze or interpret the results. We evaluated a prototype of a fully automated bioinformatics system for MRSA genome analysis versus a bespoke researcher-led manual informatics pipeline, using genomes from 777 patients over a period of 9 months. The performance was at least equivalent to the researcher-led manual genomic analysis. This indicates the feasibility of automated analysis and represents one more step toward the routine use of pathogen sequencing in infection prevention and control practice. Staphylococcus aureus (MRSA) is a globally leading cause of death due to antimicrobial resistance  practice. Methicillin-resistant sistance  and reprsistance , 3. Suchsistance , 3. Thersistance , 5. Howemec gene, and highly accurate  bioinformatics system . On compaccurate .An automated analysis tool has the advantage that results are generated without the need for an operator with any bioinformatic training. It would also provide greater standardization of analysis and less interoperator variability compared with research pipelines . Here, wOur aim was to validate the accuracy of a fully automated MRSA genome analysis pipeline. Acknowledging the absence of a “gold standard reference” against which new genome analysis tools can be assessed, we compared its performance against a manual bioinformatics approach (“pipeline”) developed in our research laboratory for bespoke analysis.n = 526), the remainder being from clinical specimens (n = 255). Metrics used in the comparison were species identification, detection of mec genes, assignment to STs, and genetic relatedness based on pairwise SNPs. Outputs were generated independently for both methods, with blinding of the results of the other method prior to comparison of findings. We defined putative patient clusters based on genomic findings.A total of 789 isolates were sequenced over 43 sequence runs. Thirty isolates failed the manual pipeline quality control (QC) metrics, 21 of which also failed the automated tool QC metrics. On further inspection, the nine discrepancies (fail/pass) had >10% contamination with another species or high numbers of heterozygous sites (not detected by the automated pipeline QC) . Twenty-10.1128/msphere.00283-22.2TABLE S2n = 30) or automated tool QC metrics (n = 21/30). Download Table S2, XLSX file, 0.01 MB.Details of sequencing metrics for 30 MRSA isolates for which genomes failed manual , the detection of a mec gene , and ST assignment , ST45  and ST59  proving the most common.The two analysis methods produced fully concordant results for species identification . An epidemiological link was made between each pair with genetically related isolates when either a case-pair was admitted to the same ward with overlapping dates of admission or within 7 days of each other in the past 6 months, or if cases shared a postcode or GP practice. Both the manual and automated method identified the same 66 clusters involving 338 isolates/334 patients. Moreover, both methods defined the same cluster sample composition with respect to samples present in each cluster.While no additional clusters were identified by the manual analysis alone a further four genetic clusters involving nine patients were identified by the automated tool alone. The SNP distance between the isolate pairs was close to the cutoff (range 21 to 25 SNPs). The timing between the positive samples was often protracted. None of the four clusters had relevant epidemiological links. Two further clusters were not clear-cut in their interpretation and were considered “indeterminate.” One cluster of 25 isolates/patients identified by the manual pipeline contained a further 2 isolates identified by the automated tool alone. These two isolates only just reached the cutoff threshold (23 to 25 SNPs) and were 26 to 29 SNPs from the cluster using the research pipeline. The timing between the positive samples in the cluster spanned more than 250 days, and the patients shared a community epidemiological link. The second indeterminate cluster involved nine isolates/patients called by the manual pipeline, which was split into two clusters separated by 29 SNPs (above the cutoff) and a singleton by the automated tool. This cluster spanned more than 100 days and there were no epidemiological links.n = 655, 83%). A total of 3,289 pairs were ≤25 SNPs apart based on CC-specific mapping or the automated tool, with a median (range) of 0 SNPs (0 to 22 SNPs) different between the two methods -specific references for any ST with more than 10 isolates ( methods . SNP resn = 21) were investigated further. There were 66 