The fear of hypoglycemia in type 2 diabetes mellitus (T2DM) patients with hypoglycemia has seriously affected their quality of life. They are always afraid of hypoglycemia and often take excessive action to avoid it. Yet, researchers have investigated the relationship between hypoglycemia worries and excessive avoiding hypoglycemia behavior using total scores on self-report measures. However, network analysis studies of hypoglycemia worries and excessive avoiding hypoglycemia behavior in T2DM patients with hypoglycemia are lacking.The present study investigated the network structure of hypoglycemia worries and avoiding hypoglycemia behavior in T2DM patients with hypoglycemia and aimed to identify bridge items to help them correctly treat hypoglycemia and properly deal with hypoglycemia fear.A total of 283 T2DM patients with hypoglycemia were enrolled in our study. Hypoglycemia worries and avoiding hypoglycemia behavior were evaluated with the Hypoglycemia Fear Scale. Network analyses were used for the statistical analysis.B9 “Had to stay at home for fear of hypoglycemia” and W12 “I am worried that hypoglycemia will affect my judgment” have the highest expected influences in the present network. In the community of hypoglycemia worries, W17 “I worry about hypoglycemia during sleep” has the highest bridge expected influence. And in the community of avoiding hypoglycemia behavior, B9 “Had to stay at home for fear of hypoglycemia” has the highest bridge expected influence.Complex patterns of associations existed in the relationship between hypoglycemia worries and avoiding hypoglycemia behavior in T2DM patients with hypoglycemia. From the perspective of network analysis, B9 “Had to stay at home for fear of hypoglycemia” and W12 “I am worried that hypoglycemia will affect my judgment” have the highest expected influence, indicating their highest importance in the network. W17 “I worry about hypoglycemia during sleep” aspect of hypoglycemia worries and B9 “Had to stay at home for fear of hypoglycemia” aspect of avoiding hypoglycemia behavior have the highest bridge expected influence, indicating they have the strongest connections with each community. These results have important implications for clinical practice, which provided potential targets for interventions to reduce hypoglycemia fear and improve the quality of life in T2DM patients with hypoglycemia.The online version contains supplementary material available at 10.1186/s12888-023-04698-9. Diabetes is characterized by hyperglycemia and can cause vascular and neurological damage, which seriously threatens the health of patients and contributed significantly to the global disease burden , 2. As rAmong the complications of type 2 diabetes, hypoglycemia is a common and potentially serious complication of diabetes , 10 whicThere are many forms of hypoglycemia fears. ‘Hypoglycemia worries’ is an important aspect of hypoglycemia fears, which refers to the patient’s concern about hypoglycemia after experiencing it, and fear of developing again. Research shows that fear of hypoglycemia is closely related to worry, that is, the more fear of hypoglycemia, the more serious the anxiety of diabetes patients . BesidesNetwork analysis can reduce these problems by fitting data with Gaussian image model . It is bTherefore, we will rely on a network analysis approach to analyze the relationship between hypoglycemia worries and avoiding hypoglycemia behavior in T2DM patients with hypoglycemia. We constructed the network model and estimated the bridge centrality to detect the important role of some specific aspects of the effect of hypoglycemic worries on avoiding hypoglycemic behavior and identified the variables connecting these two communities to reduce hypoglycemic worries and avoid hypoglycemic behavior excessively in T2DM patients with hypoglycemia.A total of 300 T2DM patients with hypoglycemia from 2 hospitals participated in a cross-sectional survey between October 2022 and December 2022; the T2DM patients were recruited via the convenience sampling method. Patients who met the following inclusion criteria were included in the research: 1) were diagnosed with type 2 diabetes; (2) without hearing or reading impairment or without cognitive dysfunction (since the research data was collected by answering questionnaires); (3) suffered from hypoglycemia within half a year (in order to avoid memory bias); (4) gave informed consent and volunteered to participate in the research. The exclusive criteria were patients with type 2 diabetes who were not using drugs with high hypoglycemic risks, such as insulin or sulfonylureas. The general information of T2DM patients is shown in Table  were diaAccording to the calculation, the sample size was at least 5 times of the number of items in the questionnaire. There were 33 variables in the study. Therefore, the calculation formula of sample size was N = 33*5 = 165, which meant that at least 165 subjects were required for this study.Before the investigation, purpose of the study was explained to the participants and verbal consent was retrieved from the participants. Besides, the participants also signed the informed consent form. In the process of the investigation, participants could terminate and withdraw from the investigation at any time and the questionnaire was completed anonymously. They were assured that their information would only be used for research. According to the voluntary principle, 300 T2DM patients with hypoglycemia participated in the survey. The digital version of the questionnaire was issued to the participants. Among the 300 T2DM patients, 13 dropped out of the research, and 4 did not complete the questionnaire. Eventually, 283 questionnaires were valid, with an effective response rate of 94.33%.The Chinese version of the Hypoglycemia Fear Survey II (HFS-II) was developed by Cox . It was We adopted SPSS 26.0 and R 4.1.1 to analysis the collected data. SPSS 26.0 software was used to analyze sociodemographic characteristics, hypoglycemia worries, avoiding hypoglycemia behavior and other descriptive data. R 4.1.1 software was used to construct the network model and estimate the bridge centrality to identify the bridge items between hypoglycemia worries and avoiding hypoglycemia behavior.bootnet [The network of hypoglycemia worries and avoiding hypoglycemia behavior was constructed by the R package qgraph . Least Abootnet .bootnet, and the stability was quantitatively evaluated by the correlation stability coefficient (CS-coefficient). The value of correlation stability coefficient preferably should be above 0.5 and should not be below 0.25 [The bridge centrality was estimated by R package networktools . The brilow 0.25 .Among the 283 T2DM patients with hypoglycemia, the mean (SD) age was 47.64 ± 11.24 years. 54.42% were male, and 45.58% were female. 31.45% had a primary school education or below, 44.17% had a junior or senior high school education, and 24.38% had a bachelor education or above. 26.50% patients had diabetes for less than 3 years, 24.38% had diabetes for 3 ~ 5 years, 31.45% had diabetes for 6 ~ 10 years, 17.67% had diabetes for more than 10 years. 32.86% had hypoglycemia once or twice in half a year; 43.82% had hypoglycemia three to six times in half a year; 23.32% had hypoglycemia more than six times in half a year. Other general demographic data are shown in Table The network model is shown in Fig. In the edges across communities, W1 was positively correlated with 2 nodes of the avoiding hypoglycemia behavior community, namely, B9 and B10, among which the correlation with B10 was stronger (edge weight: 0.07). W3 was positively correlated with 3 nodes of the avoiding hypoglycemia behavior community, namely, B7, B9, and B15, among which the correlation with B15 was the strongest (edge weight: 0.08). W4 was positively correlated with 1 node of the avoiding hypoglycemia behavior community, namely, B14 (edge weight: 0.02). W6 was positively correlated with 3 nodes of the avoiding hypoglycemia behavior community, namely, B7, B8, and B10, among which the correlation with B10 was the strongest (edge weight: 0.03). W7 was positively correlated with 3 nodes of the avoiding hypoglycemia behavior community, namely, B7-B9, among which the correlation with B9 was the strongest (edge weight: 0.10), and was negatively correlated with B1 of the avoiding hypoglycemia behavior community (edge weight: -0.08). W8 was positively correlated with 1 node of the avoiding hypoglycemia behavior community, namely, B2 (edge weight: 0.05). W9 was positively correlated with 1 node of the avoiding hypoglycemia behavior community, namely, B14 (edge weight: 0.01). W10 was positively correlated with 3 nodes of the avoiding hypoglycemia behavior community, namely, B8, B9, and B15, among which the correlation with B15 was the strongest (edge weight: 0.07). W12 was positively correlated with 2 nodes of the avoiding hypoglycemia behavior community, namely, B9 and B10, among which the correlation with B10 was stronger (edge weight: 0.11). W13 was positively correlated with 2 nodes of the avoiding hypoglycemia behavior community, namely, B7 and B9, among which the correlation with B9 was stronger (edge weight: 0.06). W14 was positively correlated with 4 nodes of the avoiding hypoglycemia behavior community, namely, B2, B7, B11 and B15, among which the correlation with B15 was the strongest (edge weight: 0.06). W15 was positively correlated with 5 nodes of the avoiding hypoglycemia behavior community, namely, B6, B9, B11, B13 and B15, among which the correlation with B15 was the strongest (edge weight: 0.07). W16 was positively correlated with 2 nodes of the avoiding hypoglycemia behavior community, namely, B9 and B10, among which the correlation with B10 was