DNA marker profiles play a crucial role in the identification and registration of germplasm, as well as in the distinctness, uniformity, and stability (DUS) testing of new plant variety protection. However, selecting minimal marker sets from large-scale SNP dataset can be challenging to distinguish a maximum number of samples. Results: Here, we developed the CoreSNP pipeline using a “divide and conquer” strategy and a “greedy” algorithm. The pipeline offers adjustable parameters to guarantee the distinction of each sample pair with at least two markers. Additionally, it allows datasets with missing loci as input. The pipeline was tested in barley, soybean, wheat, rice and maize. A few dozen of core SNPs were efficiently selected in different crops with SNP array, GBS, and WGS dataset, which can differentiate thousands of individual samples. The core SNPs were distributed across all chromosomes, exhibiting lower pairwise linkage disequilibrium (LD) and higher polymorphism information content (PIC) and minor allele frequencies (MAF). It was shown that both the genetic diversity of the population and the characteristics of the original dataset can significantly influence the number of core markers. In addition, the core SNPs capture a certain level of the original population structure.CoreSNP is an efficiency way of core marker sets selection based on Genome-wide SNP datasets of crops. Combined with low-density SNP chip or genotyping technologies, it can be a cost-effective way to simplify and expedite the evaluation of genetic resources and differentiate different crop varieties. This tool is expected to have great application prospects in the rapid comparison of germplasm and intellectual property protection of new varieties.The online version contains supplementary material available at 10.1186/s12870-023-04609-w. Accurate identification and registration of germplasm resources are essential for plant conservation efforts –3. TradiHordeum vulgare L.) were potential duplicates [With the increasing number of germplasm collections stored in genebanks, effective characterization and management have become major challenges . In the plicates . By seleplicates .Furthermore, core marker sets play a vital role in protecting new plant varieties. Due to the increasing number of crop varieties being released onto the market, the limited genetic diversity and close similarity among elite parental lines lead to fewer morphological differences that can be utilized for variety identification in modern breeding programs . The preAmong various types of molecular markers, single nucleotide polymorphisms (SNPs) have gained significant importance due to their high reproducibility, locus specificity, and wide distribution throughout the genome . The conAutomated methods based on computer programming have also been proposed to improve the efficiency of selecting core SNPs. These methods aim to construct relatively small marker sets capable of distinguishing a wide range of varieties. Hiroshi et al. (2013) employed an exhaustive method to develop MinimalMarker software for identifying minimal marker sets. They constructed a pairwise comparison matrix by calculating the number of differential alleles between each pair of varieties and consistently selected the marker with the highest discrimination to form the minimal set . This alDespite the progress made in selecting core SNPs, these studies are relatively time-consuming and not user-friendly, especially when dealing with data that contain missing loci. Here, we propose CoreSNP, a novel and customized pipeline for developing minimal core SNPs. This pipeline can largely reduce the number of SNPs essential for identifying different varieties from large-scale genotyping data. Based on publicly available genotype datasets, the pipeline has been proven to be robust in different crops and across various sequencing platforms.Marker7’ and ‘Marker8’ were used as examples. In this step, we exclude SNPs without polymorphisms and nearby SNPs linked to the selected core SNPs. Consequently, at each selection step, the process achieved the local optimal solution to maximize sample differentiation and haplotype diversity.A greedy search algorithm was applied to the core SNP selection and the specific workflow of the pipeline is summarized in Fig. After employing major allele imputation, the haplotypes without any missing values were outputted, and the chosen SNPs were designated as the primary core markers. Recognizing that missing sites could potentially result from sequencing errors, we retrieved haplotypes with missing loci based on the positions of imputed values. Subsequently, the pipeline identified pairs of haplotypes with differences caused by imputed missing data. Through an iterative process, SNPs with the highest frequency were selected and added to the primary list. Eventually, the final core sets were generated after achieving complete differentiation across all sample pairs Fig. .In this study, additional command options were introduced to configure the running parameters, thereby enhancing the applicability of the pipeline. Users can define input files using the “-v” option and customize which markers should be included or excluded using the “-i” and “-e” options, respectively. Since some SNP combinations (haplotypes) have close Shannon index values, the “-x” option was provided to allow users to specify the number of candidate SNP combinations to be selected in each round. This option provides users with more feasible solutions for downstream analysis. Furthermore, core SNPs were randomly and independently selected each round, and users have the flexibility to define the number of repetitions using the “-c” option. The “-m” option allows users to specify that at least two markers are required to effectively discriminate between every pair of varieties. This criterion can indeed mitigate potential errors or noise introduced by the raw sequencing data, thereby enhancing the reliability and robustness of the results Table .To validate the CoreSNP pipeline, we collected the public SNP genotype dataset of barley, soybean, wheat, rice, and maize from widely studied plant databases or research studies Table . The SNPI. Additionally, we obtained a GBS SNP matrix of 1,297 barley accessions from the IPK data repository, referred to as BarleyII [I & II, contains a total of 185,508 SNPs.For the barley analysis, genotypic information of 1,000 accessions was downloaded from the Germinate Barley SNP Platforms . The colBarleyII . We mergThe soybean dataset comprises 817 soybean accessions with 158,959 SNPs, including 77 parental lines, 169 nonparental lines, and 571 progenies. Genotyping was performed using the ZDX1 array genotyping platform . The wheWe extracted genotypic information from 453 high-coverage rice accessions obtained from the 3,000 Rice Genome Project . The maiHeterozygous sites were treated as missing data and processed using BCFtools version 1.10.2 . GenotypIn this study, the Shannon indices  and PIC H is the diversity index, n is the population size and Pi is the frequency of the combined haplotypes.where Pi and Pj are the frequencies of two SNP alleles among all samples. Equation where n is the population size and 2 with a maximum distance of 10 Mb using PopLDdecay version 3.40 [Allele counts and frequencies and IBS information were calculated by PLINK version 1.90 . LD estiion 3.40 . A Manteion 3.40 . Principion 3.40 . All figion 3.40  and RIdeion 3.40 .https://github.com/admy55/CoreSNP) or Gitee (https://gitee.com/admy55/CoreSNP).The CoreSNP pipeline was developed using the Python programming language and accepts compressed Variant Call Format (VCF) files or uncompressed VCF files as input. The cost-free program is readily executed from the command line, relying on specific dependencies, such as Python (version 3.6 or higher), Numpy and PLINK 1.9. Both the source code and the essential dependencies are accessible on GitHub  method using the Barley I & II dataset to evaluate their performance in selecting core SNPs. In the RS process, two markers were randomly selected from the long and short arms of each chromosome, forming a combination of 28 SNPs with aMAF greater than 0.3 RS1) and MAF greater than 0.4 (RS2). After 20 iterations of selection, the randomly selected 28 SNPs from RS1 and RS2 identified accessions ranging from 977 to 1061. Through saturation curve analysis, we found that the CoreSNP approach exhibited significantly higher efficiency in SNP selection than the RS method, as it efficiently identified the same number of germplasms with a smaller set of selected SNPs  for PIC of biallelic markers indicate better discriminatory properties.The results showed that more than 70% of the core SNPs had an MAF greater than 0.3 and PIC greater than 0.35, except for the soybean dataset Fig. . In the The 24 core markers were cover 13 chromosomes in the wheat genome, and in addition to these, the selected markers are distributed across nearly every chromosome in the genome. The majority of the core SNPs were not located in close proximity to each other, suggesting a lower LD distance between them , and identity by state (IBS) information utilizing the test dataset Fig. .No specific regularity was observed in the distribution of MAF or missing rate in soybean experiments Fig. A-B. HoweThe wheat and barley I dataset showed a relatively uniform pattern in MAF distribution Fig. A. HoweveThe fertility of spikelet florets, commonly referred to as row type, and the presence or absence of grain hulls are two crucial infraspecific morphological traits in barley. In this study, we assessed the