SNP locations identified by the automated tool but not mapped by the manual pipeline, which called an “N” (uncertain/absent base) at variable positions due to no reads mapping (these occurred over an ~450bp region with a transposase fragment (n = 19 SNP locations) and an ~8.5 kb region, including an LPXTG surface protein and a bone sialoprotein binding protein (n = 3 SNP locations)); short segments mapping in regions of the genome without any other mapping (n = 30 SNP locations); and low (2×) depth of coverage (n = 2 SNP locations). The basis for the other mismatches was unclear, although some appeared to have a mixture of base calls present in the bam file (n = 3 SNP locations).Isolate pairs >10 SNPs different between the automated pipeline and CC-specific manual pipeline (n = 24), and SNP locations relating to a mix of bases called in the bam file that were identified as a single base by the manual pipeline during variant calling (n = 6).There were 38 SNP locations identified by the manual pipeline but not by the automated tool. Seven mismatches appeared to be genuine SNP locations that were not detected by the automated tool. This was due to an error in the code such that when both isolates had a different SNP compared to the reference at the same location, or multiple alternative alleles were identified for a position, the SNP was not identified. This has been rectified in an updated version. Manual analysis of genome data identified several explanations for the remainder, including likely misalignments near to indel sites  Preventing unnecessary infection control escalation. Five patients in a neonatal intensive care unit were found to be MRSA positive over 4 months, four of which were within a 5-week period and were considered putatively linked based on time and place. Plans were developed for an urgent outbreak response, including an incident management meeting involving senior staff, staff MRSA screening, and additional hand hygiene and cleaning audits. However, sequencing and the rapid analysis enabled by the automated tool showed that the five isolates were unrelated (>495 SNPs different from each other). The planned outbreak response, including any related costs, resources and disruption, was deescalated.(ii) Supporting targeted infection control interventions. The IPC team investigated 5 MRSA-positive patients flagged over a 3-week period on a single ward. Four cases were in a four-bed bay, and the fifth case was elsewhere on the ward. The near real-time availability of strain relatedness information allowed IPC to recognize that all patients in the 4-bed bay carried the same MRSA strain, while the isolate from the 5th case was unrelated. This supported a targeted intervention in which the single bay was closed and cleaned; exclusion of case 5 prevented full ward closure and cleaning.mec genes compared to the research pipeline. There was a high degree of concordance between SNP calls, and between the designation of isolate pairs based on a cutoff ≤25 SNPs. All 66 clusters identified by the manual pipeline were identified by the automated system, which found additional clusters with relatedness at the 25 SNP border defined for the study.Here, we describe the findings of an evaluation of an automated bioinformatics system that undertakes real-time analysis of MRSA genome data to determine pathogen relatedness to provide information to confirm or exclude an outbreak. Outputs were compared with a research-led manual bespoke analysis approach that was time-consuming and required an experienced bioinformatician. The automated tool was 100% accurate in assigning species, ST and The most common reason for additional SNPs called by the automated tool was short mapping segments in otherwise unmapped regions which the manual pipeline excluded from variant calling. The most common reason for additional SNPs called by the manual pipeline was misalignment around indels. As a result, there are isolates that will be called as related by one tool and unrelated by the other. However, this largely affects designation of related or unrelated when the isolate pair is close to the cutoff, and which may have high sensitivity for clustering patients as potentially associated but low specificity for having a true epidemiological link. The cutoff used was designed to be inclusive rather than exclusive, and thus is likely to include epidemiologically unrelated isolates close to the cutoff.Genome relatedness data identified 66 patient clusters (putative outbreaks) by both methods, the majority of which had not been confirmed