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We developed a single-nucleotide polymorphism (SNP) - based linkage genetic map for American beech. Single locus Mendelian segregation was first tested using X 2 goodness-of-fit to 1:2:1 and 1:1 ratio at 5% and 1% significance levels. Linkage analysis produced 12 linkage groups (Fig. 2) using JoinMap 2.0 . Out of 3236 SNPs apparently segregating, 16 SNPs failed to be linked so the final number of linked SNPs was 3220 (Additional file 5).Fig. 2Genetic linkage map of F. grandifolia. Genetic linkage map of F. grandifolia constructed using 115 progeny individuals derived from the cross controlled experiment 1505 (R) × 1504 (R). Totally 3220 single nucleotide polymorphism markers are linked in twelve groups and presented on the right side of each linkage group. Map distances in centi-morgans are presented on the left side | study | 100.0 |
Genetic linkage map of F. grandifolia. Genetic linkage map of F. grandifolia constructed using 115 progeny individuals derived from the cross controlled experiment 1505 (R) × 1504 (R). Totally 3220 single nucleotide polymorphism markers are linked in twelve groups and presented on the right side of each linkage group. Map distances in centi-morgans are presented on the left side | study | 99.8 |
To explore the population structure of our sample population, Discriminant Analysis of Principal Components (DAPC) was applied to 506 individuals and 5838 SNPs. DAPC revealed three genetic clusters (Additional file 6A) using 40 principal components (PCs), maximum numbers of clusters and discovery clusters limited to 40 and 7, respectively and 6 discriminants. In addition, we employed a genomic control to assure for population structure by estimating an inflation factor λ (genomic control measures). Significant inflation was detected based on a QQ-plot of association p-values, which displayed systematic deviation from the expectation (Additional file 6B). | study | 100.0 |
The pairwise clustering based on identity-by-state (IBS), revealed high correlation among individuals in the sample population. The IBS test allowed the removal of individuals with the highest number of correlated “partners”, indicating high likelihood of relatedness. Our method of choice was to test for population outliers by performing IBS-based nearest neighbor analysis. In total, 179 individuals from the five different stands were identified as possible very close relatives and were removed from the further downstream analysis (Table 5).Table 5Duplicated individuals revealed by IBS test for the threshold (IBS > 0.1875)StandNumber of excluded individualsDisease statusLudington State, MI1RNABerkshire county, MA189R9SPenobscot county, Maine5533R22SRandolph county, WV3826R12SSissiboo Falls, Digby county, NS-Canada6527R38SNot classified2NA2STotal179 | study | 100.0 |
The initial logistic regression test was performed with 3220 SNPs, however after filtering SNPs to compute PCA and IBS score, an independent set of 2116 SNPs remained. No Genomic inflation from GWAS p-values expected by random chances was detected, except for the top associated SNPs (lambda = 1.13, Fig. 3a). A Fisher’s exact test revealed four markers on chromosome 5 (Fig. 3c), whose P values were above the significant genome-wide threshold of (P value >1.585 × 10−5) (Fig. 3b; Additional file 7). For the association test, the significance threshold for all 3155 SNPs was Bonferroni’s (α* = α/n) based significant threshold to adjust for multiple testing, where α represents Bonferroni’s coefficient 0.05 and n represents the number of SNPs after filtering for quality parameters (0.05 / 3155 = 1.58 × 10−5).Fig. 3 a Quantile-quantile (QQ) plot of GWA p-values. QQ-plot shows only minor deviations from the null distribution, expected for the top associated SNPs. b Manhattan plot from the GWAS analysis of Beech Bark Disease in 327 individuals. Beech bark disease is associated with a locus on chromosome 5. The x-axis represents chromosomal locations and the y-axis, −log10 p-values from genotypic associations. Four markers on chromosome 5 reached genome-wide significance (p-values >1.585 × 10−5) | study | 100.0 |
a Quantile-quantile (QQ) plot of GWA p-values. QQ-plot shows only minor deviations from the null distribution, expected for the top associated SNPs. b Manhattan plot from the GWAS analysis of Beech Bark Disease in 327 individuals. Beech bark disease is associated with a locus on chromosome 5. The x-axis represents chromosomal locations and the y-axis, −log10 p-values from genotypic associations. Four markers on chromosome 5 reached genome-wide significance (p-values >1.585 × 10−5) | study | 100.0 |
As shown in the Manhattan plot (Fig. 3b and Additional file 7), four SNPs were observed to surpass the genome-wide significance threshold of 1.585 × 10−5, which is strong evidence of association. All four SNPs are located on chromosome (Chr) 5 (Additional file 8). The strongest evidence of association is linked to AX-156994126 (P = 5.99E-6, odds ratio (OR) = 0.2573), AX-156988334 (P = 8.852E-6, odds ratio (OR) = 0.2758) and AX-157000652 (P = 8.074E-6, odds ratio (OR) = 0.2773) (Table 6). On chromosome (Chr) 5, SNPs were positioned at 12.344 cM (centimorgans) for AX-156994126, AX-156989406 and for AX-157000652 and at 13.811 cM for AX-156988334 (Additional file 8A).Table 6Top SNPs associated with Beech Bark diseaseGeneChrPosition (cM)Affymetrix IDOriginal SNPs IDLogistic regression association (P value)AnnotationMt512.344AX-156989406contig03321_5762.46E-6 Fagus sylvatica (European beech) mRNA for methallothionein-like protein, Metal ion bindingMt512.344AX-157000652contig03321_1668.07E-6Mt513.811AX-156988334contig03321_3308.85E-6Mt512.344AX-156994126contig03321_4415.99E-6 | study | 100.0 |