feasibility of the CoreSNP pipeline with three SNP markers  that are tightly linked to these two traits as included markers for core SNP selection. By establishing a criterion of a minimum of at least two markers distinguishing each pair of samples, we successfully identified 31 core SNP markers that differentiated 1081 barley samples using the merged dataset. These core SNP markers were distributed across all seven chromosomes Fig. .The genetic structure of 1081 barley collections was analyzed using both the core SNP set and the complete set of genome-wide SNPs. Principal component analysis (PCA) revealed that some samples with relatively distant genetic distances within three clusters were still well separated along the PC1 and PC2 dimensions. This observation highlights that the core SNPs capture a certain level of the original population structure Fig. .The correlation coefficient (r) values of 0.4024 indicate a significant correlation between the genetic distance matrices of the core SNP panels and the total genome-wide SNPs. This affirms the robustness and reliability of our core SNP selection process (Table S10).Appraising the discriminatory power of loci combinations is a necessary step in marker screening . When asIn addition, we conducted a comparative analysis with the Conditional Random Selection (CRS) method using the data released in their research . The resThe discrimination curve of the markers was plotted based on the haplotype count obtained at each step Fig. . This cuThe selected core SNPs exhibited a high degree of polymorphism and were distributed across the genome. In fact, within the pipeline, the evaluation of an increase in haplotype numbers involved removing redundant linked markers, ensuring that adjacent pairs of SNPs were not positioned too closely to each other. The number of core SNPs is influenced by the genetic diversity of different populations. However, it is also significantly influenced by the missing rate in the original dataset. In terms of population structure, certain closely related samples are still unable to be distinctly separated in the reduced-dimensional data. This limitation could be attributed to the insufficient number of markers in comparison to the original dataset, which hindered their ability to adequately capture the genetic variations present.The cost of genome sequencing tools remains high for routine large-scale germplasm identification. However, with the development of low-density sequencing techniques, such as KASP and ARMS-PCR, our CoreSNP pipeline provides a cost-effective solution for germplasm identification and genetic relationship analysis. By constructing a reference library using a combination of core SNP alleles (haplotypes), it becomes possible to establish a comprehensive catalog of DNA profiles and DNA fingerprints for each accession. This will greatly facilitate the analysis of variety traceability and genetic backgrounds. However, it is important to note that this reference library is not a one-size-fits-all solution, thereby necessitating additional efforts in collecting more genotypes.Moreover, these SNPs can also be utilized for parent combination selection and progeny screening during the breeding process, thus enhancing breeding efficiency and precision. Additionally, the processing of many samples inevitably introduces the issue of sample mix-up. It becomes challenging when samples contain mixtures with few identifiable characteristics. The combination of genetic profiles and core SNP alleles can be used to identify accidental sample mix-ups, ensuring the authenticity and purity of seeds. In summary, the CoreSNP pipeline takes into account sequencing platform constraints and user-specific preferences. By potentially saving time and reducing costs, it simplifies and streamlines the process of genomic identification. This tool will serve as a valuable foundation for modern breeding efforts and future germplasm management and preservation endeavors.In conclusion, we developed the CoreSNP pipeline for evolution of the discrimination power of SNP combinations. We validated it using diverse genotype files from various crops and found that it exhibited high efficiency. This tool can be efficiently used for selecting core SNPs capable of representing genome-wide variation to identify similarity and redundancy within germplasm resources.Below is the link to the electronic supplementary material.Supplementary Material 1: Fig. S1. Comparison of the CoreSNP pipeline and Random Selection(RS) method for SNP selection. Fig. S2. Frequency distribution of MAF and PIC values among the selected core SNPs. Fig. S3. Distribution of core SNPs selected from barley merged dataset with specific parameters. Fig. S4. The comparison of the Principle Component Analysis (PCA) analysis based on the raw genome-wide SNPs and core SNPs. Table S1. Multiple options for running the core SNP pipeline. Table S2. Datasets description and core SNP selection in various crop species. Table S10. Mantel’s test for comparisons among genetic distance matrices calculated using the core SNPs and the original dataset. Table S11. Comparison of MAF, PIC and Shannon index among different haplotypesSupplementary Material 2: Table S3. Description of Core SNPs Selected from the BarleyIdataset with default parameters. Table S4. Description of Core SNPs Selected from the BarleyIdataset with default parameters. Table S5. Description of Core SNPs Selected from the BarleyI&IIdataset with default parameters. Table S6. Description of Core SNPs Selected from the Soybean dataset with default parameters. Table S7. Description of Core SNPs Selected from the Wheat dataset with default parameters. Table S8. Description of Core SNPs Selected from the Rice dataset with default parameters. Table S9. Description of Core SNPs Selected from the Maize dataset with default parameters


The horse, one of the most domesticated animals, has been used for several purposes, like transportation, hunting, in sport, or for agriculture-related works. Kathiawari, Marwari, Manipuri, Zanskari, Bhutia, Spiti, and Thoroughbred are the main breeds of horses, particularly due to their agroclimatic adaptation and role in any kind of strong physical activity, and these characteristics are majorly governed by genetic factors. The genetic diversity and phylogenetic relationship of these Indian equine breeds using microsatellite markers have been reported, but further studies exploring the SNP diversity and runs of homozygosity revealing the selection signature of breeds are still warranted. In our study, the identification of genes that play a vital role in muscle development is performed through SNP detection via the whole-genome sequencing approach. A total of 96 samples, categorized under seven breeds, and 620,721 SNPs were considered to ascertain the ROH patterns amongst all the seven breeds. Over 5444 ROH islands were mined, and the maximum number of ROHs was found to be present in Zanskari, while Thoroughbred was confined to the lowest number of ROHs. Gene enrichment of these ROH islands produced 6757 functional genes, with AGPAT1, CLEC4, and CFAP20 as important gene families. However, QTL annotation revealed that the maximum QTLs were associated with Wither’s height trait ontology that falls under the growth trait in all seven breeds. An Equine SNP marker database (EqSNPDb) was developed to catalogue ROHs for all these equine breeds for the flexible and easy chromosome-wise retrieval of ROH along with the genotype details of all the SNPs. Such a study can reveal breed divergence in different climatic and ecological conditions. Equus ferus caballus, was one of the first domesticated animals and has been used for scores of purposes in human societies. It is used as a companion animal, as well as for food, transportation, draught and agricultural work, and is used in sport, hunting, and warfare [The horse, scientifically known as  warfare ,2. In In warfare ,4,5,6.The phenotypical characteristics of Indian equines are highly distinctive and unique in comparison to horses in the rest of the world, which suggests their geographical distribution and subsequent acquisition of selective characteristics over time. The ponies, Spiti and Zanskari, belong to the Himalayan region, making them susceptible to cold temperatures and scarce food availability. Both breeds have high stamina since they can move on steep terrains. Bhutia and Manipuri ponies are similarly capable, with high endurance. Marwari and Kathiawari breeds are understood to be similar to each other due to their close resemblance, as they are related to Arabian horses. Still, Marwari horses are longer than Kathiawari. Kathiawari horses are considered to be the offspring of wild horses of Gujarat and Arabian Sea .A variety of genomic tools have been used to study the genome and functions of modern equines. Considering equines across the world, several generations of high-throughput genotyping arrays known as “single nucleotide polymorphism (SNP) chips” have been released since the equine genome was first sequenced, enabling rapid and cost-effective genotyping. A genotyping array with 54,602 SNPs  was developed using SNPs identified via Sanger sequencing of a small group of horses . AdditioOne of the most common concerns about equines is their physical deformities, which occur due to their racing- and performance-based activities, as well as genetic factors . A lot oThough a study on the genetic diversity and phylogenetic relationship of Indian equine breeds like Spiti, Zanskari, Manipuri, Bhutia, Kathiawari, and Marwari using microsatellite markers was reported ,4,6, theBlood samples