Food poisoning can be caused by the potato alkaloids α-solanine (SO) and α-chaconine (CHA). Therefore, this study aimed to establish new enzyme-linked immunosorbent assays (ELISAs) for detecting these two toxins in biological samples and potato extracts. Two antibodies that bind to solanidine, a chemical compound found in both SO and CHA, were newly developed, and two types of ELISAs (Sold1 ELISA and Sold2 ELISA) were constructed. We measured SO and CHA diluted in phosphate-buffered saline (PBS), serum, and urine. The detection performance of the two ELISAs for SO and CHA in PBS was higher than in serum and urine, and the sensitivity of Sold2 ELISA was lower than that of Sold1 ELISA. Thus, we used these ELISAs to measure SO and CHA in potato part extracts and found that potato sprouts contained approximately 80-fold more SO and CHA than tubers and 8-fold more SO and CHA than peels. Although the detection sensitivity of SO and CHA depends on the sample types, these ELISAs may be effective as future clinical and food testing methods after further improvements. In 1994, Stanker et al. succeeded in developing the first monoclonal antibody against GAs . SimultaThe effects of eating stale potatoes should not be underestimated, as there have been some cases of deaths due to GA food poisoning . HoweverIn the present study, we aimed to identify new antibodies that can bind to both SO and CHA and construct two types of ELISAs using them. Further, we evaluated the detection performance of these ELISAs for SO and CHA in two biological samples (serum and urine) and potato part extracts to verify their usefulness as clinical and food testing methods. We found that although the ELISAs established in our study had low detection sensitivity for the serum and urine samples, they can be used as clinical diagnosis methods after certain improvements. Further, as they can efficiently measure the SO and CHO concentrations in potato extracts, they also have the potential to be used as food testing methods.SO and CHA powders were purchased from Sigma-Aldrich Co., LLC . Horseradish peroxidase (HRP)-labeled goat anti-rabbit polyclonal IgG (H + L) antibodies were obtained from Funakoshi Co., Ltd. . Commercial serum and urine samples of healthy volunteers were obtained from Cosmo Bio Co., Ltd. . Bio-Safe Coomassie stain, Clarity Western ECL substrate kits, Precision Plus Protein Dual Color Standards, Trans-Blot Transfer Packs, and 0.2 μm pore-size nitrocellulose membranes were obtained from Bio-Rad Laboratories, Inc. . A 96-well ELISA plate H was purchased from Sumitomo Bakelite Co., Ltd. . An HRP labeling kit was obtained from Dojindo Laboratories . Blocking One and dimethyl sulfoxide (DMSO) were purchased from Nacalai Tesque, Inc . The Prominence LC-2010 HPLC system and the reversed-phase chromatography (RPC) column Shim-pack GIST C18-AQ were purchased from Shimadzu Corporation . Freund’s complete adjuvant (FCA), Freund’s incomplete adjuvant (FIA), o-phenylenediamine dihydrochloride (OPD) tablets, 2-mercaptoethanol (2-ME), and all other reagents were obtained from Wako Pure Chemical Industries, Ltd.  and Wakenyaku Co., Ltd. .n = 12) were purchased from a supermarket and kept under fluorescent light (380–400 nm wavelength) at 22 °C during the experimental period. Their components were extracted immediately after purchase , after 30 days , and after 60 days . The extracted parts were sprouts (Day 30 and 60), peels , and tubers . Each part was removed from the potatoes , weighed, and mixed with 5 mL of 10% DMSO in 15 mL polycarbonate tubes. The parts were homogenized using a 150 Homogenizer  in the tubes and centrifuged at 1191× g for 30 min at 4 °C. These supernatants were used as the assay samples and stored at −80 °C until use. The sample preparation protocol is summarized in SO and CHA powders were individually dissolved in 10% DMSO and used in subsequent experiments. The prepared SO and CHA solutions were diluted in 10 mM phosphate-buffered saline , serum, or urine and were used as assay samples. In addition, extracts from potatoes were also used as samples. Potatoes . This experiment complied with the ARRIVE guidelines  and was approved by the Animal Experiment Committee in Carbuncle BioScienTec LLC. Briefly, solanidine, a chemical compound found in both SO and CHA, was conjugated with bovine serum albumin as an immunogen. Two female rabbits  were immunized intradermally with 0.5 mg of this immunogen (per rabbit) five times  every two weeks. During this period, blood was collected from the ear vein one week after immunization. To obtain the serum, all of these blood samples were left at room temperature (22 °C) for 30 min and then centrifuged at 1191× s report , antibods report . The pros report .TM XRS plus imaging system and Clarity Western ECL substrate .The purified antibodies were detected by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) as described in a previous study . The con2O2 was immediately added to the 10 mL chromogenic solutions, and then 100 μL of the mixture solution was added to each well. After 30 min of color development, the reaction was stopped by adding 100 μL of 3 N H2SO4 to each well. The absorbance of the color reaction was measured at a wavelength of 490 nm using a microplate reader . A schematic diagram of this experiment is illustrated in Prior to ELISA construction, anti-sold1, and anti-sold2 were labeled to HRP  using an HRP labeling kit–SH, according to the manufacturer’s protocol. In accordance with a previous report , two kinIn this study, HPLC analysis was performed using the Prominence LC-2010 HPLC system connected to a Shim-pack GIST C18-AQ column based on RPC . Analyses were performed at a room temperature of 22 °C. The flow rate was fixed at 1.0 mL/min, and 10 mM PBS (pH 7.4) was used as the mobile phase. The volume of all samples used for analysis was 100 μL. After sample injection into the HPLC system, a waveform at a wavelength of 210 nm was recorded with a retention time of 30 min. After measurement, the detected waveforms were analyzed using the LabSolutions software version 5.92 provided with the instrument. The lower areas of the waveforms of SO and CHA were calculated using this software to create a calibration curve and to measure the content of SO and CHA in the samples.n = 16) + 3SD. The recovery of SO and CHA in each ELISA was calculated according to the following equation: recovery = [/added concentration] × 100%. To create the calibration curve, this formula and Spearman’s rank correlation coefficient  were calculated using the KaleidaGraph software version 5.0.4 .All experiments except for the ones that verified the reproducibility of the ELISAs were measured in quintuplicate, and the data are shown as the mean ± standard deviation (SD). The coefficient of variation (CV) was calculated according to the following equation: (SD/mean) × 100%. In accordance with a previous report , the LODTo confirm that the rabbit polyclonal antibodies were successfully purified, SDS-PAGE and Western blotting were performed. Although the serum contained many components  using serum and urine as their solvents. Good calibration curves of SO and CHA diluted in serum could be made in the concentration range of 25–800 ng/mL A,B. The Similarly, the Sold2 ELISA calibration curves for serum and urine were re-created and verified for simultaneous repeatability. Sold2 ELISA was able to construct calibration curves for SO and CHA diluted in serum in the concentration range of 25–800 ng/mL A,B. The We examined whether it is possible to simultaneously measure both SO and CHA in each solvent. Good calibration curves could be drawn for SO and CHA samples diluted in PBS in the concentration range of 1.56–100 ng/mL in Sold1 ELISA and 3.12–100 ng/mL in Sold2 ELISA A,D. HoweThe concentration of SO and CHA in each part of the potato was determined using ELISAs. In potato tubers, the SO and CHA content increased about 1.6- to 1.8-fold from Day 0 to Day 60 A,D. SO an = 4) parts on Day 60 are shown in Finally, the concentration of SO and CHA in each part of the potato was analyzed by HPLC. When the mixture of SO and CHA dissolved in PBS was analyzed, their main waveforms were detected at a retention time of 4–6 min A. By usiHere, we successfully developed two new polyclonal antibodies that can bind to both SO and CHA and constructed two ELISA systems based on these antibodies. Although these antibodies are currently owned only by us, they can be produced by any institute by immunizing rabbits with the solanidine-BSA complex shown in As potatoes contain both SO and CHA, developing antibodies that can bind to both SO and CHA would be the best way to construct an ELISA to detect them. Therefore, we aimed to establish antibodies that can recognize a part of solanidine, a chemical structure shared by SO and CHA, as epitopes. However, the molecular weight of solanidine is considerably low (400 Da), and the epitopes to which antibodies can bind are extremely limited. Since anti-sold1 and anti-sold2 in this study probably recognized almost the same epitope, we determined that it would be difficult to construct an analytical method using two different antibodies that recognize different epitopes, such as a sandwich ELISA system. Thus, we established non-competitive direct ELISA systems using anti-sold1 and anti-sold2. Both the ELISAs in our study were more sensitive to CHA than to SO, and the Sold1 ELISA appeared to have better detection performance than the Sold2 ELISA.The constructed ELISAs in this study are comparable to the existing assays in terms of their ability to detect SO and CHA diluted in buffer solution. Our ELISA systems are about 20 times more sensitive in terms of LODs than previous ELISA kits that use monoclonal antibodies . In partAlthough the Sold1 ELISA and Sold2 ELISA values were slightly different, the combined concentrations of SO and CHA could be measured from the extracts of each part of the potato. The SO and CHA concentrations in most tubers of commercial potatoes do not exceed 100 μg/g , whereasNon-competitive direct ELISAs are advantageous because several samples can be measured simultaneously on a single ELISA plate. In this respect, ELISA is superior to HPLC because the analysis of one sample must be completed before the analysis of the next sample can begin in the HPLC method. Another advantage of ELISA is that it does not require expensive equipment and is relatively easier to perform. ELISA only requires a microplate reader, which is less expensive than HPLC or mass spectrometry instruments. Moreover, optimizing anti-sold1 and anti-sold2 as medical reagents will enable their adaption to immunological analyzers that are already installed in medical facilities. Another merit of ELISA is the easy analysis of results—they can be interpreted visually as the concentrations of SO and CHA are indicated by the degree of coloration. The interpretation of HPLC and mass spectrometry results requires a certain degree of skills and expertise, and their use in medical facilities is limited. Notably, most of the previous analytical methods for SO and CHA were reported before 2000 AD ,9,10,13,ELISA also has some limitations, the primary one being that the measurement time currently takes more than one day. The first step of overnighting the samples on the plate is time-consuming, and a future challenge would be to shorten the measurement time. The second limitation is the high running cost. ELISA requires the use of HRP-conjugated antibodies for each assay, which are costlier than HPLC reagents. Additionally, detection sensitivity is a weak point when serum or urine is used as samples.While there have been limited assay methods for SO and CHA, the development of non-competitive ELISAs is a useful achievement for the future development of potato food poisoning diagnosis and food testing. In the future, we aim to seek optimal conditions for ELISA with the goals of shortening the analysis time and improving sensitivity and specificity. Furthermore, we would like to start the construction of an immunochromatography using the antibodies developed in this study to establish a more rapid and simple method for measuring SO and CHA.In this study, non-competitive direct ELISAs that can measure SO and CHA in both serum and urine samples were developed. However, the detection sensitivity remains an issue. Although cross-reactions with other components in the biological samples may occur, these ELISAs can measure SO and CHA in potato extracts. Therefore, these ELISAs would not be applicable to clinical testing but may be useful for food testing. The improvement of the sample preparation process and assay conditions can increase the applicability of these ELISAs in the future.