The flanking sequences for these four SNPs were used in Blast analysis. The best BLAST (BLASTn) analyses were performed against the NCBI database (National Center for Biotechnology Information) for non-redundant protein database. The best hit resulted in the identification of the single gene (Mt) from a single contig (contig 03321), within which fell all four identified SNPs (see Additional file 8B). The gene (Mt) encodes an mRNA for metallothionein-like protein (metal ion binding) (Table 6). | study | 100.0 |
Support for the result from GWAS was obtained by BLASTx alignment of the RNA sequence reads for each of the 10cDNA libraries individually to contig 03321, containing the full length transcript of the candidate Mt. gene. With the exception of the individuals (1504R and DN00726S), the constitutive expression of the candidate Mt. gene was higher in the BBD resistant individuals than in the BBD susceptible individuals (Additional file 9). On average, 1602 reads mapped per BBD resistant library, and 414 reads per susceptible library, after normalization in TPM (Transcripts Per Kilobase Million). This does not imply the expression of the candidate Mt. gene alone is sufficient for BBD resistance, nor that it is the only gene differentially expressed upon attack by the insect vector. | study | 100.0 |
To measure the degree to which alleles at two loci are associated, a complete set of 3220 SNPs were included to determine whether two loci are in linkage equilibrium or disequilibrium. LD plot showed SNPs in strong linkage disequilibrium (D’ = 1) (Additional file 10). | study | 100.0 |
Genome Wide Association Analysis has identified a single locus contributing to resistance to beech bark disease (BBD). There were four SNPs in chromosome (Chr) 5 significantly associated with the scale resistance trait analyzed. A candidate gene (Mt) encoding for a metallothionein-like protein was found to be physically linked to these genetic markers and may play an important role in the resistance mechanisms against Nectria sp. - beech scale insect. This is consistent with genetic studies of several different small full-sibling families that suggest involvement of a few as two genes [5, 24, 38]. For validation of single locus trait discovery, BLASTn search of the contig EST sequences was performed against the complete NCBI database for those SNPs (see Additional file 8B). A proven functional annotation for these SNPs is essential prior to use in breeding, which will be possible when a reference genome sequence for American Beech is available. | study | 100.0 |
Disease resistance in plants can involve any number of genes, from a single major gene to many loci determining resistance. Single-gene resistance mechanisms with large effects are more common in agricultural crops but only a few have been described in forest species, which reflects the greater genetic diversity of the host and pathogen populations in forest pathosystems . In forest species, resistance is typically polygenic and durable, with few examples of simply inherited disease resistance. This is likely due to the limited potential for Mendelian analysis in forest trees and complex life cycles of many forest pathogens . However, disease resistance is not exclusively polygenic in forest pathosystems. Examples of single qualitative resistance, include loblolly pine (Pinus taeda) resistance to the fusiform rust disease [41, 42], resistance to white pine blister rust in several species of pine [43, 44] and evidence for major gene resistance to weevil in Sitka spruce . | review | 99.7 |
In this study, we used Affymetrix Axiom™ Genome-Wide 1.5 K – 50 K array (Santa Clara, CA) to genotype 327 individuals used for association mapping. Although SNP discovery was performed specifically for BBD, a very small proportion of the SNPs deemed informative in downstream analysis. The conversion rate, provided by Affymetrix genotypng facility, corresponded to 34.04% and was quite high compared to Pinus taeda (Loblolly pine) at only 5–10%. Overall, the number of informative SNPs was sufficiently high to provide us with association power on the genome scale for the disease resistance. | study | 100.0 |
The knowledge of genetic architecture is important for breeding resistant varieties to develop resistant planting stock for restoration of impacted habitats. Molecular markers have also contributed to improved breeding strategies for monogenic resistance genes when combining them in a “gene pyramiding” strategy for a more durable resistance and can also be used to develop cost-effective indirect selection techniques. | other | 99.9 |
Plant metallothioneins are proteins thought to sequester excess amounts of certain metal ions . These low molecular weight proteins (4–8 kDa) were discovered in mature wheat embryos about 30 years ago . Metallothioneins represent Cys-rich metal chelators able to coordinate metals atoms (e.g. Zn, Cd and Cu ions) and found to play a role in cellular processes such as regulation of cell growth, proliferation and DNA damage repair. But how metallothioneins fulfill these cellular roles, is yet to be discovered [49, 50]. Expression of plant metallothionein genes has been observed in a variety of senescing tissues, such as leaves and stems, ripened fruits and wounded tissues . Recent reports show that MTs (metallothionein’s) are also involved in the scavenging of reactive oxygen species (ROS) . | study | 99.6 |