through existing IPC methods alone. This corroborates previous findings that genome data provides higher resolution for outbreak detection than existing methods. If this tool was in clinical use, it would rule out the need for investigation of MRSA patients who were clustered by chance in time and space and would provide a priority flag for clusters that required further IPC investigation. The data produced in this study was accessed on several occasions by the IPC team, which resulted in implementation of targeted measures both to reduce spread and to rapidly step-down unrequired actions in cases where suspected outbreaks could be refuted. It was not the aim of the study to implement the automated tool in clinical practice or systematically record the impact of the data provided. This is the focus of a follow-on study that will span an entire year and will track how findings would alter IPC practice and provide an analysis of the consequent economic benefit to the NHS Trust.Klebsiella pneumoniae and vancomycin-resistant Enterococcus faecium, which were analyzed for antimicrobial resistance, sequence type, and outbreak clusters. The reported outbreak clusters revealed discrepancies between centers even when almost identical bioinformatic workflows were used, and some centers failed to detect extended-spectrum beta-lactamase genes and MLST loci. Furthermore, applying a standardized method to determine outbreak clusters on the reported de novo assemblies did not result in uniformity of outbreak-cluster composition . A single 2 to 3 mm colony was picked from clinical culture plates using a 1 μL loop. Where colonies were smaller than 2 mm, several colonies were picked. Where bacterial growth was confluent, a 1 μL loopful was taken. If there were several positive plates for one clinical sample, the plate with the least visible background contamination was selected. Isolates were either extracted directly from the clinical plate or subcultured overnight on CBA before processing further. A 10 μL loopful was stored at −80°C in Microbank vials. The first available MRSA isolate from each patient was selected for sequencing, together with all isolates associated with bloodstream infection if this occurred. After de-duplication we identified 789 MRSA isolates cultured from 784 patients.The study had ethical approval from the National Research Ethics Service (ref: 11/EE/0499) and the Cambridge University Hospitals NHS Foundation Trust Research and Development Department (ref: A092428). The study was conducted at the Clinical Microbiology and Public Health Laboratory at the Cambridge University Hospitals NHS Foundation Trust (CUH) in the United Kingdom (UK). Consecutive patients with MRSA-positive samples submitted by three hospitals and 65 GP surgeries during a 9-month period between 24 January and 1 November 2018 were identified using the hospital IT system . Laboratory data were collected on date and place of sampling, and sample type . During the first 3 months, MRSA isolates were retrieved from the freezer archive of the routine laboratory. Isolates were plated onto Columbia Blood Agar (CBA) using a 1 μL loop and incubated overnight at 37°C in air. A single colony was selected for sequencing, and a 10 μL loopful was stored at −80°C in Microbank vials (Pro-lab Diagnostics). During the remaining 6 months, putative or confirmed MRSA-positive culture plates were prospectively retrieved from the laboratory and confirmed as E. coli NCTC12241)). The study sequences are available in the European Nucleotide Archive (https://www.ebi.ac.uk/ena) and accession numbers are provided in Isolates were renumbered with an anonymous study code. DNA was extracted using the Qiagen DNA mini extraction kit and library preparation was performed using the Illumina Nextera DNA flex kit, as described previously . LibrariFor the researcher-led manual bioinformatics pipeline, controls and clinical MRSA isolate sequences were required to pass quality control (QC) metrics prior to further analysis, as previously described , 11. Forhttps://ccb.jhu.edu/software/kraken/) and the miniKraken database (https://ccb.jhu.edu/software/kraken/dl/minikraken_20171019_8GB.tgz). Multilocus sequence types (ST) were identified for MRSA using ARIBA version 2.12.1, as described at https://github.com/sanger-pathogens/ariba/wiki/MLST-calling-with-ARIBA. MRSA were screened for the presence of mecA , mecB  or mecC  using ARIBA, with a minimum percentage identity of 70% required based on Ito et al. 2012 . Mapping was performed using SMALT  with mapping and base calling