Sennaobtusifolia (L.) is a plant in the genus Senna that contributes to improving nutritional quality, food security, and better health protection for rural populations. However, very few studies have been devoted to it in Burkina Faso. Consequently, its genetic diversity remains poorly known. Such neglect would lead to the erosion of its genetic resource. The general objective of this study is to contribute to a better knowledge of the genetic diversity of the species in order to be able to issue scientific bases for its conservation, valorization, and genetic improvement. Sixty (60) accessions of Senna obtusifolia were collected in the wild from five provinces of three climatic zones of Burkina Faso. Molecular characterization was carried out using 18 SSR markers. Fifteen were polymorphic microsatellite markers leading one hundred and one (101) alleles in total, with an average of seven (7) alleles per locus. The number of effective alleles was 2.33. Expected heterozygosity, Shannon diversity index, and polymorphism information content averaged 0.47, 1.05, and 0.47. Molecular characterization revealed the existence of genetic diversity within the collection. This diversity has been structured into three genetic groups. Genetic group 3 presents the highest genetic diversity parameters. Senna obtusifolia (L.), also known as Foetid Cassia in French or Katr-nanguri in Mooré, is an annual or perennial herb with an erect, branched stem that can reach an average height of 2.5 m and has alternate, pinnate leaves , where N is the number of loci, n is the number of accessions, and pi is the frequency of allele i of the locus considered.The expected heterozygosity (He) and Nei's gene diversity index  which is a parameter that gives an estimate of the discriminatory power of a locus. It takes into account both the number of alleles expressed and the relative frequency of each allele . It is cP) which is the number of polymorphic loci (npj) divided by the total number of loci . Its formula is P = npj/n total.The polymorphism rate ((2) Interpopulation Diversity. The accessions were subdivided into subpopulations according to the climatic zone factor. Genetic diversity parameters were estimated for each of these subpopulations. Two parameters were estimated in the description of the genetic diversity between the defined subpopulations. These are the indices of genetic differentiation between populations (Fst) and the minimum distance of Nei between pairs of genetic groups.HT, where HS is the average of the expected heterozygosity of the subpopulations and HT is the expected heterozygosity of the total population;The index of genetic differentiation between populations (Fst): its value is between 0 and 1. It is 0 in case of strong genetic similarity between subpopulations and 1 in case of fixation of different alleles in the subpopulations. Its formula is Fst = 1−HS/The minimum distance of Nei between pairs of genetic groups: its value also varies from 0, for identical populations, to 1, for totally different populations.Among the eighteen (18) microsatellite markers tested, fifteen (15) were polymorphic . For theP value of 0.83. The factor climatic zone would therefore have a very weak influence on the expression of the genetic diversity of accessions (0%). The accessions factor plays a 100% role in the expression of this diversity  revealed a nonsignificant difference between accessions of the three climatic zones with a iversity . Thus, iiversity .The parameters, minimum distance of Nei (0.06) and differentiation index Fst (0.04), observed are higher between accessions from the Sahelian zone and those from the Sudanian zone. Low values of minimum Nei distance (0.03) and Fst differentiation index (0.02) were observed between accessions from the Sudan-Sahelian zone and those from the Sudanian zone .P value = 0.004). In the expression of genetic diversity, the intergroup genetic variance is 2% and the intragroup variance is 98% (Group 1 with six (6) accessions includes three accessions from the Sudanian zone , one accession from the Sudano-Sahelian zone (P-E2), and two accessions from the Sahelian zone (S-E6 and S-E7);Group 2 with twenty-seven (27) accessions includes eight (8) accessions from the Sudanian zone , sixteen (16) accessions from the Sudano-Sahelian zone , and three accessions from the Sahelian zone ;Group 3 with twenty-seven (27) accessions includes nine (09) accessions from the Sudanian zone , fourteen (14) accessions from the Sudano-Sahelian zone , and four accessions from the Sahelian zone .The genetic diversity obtained was structured into three groups, independent of the climatic zone . The AMOe is 98% , so the Genetic group 3 includes the majority of accessions that showed better agronomic performance during agromorphological characterization, i.e., seven accessions  out of 13.In general, the genetic diversity parameters observed were high for group 3 and low for group 1 . Thus, gSenna obtusifolia were characterized using 18 SSR markers. Fifteen microsatellites were polymorphic (VM) markers and revealed one hundred and one (101) alleles in total, and the number of alleles per locus ranged from three (3) to fourteen (14) with an average of 6.73 alleles per locus. According to FAO [Senna obtusifolia collection. Work conducted by Mohanty et al. [Senna obtusifolia. The difference between the results of the present study and previous work could be explained by the size and nature of the plant material used. According to Kalinowski [Solanum aethiopicum L.Sixty (60) accessions of g to FAO , an averg to FAO , there wy et al.  revealedlinowski , large slinowski , the numlinowski  on the slinowski . The lowlinowski  on fortyThis very high rate of polymorphism testifies to the high level of polymorphism within the accessions and the efficiency of the markers used for the analysis of the genetic diversity of the populations studied . AccordiSenna obtusifolia accessions in the present collection. The differentiation index Fst allowed the study of diversity between subpopulations. Thus, the genetic differentiation is weak between accessions of different climatic zones (average Fst of 0.03). According to Wright [P=0.83) between accessions from different climatic zones. Thus, the genetic diversity of Senna obtusifolia would indeed be due to the nature of the plant material, as it is a plant that is always found in the wild. Indeed, according to Yuan et al. and Benor et al. [Thus, there would be moderate variability within the o Wright , Fst valr et al. , 36, domAccording to Ould Ahmed et al. , total gSenna obtusifolia, which is cleistogamous, i.e., fertilization occurs before the opening of the flower buds. This phenomenon would reduce the genetic diversity within the species. The diversity of the sixty (60) accessions was structured into three distinct genetic groups. The accessions were grouped independent of their origins, thus forming composite groups. The accessions that presented better agronomic performances during agromorphological characterization were mostly found in the genetic group 3. The structuring of accessions into three genetic groups without taking into account their geographical origin confirms the low value of Fst (0.03), which reflects a weak influence of geographical origin on the total genetic diversity. Similar results were observed by Kiebre et al. [Senna obtusifolia accessions in the present study. Similar results have been found by other authors such as Ambrosi et al. [The moderate genetic diversity in the present study could be due to the reproductive mode of e et al. , 38. Thii et al. –41 on otP value (0.004) significant at 5% in AMOVA analysis. Genetic diversity parameters were high for group 3 and low for group 1. In addition, this group contains the majority of the best agronomic performance accessions from the agromorphological evaluation. According to Nacambo et al. [With a mean Fst value of 0.06, genetic diversity was moderate among the three genetic groups. This difference was confirmed by a o et al. , an agroThe results obtained in this study would therefore indicate the existence of a certain level of diversity that can be exploited by breeders.S. obtusifolia accessions was structured into three groups independent of geographic origin. Group 3 consisting of twenty-seven (27) accessions had the highest genetic parameters, which also includes the majority of the accessions that have better agronomic performances.This study revealed the existence of genetic diversity within the collection. The genetic diversity of


Pseudotaxus chienii is an endangered gymnosperm species endemic to China. In this study, we have examined the genetic and epigenetic variations in its natural populations aiming to disentangle the synergistic effects of climate and soil on its population (epi)genetic differentiation by using amplified fragment length polymorphism (AFLP) and methylation‐sensitive AFLP (MSAP) techniques. We identified 23 AFLP and 26, 7, and 5 MSAP outliers in P. chienii. Twenty‐one of the putative adaptive AFLP loci were found associated with climate and/or soil variables including precipitation, temperature, K, Fe, Zn, and Cu, whereas 21, 7, and 4 MSAP outliers were significantly related to precipitation of wettest month (Bio13), precipitation driest of month (Bio14), percent tree cover (PTC), and soil Fe, Mn, and Cu compositions. Total precipitation and precipitation in the driest seasons were the most influential factors for genetic and epigenetic variation, respectively. In addition, a high full‐methylation level and a strong correlation between genetic and epigenetic variation were detected in P. chienii. Climate is found of greater importance than soil in shaping adaptive (epi)genetic differentiation, and the synergistic effects of climate and climate–soil variables were also observed. The identified climate and soil variables should be considered when applying ex situ conservation.Whether conifers can withstand environmental changes especially temperature fluctuations has been controversial. Epigenetic analysis may provide new perspectives for solving the issue.  Pseudotaxus chienii, was selected as a representative to explore epigenetic architecture and further reveal the synergistic effects of climate and soil.Whether conifers can withstand environmental changes especially temperature fluctuations has been controversial. Epigenetic analysis can provide new perspectives for solving the issue. An endangered gymnosperm species endemic to China,  The final amplification products were separated by electrophoresis on an ABI PRISM 3130 sequencer (Applied Biosystems) with an internal size standard of ROX‐500. Electropherograms were automatically scored for presence (1) or absence (0) in a fragment range 50–500 bp using GeneMarker v2.2 .We performed AFLP analyses for all samples according to the general method of Vos et al. . Brieflyng Table , of whic2.3Hpa II and Msp I were used in standard MSAP with recognition site 5′‐CCGG‐3′ (CG and CHG contexts). In this study, we adopted new isoschizomers, Tfi I and Pfe I, which can recognize 5′‐GAWTC‐3′  in parallel reactions, which were further ligated to the Bgl II and Tfi I‐Pfe I adaptors (TaKaRa) using T4 DNA ligase . The digestion–ligation reaction was incubated for 10 h, followed by 15 min of inactivation. The ligated product was preamplified in 20 μL, including initial denaturation at 94°C for 5 min and 20 cycles with 94°C for 40 s, 56°C for 45 s, and 72°C for 1 min. The reaction mixture contained 2 μL 10 × buffer (with Mg2+), 1.5 μL dNTP (10 mM), 0.5 μL Bgl II preamplification primer (10 μM), 0.5 μL Tfi I/Pfe I preamplification primer (10 μM), 0.2 μL Taq DNA polymerase (5 U/μL), and 4 μL ligated product. The preamplified product was diluted 20‐fold as a template in subsequent selective amplification.We conducted MSAP analysis using the same AFLP DNA template. 