were collected from the jugular vein into a vacutainer with potassium ethylenediaminetetraacetic acid (K2 EDTA) from 07 horse breeds, namely Manipuri (17), Zanskari (20), Bhutia (20), Spiti (16), Kathiawari (10), Marwari (10), and Thoroughbred (3), covering 96 genotypes. All blood samples were transported to the research laboratory in an ice box and stored at 4 °C and processed for DNA isolation.According to the manufacturer’s instructions, genomic DNA was isolated from blood using a ReliaPrep™ Blood gDNA Miniprep System . The isolated DNA was stored at −20 °C to prevent DNA degradation. Agarose gel electrophoresis (0.8% agarose gel) and a Qubit4 fluorometer were used to qualitatively and quantitatively analyze DNA (at 260 nm/280 nm absorbance). Samples were genotyped using the Axiom™ Equine Genotyping Array with an average recall rate of 92.6% for each sample.For every individual, Runs of Homozygosity (ROH) were computed using PLINK v 1.9 ,18. BefoROH), the average length of ROH (AROH) and the total number of ROH per breed (NROH) were computed. The percentage of chromosomes covered by ROH was also computed. To achieve this, the mean ROH length was calculated by adding all the ROHs (in Mb) on each chromosome and dividing by the number of individuals that had ROH on that chromosome. This mean was then further divided by the length of the chromosome (in Mb).To exclude short and common ROH, which were present across the genome, the minimum ROH length was set to 1 Mb so that short ROHs induced by LD effects could be excluded . Other pHOM and FROH) were estimated using PLINK v 1.9. FROH (Inbreeding coefficient) was computed as stated by McQuillan et al. [The genomic inbreeding coefficients  was calculated using PLINK v1.90 by computing observed and expected autosomal homozygous genotypes counts for each sample as follows:Inbreeding based on the observed versus expected number of homozygous genotypes . Subsequently, significance was based on a corrected false discovery rate value set at q ≤ 0.05.All the SNPs of ROH were annotated to mine the functional genes using SnpEFF . These Ghttps://www.animalgenome.org/cgi-bin/QTLdb/EC/index) (accessed on 14 March 2022) for the EC_3.0 assembly and was plotted against top 20 ROH islands. Information used to locate the important traits within the relevant genomic regions was mined and this was carried out based on SNP markers via the filtering criteria.Known QTLs of horses, which are falling in ROH regions, were superimposed over the horse QTL map. QTL details were obtained from the Horse QTL database (The Equine SNP marker database (EqSNPDb) was developed using three-tier architecture. It consists of a database layer, an application layer (server), and a presentation layer (client). It is a relational database that provides information about the Runs of Homozygosity (ROH) deciphered from SNPs of the equine breeds under study. All the information about ROHs was saved in the form of tables in MySQL at the database level. For fetching and queries provided by the user, scripting in PHP was conducted in the middle tier, i.e., the application layer. To allow clients to browse the database, web pages were developed in the form of a presentation layer.The overall genotyping rate for these 96 samples was 99.8%. These 96 genotypes belonged to seven breeds, namely, Manipuri (17), Zanskari (20), Bhutia (20), Spiti (16), Kathiawari (10), Marwari (10) and Thoroughbred (3). A total of 620,721 SNPs were filtered and were carried further for ROH analysis.A total of 5444 homozygous segments were identified. The mean number of ROH was highest for Thoroughbred (1350.84), followed by Kathiawari (1286.31) and Bhutia (1212.11), while the mean number of ROH per animal was highest in Spiti, i.e., 67.69, and lowest in Bhutia, i.e., 48.05. After computing the average length of ROHs for all breeds, it was observed that the Marwari breed had the lowest average length of 4.6 Mb and Thoroughbred had highest average length of 5.7 Mb. A significant number of ROHs were observed on chromosome 1, i.e., 373 (6.85%) and the chromosome 30 had the least number of ROHs, i.e., 66 (1.01%). Details of the ROHs for each chromosome are given in The total number and length of genomes under ROH for each individual in a breed is given in The proportion of autosomal ROH variation within a breed and between different breeds was studied. When the frequency of ROH categorized according to the length of ROH among all the breeds was plotted, Zanskari showed the maximum length, followed by Spiti and Manipuri .While studying the percentage of genome coverage by ROH, Manipuri had minimum coverage and Spiti had maximum genome coverage. The average genomic inbreeding (FROH > 1 Mb) coefficient of Spiti was higher and Thoroughbred had the lowest. On the other hand, FROH > 8 Mb of Thoroughbred was the highest and Zanskari had minimum value. Detailed information about the inbreeding coefficient for all the species has been provided in Equus caballus based on SNPdata at K = 2, 3 and 4 is represented in To comprehend the pattern of the genetic structure, a Bayesian clustering analysis was conducted using STRUCTURE. This population structure analysis was conducted with K values ranging from 2 to 7 with an admixture model and five iterative runs for each K value. An Evanno test was further conducted to determine the log-likelihood (LnP(D)) values and ΔK between for each K number. A population structure analysis of all 96 individuals from different breeds of SNP annotation by SnpEff produced total of 18,395 candidate genes within ROH islands. In all the breeds, GO terms and pathways associated with these genes were verified for evidence of functional enrichment. This produced 6757 genes which were further categorized into Biological Processes, Molecular Functions, and Cellular Components. The details of their GO IDs have been provided in p-value were chosen and plotted. Details of all the pathways along with their p-values and gene count have been provided in Pathway analysis revealed that most of the genes were involved in metabolic pathways, followed by pathways related to cancer. Most of the genes covered under ROH islands were related to various diseases in horses, directly or indirectly. Top genes based on gene count and It was observed that the maximum number of QTLs (52) was present in Zanskari, while Manipuri possessed the minimum number of QTLs, i.e., 42. Details based on class have been provided in http://webtom.cabgrid.res.in/eqsnpdb/.In the Equine SNP marker database (EqSNPDb), ROHs have been catalogued for all seven equine breeds along with all the samples in the form of a database. The ROH page provides an interactive interface where the user can choose the breed name as well as the chromosome number to retrieve the information of a particular breed. The result table provides the sample names corresponding to that breed, chromosome number, SNP ID, SNP position, length of ROH, and the number of SNPs in that ROH. SNPs page provides the genotype details of all the 620,721 SNPs in a tabular form. EqSNPDb is freely accessible at Equus caballus) breeds. According to the study published by Willing et al., Colli et al., and Utsunomiya et al. [In this study, Axiom™ Equine Genotyping Array Chip was used to identify the ROH islands in seven Indian horse  had a greater portion of their autosome genome covered by ROH. The genomic inbreeding coefficient was low (FROH > 1 Mb) in all the breeds except Thoroughbred. These estimates of inbreeding are in agreement with the abundance and length of ROH in all the samples.Upon gene enrichment analysis, a total of 6757 genes were annotated as functional. Upon analysis, it was revealed that the maximum number of genes was a part that was responsible for cellular components. Some of the important genes that were identified are from the AGPAT1, CLEC4, and CFAP20 families ,36,37 anROH can also contribute to the conservation program of the breed by keeping in mind the level of inbreeding, pedigree comparison and heterozygosity correlation, followed by the construction of breed specific model for conservation rather than a generalized model for breed conservation. It can be used for analysis of animal admixture and monitoring of effective population size rather than census size for effective conservation programs . BesidesIt has been reported that FROH provides insight for inferring details about the history as well as about inbreeding levels of a population. Therefore, ROH has been used for the conservation of endangered populations for which genealogical information is not available .In our study, while considering QTL annotation, maximum QTLs were linked with Wither’s height trait ontology that falls under the growth trait in all seven breeds. Various other traits that were present included insect bite hypersensitivity, racing ability, osteochondrosis, semen quality, and jumping ability. ROH patterns can reveal the genomic regions containing alleles associated with the traits under selection, and hence can predict QTL. Thus, they provide insight into genes related to a variety of traits, as reported in sheep . AdditioOur study highlights the characterization of autozygosity in seven important horse breeds of India, with the main focus of short (ROH > 1 Mb) and long (ROH > 8 Mb) ROH regions. By monitoring genomic inbreeding (FROH), it was revealed that most of the breeds are mostly random bred and hence preserve sufficient genetic variability. The main genes underlying these ROH regions were mainly responsible for various metabolic functions, constituted cell parts, or were a part of major biological processes.