stronger (edge weight: 0.06). W17 was positively correlated with 4 nodes of the avoiding hypoglycemia behavior community, namely, B5, B6, B13 and B14, among which the correlation with B5 was the strongest (edge weight: 0.20). See Supplementary Table 1 of the supplemental material for more detailed information on the correlations among nodes in the network. For edges in hypoglycemia worries community, there were 92 edges ranging from < 0.01 to 0.43, and the strongest correlation was between W12 and W16. For edges in the avoiding hypoglycemia behavior community, there were 90 edges ranging from − 0.03 to 0.52, and the strongest correlation was between B13 and B14.In the network of hypoglycemia worries and avoiding hypoglycemia behavior, the 95% confidence interval of edge weights was narrow, which indicated that the accuracy of edge weights was acceptable . As the result of the difference significance test of the edge weight showed, the edge weight between W17 and B5 was the largest among the cross-community edges .P < 0.05).The node expected influence is shown in Fig. The node bridge expected influence is shown in Fig. Employing network analysis, we aim to reveal potential pathways that how different components of hypoglycemia worries are related to the avoiding hypoglycemia behavior. It is observed that different items of hypoglycemia worries are commonly but differentially related to items of avoiding hypoglycemia behavior. The results suggest that different items of hypoglycemia worries may have similar and specific pathways leading to avoiding hypoglycemia behavior. In some extent, our finding adds to emerging research showing that how hypoglycemic worries affects hypoglycemic avoidance behavior and how they interact.Within components of hypoglycemia worries, the present study finds a strongest exists between W12 “I am worried that hypoglycemia will affect my judgment” and W16 “I am worried that the occurrence of hypoglycemia will disrupt some important things I am doing”. These two items are the influence of hypoglycemia on individual cognitive ability and behavior. This is consistent with the results of Lara Coelho’s research, which shows that individuals’ cognitive impairment will limit their actions . The othW12 “I am worried that hypoglycemia will affect my judgment” of hypoglycemia worries and B9 “Had to stay at home for fear of hypoglycemia” of avoiding hypoglycemia behavior have the highest expected influences which indicates these two variables may play the most important role in the present hypoglycemia fear network consisting 18 items of hypoglycemia worries and 15 items of avoiding hypoglycemia behavior. These two variables indicate that they may be a good breakthrough point to reduce T2DM patients’ fear of hypoglycemia. The occurrence of hypoglycemia in diabetes patients will bring many serious problems, such as the damage to cognitive function and judgment , 48. ThiIn the current network, node bridge strength centrality may cast light on the specific role played by these different items of hypoglycemia worries in the context of avoiding hypoglycemia behavior. In the community of hypoglycemia worries, W17 “I worry about hypoglycemia during sleep” has the highest bridge expected influence. This suggests that W17 “I worry about hypoglycemia during sleep” has stronger associations with avoiding hypoglycemia behavior community than other items. Thus, from a network perspective, targeting W17 “I worry about hypoglycemia during sleep” may be more effective at reducing excessive avoiding hypoglycemia behavior than targeting other items of hypoglycemia worries. Therefore, clinical medical staff should help diabetes patients to keep their blood sugar stable through scientific medication and correct monitoring, and educate patients to eat dinner to prevent nocturnal hypoglycemia , 53. In The present study provides a fine-grained understanding of the relations between hypoglycemic worries and excessive hypoglycemic avoidance behavior. We broke through the existing research to take hypoglycemic fear as a whole and explore the relationship between hypoglycemic worries and excessive hypoglycemic avoidance behavior from its internal perspective. All T2DM patients should be encouraged to discuss their experiences with hypoglycemia without judgment or shame. Glucose targets, testing schedules and treatment plans should be reviewed often and individualized to the minimize risk of hypoglycemia. Medical staff should eliminate diabetes patients’ hypoglycemia worries, reduce excessive hypoglycemic avoidance behaviors, and help patients improve their quality of life.There are some limitations to our study. Firstly, our study is conducted in the form of self-report questionnaire, and the results are relatively subjective. Secondly, our study chose the convenient sampling method for the reason that there are too many hospitals in China and the research fund of our study is limited. In future studies, we will choose stratified random sampling method, which may provide a more scientific result, especially in the massive investigation. Lastly, the network in this study estimated between-subject effects on a group level. Thus, it is possible that characteristics, such as centrality and network structure, may not remain the same on an individual level.In summary, our study provides an illustration of the richness and complexity of the associations involved in the structure of hypoglycemia worries and excessive avoiding hypoglycemia behavior in T2DM patients with hypoglycemia. In the network, different aspects of hypoglycemia worries were correlated with different aspects of excessive avoiding hypoglycemia behavior. From the perspective of network analysis, B9 “Had to stay at home for fear of hypoglycemia” and W12 “I am worried that hypoglycemia will affect my judgment” have the highest expected influence, indicating their highest importance in the network. W17 “I worry about hypoglycemia during sleep” aspect of hypoglycemia worries and B9 “Had to stay at home for fear of hypoglycemia” aspect of avoiding hypoglycemia behavior have the highest bridge expected influence, indicating they have the strongest connections with each community. These results have important implications to clinical practice, which provided potential targets for interventions to reduce hypoglycemia fear and improve the quality of life in T2DM patients with hypoglycemia.Below is the link to the electronic supplementary material.Supplementary Material 1


Among these SNPs, S9_137746077, which is located upstream of the gene GRMZM2G378852 that encodes a MAPKKK protein kinase, was significantly associated with total seedling dry weight, with the same allele increasing root length and root surface area under P deficiency. The C allele of S8_88600375, mapped within GRMZM2G044531 that encodes an AGC kinase, significantly enhanced root length under low P, positively affecting root surface area and seedling weight. The broad genetic diversity evaluated in this panel suggests that candidate genes and favorable alleles could be exploited to improve P efficiency in maize breeding programs of Africa and Latin America.Maximizing soil exploration through modifications of the root system is a strategy for plants to overcome phosphorus (P) deficiency. Genome-wide association with 561 tropical maize inbred lines from Embrapa and DTMA panels was undertaken for root morphology and P acquisition traits under low- and high-P concentrations, with 353,540 SNPs. P supply modified root morphology traits, biomass and P content in the global maize panel, but root length and root surface area changed differentially in Embrapa and DTMA panels. This suggests that different root plasticity mechanisms exist for maize adaptation to low-P conditions. A total of 87 SNPs were associated to phenotypic traits in both P conditions at −log Food requirements to support continuous population growth are a great challenge for agricultural production. Abiotic stresses are important constraints for global food security, including low phosphorus (P) availability in the soil, which is the second most limiting nutrient for grain yield, after nitrogen . P is a Plants developed several strategies to withstand P deficiency  that canRoots perform important functions in plant structure, anchorage and nutrition, showing high diversity in morphology, and dynamic responses to biotic and abiotic stimuli ,16. ThusPhosphorus-starvation tolerance 1 (PSTOL1), which encodes a serine/threonine kinase that enhances early root growth and P acquisition in rice , and was placed within the predicted gene GRMZM2G037472 that encodes a kinesin motor domain containing protein, putatively involved in microtubule motor. The SNP S8_88600375 was associated with RL under low-P [–log10 = 6.30] and was located within the predicted gene GRMZM2G044531, which encodes an AGC kinase family protein.The SNP S4_3751192 was significantly associated with RD under high-P levels [−log10 = 6.43] was located 57 bp upstream the gene GRMZM2G378852 that encodes a putative MAPKKK family protein kinase. The SNP S10_77284783 was mapped within the predicted gene GRMZM2G110145, a putative cellulose synthase-like family protein. The change in the cellulose synthase pathway was one of the mechanisms for low-P tolerance in maize revealed by a combination of GWAS and transcriptome analyses , and shared a high LD  with S8_88600375.The genomic region of approximately 600 kb on maize chromosome 8 (bin 8.03) encompassing the SNP S8_88600375, which was highly associated with RL under low-P levels and presented a consistent peak in the Manhattan plot , is show from it . One of r2 threshold of approximately 0.2, which was similar to the extent of LD obtained in the entire collection of