2+), 1.5 μL dNTP (10 mM), 0.5 μL Bgl II‐N primers (10 μM), 0.5 μL Tfi I/Pfe I‐N primers (10 μM), 0.25 μL Taq DNA polymerase (5 U/μL), and 2.5 μL diluted preamplified product. The PCR conditions were as follows: an initial denaturation at 94°C for 5 min followed by 13 cycles of 94°C for 30 s, 65°C for 30 s, and 72°C for 1 min (decreasing 0.7°C per cycle), followed by 23 cycles of 94°C for 30 s, 56°C for 30 s, and 72°C for 1 min, and a final extension at 72°C for 5 min. The selective‐amplified products were visualized on an ABI3730XL capillary electrophoresis with LIZ1200 as an internal standard. The presence (1) and absence (0) were obtained using GeneMarker v2.2. We adopted 21 primer pairs generating clear polymorphic bands  or absence (0) of amplicons in both Bgl II/Tfi I and Bgl II/Pfe I. Type I (0/0) was defined as full methylation with band absent in Bgl II/Tfi I and Bgl II/Pfe I. Type II (1/0) was referred to hemimethylation with band present in Bgl II/Tfi I and band absent in Bgl II/Pfe I. Type III (1/1) and Type IV were opposite to Type I and Type II as nonmethylation and uninformative loci, respectively. Cytosine methylation status can be obtained through differences in product combinations from Bgl II/Tfi I and Bgl II/Pfe I reactions. “Mixed scoring 2” can separate methylated markers with HMeCG‐ or MeCG‐sites and markers with HMeCCG‐sites, respectively , unbiased expected heterozygosity (UHe), and percentage of polymorphisms (%P). Using the lmer function of the R lme4 package  with further detection of significance by using the glht function  between pairwise alleles by using the ia function of the R poppr package  between pairs of molecular markers. The measure was calculated based on the index of association  by the website STRUCTURE HARVESTER . For each K, 20 runs were performed with a burn‐in length of 10,000 followed by 100,000 Markov chain Monte Carlo (MCMC) cycles. For DAPC, the xvalDapc function of the R adegenet package genetic differentiation among populations was calculated via AMOVA by using the Jombart,  was used2.4.4vegdist function of the R vegan package  for 2001–2018. The slope and aspect data were downloaded from the elevation data of WordClim v.1.4 and extracted using ArcMap 10.4 , enhanced vegetation index (EVI), leaf area index (LAI), fraction of absorbed photosynthetic active radiation (fPAR), and percent tree cover (PTC). All MODIS datasets were obtained from the Land Process Distributed Active Archive Center  at MARS 6 Microwave Reaction System (CEM). Experimental treatments without soil samples were used as controls. All measurements were repeated three times  of the soil suspension, and the contents of organic matter (OM), nitrogen (N), carbon (C), phosphorus (P), sulfur (S), silicon (Si), potassium (K), calcium (Ca), sodium (Na), magnesium (Mg), aluminum (Al), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), and lead (Pb). The chemical analysis procedure was as follows: first, the water contents of fresh and air‐dried soil were measured using 10‐g samples. Second, pH and electrical conductivity were determined in a solution of 2 g of coarse soil mixed with 8 mL of ultrapure water. Third, fine‐grained soils were used to perform the following analyses: (i) measuring the percentage of organic matter using potassium dichromate volumetry; (ii) determining the content of total carbon by using a total organic carbon analyzer (SHIMADZU) under burning conditions of 720°C; (iii) estimating the proportion of total nitrogen using a Kjeltec™ 8400 Analyzer unit (Foss) after digestion by using concentrated sulfuric acid at 190°C for 2 h; and (iv) assaying the concentrations of P, S, Si, K, Ca, Mg, Al, Fe, Mn, Zn, Na, Cu, and Pb by utilizing an inductively coupled plasma optical emission spectrometer  after digestion with HNOes Table .vifstep function of the R usdm package to calculate the variance inflation in three levels, including bioclimate, ecology, and soil. Variables with VIF < 10 were retained.In view of the possible correlation between variables, we first used the 2.4.6Mantel function of the R vegan package genetic and geography (environment) while controlling for the environment (geography).Based on the Pearson correlation coefficient, the https://doi.org/10.5061/dryad.kt71r).Similar to the Mantel and partial Mantel tests, multiple matrix regression with randomization (MMRR) can also be used to estimate IBD and IBE. Its advantages lie in quantifying the effects of geographic and ecological isolation with no limitation to the number of distance matrices Wang, . We used2.4.7Fst values under neutral expectations to identify loci under positive selection from a plot of Fst against heterozygosity. First, the trimmed mean Fst  was calculated after removing the 30% highest and lowest of the initial Fst observed in the empirical dataset. Second, based on 50,000 simulated loci, a null distribution of Fst values was generated with a mean Fst similar to the trimmed mean Fst. Last, any loci outside the null distribution at 99.5% confidence levels were designated as potential outliers. DFDIST is conducted based on a symmetrical island model  was used to test the associations between outliers and environmental variables. As an extension to GLMs , the GLMM allows the analysis of nonnormal data while incorporating random effects genetic structure can be detected from environmental variables and spatial autocorrelation of populations using distance‐based Moran's eigenvector maps  is a constrained ordination approach to evaluate the explanatory power of multivariate predictors for multivariate responses  on retained variables and outliers for four datasets. When performing pRDA, geographic variables were treated as conditioned factors.To reduce collinearity, forward variable selection was applied to identify specific important environmental and spatial variables for generating selective differences across the range of Varpart function of the R vegan package .For AFLP‐based genetic variation, 312 individuals yielded 2070 polymorphic loci. The average I, UHe, and P% and their variations are shown in Table I and UHe was not significant at the population (LMM test) and dataset levels (Friedman test). AFLP‐based genetic diversity was similar to MSAP‐M‐based epigenetic variation and higher than that of the other two epigenetic datasets (H and U). In addition, GX populations possessed the lowest epigenetic diversity, while relatively higher epigenetic diversity was found in JX and ZJ , M , H , and U  datasets across the populations , 3098 (89%), and 2298 (66%) polymorphic epiloci, respectively. Corresponding to the above datasets, the average ZJ Table . Based ons Table .3.4Based on AMOVA, the variation within populations greatly contributed to the (epi)genetic differentiation, which was 83.573%, 77.442%, 73.068%, and 76.301% in H, U, M, and AFLP, respectively . In addition, a significant correlation was also detected between epigenetic variations  in the AFLP dataset, suggesting a strong “isolation by distance” pattern across populations. However, there was no correlation detected between geographic or environmental distance and epigenetic distance.Mantel tests revealed a significantly positive correlation between Nei's genetic distance and geographic distance  were also found to correlate with the outliers Table .3.9We used redundancy analysis (RDA) to quantify the relationship of (epi)genetic variation with environmental variables. The 11 sampling locations were used to generate seven eigenvectors with positive eigenvalues, which represented a positive spatial autocorrelation between populations. These eigenvectors were regarded as spatial predictors , spatial patterns alone (c), and their joint effect (b) accounted for 6.08%, 6.32%, and 60.84%, respectively. For the epigenetic variance, environment alone accounted for 9.51%, 12.26%, and 17.47% based on M, H, and U, respectively, while space correspondingly accounted for 11.87%, 3.28%, and 13.01%, respectively; and the joint effect of environment and space separately accounted for 60.24%, 41.92%, and 53.58% of the total variance in the three datasets, respectively. The results implied that 50% of the total amount of variance could be explained by both environment and space. For genetic variance, environmental variables alone could explain slightly more than spatial ones. For epigenetic variance, the explanation of environment alone increased from M to H to U, whereas the explanation of space from high to low was U, M, and H. In addition, more variance was ascribed to environment rather than space except for the M dataset‐derived outliers  of MSAP outliers was found in the M dataset, whereas only seven and five MSAP outliers were detected in the H and U datasets, respectively. In comparison to another conifer P. mariana; Prunier et al., Larix decidua; Mosca et al., Arabidopsis  in shaping genetic differentiation , followed by precipitation of warmest quarter (Bio18), and soil MgP. chienii have a wide‐range distribution, from 21° N to 29° N and 108° E to 121° E , minimum temperature of coldest month (Bio6), precipitation seasonality (Bio15), precipitation of driest quarter (Bio17), and precipitation of coldest quarter genetic and spatial structure. Specifically, soil K, Zn, Fe, and Cu were correlated with the putative outliers,The present study demonstrated that the majority of genetic and epigenetic variation  was explained by both environmental and spatial components. Liu et al.  found thTaxus media, which is closely related to P. chienii.Potassium (K) and iron (Fe) are essential macronutrients for plants, fulfilling important functions in metabolism, growth, and stress adaptation genetic differentiation were identified in rs Table . Given tP. chienii. Su et al. (Taxus cuspidata (Taxaceae), a closely related species of P. chienii, is mainly caused by recent fragmentation rather than historical climatic oscillations. In line with this, no lack of genetic and epigenetic variations was detected in the small populations of P. chienii in the present study. This raises the possibility that recent anthropogenic disturbance may also play a major role in the endangerment of P. chienii, which reserves further investigation.In addition, our results also show implications for the endangering mechanism of u et al. , 2020 ha5P. chienii populations, which was found significantly associated with climate and soil factors. A similar phenomenon was observed in its AFLP‐based genetic variation. We identified 23 AFLP and 26, 7, and 5 MSAP outliers  as putative adaptive (epi)loci in P. chienii. Twenty‐two of the putative adaptive AFLP loci, and 21, 7, and 4 putative MSAP outliers  were found associated with climate and/or soil K, Fe, Zn, and Cu contents. Our results highlight the synergistic effects between environmental factors in (epi)genetic adaptation. Under ongoing global warming, populations of P. chienii are predicted to shrink in southern China and shift to central China ; project administration ; supervision ; writing – review and editing . Li Liu: Data curation ; methodology . Qi Deng: Data curation ; methodology . Zhuyan Lü: Formal analysis ; writing – original draft . Zhen Wang: Formal analysis ; writing – review and editing . Zhiqing He: Formal analysis . Ting Wang: Funding acquisition ; project administration ; supervision ; writing – review and editing .This work was supported by the National Natural Science Foundation of China (31872670 and 32071781), Guangdong Basic and Applied Basic Research Foundation (2021A1515010911), Science and Technology Projects in Guangzhou (202206010107), and Project of Department of Science and Technology of Shenzhen City, Guangdong, China (JCYJ20190813172001780 and JCYJ20210324141000001), Science and Technology Base and Talent Special Project of Guangxi (AD19110088), and Basic Ability Improvement Project for Young and Middle‐aged Teachers in Universities of Guangxi (2018KY0334).The authors declare no conflicts of interest.Appendix S1:Click here for additional data file.