NURR1 (Nuclear receptor-related 1 protein or NR4A2) is a nuclear protein receptor transcription factor with an essential role in the development, regulation, and maintenance of dopaminergic neurons and mediates the response to stressful stimuli during the perinatal period in mammalian brain development. The dysregulation of NURR1 activity may play a role in various diseases, including the onset and progression of neurodegenerative diseases, and several other pathologies. NURR1 is regulated by multiple mechanisms, among which phosphorylation by kinases or SUMOylation are the best characterized. Both post-translational modifications can regulate the activity of NURR1, affecting its stability and transcriptional activity. Other non-post-translational regulatory mechanisms include changes in its subcellular distribution or interaction with other protein partners by heterodimerization, also affecting its transcription activity. Here, we summarize the currently known regulatory mechanisms of NURR1 and provide a brief overview of its participation in pathological alterations. Nuclear receptor-related factor 1  is an orphan nuclear receptor initially characterized as a transcription factor with critical functions in the development, differentiation, maintenance, and survival of dopaminergic (DAergic) neurons in the midbrain . It is aThe most compelling effect of NURR1 dysregulation is its association with the onset and progression of Parkinson’s disease (PD) ,26,27. GA recent study has shown that NURR1-expression is regulated in ischemic stroke . These eNurr1+/− heterozygous mice exhibited behavioural patterns associated with symptoms of schizophrenia, suggesting a potential animal model .,82.73,82p57Kip2 (cyclin-dependent kinase inhibitor 1C (CDKN1C)) is another protein that makes heterodimers with NURR1 and is essential for normal DAergic neuron development  74]. Si. Si74]. The AF1 domain of the NURR1 can bind to the steroid receptor co-activators (SRCs)  75]. In. In75]. Another group of proteins that interact with NURR1, and had been previously described to interact with NUR77, is the peptidyl-prolyl isomerase PIN1 and FHL2 . PIN1 wa+ cells is ensured by the direct interaction of NURR1 with RUNX1  . The conth RUNX1 . In macrth RUNX1  that hasIt had been reported that NURR1 has antiapoptotic functions. NURR1 overexpression decreases BAX expression, whereas NURR1 knockdown increases its expression. These effects are related to physical interaction with p53 , repressThe abundant serine (59), threonine (28), and tyrosine (20) residues in NURR1 human provide potential phosphorylation sites for various protein kinases . These mSSPPTPSTP-133), Ser 126 instead of Ser 125 should be the one phosphorylated.Several reports describe phospho-sites in the NURR1 paralogue NUR77 that can be potentially extrapolated to NURR1 ,68. In 2Lu et al. reported that the protein deglycase DJ-1, also known as Parkinson’s disease protein 7 (PARK7), enhanced the nuclear translocation of NURR1 and its transcriptional activity, in the DAergic cell line MN9D ]. AccordAlthough NURR1 can be regulated by ERK1/2 through the control of its gene expression ,96, the ERK5 is a MAP-kinase that phosphorylates NURR1 in Thr168 and Ser177  Table 2Table 2. Although the motif (124-PSSPPTPSTP-133) of NURR1 was initially correlated with possible phosphorylation and regulation by ERK1/2 (MAP-Kinase), recently it was found that GSK-3β can also phosphorylate and regulate NURR1 stability through the same motif  99]. Ga. Ga99]. AKT mutant, resistant to degradation, increased the differentiation of neural precursors to DAergic neurons . Th. Th93]. NURR1 participates in the repair of DNA double-strand (dsDNA) breaks. By analogy with NUR77, NURR1 interacts with DNA-PK, which phosphorylates NURR1 at Ser337 in the conserved NR4A sequence “TDSLKG” . OverexpAnother well-known function of the NURR1 is related to its capacity to act as a regulator of inflammation . Saijo aSUMOylation consists on the reversible covalent attachment of one or multiple SUMO  proteins to one or several lysine residues of a target protein . SUMO isNowadays, NR4A family orphan receptors are known to be regulated by SUMOylation ,106. TheNURR1 SUMOylation may have physiological consequences . The NURNucleocytoplasmic transport is another mechanism that regulates NURR1 function. . EfficieOjeda et al. described the subcellular distribution of NURR1 in adult rat brains. Hybridization was detected in cells across the brain with intense signals in the neurons of the striatum and substance nigra ,117. SubNURR1 plays a fundamental role in the differentiation and maintenance of the DAergic phenotype, cytoprotective functions, and other physiological processes. Post-translational modifications of NURR1 catalysed by several protein kinases and SUMO E3-ligases, as well as protein-protein associations are critical for the regulation of its stabilization and transcriptional activity. Regulation of NURR1 by phosphorylation is one of the most controversial aspects. Thus, NURR1 is degraded through the ubiquitin-proteasome system (UPS) by GSK-3β and AKT. The “Core 2” motif of NURR1 is apparently targeted by GSK-3β and ERK1/2. This motif is also targeted by RSK, downstream of AKT. Therefore, the mechanisms underlying how specific kinases context-dependently regulate the NURR1, the entire specific phosphorylation sites of some kinases, and the whole landscape of NURR1 phosphorylation regulation still require further exploration.The restoration or enhancement of NURR1 activity may prevent the onset and progression of several pathologies. Even though research on the mechanisms of NURR1 regulation began more than 20 years ago, it is still not possible to translate the progress on the physio-pathological mechanism of NURR1 from animal models to clinical trials. NURR1 regulation by heterodimerization with RXR is one exception that appeared in a preclinical translational study in mouse PD models , using p


Tenderloin and rib weight are important components of the economic value of pig carcasses, and selling them separately after fine segmentation further enhances the economic value of the carcasses. This study represents one of the rare attempts to conduct a genome-wide analysis focused on the economic value of pig carcasses, utilizing post-slaughter carcass phenotype values and genotype data to identify genetic variation regions. Through our investigation, we have identified several promising candidate regions and genes that have the potential to contribute valuable insights for breeding strategies and marker-assisted selection in pig production.TIMP2 and EML1, and SMN1, are associated with TLNW and RIBW, respectively. Our research not only addresses the knowledge gap regarding TLNW, but also serves as a valuable reference for studying RIBW. The identified SNP loci strongly associated with TLNW and RIBW may prove useful for marker-assisted selection in pig breeding programs.During the process of pork production, the carcasses of pigs are divided and sold, which provides better economic benefits and market competitiveness for pork production than selling the carcass as a whole. Due to the significant cost of post-slaughter phenotypic measurement, the genetic architecture of tenderloin weight (TLNW) and rib weight (RIBW)—important components of pig carcass economic value—remain unknown. In this study, we conducted genome-wide association studies (GWAS) for TLNW and RIBW traits in a population of 431 Duroc × Landrace × Yorkshire (DLY) pigs. In our study, the most significant single nucleotide polymorphism (SNP) associated with TLNW was identified as ASGA0085853 (3.28 Mb) on Sus scrofa chromosome 12 (SSC12), while for RIBW, it was Affx-1115046258 (172.45 Mb) on SSC13. Through haplotype block analysis, we discovered a novel quantitative trait locus (QTL) associated with TLNW, spanning a 5 kb region on SSC12, and a novel RIBW-associated QTL spanning 1.42 Mb on SSC13. Furthermore, we hypothesized that three candidate genes,  Pork is the most widely consumed meat globally and serves as a crucial protein source in human nutrition ,2. Due tVRTN gene, associated with vertebral development, has undergone extensive investigation and emerged as a promising candidate gene for regulating rib number. It is localized within the quantitative trait loci (QTL) region of swine chromosome 7 [LTBP2 gene, which has demonstrated its capability to enhance rib number in knockout mice [IGF2 gene plays a crucial role in muscle development in pigs. Oczkowicz et al. [IGF2 gene leads to a significant increase in tenderloin weight (11 ± 0.01 g). Furthermore, Burgos et al. [IGF2 gene to enhance tenderloin muscle tissue in pigs. Xie et al. [CD96 gene as a potential candidate gene for influencing tenderloin. However, there is currently limited genetic structural analysis directly targeting post-slaughter carcass traits, and there are still many uncertainties in the genetic mechanisms underlying these traits.However, the high costs associated with slaughter testing and the challenges in data collection have posed significant obstacles in unraveling these genetic mechanisms. Limited research has been conducted specifically focusing on post-slaughter carcass traits. Rib number, as an indirect indicator of rib weight, is influenced by a diverse array of genetic factors. Notably, the mosome 7 ,11,12,13out mice . In theiout mice  discoverz et al.  found ths et al.  reportedIn recent years, the rapid advancement and application of high-throughput sequencing technology have propelled genome-wide association studies (GWAS) as a robust strategy for identifying genetic variations associated with complex traits. This approach has found extensive use in the fields of husbandry ,18,19 anTo further pinpoint the key loci influencing carcass segmentation traits, we performed a GWAS on the post-slaughter traits of TLNW and RIBW in a cohort of 431 Duroc × Landrace × Yorkshire (DLY) pigs. The aim of this study was to unravel the genetic architecture underlying carcass segmentation traits and facilitate the rapid development of molecular breeding in pigs.The animals and experimental procedures used in this study were handled following the guidelines set