maize lines conserved at the USDA [r2~0.2) and faster within tropical materials, reaching 1 kb for the same r2 [Maize is a cross-pollinated species that normally presents fast LD decay  that is the USDA  and to tthe USDA . A large same r2 . The LD ARIA—Automatic Root Image Analyzer [The root traits  were characterized in nutrient solution with images analyzed using RootReader2D and WinRHIZO software, and the seedlings were measured for total seedling dry weight (TDW) and P content (PCont). Interestingly, the range of phenotypic values for RL (20.8–515.5 cm) and TDW (15.3–220.3 mg) were very similar to the ones obtained for 384 temperate maize lines belonging to Ames panel (RL: 16.4–536.3 cm and TDW: 16–253 mg), whose root systems were evaluated with the Maize lines from Embrapa significantly modified the root morphology under P deficiency that showed longer (increased RL and SA) and finer roots (reduced RD) compared with the high-P conditions. These changes in root morphology are expected to improve P acquisition efficiency due to the low mobility of phosphates in the soil, which could enhance the adaptation to low-P soils, prevalent in the tropical regions of Brazil. The ability to overcome low-P availability has been associated with the exploratory capacity of roots in the soil ,12,13,43Nonetheless, maize lines from DTMA showed reduced RL and SA under P deficiency, which could be explained by other mechanisms, considering a much larger genetic variance compared with the Embrapa panel. Different plasticity in response to varying soil P concentrations indicates diversification of P acquisition strategies across species . AdaptatIn the global and Embrapa panels, TDW and PCont was enhanced under high-P compared with low-P treatment, probably due to the higher P availability in the nutrient solution for biomass accumulation at the seedling stage. The superior biomass accumulation and P content under high-P availability was consistent with the positive correlation between these traits under both P treatments. The large genetic variance in the Embrapa panel for these traits contributed to the significant effect of P in the global panel.Diameter is an important feature of root systems in plants. The SNP S4_3751192 that reduced RD under high-P conditions tended to improve TDW and PCont , in agreBrassica napus [2:3 population cultivated under P deficiency [Increases in root length and root surface area enhanced P acquisition in ca napus , rice [3ca napus , sorghumca napus ,53,54, mca napus  and barlca napus . The C aca napus , to promca napus  and to rca napus . This SNficiency .The A allele of the SNP S9_137746077 significantly improved TDW, and contributed positively with RL and SA under P deficiency . This SNThe SNPs S9_137746077 and S8_88600375 reflected the positive correlations of root morphology traits (RL and SA) with biomass accumulation (TDW) under P deficiency . These pr2 = 0.18 in 1 kb), similarly to the overall mean of LD decay along the genome (r2 = 0.2). However, this SNP is in high LD (r2~0.77) with three SNPs within the predicted gene GRMZM2G057116 (WRKY transcription factor) that were separated by ~127 kb of S8_88600375 (The genomic region on maize chromosome 8 (bin 8.03) flanking the SNP S8_88600375 (GRMZM2G044531 for AGC kinase), which was highly associated with RL under low-P levels, presented a low LD  project. The maize lines from Embrapa included advanced breeding lines and founder parents of populations contrasting for yield performance under low P ,67 as wev/v) for 5 min and germinated in moistened paper rolls. After four days, the endosperm was removed and seedlings were transferred to moistened blotting paper placed in paper (24 × 33 × 0.020 cm). Each container accommodated 10 paper pouches with three uniform seedlings per pouch, whose bottoms were immersed in a half strength Magnavaca’s nutrient solution [−2 s−1.Root morphology traits in the panel were assessed using a paper pouch system in hydroponics as described in . Maize ssolution  with 2.5http://www.plantmineralnutrition.net/rootreader.htm, accessed on 17 November 2014) and WinRHIZO  software. The imaging system consists of a Nikon  D300s digital SLR Camera with 60 mm macro lens, calibrated for a fixed focal plane scale of 140 pixels cm–1. The root systems of each plant were spread out in a clear, water-filled tray that was illuminated from below and individually imaged. The captured images were batch-converted to an eight-bit grayscale format using a custom written ImageJ  plugin. The images were then batch-thresholded using the RootReader2D software and the images imported into the WinRHIZO software for root trait analysis and quantification. The images were analyzed using a calibration grid as a reference scale and changing the input settings to pale roots on a black background.After 13 days, the root images were captured with a digital photography setup and analyzed using RootReader2D  and average root diameter . The final measurements were the average of three replicates. These root traits were selected due to their relevance to represent the root system at seedling stages under P deficiency, as previously discussed in maize [Out of 24 different root traits generated by the software, only three were used in our study. The total root system, which comprises primary, seminal and initial adventitious roots, was evaluated for total root length , total root surface area . For the P concentration analysis, plant tissues were digested by nitric perchloric acid and quantified using inductively-coupled argon plasma (ICP) emission spectrometry. Total phosphorus content (PCont) was calculated by multiplying total seedling dry weight and P concentration.The maize lines were phenotyped in three different experiments, containing 192, 181 and 212 genotypes, with 12 common checks. The experimental design was in groups of experiments, with each experiment arranged in randomized complete blocks with three replicates, under low- and high-P conditions.i = 1…561), in the j = 1, 2, 3), of replicate k ; i at experiment j, including the effects of lines and common checks; Ej is the fixed effect of experiments; k at experiment j; and ijk~N for each trait and P level were used to obtain the adjusted means using GenStat 16.1  accordinDifferent structures of variance–covariance (VCOV) matrices were compared for the residual effect and the VCOV model with heterogeneous variance and uniform covariance among experiments was selected based on AIC (Akaike Information Criterion) . DiagnosThe broad-sense heritability  and PearAdditionally, joint analyses of variance were performed to test the effects of P levels within each panel (Embrapa and DTMA) and within the global panel. Phenotypic variability within panel and overall were examined through boxplots. All these phenotypic analyses were performed using GenStat 16.1 .ApeKI restriction enzyme for library preparation [Genomic DNA was isolated from young leaves using the CTAB method  and submparation  at the Gparation , based oFor association mapping, missing data in the 955,120 SNPs were imputed using the Fast Inbred Line Library Imputation (FILLIN) method in TASSEL version 5.2.21 , with thPopulation structure and familial relatedness analyses were carried out with 12,700 randomly selected non-imputed SNPs, filtered for less than 10% of missing data and for MAF ≥ 1%. Principal component analysis was used to study population structure in the maize association panel using the software TASSEL version 5.2.21 . The firr2 values for SNPs separated by pairwise distances ranging from 1 bp to 1 Mb were calculated and displayed in a violin plot per chromosome and for all 10 chromosomes. The resulting models were depicted using the package vioplot [LD decay  is y is a vector of phenotypic values, β is a vector of the fixed effects of markers, e is a vector of residual effects, v is a vector of fixed effects related to population structure and u is a vector of polygene background random effects related to familial relatedness. X and Z are the incidence matrices of 0 s and 1 s, relating to β and u, respectively, to y. Q is a matrix of population structure matrix obtained by the principal component analysis and K is the kinship (co)variance matrix between pairs of inbred lines [10 distributions.Four linear models were tested to control for false positive associations (type-I error). The naïve model is a general linear model (GLM) with no correction for population structure or familial relatedness: ed lines ,76. ModeThe Bonferroni procedure was used for multiple testing correction, considering a significance level (α) of 0.05 and the number of independent tests, which were estimated based on the number of independent LD blocks along the maize chromosomes according to . CandidaThe contrasting levels of P modified root morphology traits, biomass and P content in the global maize panel, but root length and root surface area changed differentially within Embrapa and DTMA panels, suggesting different root plasticity mechanisms for adaptation to low-P conditions. Out of 353,540 GBS-based SNPs, only seven reached the Bonferroni significance threshold, including two SNPs significantly associated with total seedling dry weight and with root length under P deficiency, which tagged a MAPKKK and an AGC protein kinases. These associations enforce the importance of protein kinases in root development changes modulated by P deficiency, such as those found for PSTOL1, a protein kinase that enhances root growth and P acquisition in rice and sorghum. The diversity panel composed by maize elite lines from Embrapa and DTMA suggest that elite lines and favorable alleles could be exploited to improve P efficiency in maize breeding programs of Africa and Latin America.