Technological advances and decreasing costs of genomic sequencing have paved the way for the increased incorporation of genomics into newborn screening (NBS). Genomic sequencing may complement current NBS laboratory analyses or may be used as a first-tier screening tool to identify disorders not detected by current approaches. As a large proportion of infant deaths occur in children with an underlying genetic disorder, earlier diagnosis of these disorders may improve neonatal and infant mortality rates. This lends an additional layer of ethical consideration regarding genomic newborn screening. We review the current understanding of genomic contributions to infant mortality and explore the potential implications of expanded access to genomic screening for infant mortality rates.  Genetic disorders underlie a substantial proportion of infant deaths, particularly infants with congenital anomalies and those admitted to an intensive care unit. Additionally, genetic diagnoses have also been identified in a large proportion of apparently healthy infants who die unexpectedly, although many genetic conditions likely remain undiscovered due to a lack of testing ,2,3,4. TPrior studies have investigated genetic diagnosis in postmortem cohorts ,7,8,9,10The spectrum of diagnoses identified also varies by cohort. Genes associated with epilepsy or cardiac arrhythmia are often implicated in cases of sudden, unexpected infant death ,13 occurAs these genetic diagnoses are typically identified by a chromosomal microarray or by massively-parallel sequencing technologies, they would be amenable to early detection via genomic sequencing from the dried blood spot obtained for traditional newborn screening, provided appropriate pre-test counseling and consent is obtained . SeveralOur understanding of the depth and breadth of genetic conditions responsible for infant deaths is limited by several factors. First and foremost, the lack of population-based approaches for a comprehensive genomic evaluation of infant deaths limits our abilities to quantify the public health impact of these diagnoses. Conclusions identified from current cohorts are therefore limited by selection bias. In addition, the interpretation of variants identified is limited in the perimortem setting due to a lack of ability for follow-up investigations. Thus, many infants with genetic conditions are never identified, and the experience with genetic diagnosis in pediatric populations suggests that this may disproportionately impact historically underserved populations, though further research is needed into inequities in this realm ,21. ThusFinally, limited outcome data preclude accurate estimates of mortality rates for conditions that are identified in the perinatal setting. Attempts to quantify these mortality rates have been undertaken for specific diseases, such as genetic leukodystrophy syndromes , though Genomic newborn screening has the potential to reduce infant mortality by identifying infants with treatable diseases prior to the onset of irreversible symptom progression, leading toward the improved management of neonates and infants with a range of genetic disorders, although the spectrum of possible impacts is not currently well understood. If applied on a population-wide scale, genomic newborn screening techniques may allow for more a comprehensive description of genetic diagnoses associated with infant deaths by eliminating the inherent bias in the access to a clinical diagnostic genetic evaluation, where many infants die before genetic diagnoses can be identified, as our current knowledge of the prognosis for these conditions is biased toward those who survive long enough to have genetic testing. Although prior research suggests that genomic sequencing is a robust method for the detection of treatable conditions , issues Nonetheless, dedicated efforts must be undertaken to move toward equitable implementation if genomic newborn screening is to be introduced. Additionally, disorders may be identified for which there are current or emerging therapies that lead to improved survival, though, again, ensuring equitable access to such therapies is paramount to upholding the ethical principle of justice in healthcare. Finally, conditions can be identified that have implications for parents’ future childbearing, and the identification of a precise genetic diagnosis can aid in pregnancy planning that can further reduce neonatal and infant mortality.Genomic newborn screening presents a unique opportunity not only to identify and manage infants at risk for long-term medical sequelae of a wider range of underlying genetic conditions, but also to understand and potentially reduce rates of infant mortality. Resource availability regarding the broader application of genomic newborn screening remains a valid concern and area of further focus. Still, understanding this approach and its potential as an important public health outcome of infant mortality may provide additional ethical justification. Critical to the success of this approach is the equitable implementation of genomic newborn screening in addition to resources devoted to accurately capturing health outcomes.


Coronary microvascular dysfunction is crucial in determining myocardial ischemia; however, the relationship between epicardial coronary diameter stenosis (DS) and the index of microcirculatory resistance (IMR) remains unclear. We sought to explore the distribution of coronary angiography‐derived IMR (angio‐IMR) in patients with suspected myocardial ischemia.The study included 480 patients with suspected myocardial ischemia, all of whom underwent coronary angiography. According to the severity of coronary DS, patients were divided into three groups: mild (DS < 50%), intermediate (DS 50%–70%), and severe (DS > 70%). Angio‐IMR and fractional flow reserve (FFR) were calculated based on coronary angiography images through the principle of computational flow and pressure simulation.p < .001). In the correlation analysis, angio‐IMR was negatively correlated with DS  and positively correlated with angio‐FFR . By multivariate logistic regression analysis, angio‐FFR ≤ 0.8  was the only independent predictor of coronary microvascular dysfunction.Of the 480 patients, the mean age was 67.23 ± 9.44 years, with 55.4% male. There were 193 (40.2%) patients in the mild group, 189 (39.4%) patients in the intermediate group, and 98 (20.4%) patients in the severe group. The average angio‐IMR of the mild group was 30.8 ± 14.9, which was significantly higher than those of the intermediate group (26.7 ± 13.0) and the severe group (17.9 ± 8.4) In patients with suspected myocardial ischemia, coronary microcirculation is significantly associated with morphological and physiological coronary stenosis. ( This study sought to explore the distribution of angiography‐derived index of microcirculatory resistance (angio‐IMR) in patients with suspected myocardial ischemia. The angio‐IMR and angiography‐derived fractional flow reserve (angio‐FFR) were calculated based on coronary angiography images through the principle of computational pressure‐flow dynamics (CFD). The results showed that angio‐IMR was negatively correlated with coronary diameter stenosis (DS) and positively correlated with angio‐FFR. To conclude that coronary microcirculation is significantly associated with morphological and physiological coronary stenosis in patients with suspected myocardial ischemia. Increased coronary microvascular resistance can impair coronary blood flow, cause angina pectoris, and lead to diastolic and systolic dysfunction.At present, noninvasive examinations such as echocardiography, positron emission tomography, cardiac magnetic resonance, and invasive tests, mainly including the index of microcirculation resistance (IMR) and coronary flow reserve (CFR), were used for the evaluation of coronary microvascular function.Previous studies showed that IMR was independent of epicardial stenosis,22.1We retrospectively recruited patients with suspected myocardial ischemia hospitalized in the Department of Cardiology, Shanghai Tenth People's Hospital from February 2021 to June 2021. All patients underwent coronary angiography and recorded real‐time mean aortic pressure (MAP). Patients with the following conditions were excluded: (1) Acute coronary syndrome; (2) Cardiomyopathy; (3) Severe myocardial bridge; (4) Chronic total occlusion of the coronary artery; (5) severe valvular heart disease; (6) A history of coronary artery bypass surgery; (7) Ostial lesions ≤3 mm from the aorta; (8) Coronary angiographic image quality of interrogated vessels did not meet the FLASH software requirements .2.2A total of 480 patients were eventually included in this study. Basic demographic information, cardiovascular risk factors, laboratory tests, echocardiography, and CA results were documented in detail for all participants. Downloaded Digital Imaging and Communications in Medicine angiography images from the picture archiving and communication system and recorded real‐time MAP during angiography. Morphological stenosis was assessed by visual assessment of coronary angiography. Vessels with the most severe DS were further analyzed for physiology, and vessels with the same degree of stenosis were randomly assigned. CAD is defined as at least one main coronary artery stenosis ≥50% in diameter, more than two vessels as multivessel disease, and DS < 50% as no‐CAD.2.3diastolic) by the thrombolysis in the myocardial infarction frame count method.hyp) was calculated based on MAP during the index procedure‐MAP × 0.2 when MAP ≥ 95 mmHg or MAP–MAP × 0.15 when MAP < 95 mmHg.hyp and Vdiastolic were used to solve the Navier‐Stokes equation using the patented computational pressure‐flow dynamics (CFD) method, computing the pressure drop (∆P). Angio‐FFR = (Pahyp − ∆P)/Pahyp, angio‐IMR = Pahyp × angio‐FFR × (vessel length/K × Vdiastole).The angio‐IMR and angio‐FFR measurements were conducted using the FlashAngio system . Details of measurement and procedures of angio‐IMR and angio‐FFR have been published previously.2.4t‐test or one‐way analysis of variance was used as appropriate, and non‐normally distributed continuous data were compared with the Wilcoxon rank‐sum test. For comparisons of categorical variables, the Chi‐square test and Fisher's exact tests were used, with a Bonferroni‐adjusted significance level. Spearman or Pearson correlation was applied for correlation analysis as appropriate. CMD was defined as angio‐IMR > 25,p‐value < .05. All statistical analyses were performed using SPSS v.22 (IBM Inc). Data were visualized using GraphPad Prism v.9.0 (GraphPad Software Inc).Numerical data were presented as the mean ± SD or medians and interquartile ranges, and categorical variables were presented as percentages. For intergroup comparisons of numerical variables, independent sample Student's 3A total of 480 patients with suspected myocardial ischemia were included. The baseline characteristics are listed in Table 3.1p = .022). In the mild group, the proportion of male patients was significantly less than that of the severe group  (p = .001). The systolic blood pressure in the severe group was also higher than that of the mild and intermediate groups (p = .005), while there was no difference in the diastolic blood pressure and body mass index . There was a less prevalence of cardiovascular risk factors in the mild group than in the intermediate and severe groups: hypertension (p = .049), diabetes mellitus (p = .002), and prior percutaneous coronary intervention history (p = .006). Other risk factors were not significantly different among the three groups.Patients' baseline characteristics stratified by coronary stenosis categories are shown in Table p = .044), antiplatelet aggregation drugs (p < .001), and angiotensin‐converting enzyme inhibitors or angiotensin II receptor blockers (p < .001) in the intermediate group