forth by the Animal Care and Use Committee of the South China Agricultural University (SCAU) . The ethics committee of SCAU approved all animal experiments. The experimental animals were not anesthetized or euthanized during this study.In the present study, we collected 431 three-way crossbred DLY pigs from Wens Foodstuff Group Co., Ltd. . All pigs were subjected to the same growth and feeding conditions. After that, the unified slaughtering was carried out according to the standard slaughtering flow at (110 ± 5) kg body mass. Pigs fasted for 24 h before slaughter, were provided with free drinking water, and were subjected to neither beating nor driving before slaughtering, so as not to affect the meat slaughtering experiment carried out in the slaughterhouse. All pigs were divided into five batches and slaughtered in a commercial abattoir in Chifeng, Inner Mongolia. Electric shock anesthesia and heart bloodletting were used for slaughtering, and fine segmentation of each pork carcass was subsequently carried out. After fine segmentation, the weight of either the tenderloin or the ribs was recorded, as TLNW and RIBW, respectively . Further details on the segmentation position are shown in p < 10−6) and were unmapped or located on the sex chromosome were also removed. After QC, 4188 SNPs not located on autosome chromosomes were discarded. Moreover, we removed 211 (TLNW) and 213 (RIBW) SNPs because of missing genotype data; 11,202 SNPs due to failing the Hardy–Weinberg exact test; and 64 SNPs due to their minor allele threshold. All animals passed the QC (431 for TLNW and 408 for RIBW). Finally, 34,978 TLNW SNPs and 34,976 RIBW SNPs were retained for subsequent analysis.The genomic DNA needed in this experiment was isolated and extracted from the ear tissue of 431 pigs using the standard phenol/chloroform method. All 431 DNA samples were subjected to DNA quality control according to light absorption ratio (A260/280 and A260/230), gel electrophoresis, and DNA concentration of 50 ng/μL. The GeneSeek pig 50 K SNP chip was used for genotyping with a total of 50,643 SNPs. The genotype quality control of the 431 DLY pigs was conducted using PLINK v1.9 software . IndividPopulation stratification is one of the main reasons for unreliable GWAS results, as it can cause false positive results. Principal component analysis (PCA) and LD analysis were performed using the SNPs that met the QC standards to investigate the population structure. PCA was performed with GCTA software (version 1.93.2 beta) . In addin × n identity matrix; and n refers to the number of pigs. In the study, Bonferroni correction was used to determine the threshold p values of single-locus GWAS. At a stringent genome-wide Bonferroni threshold, p < (0.05/N). At a more lenient threshold, p < (1/N) for chromosome-wide (suggestive) associations, and N means the number of SNPs [GEMMA software (version 0.98.5) was used to implement a Mixed Linear Model (MLM) for single-locus GWAS of TLNW and RIBW . GEMMA c of SNPs . Haplovi of SNPs . LD amony is the vector of tenderloin weight or ribs weight; β is the vector including fixed effects; X is an incidence matrix for β; g is the vector of the aggregate effects of all the qualified 50K SNPs for the pigs within one population; Ι is the identity matrix; Ag is the genomic relatedness matrix estimated by these SNPs; 2 = , where The model in GCTA software, as following, is used to estimate the SNP-based heritability and the phenotypic variance explained by genome-wide SNPs (based on SNP inheritance), the proportion of phenotypic variation explained by significant SNPs:http://ensembl.org/Sus_scrofa/Info/Index, accessed on 3 August 2023). Functional gene annotation (v105) was downloaded in GIFF3 format from the Ensembl website . The R package BioMart (version 2.56.1) [p value < 0.05 were selected to further explore the genes invoked in pathway and biological processes. Subsequently, we employed REVIGO  in conjunction with the Mus musculus database to eliminate GO term redundancy  and cluster the remaining terms in a 2D space [https://www.informatics.jax.org/, accessed on 10 August 2023), GeneCards , and Ensembl  were used to query gene functions.All SNPs refer to the latest version of the Sus scrofa 11.1 genome ( 2.56.1)  efficien 2.56.1)  to inves2D space ,37. ThisPopulation stratification is a significant factor contributing to the unreliability of GWAS data, as it can result in false positive findings. The quantile–quantile (Q–Q) plots serve as a valuable tool for assessing the presence of population stratification . In our p < 2.86 × 10−5  and p < 2.86 × 10−5, respectively. Furthermore, two suggestive SNPs (ASGA0085853 and ALGA0112188) were found to be associated with TLNW on Sus scrofa chromosome (SSC) 12 and SSC7, respectively, while one suggestive SNP (Affx-1115046258) was related to RIBW on SSC13. Moreover, p-value) of 5.16. On SSC13, Affx-115046258 is located at 172.45 Mb with a MAF of 0.268 and a −log10  of 5.31. The most significant SNPs (ASGA0085853 and Affx-1115046258) for the above characterized haplotype block an explained 4.88% and 5.19% of the phenotypic variance for TLNW and RIBW, respectively. Among these, carriers of the A allele (AA and AG) of ASGA0085853 had significantly greater loin weight than those with the GG genotype, with highly significant phenotypic differences observed among the three genotypes  and ASGA0084858 . For RIBW, the QTL region on SSC13 was composed of six SNPs; the most significant, Affx-115046258 (172.45 Mb), was linked closely with the other five SNPs  in the QTL region  and the Gene Ontology (GO) knowledgebase that are relevant to TLNW. These enriched terms include cellular division and protein translation . While the demand for pork is gradually rising worldwide, many nations have preferences for specific varieties of meat from pig carcasses [According to the 2021 edition of the OECD-FAO Agricultural Outlook, the global meat supply is projected to expand, reaching 374 million tons by 2030  gene on SSC12 through Sus scrofa 11.1 delivered from the Ensembl database. In mice, deficiency of TIMP2 leads to increased cardiac hypertrophy and subsequent heart enlargement [TIMP2 is associated with intramuscular fat content and explains 32% of the variation in intramuscular fat [TIMP2 also affects feed conversion efficiency in cattle by regulating cell growth and proliferation networks [TIMP2 may regulate the size of cardiomyocytes by influencing the molecular pathways of cell growth and proliferation, ultimately affecting the development of the TLNW trait. On SSC12, the candidate gene CANT1 is located 79.8 kb upstream of the leading SNP and is associated with abnormal skeletal morphology and body size in mice [EML1—found on SSC7, a candidate gene associated with TLNW—is located within ALGA0112188. EML1 is associated with brain overgrowth syndrome [EML1 suggested by this study requires further investigation for functional validation. On SSC7, the candidate gene, YY1, has been shown to be involved in cell proliferation and body size in mice [YY1 might regulate tenderloin growth through processes related to the proliferation and development of muscle cells. These results indicate that the TIMP2 and EML1 genes may play an important role in TLNW and should be considered strong candidate genes for this trait. The leading SNP (ASGA0085853) was annotated within the first intron of the argement . In cowsular fat . TIMP2 anetworks ,46. Base in mice . Additiosyndrome  and playsyndrome  in human in mice . It is sENSSSCG00000049210 and ENSSSCG00000050907 as noncoding genes within the QTL on SSC13. However, we discovered a protein-coding gene, ENSSSCG00000029127, located 150.8 kb downstream of the leading SNP (Affx-115046258). Interestingly, ENSSSCG00000029127 exhibits homology to the protein encoded by the SMN1 gene. Our literature search revealed that the SMN1 gene is the determinant gene for spinal muscular atrophy in humans [SMN1 [SMN1 gene has been demonstrated to be associated with physiological conditions such as abnormal muscle physiology, decreased body weight and size, and abnormal motor neuron morphology [SMN1 gene, showing significant sequence homology between porcine and human SMN1 in the entire coding region. Schrank et al. [SMN1 gene may be involved in early embryonic death. It is possible that the SMN1 gene may influence the development of the RIBW trait by affecting proximal muscle atrophy caused by the degeneration of spinal motoneurons in pigs. However, the specific molecular mechanism underlying this relationship requires further investigation.After conducting haplotype block analysis, we identified n humans , a rare ns [SMN1 . The SMNrphology ,54,55. Arphology  reportedk et al.  demonstrTIMP2 and EML1 for TLNW, and SMN1 for RIBW. This research provides valuable insights for segmenting carcass molecular breeding strategies.In this study, we performed a GWAS to investigate the TLNW trait and RIBW trait in a population of 431 DLY pigs. We identified two suggestive SNPs (ASGA0085853 and ALGA0112188) associated with TLNW, and one SNP (Affx-1115046258) associated with RIBW. Furthermore, we discovered two novel QTL regions on SSC12 (5 kb) and SSC13 (1.42 Mb) that were significantly related to TLNW and RIBW, respectively. Notably, the QTL region on SSC12 represents the first association with the TLNW trait reported to date. During further analysis, we identified three major candidate genes:


Whole genome analysis of Leishmania hybrids generated experimentally in sand flies supports a meiotic mechanism of genetic exchange, with Mendelian segregation of the nuclear genome. Here, we perform functional analyses through the generation of double drug-resistant hybrids in vitro and in vivo (during sand fly infections) to assess the importance of conserved meiosis-related genes in recombination and plasmogamy. We report that HOP1 and a HAP2-paralog (HAP2-2) are essential components of the Leishmania meiosis machinery and cell-to-cell fusion mechanism, respectively, since deletion of either gene in one or both parents significantly reduces or completely abrogates mating competence. These findings significantly advance our understanding of sexual reproduction in Leishmania, with likely relevance to other trypanosomatids, by formally demonstrating the involvement of a meiotic protein homolog and a distinct fusogen that mediates non-canonical, bilateral fusion in the hybridizing cells. Leishmania during sand fly development, indicating a meiotic mechanism. Here the authors show that meiosis-related genes HOP1 and HAP2-2 are essential for Leishmania hybridization in vitro and in sand flies and that their deletion in one or both parents hinders mating competence.Genetic exchange has been experimentally demonstrated for  Leishmania genus, genetic exchange is thought to contribute to the extraordinary diversity of species and strains which can produce a spectrum of diseases in their mammalian hosts, ranging from self-limiting cutaneous lesions to fatal forms of visceral disease. Leishmania parasites have a dimorphic life cycle, reproducing asexually as extracellular promastigotes within the alimentary tract of their sand fly vectors, and as intracellular amastigotes within the phagolysosomes of their mammalian host mononuclear phagocytes. Genetic exchange has been inferred from the analyses of genotypes observed in natural isolates, providing evidence for outcrossing and inbreeding6. In some instances the hybrid genomes have been associated with phenotype changes within their host environments, including increased transmission potential of infected sand flies and altered tissue tropism in infected humans8.For parasites of the Leishmania strains and species, as well as between the same clone (self-hybridization), in the laboratory14. Experimentally, genetic exchange in Leishmania is non-obligatory, relatively rare, and thought to be naturally confined to life-cycle stages present in the sand fly midgut, although hybridization using promastigote and amastigote culture forms in vitro has recently been described17. Based on whole genome sequencing analyses, the allele inheritance patterns of experimental hybrids generated in sand flies provide strong evidence that the system of genetic exchange in Leishmania is Mendelian and involves meiosis-like sexual recombination18. This conclusion is reinforced by the identification of Leishmania homologs for meiotic genes19, and their expression by promastigote stages recovered from sand flies20, including the core meiotic genes SPO11, HOP1 and DMC1, involved in creating DNA double-strand breaks, homologous chromosome alignment and recombination, respectively. Upregulated expression of the Leishmania homolog gene encoding the cell fusion protein HAP2/GCS1 (HAPLESS 2/Generative Cell-Specific 1) has also been described for promastigotes able to hybridize in vitro16. Critically, haploid gametes have never been identified in sand flies or in vitro, and the presence and expression of these homologs does not necessarily mean that they function in meiosis, as expression of meiotic genes in some organisms can be detected in asexually growing cultures, e.g. Acanthamoeba21. The homologs may have also acquired non-meiotic functions, e.g., SPO11 paralogs required for vegetative growth in Arabidopsis22. Furthermore, the fact that some sexually reproducing organisms, e.g. Drosophila melanogaster, lack many meiotic homologs, including HOP1, HOP2, MND1, DMC1, means that many core meiotic genes are not required for sex. Similarly, HAP2, which is an ancestral gamete fusogen required for sexual reproduction in major eukaryotic taxa, including protozoa, green algae, and flowering plants24, is missing from other sexual taxa, including Fungi and most Metazoa25. Lastly, even if Leishmania express meiotic homologs, aneuploidy, which is a constitutive feature of Leishmania genomes, may disrupt normal meiotic processes, producing sterile gametes with unbalanced chromosome complements, as observed for the effects of polyploidization in some flowering plants26.Genetic exchange has been directly demonstrated by the generation of hybrids between different 27 to their functional roles in a meiotic process has not been formally validated for any parasites in the Trypanosoma and Leishmania genera. This includes the subspecies of Trypanosoma brucei, the agents of African trypanosomiasis, for which haploid gametes expressing meiotic gene homologs, including MND1, DMC1, HOP1 and HAP2, have been observed in the salivary glands of the tsetse fly vector where mating is believed to take place30. In the current studies, we employed a functional genomics approach to identify genes required for Leishmania mating in vitro and in vivo. Using CRISPR/Cas9 editing tools to generate null mutants of core meiosis gene homologs , as well as two genes related to the HAP2 fusogen (here named HAP2-1 and HAP2-2), we identified HOP1, a synaptonemal complex (SC) protein involved in chromosome pairing, and HAP2-2, as essential to Leishmania mating in vitro and in the sand fly. Complementation with the targeted genes could in each case partially rescue mating competence, and reporter constructs permitted detection of subpopulations of expressing cells and their localization to midgut microenvironments.The linkage of genes in the “meiosis toolkit”16, including components of the synaptonemal complex , and two membrane proteins containing domains related to gamete fusion in several eukaryotes (here named HAP2-1 and HAP2-2). Using InterPro31, we confirmed the main expected conserved domains for all genes of interest , HAP2-2 only has two . The signal peptide located at the N-terminus of HAP2 proteins was notably absent in both Leishmania HAP2-1 and HAP2-2. Additionally, the variability in the number of transmembrane (TM) domains is also observed in one of the HAP2 paralogues of P. falciparum  transcription34.To investigate the role of meiotic proteins in For the design of sgRNA and recombination regions to generate the null mutants, we considered strain specific SNPs (Single Nucleotide Polymorphisms) to ensure correct targeting and gene deletion. We were able to obtain null mutants clones for each of the 7 genes on both strains , namely HOP1 (p < 0.0001), HOP2 (p = 0.026), HAP2-1 (p = 0.0003) and HAP2-2 (p < 0.0001), , , while for HOP1, these frequencies were reduced when the gene was deleted in both parents (p = 0.005) or only L747 (p = 0.02).To facilitate the hybrid screening, we generated fluorescent cell lines in each of the null mutants and T7Cas9 background controls. The resulting transfectants constitutively express either eGFP-Neomycin (Neo) resistance in MA37 strain or mCherry-Nourseothricin (Sat) resistance in L747 strain, integrated into the 18 S rDNA locus , Fig. . Interes1), Fig. . For cro5), Fig. . For HAPHOP1 or HAP2-2 by integrating the CDS plus the Sat resistance gene into the 18 S rDNA locus of each respective L747 null mutant, the strain showing the strongest hybridization defects  or HAP2-2 (L747 Δhap2-2::HAP2-2) conferred significant, though partial, reconstitution of L747 mating competence in vitro when paired with MA37 control (eGFP-Neo) or MA37 null mutants (Δhap2-2 eGFP-Neo or Δhop1 eGFP-Neo), , Fig. . The preo), Fig. . The parine Fig.  indicate9. Therefore, we tested if the hybridization deficits observed for the HOP1 and HAP2-2 null mutants would be reproduced during infections in Lutzomyia longipalpis, a permissive vector for experimental L. tropica infections and for hybrids generation in vivo18. Each parental line was tested alone for its ability to colonize the insect midgut. None of the mutant lines showed significant differences in parasite numbers after 8 days of infection  . Deletion of HOP1 in only the MA37 parent allowed hybrid recovery in 14% of flies (p < 0.0001). HOP1 re-expression in the L747 null mutant significantly reconstituted hybrid recovery when paired with MA37 eGFP-Neo (p = 0.0012), albeit with lower efficiency compared to controls (MA37 eGFP-Neo and L747 mCherry-Sat). HOP1 re-expression in the L747 null mutant line was sufficient to reconstitute mating compatibility even when paired with the MA37 HOP1 null mutant (p = 0.0295), and at frequencies comparable to MA37 null mutant mating with the L747-Sat control parent  Fig. . Consistent Fig. .Fig. 3EfHAP2-2 deletion in either one or both parental lines significantly impaired hybridization compared to controls (51.5% hybrid+ flies), , while deletion only in MA37 allowed for hybrid recovery in 5% of flies (p = 0.0002). Re-expression of HAP2-2 in L747 partially rescued mating competence when crossed with MA37 control (p = 0.0133), but not with MA37 Δhap2-2 eGFP-Neo , Fig. . When saNeo Fig. , supportL. tropica MA37 deleted for either HOP1 or HAP2-2, the few hybrids recovered from in vivo crosses with the gene sufficient L747 parent suggested that these genes were not strictly required in the MA37 strain for hybridization to occur. To determine the genome alterations of the hybrids resulting from the absence of HOP1 or HAP2-2 in the MA37 parent, we first performed propidium iodide (PI) staining to estimate ploidy based on the total DNA content of each hybrid, using as references the diploid parental lines, as well as diploid and tetraploid hybrids generated previously. From the sand fly experiments involving the HOP1 mutants, we tested hybrids generated in two independent experiments , although triploid (n = 16) and tetraploid (n = 1) hybrids were also observed  or tetraploids , triploid (n = 2) and tetraploid hybrids (n = 7), similar to control crosses  and tetraploid hybrids  analysis of the parents and individual triploid hybrids to estimate the genomic contribution from each parent. This analysis confirmed that the two triploid hybrids were full genome hybrids, with biallelic inheritance of all the SNPs that were homozygous and different between the parents , while 38 of the 49 hybrids generated between the parental controls were diploid  and tetraploid hybrids (n = 10) were observed, with a significant decrease in diploids when compared to controls  tag at the N-terminus of HOP1 and HAP2-2 in both MA37 and L747 non-fluorescent T7Cas9 strains. After confirming the correct integration in cultured cells , and the main structural components of the SC, including cohesins and meiotic HORMA domain proteins, are evolutionarily conserved. The membrane proteins involved in the fusion of male