Hair loss disorders such as androgenetic alopecia have caused serious disturbances to normal human life. Animal models play an important role in exploring pathogenesis of disease and evaluating new therapies. NIH hairless mice are a spontaneous hairless mouse discovered and bred in our laboratory. In this study, we resequenced the genomes of NIH normal mice and NIH hairless mice and obtained 3,575,560 high-quality, plausible SNP loci and 995,475 InDels. The Euclidean distance algorithm was used to assess the association of SNP loci with the hairless phenotype, at a threshold of 0.62. Two regions of chromosome 18 having the highest association with the phenotype contained 345 genes with a total length of 13.98 Mb. The same algorithm was used to assess the association of InDels with the hairless phenotype at a threshold of 0.54 and revealed a region of 25.45 Mb in length, containing 518 genes. The mutation candidate gene Lama3  was selected based on the results of functional gene analysis and mutation prediction screening. Lama3 (R217C) mutant mice were further constructed using CRISPR/Cas9 technology, and the relationship between Lama3 point mutations and the hairless phenotype were clarified by phenotypic observation. The results showed that male Lama3 point mutation mice started to lose hair on the 80th day after birth, and the hair loss area gradually expanded over time. H&E staining of skin sections showed that the point mutation mice had increased sebaceous glands in the dermis and missing hair follicle structure . This study is a good extension of the current body of knowledge about the function of Lama3, and the constructed Lama3 (R217C) mutant mice may be a good animal model for studying androgenetic alopecia. There are numerous human hair-related diseases, including alopecia, alopecia areata, and structural abnormalities of the hair shaft and hirsutism, which can cause serious disturbances to normal human life2. Androgenetic alopecia (AGA), is one of the most common hair loss disorders worldwide. AGA is a polygenic recessive disorder, but the gene for its pathogenesis has not been identified. AGA is highly heterogeneous in terms of severity, age of onset, and location of hair loss3. Currently, the main treatments for AGA include hair transplantation surgery and medication, which are expensive, have significant side effects and the therapeutic effect is very limited. Animal models play an important role in exploring the underlying mechanisms of disease development and evaluating new therapies.Hair is an important accessory structure in the skin with physiological functions such as sensing stimuli, promoting damage repair, and immune regulation4. The HL mouse presents an autosomal recessive hairless phenotype from birth, can be accurately distinguished from normal mice at 7 days of age, and has normal thymus development. These characteristics are significantly different from those of the coat color mutant mice, indicating that HL mice are a novel type of hair mutant mouse. The breeding of animals and the localization of mutant genes provide important animal models and basic information for the study of the pathogenesis of hair loss, epidermal abnormalities and other skin-related diseases.The NIH hairless mouse (hereafter referred to as the HL mouse) is a spontaneous mutant mouse discovered in our laboratory during the breeding of NIH mice in 20076. Supplementation with laminin-511 inhibits the transition from anagen stage VI to catagen of hair growth7, and also alleviates chemotherapy-induced hair loss8. And existing studies have shown that Laminin-511 deletion leads to defective hair papilla growth, which interferes with primary cilia formation and hair development9. Nonsense mutations in the Lama3 gene are a major cause of epidermal macrovesicular loosening11, and targeted disruption of the Lama3 gene in adult mice is sufficient to induce skin inflammation and fibrosis12. However, the function of the Lama3 gene in hair follicle morphogenesis and hair cycle transition remain to be elucidated.In this study, we used hairless and normal as extreme phenotypes and located the mutated gene on chromosome 18 by bulk segregant analysis (BSA). Based on the results of functional analysis and mutation prediction, we obtained the candidate gene Lama3, whose base 854 in exon 4 was mutated from C to T . This is an unreported SNP locus that is completely linked to the phenotype. Laminin, a major component of the basement membrane, includes three isoforms, laminin-511, laminin-332, and laminin-211, and the Lama3 gene encodes the alpha subunit of laminin-332. Basement membrane-mediated molecular signaling between the epidermis and dermis is a major driver of skin development and hair dynamic homeostasis. Expression of laminin-511 and -332 changes dynamically with the hair growth cycleTherefore, to clarify the relationship between the Lama3 (R217C) mutation and the hairless phenotype, this study used CRISPR/Cas9 technology to construct mutant mice with Lama3 (R217C) and observed the effect of the mutation on the skin and hair phenotype of mice at the animal and tissue levels. The results showed that Lama3 mutant mice started to show signs of androgenetic alopecia on the 80th day after birth. This study is a good extension of the current body of knowledge about the function of the Lama3 gene, and the constructed Lama3 single-base mutant mouse may be an animal model for the study of androgenetic alopecia.HL mice were bred at the Experimental Animal Center of Jilin University (SCXK (Ji) 2021-0001), and specific pathogen free (SPF)-grade NIH mice were purchased from Changchun Institute of Biological Products, Ltd. (SCXK (Ji) 2017-0005).The experiments were conducted under the supervision of the Animal Ethics and Welfare Committee of Jilin University , all methods were carried out in accordance with relevant guidelines and regulations. This study was carried out in compliance with the ARRIVE guidelines, mice were anesthetized with isoflurane and euthanized by inhalation of excess carbon dioxide.Two mixed pools were constructed for BSA with hairless and normal as extreme characteristics; the normal pool was named R03, and the hairless pool was named R04, and each pool contained DNA samples from 30 mice  with the same amount of DNA in each sample.13, the positions of the clean reads on the reference genome were located by comparison, the sequencing depth and genome coverage of each sample was counted, and the variant sites were detected and statistically analyzed.Liver DNA was extracted by the CTAB method, and after the samples were tested and quantified, DNA was randomly broken into 350 bp fragments by ultrasonic fragmentation, the DNA fragments were end-repaired, an A was added at the 3ʹ end, and sequencing connectors were added, purified, and PCR amplified to complete the construction of sequencing libraries. The libraries were sequenced by Illumina HiSeq after passing quality control. The raw image data files obtained from high-throughput sequencing were analyzed and transformed into raw sequenced sequences (sequenced reads), raw reads were converted to FASTAQ format, base identification  was performed by Illunima Casava 1.8 software, and clean reads were obtained after filtering. The short sequences obtained were compared with the reference genome using BWA software14. Based on the localization results of the clean reads to the reference genome, we used Picard software (http://sourceforge.net/projects/picard/) to mark duplicates and GATK for local realignment. Base preprocessing, such as base recalibration, was performed to ensure the accuracy of the detected SNPs/InDels, and then GATK was used to detect the SNPs/InDels and obtain the final SNP/InDel locus set. Subsequently, the SNPs/InDels were filtered to select high-quality loci for subsequent association analysis. The filtering criteria were as follows: first, SNP loci with multiple genotypes were filtered out; second, SNP loci with read support less than 4 were filtered out; third, SNP loci with consistent genotypes between the mixed pools were filtered out; and finally, high-quality SNP loci were obtained. Before using the InDels for association analysis, InDels was first filtered with the same filtering criteria as used for SNP analysis, and finally, high-quality, plausible InDel loci were obtained.SNP/InDel detection was mainly implemented using the GATK software toolkit15. Theoretically, the ED value of non-target loci should tend to be 0. The formula of the ED method is shown below, and the larger the ED value, the greater the difference in the marker between the two pools.In this study, the Euclidean distance (ED) algorithm was utilized, which is a method that uses sequencing data to find significantly different markers between the hybrid pools and subsequently uses this information to assess the region associated with the trait15, and in this project, to achieve the function of eliminating background noise, the 5th power of the original ED was taken as the association value, and then the ED value was fitted using the DISTANCE method.When conducting the analysis, SNPs and InDels with differences in genotypes between the two mixed pools were used to count the depth of each base in the two pools and to calculate the ED value of each locus. To eliminate background noise, the original ED value was multiplied by a constant16 software was applied to perform deep annotation of genes within the candidate region in multiple databases. GO17, KEGG18 and COG19 enrichment analyses were performed for genes within the candidate region.BLASTThe mouse Lama3 gene  is located on mouse chromosome 18. Seventy-five exons have been identified, with the ATG start codon in exon 1 and TAA stop codon in exon 75. R217C is located on exon 4; therefore, Exon 4 was selected as the target site (sequences shown on the next page). A gRNA targeting vector and donor oligo  were designed. The p. R217C (CGT to TGT) mutation sites in the donor oligo was introduced into exon 4 by homology-directed repair. Two silent mutations  were also introduced to prevent the binding and cleavage of the sequence by gRNA after homology-directed repair. Cas9 mRNA and gRNA generated by in vitro transcription and donor oligo, were coinjected into fertilized eggs for KI mouse production. The pups were genotyped by PCR followed by sequence analysis. The gRNA (matching the reverse strand of the gene) was ATTACCTCGCCATTTTCCAGAGG.Donor oligo:Forward primer (F1): 5ʹ-TGCTTACTTGAGACATGAAGAG-3ʹ.Reverse primer (R1): 