and the severe group were significantly higher than that in the mild group, while there was no difference in statin use (p = .292). In the laboratory test, the glycated hemoglobin (6.80 ± 1.36 mmol/L) in the severe group was significantly higher than that in the mild group and the intermediate group  (p = .001). The creatinine level in the severe group was also significantly higher than that in the mild group or the intermediate group . Among the three groups, the angio‐FFR and angio‐IMR levels were significantly different (All p < .001). The angio‐FFR of the mild group was 0.92 ± 0.04, and the intermediate and severe groups were 0.87 ± 0.10 and 0.65 ± 0.17, respectively. The angio‐IMR of the mild group was 21.3 ± 18.7, while the latter two groups were 26.7 ± 13.3 and 17.9 ± 8.4, respectively.In terms of drug use, the rates of beta‐blockers (3.2p = .030) and systolic blood pressure . In the laboratory test, glycosylated hemoglobin , fasting blood glucose , creatinine , and B‐type natriuretic peptide  had a weak negative correlation with angio‐IMR. Angio‐IMR was moderately positively correlated with angio‐FFR  and negatively correlated with DS .In the correlation analysis between angio‐IMR and continuous variables Table , angio‐I3.3p < .05)  . The angio‐IMR of patients with the angio‐FFR > 0.8 was higher than that of the patients with the angio‐FFR ≤ 0.8 (p < .001)  Figure . However) Figure . Based o) Figure .Based on different stenosis degrees, the incidence of CMD in groups mild, intermediate, and severe groups was 57%, 49%, and 13%, respectively , DS < 50% , and angio‐FFR  were significant predictors of CMD. However, multivariate logistic regression analysis showed that angio‐FFR < 0.8  was an independent risk factor of CMD  were included for univariable and multivariable logistic regression analysis to predict CMD (angio‐IMR > 25) Table . By univMD Table .4In this study, we pinpointed the following findings: (1) in patients with suspected myocardial ischemia, the distribution of angio‐IMR was different in the stratification of the degree of epicardial stenosis by visual assessment; (2) angio‐IMR and angio‐FFR was moderately positively correlated, and angio‐IMR was negatively correlated with DS; and (3) Angio‐FFR > 0.8 was an independent predictor of CMD.CMD is an essential mechanism of myocardial ischemia.r = .451, p < .001).r = .16, p < .01) between FFR and IMR.The present study suggests that DS was negatively correlated with angio‐IMR; angio‐FFR was positively correlated with angio‐IMR. Patients with stenosis <50% had higher angio‐IMR levels. In contrast, patients with severe stenosis had lower angio‐IMR levels, suggesting more microcirculatory disturbances in patients with non‐obstructive CAD, whereas patients with morphologically severe epicardial vessel stenosis versus functional stenosis have preserved microvascular function. Similarly, previous studies have also shown an association between IMR and functional or structural stenosis of epicardial vessels. In a cohort with moderate coronary stenosis, FFR and IMR were moderately positively correlated  patients were excluded from angio‐IMR analysis due to the noncompliant quality of angiographic images and lack of aortic pressure.5Angio‐IMR correlates with morphological and physiological stenosis of coronary arteries in patients with suspected myocardial ischemia. The distribution of angio‐IMR was distinguished in patients with different degrees of stenosis. In patients with suspected myocardial ischemia, angio‐FFR was an independent predictor of CMD.The authors declare no conflict of interest.Supplementary information.Click here for additional data file.


The molecular mechanism of the significant role of long noncoding RNAs (lncRNAs) in the progression and metastasis of gastric cancer (GC) remains largely elusive. Our objective is to detect overexpressed lncRNA in GC and investigate its role in promoting epithelial‐mesenchymal transition and tumour microenvironment remodel.LINC00501 was evaluated in both GC patient tissues and GC cell lines by quantitative reverse transcription PCR and large‐scale (n = 304) tissue microarray. To explore the biological role and regulatory driver of LINC00501 in GC, various experimental techniques including Chromatin isolation by RNA purification (ChIRP), RNA immunoprecipitation (RIP), chromatin immunoprecipitation (ChIP) assay, dual luciferase assays were performed.LncRNA differential expression profile in GC was analysed using RNA microarrays. The level of LINC00501 level was abnormal overexpression in GC tissue and was associated with GC progression and distant metastasis. Gain and loss molecular biological experiments suggested that LINC00501, promoted EMT process and angiogenesis of GC. Mechanically, the enrichment of H3K27 acetylation in LINC00501 promoter region contributed to the increase of LINC00501 in GC. LINC00501 transactivated transcription of SLUG, by recruiting hnRNPR to its promoter. The growth of GC was inhibited both in vitro and in vivo by suppressing the level of LINC00501 using pharmacological intervention from the histone acetyltransferase (HAT) inhibitor ‐C646.Clinically, it was observed that LINC00501 promotes GC progression via hnRNPR/SLUG pathway, which indicates a promising biomarker and target for GC.This study suggests that  Our study revealed that LINC00501 is highly expressed in GC and promotes GC metastasis by enhancing the EMT process and tumour environment remodel. To describe the underlying mechanism, LINC00501 promoted SLUG expression through hnRNPR. During this, tumour cells invade locally through extracellular matrix, intravasate into blood vessels, survive and extravasate into distant organs and colonise in distant organs. During this cascade, epithelial‐mesenchymal transition (EMT), a multistep, complex physiopathological process, is confirmed to play important roles in the both the initial and later stage of metastasis.lncRNA‐BX111 upregulates ZEB1 and promotes progression of pancreatic cancer.AFAP1‐AS1 decreases ubiquitination and degradation of c‐Myc protein and thus accelerates progression of lung cancer.RAB11B‐AS1 enhances the angiogenesis process and metastasis in breast cancer by enhancing translation of VEGFA and ANGPTL4.Long noncoding RNAs (lncRNAs) are a large type of RNA that exceed 200 nucleotides and do not possess any evident ability to encode proteins.LINC00501, which is overexpressed in gastric tumour tissues compared with the adjacent nontumour tissues by performing genome‐wide lncRNAs profile microarray screening, qRT‐PCR, and large‐scale tissue microarray assay. Further analysis revealed that LINC00501 was elevated in advanced GC patient tissues and correlated with metastasis status and poorer outcomes. Furthermore, the assessment of LINC00501 function assays indicated LINC00501 facilitated EMT, invasion, migration and angiogenesis in GC. Mechanically, upstream analysis of LINC00501 revealed that LINC00501 was upregulated in GC because of the enrichment of H3K27ac mediated by P300 in its promoter. LINC00501, which is situated in the nucleus, increases the expression of SLUG by enlisting the hnRNPR protein to the promoter area of SLUG thereby stimulating EMT and angiogenesis. Importantly, inhibition of LINC00501 by lentivirus or P300 inhibitor C646 significantly suppressed tumour growth of GC, which indicates that targeting P300/LINC00501 axis may be an optional target for GC.Herein, we discovered a lncRNA called 22.1n = 304) from patients who had surgery at our clinical centre, the diagnosis of gastric cancer of all samples was confirmed by two pathologists. All included patients were devoid from chemotherapy or radio‐therapy before surgery. The written informed consent of patients was obtained before surgery operation. After obtaining approval from the Medical Ethical Committee of our centre , this study was conducted.Six pairs of fresh gastric cancer tissues was collected for microarray analysis, 40 sets of fresh primary GC tissue samples (cohort 1), and a large‐scale GC tissue microarray sample  for 20 min at 37°C. Then the cell was collected with centrifugation and lysed with specific lysis buffer consists of 15 mM MgCl2, 0.3 M NaCl, 1% Triton X‐100, 1 mg/mL heparin, 100 μg/mL cycloheximide, 1× proteinase inhibitor and RNase inhibitor, 15 mM Tris‐HCl. After 10 min, the mixture was centrifuged for 10 min at the speed of 13 000 rpm. The supernatant contained equal amount of RNA (100–300 μg) was retracted and added into 10−50% continuous, linear sucrose gradients. After centrifugation with a speed of 35 000 rpm for 190 min, the RNA distribution was monitored with an ISCO fractionator .Polysomes fraction was modified and performed as described.Distant metastasis is still the leading cause of gastric cancer, which is dependent on tumour cell invasive phenotype and tumour microenvironment.Epithelial‐mesenchymal transition (EMT) is a pathological process in tumour cells, which not only enhances tumour invasive ability but also remodels tumour microenvironment.LINC00501 regulates the EMT process and thus promotes tumour cell invasion and angiogenesis, ultimately enhancing gastric cancer progression.2.4LINC00501 and LacZ probe were synthesised by BersinBio and followed by incubation with extracts from gastric cancer cells. Next, the proteins extract from the pulldown were separated using an SDS‐PAGE gel and subsequently silver‐stained for band visualisation. Finally, the specific band was cut out followed by mass spectrometry or western blot.RNA pulldown assay kit was purchased from the BersinBio CO., Ltd  followed the instruction. Biotin‐labelled 2.5β‐ACTIN was selected as control; for the nucleus fraction, U6 was selected as control.Around five million cells were digested, followed by a PBS wash and centrifugation. The cells that were gathered were broken down using 800 μL hypotonic lysis buffer  and 10% Nonidet P‐40  was added in 0.4% concentration; after being centrifuged, 450 μL of the supernatant was taken and marked as a cytoplasmic fraction, and the remaining deposit was washed for three times and retained as the nucleus fraction. In the further qRT‐PCR analysis, for the cytoplasmic fraction, 2.6The sequence of qRT‐PCR primers and siRNA/shRNA can be retrieved from Supplementary Table 33.1LINC00501 has been reported to show the potential to become gastric cancer biomarker,LINC00501 were also analysed and indicated LINC00501 exhibited low‐coding potential and high expression in GI cancers . Moreover, LINC00501 level in GC tissues was markedly higher in patients with metastasis  and LINC00501‐knockdown MKN‐45 (MKN45‐sh‐LINC00501) cell lines were constructed using lentivirus. The efficiency of overexpression and knockdown was verified using qRT‐PCR .SLUG was significantly upregulated or downregulated, respectively, in accordance with the overexpression or knockdown of LINC00501  revealed that LINC00501 was mainly located in the nucleus of cells. Subcellular fractionation analyses and FISH assays further confirmed these observations . Indeed, the qRT‐PCR results indicated that LINC00501 knockdown resulted in a moderate upregulation of ZMAT3 (approximately 50%), but it did not affect other nearby genes ] was constructed  and proteasome inhibitor MG132 (10 μM) to block the internal protein synthesis and degradation activity, respectively. SLUG protein level was detected using western blot analysis. LINC00501 had no obvious effect on the stability of SLUG protein  luciferase reporter plasmids were ab initio constructed  was applied to knockdown LINC00501 in MKN45 cell lines. Female nude mice were inoculated with MKN45‐sh‐LINC00501/NC cells in the right flank via subcutaneous injection. The xenografts produced from MKN45 cells with LINC00501 knockdown had lighter weight and smaller volume compared with MKN45‐NC cells , indicating that LINC00501 may have different cell distribution in tumour and unrevealed