performed as described in Klemm et al. 2018  were identified using the script available at https://github.com/sanger-pathogens/snp-sites. The depth and percentage coverage of the mapping reference were determined using the script available at https://github.com/sanger-pathogens/vr-codebase/blob/master/lib/VertRes/Pipelines/Mapping.pm. The number of heterozygous sites >50bp apart was determined using the script available at Github . This script also provided depth of coverage and standard deviation of coverage for the mec gene, which could be used to calculate whether the mec gene had sufficient depth to pass QC (see above).Isolates were mapped to CC22 HO5096 0412 , CC5 N315 (accession number BA000018), CC8 USA300 (accession number CP000255), CC30 MRSA252 (accession number BX571856), CC45 CA347 (accession number CP006044), and CC59 M013 (accession number CP003166). Mobile genetic element files used for these references are available at http://figshare.com/authors/Francesc_Coll/5727779.CC-specific mapping was performed as described above with the following references: CC1 MW2 (accession number The fully automated tool used here has been updated since our previous report on 17 genomes . The clo


FST, and XP-EHH), we explored positive selection regions harboring genes in Xiangxi cattle, which were related to reproduction, growth, meat quality, heat tolerance, and immune response. Our findings revealed the extent of sequence variation in Xiangxi cattle at the genome-wide level. All of our fruitful results can bring about a valuable genomic resource for genetic studies and breed protection in the future.Understanding the genetic diversity in Xiangxi cattle may facilitate our efforts toward further breeding programs. Here we compared 23 Xiangxi cattle with 78 published genomes of 6 worldwide representative breeds to characterize the genomic variations of Xiangxi cattle. Based on clustering models in population structure analysis, we displayed that Xiangxi cattle had a mutual genome ancestor with Chinese indicine, Indian indicine, and East Asian taurine. Population genetic diversity was analyzed by four methods , and we found that Xiangxi cattle had higher genomic diversity and weaker artificial selection than commercial breed cattle. Using four testing methods (θπ, CLR,  Bos taurus) and humped indicine (Bos indicus)  , which aindicus) . As a siindicus) . Xiangxiindicus) . Previouindicus)  to demonWith the application of whole-genome sequencing, it has become possible to study the genetic diversity of various breeds on the group size . Since tBos taurus reference genome assembly (ARS-UCD1.2). Then SNPs of Xiangxi cattle made a comparison with those of commercial and native breeds previously collected from around the world. Finally, by scanning the whole genome of Xiangxi cattle, the selective sweeping results were determined. Our analyses fully described the genomic diversity and population structure, revealed the possible signs of natural and artificial selection, as well as provided new insights for breeding Xiangxi cattle.Considering that the entire genome variation of Xiangxi cattle was largely unexplored, we performed whole-genome sequencing on 23 Xiangxi cattle. SNPs were identified in Xiangxi cattle by basing on n = 23) were collected from the Xiangxi cattle engineering technology center of Hunan Province, Huayuan, China. Genomic DNA of the ear tissue samples was extracted using a standard phenol/chloroform-based protocol. The DNA library was constructed for each sample (500 bp insert size). Sequencing via Illumina NovaSeq 6000 with 2 × 150 bp model at Novogene Bioinformatics Institute, Beijing, China, and 150 bp paired-end sequence data were generated.Ear tissue samples of Xiangxi cattle , 15 Hanwoo, 31 Chinese native cattle , and 7 India-Pakistan cattle  publicly. In total, 101 whole genomes of cattle were used for the subsequent analysis.Bos taurus reference genome assembly ARS-UCD1.2 using BWA-MEM (0.7.13-r1126) with default parameters (http://broadinstitute.github.io/picard), and Genome Analysis Toolkit . All SNPs were filtered using the module “Variant Filtration” of GATK to obtain high-quality SNPs. By using the ANNOVAR, a set of SNPs were generated for the following analyses. An unrooted neighbor-joining (NJ) was constructed based on the matrix of pairwise genetic distances using MEGA v7.0  and iTOLhttp://www.r-project.org).Linkage disequilibrium (LD) decay was calculated using PopLDdecay  with defTo identify the selective sweep regions in Xiangxi cattle, two different statistics were used, including