5ʹ-GTCCTGGATAGAGCTACTTGAG-3ʹ.PCR Primers :Sequencing Primer: SF1: 5ʹ-TTGTCCTAAGCCTTCACCTAG-3ʹ.PCR product size 746 bp:Skin appearance was photographed at different time points, and the skin of the male Lama3 mutant mice and the corresponding control skin was cut at P150 from the dorsal hair loss area, fixed in 4% paraformaldehyde for 24 h, embedded in paraffin, and sectioned for H&E staining. The skin was subsequently observed and photographed by light microscopy .Testosterone and Dihydrotestosterone levels in serum were measured according to the operating instructions of an ELISA kit .The experimental data are presented as the mean ± standard deviation. Unpaired t tests were used for analysis of differences between the two groups, and Welch’s test was used to correct for P values when F > 0.05. The data were analyzed and plotted using GraphPad Prism8 . P < 0.05 was considered to indicate a significant difference.The experiments were conducted under the supervision of the Animal Ethics and Welfare Committee of Jilin University (SY201912015), all methods were carried out in accordance with relevant guidelines and regulations. This study was carried out in compliance with the ARRIVE guidelines.NIH mice were completely covered with hair on the body surface on the 7th day after birth (P7), while HL mice remained completely naked Fig. A. In addThe results in Table The results in Table The evaluation results of the sample base distribution ratio, insertion fragment distribution, and the base coverage and coverage depth proved that the library from the sequencing data was properly constructed with good randomness that covered the genome evenly Fig. . The seqBased on the localization results for the samples on the reference genome from the clean reads, the SNPs and InDels with between sample differences were analyzed and summarized. The statistical results of SNPs and InDel between samples are shown in Fig. The association of SNP loci with phenotypes was assessed according to the Euclidean distance (ED) algorithm. In this study, to eliminate background noise, the 5th power of the original ED was taken as the association value, and then the ED values were fitted using the DISTANCE method. The distribution of association values in all chromosomes is shown in Fig. Some regions of chromosome 18 had the highest degree of correlation with the phenotype Fig. B. Using The same algorithm assessed the association of InDels with traits, and the distribution of association values across all chromosomes is shown in Fig. The analysis was performed with an association threshold of 0.54, and six regions with a total length of 25.45 Mb containing 518 genes were obtained, and the specifics are shown in Table The region with the highest correlation with the phenotype on chromosome 18 was selected as the candidate region, and BLAST software was applied to deeply annotate the coding genes in this region with multiple databases . A total of 309 genes were annotated in the candidate region, among which a total of 41 genes were annotated with nonsynonymous mutations between the mixed pools and 2 genes were annotated with code-shifting mutations. The annotation results are shown in Table The mutation candidate gene Lama3  was selected based on the results of gene function analysis and mutation prediction screening. PCR amplification followed by Sanger sequencing verified that the missense mutation was indeed present in Lama3 in HL mice Fig. A,B, wher22 and parasitic infections23, and are an important part of animal models for human diseases. Hairless (Hr)25, Gsdma327, and Foxn128 are several hair-related functional genes reported in the study. Their nonsense mutations or missense mutations cause hairlessness or progressive hair loss. However, animal models related to hair abnormalities are still scarce. We also have very limited knowledge of hair function genes.Hair mutation mouse models have a wide range of applications in life science and medical research, such as skin photoaging damage29. It has been shown that the PI3K/Akt signaling pathway is essential for the morphogenesis of hair follicles. The PI3K-Akt pathway inhibitor treatment in hair follicle reconstruction experiments results in failure of follicle regeneration30. Additionally, our previous transcriptome sequencing results showed that the PI3K-Akt pathway was in a repressed state in HL mouse skin4. Therefore, we initially selected the Lama3 gene as a mutation candidate. Furthermore, we constructed Lama3 (R217C) mutant mice by CRISPR/Cas9 technology, and the observation of the hair phenotype showed a causal relationship between the Lama3 (R217C) mutation and AGA.In the present study, we used normal and hairless as extreme phenotypes and association analysis of variant loci and phenotypes by the BSA technique, and the mutated genes were initially localized to chromosome 18. Lama3, an extracellular ligand molecule of the PI3K/Akt signaling pathway, can activate the PI3K pathway by binding to integrin receptors on the membrane33. For example, a study by Seok et al. showed that the HSPA1B SNP rs6457452 was associated with patchy baldness in a Korean population34, and a study by Antonio Prodi et al. showed that the EDAR SNP rs1352015 was associated with AGA35. These studies analyzed the correlation of these SNPs with hair loss disorders such as AGA, but the causal relationship between the two remains uncertain.AGA is a common polygenic disorder whose genetics are not fully understood. Several studies have revealed the association of genetic SNPs with AGA and patchy baldnessLama3 (R217C) is a missense mutation newly identified by us, and the site is highly conserved among species, corresponding to the human LAMA3 protein R220C. In the present study, following a classical forward genetics approach, we formed a complete logical closure loop from the new hairless phenotype discovered and performed mutant gene screening for the subsequent verification of the causal relationship between the gene and the phenotype. We demonstrated for the first time that the Lama3 (R217C) mutation causes AGA. This locus has screening and diagnostic value for AGA.Of course, there are limitations to our study, and there are many meaningful questions that still need to be examined. First, the mutation of Lama3 (R217C) did not fully recapitulate the hairless phenotype of HL mice, which suggests that the combination of multiple genes may be behind the hairless phenotype of HL mice; however, the whole-genome resequencing results from the HL mice still have great value for excavation and exploration. Second, we established a causal link between Lama3 (R217C) mutations and AGA, but the underlying molecular mechanisms remain unresolved. In this regard, we also went through additional steps. First, we found through bioinformatics analysis that the mutation of Lama3 (R217C) might lead to the loss of ADP-ribosylation modification at this site, which in turn may have affected the binding of the Lama3 ligand to the integrin receptor on the cell membrane and led to the inhibition of the downstream PI3K-AKT pathway. However, the lack of Lama3 IP-level commercial antibodies and its extra-long CDS region (9993 bp) limited our experiments. We may subsequently analyze the effect of point mutations on their ability to bind to the receptor by constructing truncated bodies containing labeled antibodies. Third, the question remains whether the mutant locus is present in patients with clinical AGA. Establishing the answer to this question directly determines the clinical significance and subsequent application value of the locus.In conclusion, in this study, we performed resequencing analysis of HL mice, screened for SNPs and InDels with linkage to the phenotype, and further constructed Lama3 (R217C) mutant mice by CRISPR/Cas9 technology to clarify the causal relationship between this point mutation and AGA. Lama3 (R217C) mutant mice may be an important model for studying the pathogenesis of AGA, and this locus has potential as a diagnostic and screening test for AGA.Supplementary Information.


Larix sibirica Ledeb.), whose distribution covers different altitudes, makes it possible to directly compare lowland with highland populations. This paper presents for the first time the results of studying the genetic differentiation of Siberian larch populations, presumably associated with adaptation to the altitudinal gradient of climatic conditions, based on a joint analysis of altitude and six other bioclimatic variables, together with a large number of genetic markers, single nucleotide polymorphisms (SNPs), obtained from double digest restriction-site-associated DNA sequencing (ddRADseq). In total, 25,143 SNPs were genotyped in 231 trees. In addition, a dataset of 761 supposedly selectively neutral SNPs was assembled by selecting SNPs located outside coding regions in the Siberian larch genome and mapped to different contigs. The analysis using four different methods  revealed 550 outlier SNPs, including 207 SNPs whose variation was significantly correlated with the variation of some of environmental factors and presumably associated with local adaptation, including 67 SNPs that correlated with altitude based on either LFMM or BayeScEnv and 23 SNPs based on both of them. Twenty SNPs were found in the coding regions of genes, and 16 of them represented non-synonymous nucleotide substitutions. They are located in genes involved in the processes of macromolecular cell metabolism and organic biosynthesis associated with reproduction and development, as well as organismal response to stress. Among these 20 SNPs, nine were possibly associated with altitude, but only one of them was identified as associated with altitude by all four methods used in the study, a nonsynonymous SNP in scaffold_31130 in position 28092, a gene encoding a cell membrane protein with uncertain function. Among the studied populations, at least two main groups (clusters), the Altai populations and all others, were significantly genetically different according to the admixture analysis based on any of the three SNP datasets as follows: 761 supposedly selectively neutral SNPs, all 25,143 SNPs and 550 adaptive SNPs. In general, according to the AMOVA results, genetic differentiation between transects or regions or between population samples was relatively low, although statistically significant, based on 761 neutral SNPs (FST = 0.036) and all 25,143 SNPs (FST = 0.017). Meanwhile, the differentiation based on 550 adaptive SNPs was much higher (FST = 0.218). The data showed a