mechanisms should be further studied. We believe, with the advancement and employment of single‐cell lncRNA sequence, we will have a better understanding of LINC00501 regarding the distribution and comprehensive function.It is well known that EMT process enhances tumour metastasis by increasing tumour cell invasion and migration in various cancers, including GC.LINC00501, as an upregulated oncogenic lncRNA, was reported to distinguish GC from normal stomach tissues.LINC00501 was significantly upregulated in GC tissues than in normal stomach tissues. Importantly, LINC00501 exhibited the further potential to identify advanced and metastatic GC; the AUCs of the ROC curve were 0.767 and 0.737, respectively. Recently, specific noncoding‐RNA‐targeted agents have been designed as candidates for antitumour therapy.LINC00501 with LINC00501‐specific lentivirus repressed the invasion and migration of tumour cells. Furthermore, P300 as a transcriptional coactivator and histone acetyltransferases has been proved to involve in several pathological processes, and targeting P300 and thereby inhibiting subsequent downstream oncogenes is emerging as an efficient therapy intervention in several cancers including prostate cancer,LINC00501 axis by C646 inhibits the tumour growth of GC without significant sign of side effect, indicating that the P300/LINC00501 axis as a therapeutic target in GC.Emerging studies reported that lncRNA is a potential candidate biomarker for GC.5LINC00501/hnRNPR/SLUG regulatory axis in advanced GC will not only expand our knowledge on the underlying mechanism of progression and metastasis of GC but also help to develop a promising biomarker and potential therapeutic target in GC.Our study revealed that LINC00501 is highly expressed in GC and promotes GC metastasis by enhancing the EMT process and tumour environment remodel. To describe the underlying mechanism, LINC00501 promoted SLUG expression through hnRNPR. Understanding the precise role of We appreciate the funds from National Natural Science Fund Youth Fund of China (81702411); National Natural Science Foundation of China (82173330); the mainstay project of young and middle‐aged medicine in Wuhan (WHQG202003); and Zhongnan Hospital of Wuhan University, Technology Innovation Seed Found (znpy2019076).No conflicts to declaration.Supporting InformationClick here for additional data file.


ChromaX is a Python library that enables the simulation of genetic recombination, genomic estimated breeding value calculations, and selection processes. By utilizing GPU processing, it can perform these simulations up to two orders of magnitude faster than existing tools with standard hardware. This offers breeders and scientists new opportunities to simulate genetic gain and optimize breeding schemes.https://chromax.readthedocs.io. The code is available at https://github.com/kora-labs/chromax.The documentation is available at  Livestock and plant breeding is crucial to sustainable agriculture  and to dchromax. In the following sections, we describe the core functions available with ChromaX; for a complete list see the documentation.ChromaX is based on the high-performance numerical computing library JAX , a compitrait_names can be used to specify a list of trait names; every element must match a column name in the genetic linkage map. A heritability value for each trait can also be specified.The simulator is initialized by providing a genetic linkage map supplied as a Pandas DataFrame . The marker effects are represented as an array of shape ChromaX can further model phenotypes that have a genotype-by-environment interaction component, as described in n scores, where n is the number of individuals in the population. Examples of scoring functions included in the software are breeding values , and a commonly available GPU (Quadro RTX 6000). The performance of ChromaX is compared to AlphaSimR (As a further comparison, we implement the breeding program described in Our benchmarking experiments show that ChromaX can utilize modern hardware and parallelism to run simulations many times faster than AlphaSimR, in some cases by orders of magnitude. Crucially, this will pave the way for the systematic exploration and optimization of complex designs in modern breeding programs.in silico by making some assumptions about the genetic features of the species. Second, ChromaX models additive traits and genotype-by-environment effects to simulate population phenotypes. ChromaX does not yet implement other biological effects, such as dominance or epistasis. Finally, ChromaX is designed for breeding programs of self- or open-pollinated species; other systems, such as hybrid breeding from distant heterotic groups, or monoecious/dioecious reproductive systems are not supported. Addressing these limitations will enable widespread use in a wider community.ChromaX can simulate standard breeding programs but has limitations. First, ChromaX requires a genetic linkage map with marker effects and genetic data of the populations to simulate breeding cycles. To circumvent these requirements, other programs can simulate these data


Platichthys flesus), which is closely related to the recently discovered Baltic flounder , is currently the third most commercially fished species in the Baltic Sea. The aim of this study was to obtain information on the current patterns of genetic diversity and the population structure of the European flounder and to verify whether the Baltic flounder is present in the southern Baltic Sea. Moreover, we aimed to verify whether the observed decline in the body condition indices of the species in the Baltic Sea might be associated with adaptive changes in its gene pool due to increased fishing pressure. The examined European flounder specimens displayed a high level of genetic diversity and represented a single genetic cluster. The applied molecular markers did not detect the presence of the Baltic flounder among the fish sampled from the studied area. Correlation analysis between genetic and morphological characteristics did not detect any signs of directional selection or density-dependent adaptive changes in the gene pool of the examined fish.The European flounder (Platichthys flesus), which is closely related to the recently discovered Baltic flounder , is currently the third most commercially fished species in the Baltic Sea. According to the available data from the Polish Fisheries Monitoring Center and fishermen’s observations, the body condition indices of the species in the Baltic Sea have declined in recent years. The aim of the present study was to obtain information on the current patterns of genetic variability and the population structure of the European flounder and to verify whether the Baltic flounder is present in the southern Baltic Sea. Moreover, we aimed to verify whether the observed decline in the body condition indices of the species in the Baltic Sea might be associated with adaptive alterations in its gene pool due to increased fishing pressure. For this purpose, 190 fish were collected from four locations along the central coastline of Poland, i.e., Mechelinki, Władysławowo, the Vistula Lagoon in 2018, and the Słupsk Bank in 2020. The fish were morphologically analyzed and then genetically screened by the application of nineteen microsatellite DNA and two diagnostic SNP markers. The examined European flounder specimens displayed a high level of genetic diversity . A lack of significant genetic differentiation  was observed in all the examined fish, indicating that the European flounder in the sampled area constitutes a single genetic cluster. A significant deficiency in heterozygotes  and overall deviations from Hardy–Weinberg expectations (H-WE) were only detected in fish sampled from the Słupsk Bank. The estimated effective population size (Ne) among the sampled fish groups varied from 712 (Słupsk Bank) to 10,115 (Władysławowo and Mechelinki). However, the recorded values of the Garza–Williamson indicator (M = 0.574–0.600) and the lack of significant (p > 0.05) differences in Heq > He under the SMM model did not support the species’ population size changes in the past. The applied SNP markers did not detect the presence of the Baltic flounder among the fish sampled from the studied area. The analysis of an association between biological traits and patterns of genetic diversity did not detect any signs of directional selection or density-dependent adaptive changes in the gene pool of the examined fish that might be caused by increased fishing pressure.The European flounder ( Platichthys flesus) belongs to the Pleuronectidae family that is native to the coastline of the northeastern Atlantic Ocean and the Baltic Sea [Platichthys solemdali) [The European flounder  .Gadus morhua) fishery, the European flounder has become the third most exploited species in the Baltic Sea [The European flounder is an economically important, non-quota species fished in the Baltic Sea . Accordiltic Sea . Unfortultic Sea . Other dltic Sea . In addiTo date, at least three main hypotheses have been proposed to explain the observed decline in the population size/abundance and body condition indices of flounder in the Baltic Sea. The first hypothesis proposes that this decline is caused by the increasing deterioration of environmental conditions due to climate change and eutrophication, which shrink the species’ spawning grounds; cyanobacterial blooms in the nursery grounds; and higher rates of disease occurrence in the fish caused by invasive parasitic organisms ,9. The sA research project recently launched by the National Marine Fisheries Research Institute (Poland) has indicated that the observed decrease in the flounders’ body condition indices in the Baltic Sea may be associated with environmental changes caused by climate change and anthropogenic pressure . HoweverTherefore, the purpose of the present research was to obtain information on the current genetic status of the European flounder from the southern Baltic Sea and compare it with previous reports ,15. The Oceanograf, (University of Gdańsk). The fish sampled from Mechelinki, Władysławowo, and the Słupsk Bank represented mature fish that were at least 3+ years old. The fish sampled from the Vistula Lagoon were all young and immature, aged 1+ or 2+ years, indicating that the latter sampling site may be the nursery ground of the species.In total, 190 fish were sampled from four sampling sites along the central coastline of Poland, i.e., Mechelinki (54°37′07.0″ N 18°33′01.4″ E) (N = 50), Władysławowo (54°48′17.6″ N 18°24′33.1″ E) (N = 50), the Słupsk Bank (55°02′22.6″ N 16°22′03.0″ E) (N = 50), and the Vistula Lagoon (54°21′00.0″ N 19°33′00.0″ E) (N = 40) . SamplesThe collected fish were measured to determine total body length (±0.1 cm) and mass (±0.01 g). The fish were then dissected to determine their sex and liver mass (± 0.01 g) . For theGenomic DNA were extracted and purified using the DNeasy Blood & Tissue Kit  following the manufacturer’s protocol. The quality of the isolated DNA was checked using electrophoresis in 1.5% agarose gel.StPf1001, StPf1002, StPf1003, StPf1004, StPf1005, StPf1006, StPf1015, StPf1016, StPf1022, PL142, PL167 [FLAG5-83, FLAG4-71, FLAG2-76, FLAG4-65, FLAG8-37, Nplaf-33, FLAC4-67, FLAC4-69, originated from other studies on flounder along the Atlantic coast [Nineteen microsatellites that displayed satisfactory amplification performance were used for the genetic population study on the European flounder. Eleven of these had been previously used in studies on the species in the Baltic Sea: ic coast   and varying primer concentrations, according to the recommendations described by Schuelke [PCRs were prepared in 12.5 µL volume mixtures that contained 1X GoTaqSchuelke  . Electropherograms were analyzed using Peak Scanner Software ver. 1.0 (Applied Biosystems).To analyze stock composition, we used two diagnostic SNP