the nucleotide diversity (θπ) with 50 kb sliding window and 20 kb step in VCFtools  and the FST) and cross-population extended haplotype homozygosity (XP-EHH). We estimated the FST values using VCFtools (50 kb windows with 20 kb step) and the XP-EHH using selscan v1.1 (p < 0.005). In order to make the results more reliable, we used two or more methods to determine that the overlapping regions had been used as candidate regions. We calculated the Tajima’s D statistic to consolidate our results by using VCFtools.For comparison in Xiangxi cattle and Qinchuan cattle, we separately calculated fixation index (can v1.1  with defhttp://kobas.cbi.pku.edu.cn/) was used to understand the function and complex pathways of candidate genes, including the Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Gene Ontology (GO) in the study .Moreover, KOBAS 3.0 (B. taurus reference genome (ARS-UCD1.2), resulting in a total of ∼4.7 billion reads and ∼10.9 × coverage each  or intron region (38.2%). Exon accounted for 0.7% of the total SNPs, including 112572 nonsynonymous and 179112 synonymous SNPs . In addiBased on the study of autosomal SNPs, we discussed the phylogenetic relationships among 101 samples from 7 populations . The NJ We estimated the ancestral populations of all cattle samples by using clustering models. When K = 2, the lineage of cattle can be fundamentally distinguished from that of taurine and indicine cattle. When K = 5, five “core” cattle herds were separated, while Xiangxi cattle exhibited mixed phenomena . The resAs shown in Meanwhile, the ROH of each breed was divided by length . In order of the number of ROH, it demonstrated that the European taurine took the largest number. By contrast, the number of the Indian indicine is lowest, that of the Chinese Crossbreed and Chinese indicine are in the middle respectively . The XiaKHDRBS2, GOLGA4, BBX, BANF2, INSL6)  (PLIN4) (SYN3)   and the composite likelihood ratio (CLR) were used to explore the selected genomic regions in Xiangxi cattle  Figure . We foun, INSL6) , growth , FGFR3) , meat qu (PLIN4) , and som) (SYN3) . We alsoE, AZU1)  related FST  and XP-EHH , to elucidate further the positive selection characteristics between Xiangxi cattle and Qinchuan cattle (DNAJC8)  and 5 genes were performed , which were related to meat quality, growth efficiency, and the balance of ionic concentrations  were found in Xiangxi cattle which were associated with the development of sebum, sebaceous glands, and epidermal cells. In addition, we also detected significant GO terms responsible for meat quality  .Furthermore, we implemented two methods, n cattle , which cn cattle . 102 oven cattle . Among t(DNAJC8)  showed i(DNAJC8) , the KEGtrations . GO term005516”)  involvinADRB2 gene was associated with muscle pH , East Asian taurine (∼20%), and Indian indicine (∼7%) . The hetBos javanicus) , which was consistent with the previous study . By contvanicus) . Similarvanicus) . Our resVGLL2) played a vital role in the process of locomotion. The VGLL2 gene can affect the development of skeletal muscles   in skin fibroblasts, thereby controlling the process of cutaneous wound healing  associated with the muscle contraction. SLC9A1 improves the adjustment capacity of muscle pH value and maintains body endurance  were enriched, involving epidermal cell related genes  . The SFN healing . FA2H is barrier . Xiangxindurance . It was ormation . The genormation . Besidesthe gene . Compareulations . TherefoFST, and XP-EHH), six genes  also showed positive selection, which may be related to superior meat quality traits in Xiangxi cattle. Two genes  were related to the disease-resistance of Xiangxi cattle. PTAFR gene, platelet activating factor receptor, can regulate inflammatory, smooth-muscle contractile and hypotensive activity . Our selective sweep identification of Xiangxi cattle may promote the cognition of the hereditary mechanism of potential population characteristics, and hopefully serve as a theoretical reference for the breeding in the future.In this study, we used WGS data to study the population structure of Xiangxi cattle, so as to provide the first in-depth study of its gene diversity, phylogenetic relationship, and Selective Sweep Test. Some genes were identified that will not only be helpful to us for a better understanding of the features of Xiangxi cattle, but also further study the characteristics of other native cattle of China. In conclusion, the revelation of the genetic diversity of Xiangxi cattle will establish a sound foundation for conservation breeding in the future.