relatively weak but highly significant linear correlation between genetic and geographic distances .Forest trees growing in high altitude conditions offer a convenient model for studying adaptation processes. They are subject to a whole range of adverse factors that are likely to cause local adaptation and related genetic changes. Siberian larch ( Larix sibirica Ledeb.) is one of the key conifer species of Siberian boreal forests, playing a very important ecological and economical role. Siberian larch has a high level of phenotypic variation, the genetic mechanisms of which are still poorly understood. The genetic study of this species is hampered by the huge size of the genome, ~12 Gbp [Siberian larch  is widely used . The appAt the same time, landscape genomics methods are widely used. They are based on a relatively new approach that simultaneously analyzes variation of a large number of genes and environmental factors to detect genes whose variation is under selection and presumably associated with adaptation to environmental factors, the so-called candidate adaptive genes ,6,7. TheFST-outlier test) [Two main strategies have been developed to identify loci under selection, the variation of which may have an adaptive value (or loci linked to those). The first strategy is to search for loci with striking values of genetic differentiation that cannot be explained by only random selectively neutral processes such as genetic drift and isolation  , while ter test) .The first approach is based on the assumption that positive selection for different alleles in populations living in different ecological environments increases the divergence between them to a level that cannot be explained by selectively neutral processes such as genetic drift or isolation, while stabilizing selection preserves the degree of divergence at a level statistically lower than can be expected by chance . The secPinus taeda L.) and climatic variables. De Kort et al. [Alnus glutinosa L.) across Europe and reported significant associations between variation of several loci of this tree species and temperature and latitude. Zheng et al. [Populus szechuanica var. tibetica) genome associated with altitude and response to the solar radiation level, and gained insight into the genetic mechanisms underlying the adaptation to highlands in plants.For example, Eckert et al.  found a t et al.  studied g et al.  discoverPlants growing in different altitude conditions are subjected to different environmental factors and are a convenient model for studying of the adaptation process. Highland plants are subject to a whole range of adverse factors: high-intensity exposure to solar radiation and wind, low atmospheric pressure, low temperatures, sharp fluctuations in daily and seasonal temperatures, humidity, and a short growing season. These climatic factors act as major forces in the selection of fitness-enhancing variants from the gene pool and hence stimulate local adaptation and genetic differentiation. Plant species whose habitat spans different altitudes (from sea level to above 2000 m) are of the greatest interest, allowing direct comparisons of lowland and highland populations.This study was aimed at identifying signs of local adaptation in the Siberian larch populations using genome-wide genotyping and landscape genomics approaches. We studied genetic differentiation of Siberian larch populations associated with adaptation to the altitudinal gradient of climatic conditions based on joint analysis of six bioclimatic variables and double digest restriction-site-associated DNA sequencing (ddRADseq) data. Specifically, we analyzed Siberian larch population structure, genetic diversity and genetic traits of adaptation to growing conditions within the Altai-Sayan mountain system in southeastern Siberia.PCA was performed to pre-test the relationship between climate variables and altitude at the collection sites. The first principle component (PC1) explained 53.6% of the variation primarily related to the average annual precipitation (PREC) and the average temperature of the coldest quarter (MTofCQ)—lower PREC and higher MTofCQ correspond to positive PC1 values. The second principle component (PC2) explained 32.2% of the variation primarily related to the altitude (ALT) and average temperature of the warmest quarter (MTofWQ)—higher ALT and lower MTofWQ correspond to positive PC2 values . The corr) between six bioclimatic variables and altitude (ALT) are shown in Pairwise Pearson’s correlation coefficients (p ≪ 0.001) and 0.89 (p ≪ 0.001), respectively. The altitude (ALT) and the MTofWQ, as well as the temperature seasonality (TEMPSEAS) and the MTofCQ, were also significantly but negatively correlated with each other .The average annual temperature (TEMP) was significantly positively correlated with the average temperature of the warmest (MTofWQ) and coldest (MTofCQ) quarters with correlation coefficients of 0.81  and an average sequence length of 85 bp (ranging from 32 to 92 bp). About 97% of the reads for each sample on average were successfully mapped to the reference Siberian larch genome [Data for 19 trees were completely removed from further analysis due to the small number of reads (≤1 million) and the insufficient level of mapping to the reference genome. Finally, 19,743 loci containing 25,143 biallelic SNPs were selected for genotyping of 231 trees in total .In addition, in order to infer a potential population genetic structure that resulted due to random selectively neutral factors such as genetic drift and isolation, a dataset of 761 supposedly selectively neutral SNPs was assembled by selecting SNPs located outside coding regions in the genome and mapped to different contigs. In addition, a dataset of 550 supposedly adaptive SNPs was assembled as described below based on the outlier SNPs and SNPs whose variation correlated with altitude and/or bioclimatic variables  to compaSNPs whose level of variation and differentiation cannot be explained by selective-neutral processes are likely to be under selection and possibly involved in local biological adaptation. We used several of the most efficient population genetic approaches to find such candidate adaptive SNPs.K = 2) explained the most genetic variance; for that reason, they were retained for further analysis.The PCAdapt  program q-value < 0.05.Then, all SNPs were regressed against the retained ordination axes, and outlier SNPs were selected based on their significant correlation with these axes. The Manhattan plot in q-value < 0.05), three of which were common for both predictors, PC1 and ALT.Using the latent factor mixed model program LFMM2 , 40 SNPsq-value < 0.05).Similarly, when using the BayeScEnv program , 94 SNPsThe correlation of environmental factors with the first three redundancy analysis (RDA) axes is presented in p-value = 0.0027). Alt, Prec and Isotermality were most closely related to the SNPs found  and 46 for all four methods (previous three plus PCAdapt) . Among tThe summary of genetic variation parameters for each of the 24 population samples based on 25,143 SNPs is presented in K) was searched using three different datasets of SNPs—all 25,143 SNPs, 761 supposedly neutral SNPs and 550 adaptive SNPs whose variation is supposedly under selection (described below in detail)—by checking the value of the parameter K (from 1 to 24) in a computer simulation with 20 repetitions (iterations) for each number of K. Various methods for selecting K showed that the most probable number of clusters was K = 2 (The most probable number of subpopulation clusters (as K = 2 .K (from K = 1 to K = 6), based on three different datasets of SNPs and sorted differently according to their geographic origin and altitude, respectively, are presented in A plot demonstrating the admixture of each of the two clusters to individual trees  is presented in PCA and DAPC were also performed to identify the population structure based on 761 neutral SNPs. It was revealed that the studied samples did not form pronounced, unambiguous clusters; however, we could infer the presence of two or three conditional clusters: the samples belonging to transects A, C and K formed one cluster, the samples of transects D, E and F formed the second and transect G, according to the DAPC results, occupied a separate position .FST parameter [K = 2), when geographic transects D, E and F were in one cluster (Altai Mountains) and A, C, G and K were in another (FST = 0.018); (2) into three clusters (K = 3), in which transects D, E and F formed one cluster (Altai Mountains) and transects A, C (Western Sayan Mountains) and K (East Tuva Highlands) the second, while transect G (Kuznetsk Alatau) formed an independent third cluster ; (3) into seven geographic transect clusters , with the lowest FST between transects D and F (0.004) and the highest between G and E (0.061); (4) into 24 clusters corresponding the 24 samples, respectively ,F-indices) for each hierarchical level (p ≪ 0.001).Based on three different SNP datasets, a hierarchical AMOVA was also carried out by partitioning the total genetic variance into among 7 geographic transects (regions), among 3–4 population samples within transects, and within and among all 24 population samples, then calculating Wright’s fixation indices  linear relationship between genetic distance and geographic distance  within a coding region of gene LS_31130-0.0 .Cellular components where the functioning of the products of these genes was localized included a wide range of membrane complexes .The main biological processes in which they are involved are the processes of macromolecular cell metabolism and organic biosynthesis, including metabolism of nitrogenous and aromatic compounds, phosphorus, nitrogenous bases, carbohydrates and proteins, as well as related processes of regulation of gene expression .Among the 61 SNPs, 16 represented non-synonymous single nucleotide substitutions that potentially affect the function of the corresponding proteins and are of the most interest for further study.A BLAST search in the NCBI GenBank  for sequThe results of the presented genome-wide analysis of the structure and genetic variation of natural populations of Siberian larch are generally consistent with previous conclusions about the relatively weak selectively neutral structure of closely located populations of conifers, including larch ,26, explPrA), which was the highest for the SNP dataset based on all SNPs (Most of the genetic variance (approximately 96%) when studying the neutral, adaptive and all SNPs in the 