markers (SNPs 886_19 and 3599_4) that have been previously identified as being under divergent selection in the Baltic and European flounders . Both thAo), Shannon’s index (I), the polymorphism information content , and the inbreeding coefficient (Fis) were calculated using GenAlEx (version 6.5) and PowerMarker (version 3.25) [Ho), and the expected heterozygosity (He), as well as the Hardy–Weinberg equilibrium (H–WE) test for each locus were performed using Arlequin software (version 3.5) [Fis indicator were adjusted using the sequential Bonferroni correction [The obtained microsatellite raw data were checked for the presence of microsatellite null alleles, inconsistent values, scoring errors, and large allele dropouts using Micro-Checker software ver. 2.2.3 . The obson 3.25) ,23. The ion 3.5) . To asseion 3.5)  computerrrection .Fst) and the analysis of molecular variance (AMOVA) using Arlequin software (ver. 3.5). The genetic heterogeneity among tested fish groups was examined using ONCOR (ver. 2.0) and GeneClass (ver. 2.0) software [K) without a priori information on the origin of the examined fish. For this purpose, we used two computer programs that apply different clustering principles, i.e., Structure (ver. 2.3.4) and Flock (ver. 3.1.) [K within the sampled fish group. Ten runs were completed for each tested number of K (K = 1–8), setting the admixture model with 250,000 burn-in periods and one million Markov chain Monte Carlo (MCMC) replicates. The ΔK method of Evanno et al. [K is identified using Plateau analyses that are based on the repetition of identical cluster solutions. Additionally, the representation of the genetic structure independent of H–WE optimization––i.e., allele sharing distances (DAS), principal component analysis (PCA), and principal coordinate analysis (PCoA)––was also carried out using Populations (ver. 1.2.32) [The genetic structure and differentiation among the fish from each sampling site were assessed using the estimation of genetic differentiation index  ,31. The o et al.  was usedo et al. . Flock s 1.2.32) , R packa 1.2.32) , and GenNe) was estimated using the NeEstimator computer program (ver. 2.01) [critP = 0.02 and 0.05 was chosen, in line with Waples and Do [IAM), stepwise mutation model (SMM), and two-phase mutation model (TPM). This method assumes that the excess of expected heterozygosity (He) over expected heterozygosity under a mutation–drift equilibrium (Heq) in the population indicates the population size reduction and associated bottlenecks.To delineate the historical demography of the studied fish group, effective population size (r. 2.01) . The sins and Do . Additios and Do . For thiThe obtained DNA sequences of genomic regions bearing analyzed SNP markers were viewed using BioEdit software (ver. 7.2.5)  to recovPIC, I, Ho, Fis) to assess the possible effect of selective pressure on the genetic structure of the studied fish. For this purpose, the recorded meristic parameters of the studied fish were transformed into relative values, i.e., Fulton’s condition factor (K) and the hepatosomatic index (HSI) [K;  × 100), and the hepatosomatic index  × 100) (PCA/PCoA), correspondence analysis (DA), and discriminant function analysis (DFA), were implemented using Statistica (ver. 12.0)  and GenAlEx software. These analyses were conducted to check for any significant (p < 0.05) associations in the estimated values of genetic parameters with any of the established fish groups as a sign of possible density-dependent genetic adaptation toward body condition indices. For PCoA analysis, individual pairwise genetic matrices for all analyzed fish were calculated, then each fish in the matrix was labeled according to the series group in their division. Lastly, a 2-dimensional plot was constructed. Because fish from the Vistula Lagoon displayed significant differences in terms of cohort composition, the analysis of correspondence between biological traits was carried out for all fish except for those sampled from the Vistula Lagoon.An analysis was conducted to identify an association between biological traits  and the pattern of genetic diversity  ,41. Next) × 100) . All theThe Micro-Checker software did not detect any data consistency failures associated with stutter miscalls, scoring errors, or allelic dropout in the collected microsatellite DNA data. The estimated frequencies of possible null alleles across all the examined loci were below 5%, excluding the influence of possible amplification issues on the estimated population genetic parameters ,43. The Ao = 17.9 and 19.4. The average values of polymorphism information content (PIC) and Shannon’s index ranged from 0.832 to 0.903 and from 2.579 to 2.768, respectively. The multilocus analysis revealed significant (p < 0.05) overall values of the fixation index (Fis = 0.074–0.093) in fish sampled from Mechelinki and the Słupsk Bank. However, after Bonferroni correction, significant values were observed only in fish from the latter sampling site. The recorded values of the fixation index (0.031 and 0.036) in fish sampled from Władysławowo and the Vistula Lagoon were not significant (p > 0.05). The mean values of observed heterozygosity (Ho) and expected heterozygosity (He) varied from 0.751 to 0.815 and from 0.841 to 0.847, respectively. Significant differences between values of observed heterozygosity (Ho) and expected (He) heterozygosity were observed across four (Władysławowo and the Vistula Lagoon) and five loci (Mechelinki and the Słupsk Bank). After Bonferroni correction, significant differences were observed only in one locus (Słupsk Bank). The H–WE probability global tests detected significant (p < 0.05) deviations from the H–WE expectations in fish sampled from Mechelinki and the Słupsk Bank. These deviations remained significant after Bonferroni correction only for fish from the latter group.The mean number of alleles per locus among fish from each sampling site varied between Fst genetic differentiation between the examined groups of fish from each sampling site varied from 0.001 to 0.003, while the global test of differentiation revealed an insignificant value . The individual assignments test showed a low level of fish classification correctness for each sampling location, ranging from 26% (Władysławowo) to 40% (Słupsk Bank) . The resolved individual’s tree based on DAS genetic distances, PCoA, and PCA analyses revealed that all the examined fish formed one common genetic cluster without any signs of significant genetic differentiation , while only 0.04% occurred between fish groups originating from each sampling site. Similarly, the pairwise ntiation . Similarntiation .Ne) for all examined fish equaled Ne = 9101 . In the fish sampled from Mechelinki, Władysławowo, and the Vistula Lagoon in 2018, the estimated value of Ne equaled Ne = 10115 . In the fish sampled from the Słupsk Bank in 2020, the estimated value of the indicator was considerably lower, i.e., Ne = 712 . Significant (p > 0.05) He > Heq differences were recorded only under the infinite alleles model (IAM) and the two-phase mutation model (TPM), where nine loci  exhibited significant heterozygosity excess (p < 0.05) overall He > Heq excess only under the IAM mutation model. The mean values of the M-ratio in all examined fish groups were similar, ranging from 0.574 to 0.647. The lowest values of this indicator (M < 0.5) were recorded for FLAG5-83, FLAG4-65, FLAG8-37, Nplaf-33, Stpf1001, Stpf1002, Stpf1015, Stpf1016, and FLAC4-69  in estimated genetic parameters among the predefined series of fish groups according to sex, the values of Fulton’s condition factor, and hepatosomatic index body condition indices in all comparisons. The performed multidimensional analyses showed no association between genetic parameters and biological characteristics in the examined fish  in the flounder from all sampling sites were high, and comparable with data obtained over a decade ago from fish sampled in other parts of the Baltic Sea [The results of our study show that despite a significant increase in fishing pressure on the European flounder in the Baltic Sea over the last 20 years, the recorded values of genetic variability indicators  but significant and stable levels of genetic differentiation, supporting the existence of at least eight genetic clusters of the species in Europe, i.e., (1) Iberian Peninsula, (2) Bay of Biscay, (3) English Channel, (4) North Sea and Irish Sea, (5) Baltic Sea, (6) Faroe Islands, (7) central coast of Norway, and (8) northern coast of Norway [Fst = 0.005–0.015) have been reported among European flounder populations in the North Sea and Irish Sea [The available studies indicate that the genetic structure of flounder populations along the European coastline from the Bay of Biscay to northern Norway is mainly shaped by the latitudinal temperature gradient; however, factors such as migratory behavior, the strict association of juveniles with estuaries or freshwater habitats, and the adaptive response to environmental pollution may also play an important role in shaping the genetic structure of the species across its distribution range ,60,61,62f Norway ,52,63,64rish Sea . The resrish Sea .Ne) of marine fish populations is frequently several times lower than census data. This makes them particularly vulnerable to the loss of genetic diversity due to population size bottlenecks caused by the impact of increased fishing pressure or changes in the environment [Ne) of fish from Władysławowo, Mechelinki, and the Vistula Lagoon sampled in 2018 was high, and comparable with the previously estimated values of Ne for flounder populations in the North and Irish Seas [Ne for fish sampled from the Słupsk Bank in 2020 displayed visibly lower values of this indicator, i.e., Ne = 712. This finding might indicate a change in the population size of European flounder in the southern Baltic Sea. However, this conclusion is very speculative and requires further investigation. Interestingly, a similar number of differences in Ne values was reported in overexploited plaice populations from Iceland (Ne = c.a. 2000) and landlocked populations of Atlantic cod from Mogilnoe Lake (Ne = c.a. 200), where the decrease in this parameter was assumed to be accelerated by the fish non-random mating and the associated cryptic mating structure [In contrast to freshwater fish species, the estimated effective population size  and expected heterozygosity (He) may indicate the presence of a recent demographic disturbance [Fis = 0.093, p < 0.05), suggesting a recent reduction in population size in the sampled area. However, the recorded values of the Garza–Williamson indicator (M = 0.574–0.600) and the lack of significant differences of Heq > He under the SMM model do not support this conclusion [The uncoupling between observed heterozygosity  between fish sampled in 2018 and those sampled in 2020 might indicate recent changes in the population size of the European flounder in the southern Baltic Sea. However, this conclusion is speculative and requires further studies to be verified. The results do not support the hypothesis of adaptive genetic changes in the flounder’s gene pool toward a limited growth rate, body size, and/or reduced population body condition indices caused by fishery-mediated heavy selective pressure on the species’ populations in the southern Baltic Sea. Our study did not detect the presence of the Baltic flounder in the southern Baltic Sea. Broader geographic and time scale research based on more sensitive genetic analysis methods, such as whole genome sequencing or gene expression, is required to provide more detailed data on the European flounder’s status in the Baltic Sea.In the present study, the recorded levels of genetic diversity among the studied European flounder from the southern Baltic Sea were high and temporally stable in relation to the data published about a decade ago. Moreover, the recorded levels of homozygosity were similar to previously reported genetic data. The results indicate that the European flounder from the southern Baltic Sea represent a single and homogenous genetic cluster. Significant differences in the effective population sizes (