The plant material was developed at the EEAD–CSIC Prunus rootstocks breeding program, aiming to obtain a segregating progeny for a range of characters of agronomical interest to rootstock breeding . Sequence reads obtained from double-digest SBG were aligned to the P. persica reference genome (Peach v2.0). While eight linkage groups were constructed for “Adafuel,” only four linkage groups were constructed for “Flordaguard,” given the low heterozygosity of this last genotype. High synteny and co-linearity were observed between obtained maps and Peach v2.0. On the other hand, this work aimed to elucidate the genetic basis of leaf chlorosis tolerance using the phenotypic segregation of the progeny to iron-chlorosis tolerance, along with the QTLs responsible for leaf chlorosis. The F1 mapping population, composed initially of 131 seedlings, was growing in four field trials established on calcareous soils at the experimental field of the EEAD-CSIC in Zaragoza, Spain. From the initial mapping population, 131 individuals were selected for their phenotypical characterization with SPAD measurements of plants grown in the field, exhibiting a great variability. Significant QTLs associated with tolerance to iron chlorosis were found in LG1, LG5, LG7, and LG8. The significant QTLs detected in LG5 and LG7 have not been associated with this abiotic stress before in Prunus. Several candidate genes such as Prupe.1G541100, predicted as glutamyl-tRNA reductase 1, Prupe.1G468200, encoding a 2-oxoglutarate (2OG), and Fe(II)-dependent oxygenase superfamily protein or Prupe.1G577000 (ppa011050.m), a NIFU-like protein 2 (NIFU2) were detected. The exact biological function of some of these genes should be verified for the future development of marker-assisted selection for peach iron chlorosis tolerance.Linkage maps are highly appreciated tools for cultivar and rootstock breeding programs because they are suitable for genetic and genomic studies. In this study, we report on using sequence-based genotyping (SBG) approach to simultaneously discover and genotype SNPs from two peach-based rootstocks (“Adafuel” and “Flordaguard”) and their progeny ( Prunus is one of the largest genera of the family Rosaceae and subfamily Prunoideae , formerly named “GxN 22”  markers  Franch, C-26712. Chico 11 was a seedling of the ornamental P. persica cv. Shau Thai, PI 65821  seedlings obtained from the cross between the peach–almond rootstock “Adafuel”  in ungrafted clonally propagated plants of 131 genotypes grown in the field. The chlorophyll (Chl) concentration per unit leaf area was estimated using 10 leaves per tree, selected from the middle of bearing shoots located all around the crown, to obtain an average leaf value representative of the leaves belonging to the outer part of the tree canopy. SPAD measurements were carried out ~120 days after full bloom in the four experimental trials (blocks) for two years. For each genotype, at least three ungrafted trees were assessed in each experimental trial.A mixed model was developed to represent our experimental design and to obtain the adjusted means (lsmeans) using the procedure PROC MIXED within SAS _im the random effect of the i-th clone error at the m-th year, β_ik is the random effect of the k-th block within the i-th year, and ε_imk is the effect of the error of plot mean y_ikm. For all random effects including the error, year-specific variances and an additional covariance were fitted, if these extensions increase the model  Akaike, .Genomic DNA was extracted from young leaves collected from the parents and seedlings and stored at−80°C until use. The homogenization of leaf samples was reached after grinding around 100 mg of tissue with the Mixer Mill MM 400 , and the tubes were immersed in liquid nitrogen to freeze the tissue. The NucleoSpin Plant II kit  was used for DNA extraction according to the manufacturer's instructions using the Lysis Buffer PL1, which is based on the CTAB method. DNA was eluted in 50 μL buffer PE and it was quantified using spectrophotometry . DNA quality was determined using 1% agarose electrophoresis.PstI (methylation sensitive) and MspI , as described by Poland et al. . A reduced representation SBG library was prepared using restriction enzymes, d et al. . A totald et al. . Sample d et al.  with thed et al. . The resd et al. , and intd et al. , and acchttps://www.kyazma.nl/index.php/JoinMap/Evaluate). Linkage maps were constructed following the protocol described for sweet cherry by Guajardo et al.  similar or higher than 0.005 was used to identify markers significantly associated with both traits by the K-W test , moderate (non-synonymous substitution), low (synonymous substitution), or modifier (with impact on noncoding regions). To examine the putative function of the genes containing high impact SNPs, a eukaryotic orthologous group (KOG) analysis was carried out using tools from Join Genome Institute . Information about GO, PFAM and Phanter for each gene of this group was obtained from Phytozome (https://jgi.doe.gov).The physical position of each SNP was used to identify common markers in this study and the IRSC 9K peach SNP