24 population samples was within samples. However, it is interesting to note that all mean parameters of genetic variation were higher for the SNP dataset based on 550 adaptive SNPs compared to the other two datasets, except for the number of private alleles , between population samples within transects (FSC) and between all population samples (FST) was relatively low, although statistically significant, based on 761 neutral SNPs and all 25,143 SNPs, as expected for conifers with large continuous populations and high gene flow. However, the differentiation based on 550 adaptive SNPs was much higher, which verifies their role in local adaptation leading to higher differentiation.In general, genetic differentiation between transects or regions  correlation between genetic and geographic distances assuming that isolation by distance plays an important role in genetic differentiation between these populations.The studied population samples could be divided into at least two main groups (clusters)—the Altai populations and all other populations. That was in agreement with their altitudinal–latitudinal location, which likely leads to strong isolation of the Altai populations from all other populations. The data showed a relatively weak but significant . They were located in the gene LS_9849-0.1 , directly regulating the ability of cells to detoxify H2O2 [Nonsynonymous SNP scaffold_36255_2146 was associated with synthetic predictor PC1 in the LFMM analysis and located in the gene LS_36255-0.2 [ify H2O2  and therNonsynonymous SNPs scaffold_73031_9433 and scaffold_73031_9438 were associated with all environmental predictors and located in the gene LS_73031-0.1 [Nonsynonymous SNP scaffold_118661_524 was an outlier based on PCAdapt but was not selected by the three GEA methods. It is located in the gene LS_118661-0.1  that encArabidopsis thaliana, the COG complex functions during cell growth, reproduction and other processes including direct interaction with the components of the secretion system. Recent experiments have revealed the protective role of the COG complex in plants, including plant–pathogen interactions [Nonsynonymous SNP scaffold_3984510_7337 was an outlier based on PCAdapt but was not selected by the three GEA methods. It is located in the gene LS_3984510-0.1 [ractions .Arabidopsis [Nonsynonymous SNP scaffold_4015301_864 was associated with all environmental predictors and located in the gene LS_4015301-0.0 [bidopsis , FAR4  receptor [Nonsynonymous SNP scaffold_4023983_5884 was an outlier based on PCAdapt but was not selected by the three GEA methods. It is located in the gene LS_4023983-0.1 [receptor , which iNonsynonymous SNP scaffold_4033175_6319 was an outlier based on PCAdapt but was not selected by the three GEA methods. It is located in the gene LS_4033175-0.1 .Synonymous SNP scaffold_4078980_3679 was an outlier based on PCAdapt but was not selected by the three GEA methods. It is located in the gene LS_4078980-0.1 [Arabidopsis provides a link between the ABA-mediated response to abiotic stress and oxylipin signaling.Nonsynonymous SNP scaffold_5133697_5690 was an outlier based on PCAdapt but was not selected by the three GEA methods. It is located in the gene LS_5133697-0.1 . PLIP2 iNonsynonymous SNP scaffold_5135911_1098 was an outlier based on PCAdapt but was not selected by the three GEA methods. It is located in the gene LS_5135911-0.1 .Arabidopsis suggests that they may play different functional roles [Nonsynonymous SNP scaffold_5188799_2951 was an outlier based on PCAdapt but was not selected by the three GEA methods. It is located in the gene LS_5188799-0.1  that encal roles .Among the 550 candidate adaptive SNPs found in this study, 41 were located in the intergenic regions of the genome. Some of these SNPs are likely to be located in regulatory regions and affect gene expression, which requires additional research.A. thaliana; the LRR protein encoded by this gene interacts with ferric reductase defective 3 (FRD3), which is involved in citrate transport and stable development of microspores during pollen tube growth. Another gene encodes MADS-box transcription factor 47, which is involved in the formation of floral organs, in part through downregulation of the brassinosteroid signaling pathway. Phospholipid hydroperoxide glutathione peroxidase 1 (GPX) is a group of proteins that protect cells from oxidative damage caused by reactive oxygen species (ROS).Previously, Zheng et al.  revealedIndividual needle samples were collected from 250 trees (~20–100 years old) of Siberian larch along seven high-altitude transects  located in the Altai-Sayan region in southeastern Siberia in native unprotected area .Along each transect, 3–4 population samples of 10 trees each were collected at different altitudes, where each sample corresponded approximately to 500, 1000, 1500 or 2000 m above sea level .DNA from the collected larch needles was isolated using the CTAB method . The DNAEcoRI and MseI [Preparation of ddRADseq libraries was carried out according to a modified version of the protocol described in . DNA samand MseI , selecteand MseI  and the and MseI . After tThe obtained pools of ddRADseq libraries were checked for quality by capillary electrophoresis on a Bioanalyzer 2100 instrument using a High Sensitivity DNA Kit . The pool concentration was measured on a Qubit 2.0 fluorimeter using a Qubit dsDNA BR Assay Kit . The final check of the prepared pools before sequencing was carried out on an Agilent 2200 TapeStation System .Single-end sequencing of ddRADseq libraries was performed using 100 cycles on a NovaSeq 6000 sequencer . Accordingly, the length of the reads was 100 bp, and the number of reads varied in the range of 400–500 million per transect.In addition to the geographic coordinates and altitude measured at the collection sites for each tree, by using their exact coordinates and the R “raster” v. 3.4.5  and “sp”All values were standardized for further calculations by subtracting the arithmetic mean and dividing by the standard deviation using the basic R scale function. Genes associated with environmental variables and potentially reflecting local adaptation were detected using the LFMM2  and BayeEcoRI and MseI restriction sites. Demultiplexing was performed based on the barcoded adapter sequences unique for each sample using the process_radtags utility included in the Stacks software [The raw sequencing data went through several steps of initial processing. The original reads were filtered and trimmed according to quality scores in the Trimmomatic-0.39 program  with parsoftware . Statistsoftware . The avesoftware  in the “Alignment results were sorted, and the genomic assembly was indexed in Samtools . SNP calk-nearest neighbor genotype imputation method (LD-kNNi) in the TASSEL v. 5.0 program [The search for SNPs located in intergenic areas was carried out using the annotation of Siberian larch  in SNPda program .K clusters. BayeScEnv is an FST-based, genome scan method that uses environmental variables to detect local adaptation. BayeScEnv models were run separately for PC1 and ALT variables using the same parameters: 20 pilot runs with 5000 iterations, thinning interval size 10, 5000 outputted iterations and burn-in length 50,000.The search for SNPs with striking values of genetic differentiation, which could not be explained only by selectively neutral processes, the so-called outlier genes, was carried out using four approaches: (1) PCA-based genome scans for selection using the PCAdapt v4.3.3 program , (2) regPrA), allelic richness (AR), observed (oH) and expected (eH) heterozygosity and fixation index (FIS). To reveal the population structure, we also performed principal component analysis (PCA) and discriminant analysis of principal components (DAPC) using the R ade4 package [The following R packages were used for the analysis of population genetic variation: adegenet , poppr , and vcf package .K) of clusters (“subpopulations”) was carried out by checking the value of the parameter K from 1 to 24, with 20 repetitions for each K. The most probable value of K was chosen based on the values of the cross-validation error and the ΔK method [In addition, the structure of the population was studied using the admixture algorithm implemented in the AdmixPipe program , which eK method  calculatK method .FST coefficients based on 1000 permutations were performed using Arlequin v. 3.5.1.2 [A pairwise population genetic distance matrix for the Mantel test was built in the TASSEL program, where genetic distance was calculated as 1-IBS (identity by state) based on all 25,143 SNPs. This was used in the Mantel test to check for correlation with pairwise geographic distance using the R vegan v. 2.6-2 package. Hierarchical analysis of molecular variance (AMOVA) and calculation of pairwise  3.5.1.2 .https://www.biobam.com/omicsbox, accessed on 30 December 2022). The selected SNPs were annotated using SNPdat.To analyze genomic regions in the contigs where adaptive SNPs were located, we used these contigs to search for homologs in the “nr” database of the NCBI GenBank . Gene moWe identified several SNPs in candidate genes whose variation was associated with altitude and other bioclimatic variables, such as LEA, NRX1, F-box, EXO, COG1, FAR4, AP2A1, CSL, ISWI, PLIP2, PIRL1 and OPT7-like genes. Based on these results, it can be assumed that Siberian larch has adapted to high altitudes in part through supportive functions associated with reproduction under abiotic stress, such as chaperone protection against cellular damage, cell growth support and stimulation, stress signaling, epigenetic regulation via chromatin remodeling, etc., although more information about how these genes regulate altitude adaptation in Siberian larch must be ascertained. The results of this study will allow for a deeper understanding of the genetic mechanisms underlying the formation of adaptations in larch to various environmental conditions. Considering that many environmental factors affecting larch in high-altitude conditions can be confidently attributed to stress, this study allows us to detect important genes and SNP markers for breeding, as well as lays the foundation for creating a SNP genotyping chip for monitoring neutral and adaptive genetic variability in other larch populations. The presented data can serve as a scientific basis for optimizing nature management, developing methods for the rational use of the studied species, identifying populations with good genetic potential and conducting environmental monitoring.