The production of animal-based foods from native breeds have a synergistic relationship with the regional culture, the local climate, and mainly the maintenance of alternative genetic resources for a system with a lower environmental impact. Thus the efficiency of conservation and production depends on assessing the variability of these local breeds. In the case of Curraleiro Pé-duro cattle, the most adapted individuals have undergone natural selection over five hundred years in the Brazilian savannas, mating with little or no human interference. The peculiarities of these biomes, where the regional flora is the food base and cattle is raised in extensive areas, likely influenced the genetic composition of the different groups that make up the first cattle breed of Brazil.To evaluate the composition, diversity, variation, differentiation, and genetic structure of the populations studied, samples of hair follicles from 474 individuals of different animal categories  from three farms, defined as subpopulations “A”, “B”, and “C”, were collected. The animals were genotyped for 17 microsatellite markers using a DNA sequencer. After verification of monomorphic alleles, alleles outside the expected size range, and for the presence of stutter bands, the results were subjected to statistical analysis.STranging from 0.00723 and 0.03198; p-values < 0.05). However no significant differences among herds where found with the Mantel test based on geographic distances. The formation of genetic clusters of all animals sampled with the software Structure resulted in minimum cluster values, with two main genetic groups (K = 2) observed among the evaluated animals. Therefore, based on PIC and heterozygosity values, a wide genetic diversity was observed, despite little differences in population structure  among sampling sites.The markers used were suitable for the proposed application with a mean Polymorphism Information Content (PIC) of 0.62. On average, the effective alleles were 4.25 per marker, with mean heterozygosities of 0.74 (observed and expected), which was lower in herd A (0.70) in comparison to herds B (0.77) and C (0.74). The analysis of molecular variance (AMOVA) revealed a higher rate of variation within herds (98.5%) and lower among herds (1.5%) (F Food security once again has become a global priority, as the world population is estimated to peak in the next 40 years, requiring food to be produced in quantity and quality accessible to 9.7 billion people, while preserving genetic resources . Among aThe clustering of similar individuals and the consequent emergence of genetic groups of domestic animals are associated with human and environmental dynamics. The Curraleiro Pé-duro (CPD) is descendant of the first cattle raised in the Americas and part of the history of colonization of the cerrado and caatinga . These bThe types of crossing and environmental variables may affect the population structure or population subdivision of these naturally occurring groups, with genetic differentiation due to variations in allele frequencies among different subpopulations. Many factors can influence the formation of subpopulations including geographic location and parental origin .The conservation and use of genetic resources require genetic diversity. The Curraleiro Pé-duro (CPD) cattle are genetically distinct from other breeds, with substantial differences mainly due to its interaction with the environment .Also, nearly 500 years of uncontrolled mating with the formation and extinction of subpopulations had repercussions on its current genetic composition .The genetic diversities of animal resources can enhance or limit their conservation and/or use for food production in the face of global challenges. This study was aimed at evaluating the composition, diversity, variation, differentiation and genetic structure of the Curraleiro Pé-duro cattle raised in the states of Maranhão and Piauí, where it originated and evolved.The sampling of the specimens used in this study was designed to avoid the Wahlund effect , and conHair follicle samples from males and females were taken from various categories , with the adults registered as base herd with the Associação Brasileiro de Criadores de Curraleiro Pé-duro.https://doi.org/10.5281/zenodo.7115391The database is available at The three herds visited  are participants in the genetic improvement program of the breed, conducted by ABCPD, the Federal University of Piauí (FUP), and the State Univerity of Piauí (UESPI). This research project has been approved by the Research Ethics Committee of the Federal University of Piauí under #683/21 CEUA-UFPI. Farm “A” is located in the Carnaubais region of Piauí , Farm “B” in the Cocais region of Maranhão  and Farm “C” in the Sambito Valley of Piauí . Each farm represents a subpopulation, and exchanges of a small number of individuals occur irregularly and sporadically among farms, during the process of commercialization of animals.® program, generating a file with the alleles present in each animal, for each marker tested.PCR products were obtained with primers marked with fluorophores and, after purification, were examined with a Thermo Fisher ABI 3730 DNA analyzer . Fragment size was determined with the GeneMapperThe genotyping of the animals was outsourced to Laboratório Raça , accredited by the Ministry of Agriculture, Livestock and Supply, Brazil (MAPA).Seventeen microsatellite markers were used  followinMicro-Checker 2.2.3 software  was usedThe number of alleles (Na), effective alleles (Ne), and expected (He) and observed (Ho) heterozygosity were estimated with the GenAIEx 6.5 software .Wright’s F statistics  were carTo examine differences among individuals and populations, the analysis of molecular variance (AMOVA) by Genetic differences among populations were examined as a function of the geographic distances among them using the Mantel test. Geographic distances among populations  were logStructure 2.3.4  was usedBecause animals are exchanged among properties (subpopulations), the program was adjusted to run the admixture model (Admixture) that aims to infer a single Alpha and use it for all populations. However, it was tested with an alpha estimate for each population (farm) .The results were analyzed using the main interpretive methodology by The graphical representation of the results was carried out using the Structure Selector software .Seventeen microsatellite loci showed high level of polymorphism to evaluate the genetic diversity of the sampled populations , with a Indications of null alleles were identified in only a portion of the loci used and in low incidence (The estimated expected heterozygosity (He) was 0.74 .Based on the number of heterozygous individuals, the observed heterozygosity (Ho) was estimated, and was observed to be same as He.The fixation indices proposed by Wright  are showFIS is the product of the difference between He and Ho in relation to He, it indicates whether there are changes in population structure. Using a population in Hardy-Weiberg equilibrium (E-HW) as a model, the expected heterozygosity of the subpopulations  was higher than expected in a theoretical population  revealed the degree of genetic structure of the subpopulations sampled .The 98.5% of the variation arising from individuals within populations suggests that there is greater variation within the herd than among herd. Thus herds are relatively similar, with greater variation occurring among the individuals that make up each herd. This condition mischaracterizes the degree of genetic structuring among sampled population.FST values found were significant . Despite being low, the value observed for the population of Maranhão was higher than that of the populations of Piauí compared between them ; p-values <0.05)FST . It should be pointed out that there are no other variables that notoriously affect the distance among groups (subpopulations). Therefore, there is no evidence of isolation by geographic distance.Although almost the entire genetic distance among groups is explained by geographic distance, it is not statistically significant  indicating the same cluster, following Therefore, in both methods, Considering two main groups, the participation of each individual  within each rectangle was evaluated into subgroupings in The genetic compositions of each of the subpopulations within each cluster is shown in F , compromising the estimation of the causes of deviations from the expected Hardy–Weinberg genotypic proportions, among others  is the most frequently used indicator for genetic diversity analysis, as it is associated with other non-estimated indicators such as the proportion of polymorphic loci because they are interrelated . Since aBos taurus indicus) and inbreeding within herds, an unfavorable condition for genetic diversity. In the case of CPD, heterozygosity was higher (0.74), indicating a greater level of variation.This discussion is relevant, and expansion with new microsatellites to replace less informative markers should be carried out. Investigations like the present one applied little-used markers, that in the case of CPD were very informative (estimated PIC), such as five of the loci used .FIS per loci reported by Comparing with the results of Artificial selection  tends to interfere with genetic diversity, which was not observed. Reduced inbreeding rates was found in the sampled population, similar to that identified by FST, with the mean among the 17 markers being 0.015 (± 0.010), which according to the classification by The locus ETH10 had the highest estimated Other researchers working with the same breed (CPD) identified the presence of three genetic groups k = 2). However, k = 2).The number of K can be differ depending on the methodology and the most common one is based on ΔK, used in all studies cited  and thatClustered alleles in the two subpopulations comprise the genotype of individuals from the three different populations (farms) sampled. There are sets of lines representing individuals with a single predominant color, for example, at two occasions in population A and another in population C with individuals grouped in purple . At these points you have close individuals. This pattern can initially be visualized when Δk =3 and is maintained throughout the simulations of relatively individualized clusters from the others, suggesting secondary additional clusters in the sampled populations.These clusters are notorious in studies with individuals of different breeds, in which clusters almost always make up a different race from the others. In the case of animals of a single breed, clusters indicate conditions for grouping individuals within the same breed into different groups based on similarities.It should also be noted that the clusters were not predominant in any of the subpopulations, with specimens of all clusters occurring in all sampled farms.The microsatellite markers used by MAPA (Ministério da Agricultura e Pecuária - Brazil) to assess kinship in bovine species were highly polymorphic and suitable for studies of structure and genetic diversity.Some little known markers proved to be highly efficient in the variability of genotypes among the population studied.FIS).Heterozygosities were high for all loci in the three subpopulations, including excess heterozygosity in some cases, which resulted in low inbreeding rates  for the genotyped animals (rows). Format compatible with some software, such as Structure Click here for additional data file.10.7717/peerj.14768/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.14768/supp-3Supplemental Information 3Click here for additional data file.