array v1 . Lsmeans ranged significantly among genotypes and years from 22.013 to 37.40 .Different measurements were implemented to guarantee an accurate data set for the genetic analysis of 131 genotypes, clonally propagated and growing in three experimental fields. Leaf chlorophyll content, used as a good indicator of leaf chlorosis in trees, was estimated by using a SPAD meter. According to the Lsmeans obtained from our model, the values showed a great segregation response and significant differences between genotypes , evenly distributed along the eight peach pseudomolecules, were used for mapping . The nump < 0.01) was mapped in the bottom part of Ad-LG5  exhibited the maternal segregation type < lmxll>, 868 (4.6%) showed the paternal segregation type < nnxnp> and 505 (2.7%) were heterozygous for both parents, with segregation type < hkxhk>. The last group of SNPs was not used for linkage maps construction. The linkage map obtained for the “Adafuel” genotype was composed of eight linkage groups  with this trait , covering a total of 450.6 cM. The results obtained from the non-parametric Kruskal–Wallis for iron chlorosis revealed several QTLs in Ad-LG1, Ad-LG5, Ad-LG7, and Ad-LG8. The number of SNPs identified and the length of each QTL in each linkage group was different. In total, 1,263 SNP markers, located in 95 unique map positions, were strongly associated , low impact (12.12%), moderate impact (8.62%), and high impact (0.05%). In Ad-LG1, a total of 3,522 effects were detected. The number of effects was lower in the rest of pseudomolecules, with 103 effects in Ad-LG5, 93 in Ad-LG7, and 28 effects in Ad-LG8. These effects affected a total of 970 unique peach (Prupe) genes, as commented before, the majority of them located in Pp01. Several annotations for homologs of these peach genes have been provided, and in total, this list of genes matches with 754 homologs is genes . A totalis genes .Prunus. The physical position and distribution of each SNP along the eight peach pseudomolecules were determined to identify SNP-carrying genes and to predict the effect of each SNP. To our knowledge, this is the first report on linkage map construction and QTL analysis using SNPs obtained from SBG for Prunus rootstocks.In this work, a successful strategy covering the discovery of single nucleotide polymorphism (SNP) through double-digest sequence-based genotyping (SBG), genetic linkage maps construction, and QTL identification has been performed in a segregating peach–almond progeny to improve the knowledge about the genetic control of the tolerance to iron chlorosis in Prunus rootstocks. A high mean depth  per SNP and high coverage of peach pseudomolecules were reached, which was higher than a coverage of 12.1x obtained by Núñez-Lillo et al.  was mapped at the bottom part of Ad-LG5  deficiency-induced chlorosis, which has a large impact on yield in tree fruit crops, such as peach  to ferrous iron (Fe2+) via its reductase activity .Several studies using commercially available Affymetrix ATH1 GeneChips , predicted as glutamyl-tRNA reductase 1, which had a modifier (3'-UTR variant) effect by a significant SNP located in Pp01  and mapped at 63.65 cM. The study of the transcriptional response of Arabidopsis to Fe deficiency has indicated that glutamyl-tRNA reductase (HEMA1), NYC1, and CGLD27 may represent key players in preventing photooxidative damage in Fe-deficient leaf cells , encoding a ferredoxin protein (FdC2) and a NIFU-like protein 2 (NIFU2), respectively. The interaction of these both genes in peach and their association with iron chlorosis response should be corroborated in further analysis. In this sense, in Prunus rootstocks linkage maps using SNPs obtained from double-digest SBG. These linkage maps were the basis to identify QTLs and putative genes related to iron chlorosis in a segregating peach–almond progeny. SNPs identified in this study present a valuable set of new SNPs identified in Prunus that would be useful for genetic studies in the future. Candidate genes for iron chlorosis QTL were proposed; however, future fine mapping and transcriptomic analysis are needed to enable the identification of the underlying gene(s).In the present study, we have constructed, to the best of our knowledge, the first The dataset generated for this study can be found in the NCBI-SRA database, BioProject number PRJNA773812.VG, PM-G, and MM conceived and designed the experiments. VG, PM-G, and SS performed the experiments. VG, SS, CS, PM-G, and AC-S analyzed the data. CS and MM contributed with reagents, materials, and analysis tools. All authors contributed to the manuscript preparation, read, and approved the final version.This research was funded by the Spanish Ministry of Science, Innovation and Universities (MICINN) grants RFP2015-00019 and RTI2018-094176-R-C33, co-funded by ERDF A way of making EUROPE and the Regional Government of Aragon (A44 and T07-17R). It was also funded by CONICYT-REGIONAL/GORE O'HIGGINS/CEAF/R19A10003, FONDECYT 3160316, and CONICYT R16F20006 from Chile.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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