Climate change, along with increased demand for tropical fruits like mangoes, has led to an increase in greenhouse cultivation in South Korea, raising the risk of yellow tea thrips infestations. Our study explored ethyl formate fumigation as a safe, effective alternative to traditional pesticides in mango greenhouse cultivation and post-harvest storage. This novel fumigation method was successfully able to manage thrips without harming mango trees or fruits, offering an environmentally friendly pest management approach benefiting both farmers and consumers.Scirtothrips dorsalis), which is considered a surrogate pest in the thrips group according to the PRA. The efficacy and phytotoxicity of EF were evaluated in greenhouse-cultivated mango tree (Irwin variety) and post-harvest mango fruit scenarios. EF efficacy ranged from 6.25 to 6.89 g∙h/m³ for lethal concentration time (LCt)50 and from 17.10 to 18.18 g∙h/m³ for LCt99, indicating similar efficacy across both scenarios. Application of 10 g/m³ EF for 4 h at 23 °C could effectively control S. dorsalis  without causing phytotoxic damage to the greenhouse-cultivated mango trees, while post-harvest mango fruit fumigation with 15 g/m³ EF for 4 h at 10 °C showed potential for complete disinfestation of S. dorsalis without compromising fruit quality.The effects of climate change and shifting consumer preferences for tropical/subtropical mango fruits have accelerated their greenhouse cultivation in South Korea, which has consequently exacerbated the risk of unexpected or exotic insect pest outbreaks. This study used the pest risk analysis (PRA) of greenhouse-cultivated mangoes provided by the Animal & Plant Quarantine Agency in Korea to evaluate the potential of ethyl formate (EF) fumigation as a new pest management strategy against the yellow tea thrips ( Climate change predictions indicate that temperatures will have risen by 4 °C at the end of the 21st century, with the central region of the Korean Peninsula experiencing a warm temperate climate and the southern coast transitioning to a subtropical climate .Mangifera indica, Variety: Irwin) is a native tropical Asian crop and one of the most commercialized tropical fruits in the world. India is the largest global mango producer, followed by China, Thailand, Mexico, Pakistan, and Indonesia. Mango production in India accounts for about 38% of global mango production /2) from the values from ten different mango trees after fumigation.The damage indexes were evaluated with the following scale: 0, 1, 2, 3, and 4, representing no leaf damage, <5% of total leaves/plant dropped/browned/shriveled, 5–25% leaves affected, 25–50% leaves affected, and >50% leaves affected, respectively. Chlorophyll content was measured the 10 points of mango tree leaves using SPAD-502 Plus . The color changes were calculated as Hue value  to assess worker safety during ventilation at different time intervals after the end of fumigation experiments.S. dorsalis against EF dosage was analyzed using a Probit analysis. Assessment of all data-related quality parameters, such as weight loss, firmness, sugar content, and surface color change were calculated using Proc Univariate in SAS . To analyze the phytotoxic damage of EF fumigation on mangoes, a one-way ANOVA was performed in SAS  based on Tukey’s Studentized Range (HSD) test at p = 0.05.The toxicological response of 50 and LCt99 values of EF were 6.89 and 18.18 g h/m3 for adult S. dorsalis, with a fitted slope value of 5.52 ± 0.4 after 4 h fumigation at 11 °C in the post-harvest storage scenario (w/v) loading ratios of mango fruits (w/v) and an EF applied dose target at the LCt99 level exhibited sorption of about 80% at the end of the fumigation treatment , 0.38% LSD (p = 0.05). The mean fruit sugar contents in the control and 15 or 30 g/m3 EF treatments were 20.2, 20.3, and 20.8%, respectively  treated with 15 and 30 g/mectively a. Means ectively b. No sigectively b.p = 0.05) in sugar content  was observed. Similarly, the means of fruit color changes in the control and 15 or 30 g/m3 EF treatments were 24.1, 22.8, and 23.7, respectively, indicating no significant difference in the color change  based on LSD   based on LSD . Overall, these results showed no evidence of phytotoxic damage on firmness, sugar content, color change, and weight loss during post-harvest storage of mango fruits after 4 h EF fumigation  b. Moreovmigation b.3, respectively. The EF concentration at the top location of the greenhouse was constantly higher than at the middle and bottom locations throughout the 4 h treatment period  within 10 min, but more than 1 h was still needed to lower its potential hazardous conditions, which could cause unexpected acute inhalation risk in the greenhouse .3 EF treatment trees were 16.8 and 18.1, respectively, which indicated no statistical difference in chlorophyll contents  between treatments. Color changes in the cultivation scenario were investigated after 1 week. No significant difference  in the means of color change was observed between the untreated (32.6) and 10 g/m3 EF treated (34.5) greenhouse mango trees after one-week fumigation. Overall, these results demonstrate that continuous EF fumigation could control S. dorsalis  without residual or phytotoxic effects in greenhouse-cultivated mangoes (Chlorophyll content was quantified in the greenhouse mango trees after one-week treatment to assess EF phytotoxicity . Mean ch mangoes .Thysanoptera: Thrididae) [S. dorsalis, the target pest of this study, is known to infest a wide range of over 225 host plants, including mango, avocado, strawberry, and chili pepper [Various traditional insecticides such as abamectin, acetamiprid, and spinosad have been used to manage thrips (rididae) . Howeverrididae) . In parti pepper . The slei pepper ,36. GiveS. dorsalis and various other thrips species. For instance, the Probit 9 values for adult bean thrips  treated with EF fumigation for one hour at 5, 10, and 15 °C were 12.99, 17.05, and 14.32 g h/m3, respectively, showcasing successful pest control without damaging the host plant, citrus [Frankliniella occidentalis) on post-harvest strawberries, achieving full mortality without any negative impacts on the strawberries’ taste, odor, or decay rate [3, respectively [F. occidentalis could be controlled at 10% CO2 with 6.51 g/m3 of EF [3 for over 30 min at 20 °C was effective against adult onion thrips (Thrips tabaci Lindeman) [Existing studies have highlighted the efficacy of EF against , citrus . Aharonicay rate . Similarectively . Simpsonm3 of EF . In anotindeman) .The potential for ethyl formate (EF) to cause phytotoxicity has previously been reported in the leaves of imported foliage nursery plants . HoweverAdditionally, in this study, we assessed phytotoxic effects, including changes in firmness, sugar content, and color of the mango fruits . It is i99 value for controlling S. dorsalis was 17.10 (14.83–20.63) g h/m3  g h/m3 , the tareenhouse . The cona tabaci . The reda tabaci . This no3 EF for 4 h at 23 °C under the cultivation scenario caused 100% S. dorsalis mortality on greenhouse mango trees with no phytotoxic damage at levels higher than the EF LCt99 value (17.10 g h/m3 for 4 h at 23 °C). Mango fruits treated with 15 and 30 g/m3 EF for 4 h at 11 ± 1 °C under the post-harvest storage scenarios showed no phytotoxic damage at higher levels than the EF LCt99 value (18.18 g h/m3 for 4 h at 11 °C). We speculate that EF fumigation technology, which is currently replacing usage of MB as QPS in Korea, is potentially an effective fumigant against S. dorsalis that can be used to control unexpected outbreaks of exotic pests in greenhouse-cultivated plants or during post-harvest fruit storage without altering their quality.In summary, fumigation with 10 g/m


Malic acid (MA) is an important flavor acid in fruits and acts as a mediator in a series of metabolic pathways. It is important to understand the factors affecting MA metabolism for fruit flavor improvement and to understand MA-mediated biological processes. However, the metabolic accumulation of MA is controlled by complex heredity and environmental factors, making it difficult to predict and regulate the metabolism of MA. In this study, we carried out a genome-wide association study (GWAS) on MA using eight milestone models with two-environment repeats. A series of associated SNP variations were identified from the GWAS, and 15 high-confidence annotated genes were further predicted based on linkage disequilibrium and lead SNPs. The transcriptome data of candidate genes were explored within different tomato organs as well as various fruit tissues, and suggested specific expression patterns in fruit pericarp. Based on the genetic parameters of population differentiation and SNP distribution, tomato MA content has been more influenced by domestication sweeps and less affected by improvement sweeps in the long-term history of tomato breeding. In addition, genotype × environment interaction might contribute to the difference in domestication phenotypic data under different environments. This study provides new genetic insights into how tomato changed its MA content during breeding and makes available function-based markers for breeding by marker-assisted selection. In ripe fruits, malic, citric, and tartaric acids are the main organic acids, and act as important indicators of the flavor and nutritional qualities of fruits. Fruit acidity is due to the presence of organic acids, and different acidic substances are not equally acidic and differ significantly in acidity . DiffereMdVHA-Bs, MdVHA-E2, and MdtDT12 to control MA content in apple . Fruit spatiotemporal expressions of candidate genes were also provided in another profile with the transcript of cultivated variety S. lycopersicum cv. ‘M82’ [http://hemi.biocuckoo.org/down.php).We further cross-checked the expression patterns of candidate genes. Gene expression in different organs and fruits at different stages in tomatoes with three genomic backgrounds was studied, including cro-Tom’ , S. pimpv. ‘M82’ . HemI soπ) is often used as a measure of the degree of variation in a population [FST) is also applied to confirm highly differentiated regions of molecular diversity [π and FST were performed to identify the selective sweeps about MA content at two key stages (domestication and improvement) in tomato evolution [πPIM/πCER) as well as between CER and BIG (πCER/πBIG) , and population differentiation ratios FST (PIM versus CER) and FST (CER versus BIG)  were identified as selective sweep regions.Nucleotide diversity ; the National Natural Science Foundation of China ; the Wuhan Biological Breeding Major Project (2022021302024852); the Key Project of Hubei Hongshan Laboratory (2021hszd007); the Hubei Key Research & Development Plan ; the Fundamental Research Funds for the Central Universities (2662022YLPY001) and the International Cooperation Promotion Plan of Shihezi University (GJHZ202104)..W.G. and Y.Z. conceived and designed the experiments. W.G. and F.Y. analyzed the data and carried on the analyses. J.T. and X.Z. performed the data collection. S.H., Yaru W., Ying W., F.Y., L.Y., L.S., F.L., P.G., F.W. and H.D. conducted the literature review and edited the manuscript. Y.Z. was responsible for funding acquisition and article revision.The datasets supporting the findings in this study are available in the supplementary materials.The authors declare no competing interests.Horticulture Research online.Web_Material_uhad021Click here for additional data file.