and female gametes or mating types are also broadly conserved38. We show here evidence that the Leishmania homolog of HOP1, a meiotic core protein that functions in pairing of homologous chromosomes, and HAP2-2, a HAP2 family member involved in gamete fusion, are essential for genetic exchange in Leishmania.For several decades, genetic exchange in Saccharomyces cerevisiae), flowering plants (Arabidopsis), and worms (Caenorhabditis elegans)41. HORMA domain proteins are characterized in these and other organisms as essential for the recruitment of key components of the SC, which is a zipper-like multiprotein complex responsible for the synapsis, recombination, and segregation of homologous chromosomes at prophase of meiosis I42. The presence of HOP1 on the meiotic chromatin is described as a hotspot for the programmed DNA double strand break (DSB) machinery, and its displacement after SC establishment is associated with down-regulation of DSB activity43. Crossover formation during DSB repair, in addition to recombining genes along each chromosome, establishes the physical linkages required for accurate segregation of the homologs in meiosis I. In Leishmania, balanced segregation of the homologs in each parent is consistent with the whole genome sequencing analyses of large numbers of diploid hybrids recovered from sand flies, indicating that in each case the progeny have received one full genome complement from each parent18. The severe mating defect displayed by the HOP1 null mutants in sand flies provides direct evidence that hybridization of wild type parasites is dependent on the SC meiotic machinery. While a few hybrids could be recovered when the MA37 HOP1 null mutant was crossed with L747 wild type, the products were all polyploid, with the deleted parent shown to be the source of the extra genome in the triploid hybrids. Thus, HOP1 in MA37 is required for reductional division even if it is not absolutely essential for hybridization to occur. These results are similar to the outcome of the deletion of meiosis-specific genes in plants, for which defects in chromosome segregation produced hybrid progeny with aneuploid or polyploid genomes44. Still, since the hybridizing potential of the HOP1 deleted lines was either strongly reduced or completely ablated, this would link a component of the meiotic machinery to the generation of fusogenic cells, and argue that the occasional polyploid hybrids generated in flies between wild type parents, observed in this and prior reports, are still products of fusion between cells that are committed to a meiotic program, even if they cannot complete meiotic division(s). This would explain the requirement for HOP1 in the generation of hybrids in vitro, which yields mainly tetraploid products, consistent with the fusion of diploid cells. The inability of the hybridizing cell to complete a meiotic program might be associated with the γ-irradiation treatment that was employed to facilitate hybrids generation in vitro, and that may produce DSBs that redirect DNA repair to a non-meiotic DNA damage response, including single-strand annealing and microhomology-mediated end joining45, that does not involve crossovers. The fact that HOP1 is still required for hybrid generation suggests that the hybridizing cells are marked as gamete-like, even if they are unable to undergo reductional division(s). Unreduced gamete formation can occur naturally and is a common mechanism of polyploidization in plants46.HOP1 is a meiotic chromosome axis protein containing the HORMA domain, named after three proteins that harbor it . HOP1 is known to perform meiosis specific functions in yeast , synonymous with GCS1 (Generative cell specific 1), was first described in Arabidopsis and is now known to catalyze zygote formation across major eukaryotic taxa, from protists to insects49. The slime mold Dictyostelium discoideum50 and Plasmodium spp.51 express two HAP2-related genes, both of which are critical for mating. We previously reported that Leishmania also possesses two HAP2-like encoding genes16, designated here as HAP2-1 and HAP2-2. In our prior study, positive selection of HAP2-1 expressing cells in one or both parents was associated with hybridization competence in vitro; association with HAP2-2 expression was not addressed52. The current studies directly address and clarify the roles of these fusogens in Leishmania hybridization in vitro. Deletion of HAP2-1 in either or both parents produced a significant, though modest reduction in the number of in vitro hybrids recovered. When calculated as the minimum frequency of hybridizing cells in the co-cultures, a significant reduction was observed only when the gene was deleted in both parental lines. By contrast, deletion of HAP2-2 in either or both parents produced a near complete hybridization defect in culture, a phenotype that was extended to sand flies. The data suggest that HAP2-1 expression is required on only one of the fusing cells, while HAP2-2 expression is required on both. It is possible that HAP2-2 function may have mitigated the effects of the HAP2-1 deletion, although the reverse does not seem to be the case, with the possible exception of the few hybrids that were generated between MA37 HAP2-2 null mutant and L747 wildtype. The requirement for expression of HAP2-2 on each hybridizing partner is unusual, as other organisms require HAP2 only on one of the two gametes, such as the mating type minus gamete in Chlamydomonas algae, or the male gametes in Plasmodium, Tetrahymena, or Arabidopsis, consistent with its functional similarity to fusion proteins of enveloped viruses55. By contrast, and similar to our observations regarding HAP2-2, fusion in Dictyostelium was completely blocked by single disruption of the HAP2 locus, suggesting that bilateral interactions were needed to mediate membrane fusion50. It is interesting that the few hybrids that could be generated in flies between MA37 HAP2-2 null mutant and L747 wildtype, were all either triploid or tetraploid, while the crosses between the wildtype lines were mainly diploid. HAP2-2 deletion did not appear to be directly linked to defective reductional division, as our WGS analysis of the 3 triploid hybrids determined that the extra genome was contributed in each case by the wild type parent, not the deleted line. It is possible that in contrast to HAP2-2 mediated fusion, the HAP2-1 dependent fusion that may operate following disruption of HAP2-2 is not confined to cells that are committed to a meiotic program. HAP2-1 was in fact shown to be constitutively expressed on a high frequency of cultured cells52. Future studies will explore the cooperative roles of the two fusogens during sexual mating in Leishmania.The cell fusion of male and female gametes culminates the classical meiotic program. The gene encoding HAP2 Tetrahymena and Chlamydomonas is expressed on apically localized regions of the plasma membrane where the cells fuse54. For the L. tropica HAP2-2 reporter lines recovered from infected sand flies, we observed uniform expression throughout the cell body, including the flagellum, which might reflect an indiscriminate gamete attachment site, similar to gamete fusion in Plasmodium. In P. falciparum, HAP2 is expressed throughout the plasma membrane of male gametocytes beginning at stage II of development, and in fully differentiated microgametes. Its paralog, HAP2p, shows a patchier membrane expression pattern, and begins to be expressed later during gametocyte development, and interestingly on stage V female gametocytes51. Whether HAP2p functions bilaterally to promote gamete fusion in P. falciparum, as described here for HAP2-2 in L. tropica, has not been addressed. In T. brucei, HAP2 expression is concentrated in some gametes at the cell posterior, while in other gametes and meiotic intermediates, it is expressed throughout the cytoplasm30. Whether it needs to be expressed on both gametes is not known. HAP2 expression in T. brucei is confined to life cycle stages in the tsetse salivary glands where gametes and meiotic intermediates are found. Similarly, HOP1 expression in T. brucei is restricted to cells in the salivary glands undergoing meiotic division, marked by the presence of two kinetoplasts28. By comparison, the HAP2-2 expressing cells in L. tropica were distributed throughout the posterior and anterior midgut, including the plug, while the HOP1 expressing cells were mainly in the posterior midgut and did not show signs of cell division. We so far lack evidence that HAP2-2 and/or HOP1 expression is restricted to gametes or cells committed to a meiotic program.HAP2 in Leishmania. As these are also the first functional studies of meiotic stage-related proteins in trypanosomatids, they are relevant to the mechanisms of meiotic recombination and plasmogamy in other pathogenic species, including Trypanosoma cruzi and African trypanosomes.In summary, using reverse genetics and in vitro and in vivo protocols, we provide evidence that HOP1, a meiosis machinery protein, as well as a HAP2 fusogen family member, are essential for mating in L. tropica MA37 T7Cas9-Hyg and L. tropica L747 T7Cas9-Hyg, generated from strains (MHOM/IL/02/LRC-747 and MHOM/JO/94/MA37) originating from Jordan and Israel, respectively16. Axenic cultures were maintained at 26 °C in cM199 . Cell lines were not kept in culture for more than 3 passages. For all experiments frozen stocks of cells were thawed and after 48-60 h recovery, cultures were diluted (1:100) in the presence of selective antibiotics.For this study we used two background cell lines previously reported: 6 mL−¹) were cultivated without selection pressure for 24 h before exposure to 6.5 Gy of γ-irradiation. The cell concentration was estimated before mixing together equal volumes of each parental line and distributing it in 96-well microplates (100 µL/well). Co-cultures were diluted (1:10) in double-drugged cM199  after 24 h. For control crosses with non-fluorescent MA37 SSU-Bsd control parental line and L747 SSU-Pac, L747 Δhop1-Pac or L747 Δhap2-2-Pac, co-cultures were selected and maintained in cM199 with Blasticidin, Fisher Scientific  and Puromycin, Sigma-Aldrich . Drug selection controls were performed in parallel with γ-irradiated parental lines submitted to pressure of selective antibiotics separately or combined. Selection plates were diluted weekly in fresh, double-drugged media and hybrids confirmed by the co-expression of fluorescent markers using flow cytometry (FACSCanto II and FACSDiva software {SCR_001456}). For crosses using non-fluorescent parental cell lines, hybrids were cloned and one clone from each well was confirmed by PCR for correct integration of the resistance markers, as described below for the confirmation of the parental lines  were resuspended in heparinized mouse blood reconstituted with heat-inactivated serum and used to fed 2-4 days old female sand flies through a chick-skin membrane. Unfed sand flies were discarded after 24 h, and engorged females kept at 26 °C in a wet-chamber and provided 30% sucrose ad libitum. Sand fly midguts were dissected at day 5 for imaging, and at day 8 post-infection for counting and selection of hybrids.Infection of The mice used in this study were used under a study protocol approved by the NIAID Animal Care and Use Committee (protocol number LPD 68E). All aspects of the use of mice in this research were monitored for compliance with The Animal Welfare Act, the PHS Policy, the U.S. Government Principles for the Utilization and Care of Vertebrate Animals Used in Testing, Research, and Training, and the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals. Housing conditions included a standard 12-hour dark/light cycle, with the ambient temperature maintained between 22–24 °C, and the humidity level set at 60%.For single cell line infections, the total number of promastigotes was estimated on a hemocytometer. For recovery of hybrids, sand flies were infected with equal concentration of each of the parental cell lines, and after 8 days each midgut was homogenized and added to double drugged cM199 (Neomycin - Neo 50 μg/mL and Nourseothricin - Sat 300 μg/mL) containing 0.2% 50 mg/mL Gentamicin before plating into a single well of a 96-well microplate. Wells positive for promastigote growth after 7–10 days of culture were checked by flow cytometry (FACSCanto II and FACSDiva software) for co-expression of mCherry and GFP. Hybrids generated using the re-expressors were confirmed by PCR. Results were plotted as the percentage of sand flies yielding a hybrid.L. tropica L590 reference genome in TriTrypDB (https://tritrypdb.org/tritrypdb/) and the conserved domains identified using InterproScan searches. The percentage identity of HAP2-1 and HAP2-2 GCS domains were determined by alignment using MUSCLE  {SCR_011812}.Genomic and protein sequences were retrieved from the 34. Primers were designed using LeishGEdit (http://www.leishgedit.net/) based on the L. tropica L590 reference strain and manually corrected for SNPs of L747 and MA37 strains. A limit of 3 SNPs per sgRNA plus homologous recombination region was tolerated between the strains as long as they were not mapped to the PAM region. Repair cassettes produced to generate null and endogenously tagged mutants were generated using pTPuro_v1 and pPLOTv1 blast-mNeonGreen-blast as template, respectively.For CRISPR-Cas9 mediated genomic modifications, MA37 T7Cas9 and L747 T7Cas9 background cell lines were transfected with repair cassettes and sgRNA linear DNA produced using protocols described in Beneke et al. To generate fluorescent and re-expressors cell lines in wildtype (T7Cas9) and null mutants cell lines, pLEXSY-cherry-Sat2, pLEXSY-LtroL747_HOP1-Sat2, pLEXSY-LtroL747_HAP2-2-Sat2, A2-GFP-Neo plasmids were SwaI-digested (Thermo Fisher) for linearization and purified from gel with QIAquick Gel Extraction Kit (QIAGEN) before transfection for integration in the small subunit rRNA (SSU).7 mL−¹) were resuspended in P3 primary cell 4D buffer (Lonza), mixed with DNA fragments  immediately before transferring to a Nucleofector strip and transfected using program FI-115 . Cells were recovered in 5 mL of cM199 for 18 h before selection with antibiotics: Puromycin (Pac) - 40 μg/mL; Blasticidin (Bsd) - 20 μg/mL; Nourseothricin (Sat) - 300 μg/mL; Neomycin (Neo) - 50 μg/mL. Transfectants were immediately cloned by dilution (1:10 and 1:100) in cM199 with appropriate antibiotics before distributing 100 μL per well in 96-well microplates. Population and control selections were performed in 25 cm³ flasks.Log-phase promastigotes  and mutants were confirmed by PCR. For null mutants the presence/absence of the target gene was confirmed with primers that anneal inside the CDS, and the integration of the repair cassette in the gene locus confirmed by amplification from the 5’-UTR of the gene deleted until the 5’ end of the Pac resistance gene (CP-OL091). Integration of mNeonGreen-Bsd repair cassette was confirmed using a similar strategy but using a Bsd specific reverse primer (CP-OL062). To confirm fluorescent cell lines and re-expressors, integration in the SSU was assessed by combining a forward primer annealing to the resistance marker  with a reverse primer annealing downstream of the recombination site in the SSU (CP-OL243). For re-expressors, PCR reactions using CDS specific primers were performed in parallel. Primer details are described in the Supplementary Data HOP1 and L747 HAP2-2 were amplified from L. tropica L747 T7Cas9 genomic DNA using primers designed with the NEBuilder assembly tool . Constructs were confirmed by Sanger sequencing before preparing DNA for transfection.To generate re-expressor constructs, the backbone vector was prepared from pLEXSY-mCherry-Sat2 by double digestion with BglII and NotI (Thermo Fisher) and gel purification with QIAquick Gel Extraction Kit (QIAGEN). The CDS of L747 Ploidy of parental lines and hybrids was estimated from propidium iodide (PI) stained mid-log phase promastigotes. Cells were incubated in 0.4% paraformaldehyde for 1 min at room temperature. After centrifugation, cells were resuspended in 100 µL of PBS, topped with 1 mL of cold 100% methanol and kept on ice for 15 min. Next, cells were sedimented and stained with PI and treated with RNase  at room temperature for 15 min. Cells were washed and resuspended in cold PBS and data collected by flow cytometry (FACSCanto II and FACSDiva software). DNA content analysis was performed using FlowJo software v.10.8 {SCR_008520}  comparing parental lines and hybrids.Midguts were dissected and transferred to the VectaVIEW (Vector) working solution (10 µL Reagent A: 10 µL Reagent B: 10 µL Reagent C) and incubated at room temperature for 10 min. Midguts were quickly transferred to PBS before incubation in 10 µg/mL of Hoechst 33342 (Thermo Fisher) for 20 min. After rinsing in PBS, the midguts were mounted on ice-chilled glass slides containing 10 µL of CyGEL Sustain (Abcam), covered with coverslips, and sealed to keep parasites immobile during imaging. Images were obtained using a 20 x /1.3 oil objective with the following acquisition mode: bidirectional scan, scan speed 600 Hz, line average 3 and sequential scan between stacks. We used the diode (405 nm) and the WLL (70%) lasers, with laser line set for Hoescht 33342 1% (410–450 nm) and mNeonGreen 5% (480–510 nm) on a Leica SP8 WLL FLIM confocal microscope in LAX software {SCR_013673} (Leica Microsystems). Projections and videos of the Z-axis were processed using Imaris 9.8.2 {SCR_007377} (Oxford Instruments).Parental cell lines and sand fly derived hybrids were cloned by limiting dilution and their genomic DNA was extracted using DNeasy Blood and Tissue kit (QIAGEN) and submitted to Psomagen for next-generation sequencing . DNA libraries were generated using TruSeq Nano DNA Library Prep kit (Illumina) and the 150 bp-paired-end reads were sequenced on a NovaSeq6000 (Illumina).L. tropica Ltr590 v.57 reference genome available on TritrypDB (tritrypdb.org), using the BWA-MEM aligner v.0.7.17 with default parameters. Mean sequencing coverage of mapped reads was 30.10 (SD = 17.03), according to Qualimap v.2.2.1. Single nucleotide polymorphisms (SNPs) were determined using the PAINT software suite designed for studying inheritance patterns in aneuploid genomes {PMID: 33530584}. PAINT was also used to find and extract the homozygous SNP marker differences between the parental strains and to estimate the chromosome copy numbers (somy). Chromosome somies were determined by calculating the normalized median read depth multiplied by 2 (for a diploid genome) using the ConcatenatedPloidyMatrix utility with a 5-kb window size. Genomic regions of multiple sequence repeats and high copy number variation (CNV) were filtered out from the analysis by eliminating positions with coverage levels ≥2-fold and ≤0.5-fold the average chromosome coverage. In the case of the polyploid hybrids (≥3n), the somy values were divided by 2 and multiplied by the ploidy estimated from the DNA content analysis (PI staining) and the parental contribution profile .Paired-end reads were aligned to the getParentAllelFrequencies utility in PAINT. The parental allele frequencies were formatted to be compatible with Circos software v.0.69 {PMID: 19541911} and inheritance circos plots were generated with 372,866 homozygous marker differences between L. tropica MA37-GFP and L747-mCherry cell lines labeled in green and red, respectively.SNPs were considered heterozygous if the allele frequencies were between 0.15–0.85 and homozygous if >0.85 for each genomic position. Allele frequencies of <0.15, read depth lower than 10 or represented by <25% of reads in either forward or reverse direction were filtered out of the analysis. The allelic inheritance of each homozygous parental SNP in the hybrid progenies was determined using the p-values < 0.05 were considered significant.Hybridization experiments in vitro were independently repeated three times, while in vivo mating experiments were repeated twice. No randomization or blinding procedures were used in this study. For statistical analysis, data was plotted and analyzed on GraphPad Prism 9.3. We used Ordinary one-way ANOVA for multiple comparison of preselected pairs and a two-step step-up method of Benjamini, Krieger and Yekutieli to correct false discovery. Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Movie 1Supplementary Movie 2Reporting SummaryPeer Review File