Metallothionein-like protein class II (Fagus sylvatica type) was described in Norway spruce (Picea abies), whose expression pattern was analyzed via ESTs from cDNA libraries . Type 4 metallothionein-like protein genes are expressed in inner bark tissue of Japanese cedar (Cryptomeria japonica) . ESTs encoding metallothionein-like proteins were the most frequently found hits in both early and late flushing libraries. Metallothionein-like protein activity is probably initiated by some cellular events during late flushing . | study | 100.0 |
Changes in expression of metallothioneins and metallothionein-like proteins have been previously reported in response to biotic stresses in plants, including insect herbivory and fungal infections (reviewed in ). There is not consensus of the role of metallothionein-like proteins in biotic stress response, but a role in oxidative stress have been proposed . | review | 99.9 |
A number of insects and diseases cause significant loss to forest productivity. Most of the current operational strategies for insect and disease control rely on classical breeding methods to develop populations enriched for resistance . With emergence of genomics-based approaches, such as genome-wide association studies (GWAS) and genomic selection (GS), a broader range of applications is now available for plant breeding and genetic research [55, 56]. In Fagus, mapping populations have been developed to discover QTLs for traits correlated to BBD. The tree improvement program included crosses to study inheritance of resistance to Cryptococcus fagisuga (see [19, 57]). However, association mapping like GWAS for QTLs underlying disease resistance to the BBD, has not been previously reported. In the present study, we used a GWAS mapping approach and a SNP linkage map to identify candidate resistance genes. To confirm the SNPs identified are truly associated with the scale-resistant trait, replication of this GWAS study is necessary, using independent case-control data from the initial population of unrelated individuals (see [58, 59]). | study | 99.94 |
Deployment of resistant planting stock can help to reduce disease incidence throughout natural stands of American beech. Markers found in this study that exhibit a significant association with the resistant phenotype, can be further refined to develop efficient and cost effective indirect selection techniques such as MAS (marker assisted selection) and genomic selection (GS) or combination of both (see ). | study | 83.06 |
To our knowledge, this is the first study designed to determine the genetic factors of disease resistance to beech bark disease (BBD) with genome scan analysis in American beech tree. The results presented identified four highly significant markers associated with a single locus located on chromosome (Chr) 5. All four loci were localized to the same contig within a single gene (Mt), that encodes for Fagus sylvatica mRNA for metallothionein-like protein (metal ion binding). Once a reference genome sequence is available, it will be possible to gain more insight into functional annotation of the four SNPs and determine the exact number of genes associated to BBD. | study | 100.0 |
Additional file 1:The list of sampled trees and their phenotypes. (XLSX 146 kb) Additional file 2:RNA‐seq data quality assessment. (PDF 853 kb) Additional file 3:Examples of RNA vs. mRNA Agilent Bioanalyzer quality profiles. (PDF 288 kb) Additional file 4:The sequences and statistics for the high quality and most informative SNPs used in the GWAS. (XLSX 1080 kb) Additional file 5:The list of mapped SNPs in 12 linkage groups (LGs). (XLSX 91 kb) Additional file 6:(A) DAPC analysis revealed three main genetic clusters where the individuals shown as dots and the groups as inertia ellipses. Eigenvalues of the analyses are displayed inset. (B) Quantile-quantile (QQ) plot of GWA p-values where on x-axis, are expected –log10 P values and on y-axis observed –log10 P values. The plot is showing large deviation from the null distribution where the inflation factor was higher than the threshold of 1, indicating a high genomic inflation in Beech association data and an existence of the population stratification. (PDF 90 kb) Additional file 7:The statistics for Fisher exact test and Logistic regression test. (XLSX 4534 kb) Additional file 8:(A) Four highlighted markers with significance level higher than genome-wide threshold (P value >1.585 × 10–5) located on the chromosome (Chr) 5. (B) Alignment of four nucleotide sequences to reference sequence Fagus sylvatica mRNA (Sequence ID: AJ130886.1). The FASTA sequence order corresponds as AX-156994126 (SEQ_1), AX-156989406 (SEQ_2), AX-156988334 (SEQ_3) and AX-157000652 (SEQ_4). Highlighted red nucleotides refer to polymorphism to reference sequence and green nucleotides present diagnostics SNPs, respectively. (DOCX 4155 kb) Additional file 9:RNA sequence reads from each cDNA library mapped to the full-length copy of the candidate gene transcript sequence from contig 03321, representing the expression of the candidate Mt gene after the challenge by the insect vector. (DOCX 161 kb) Additional file 10:Linkage disequilibrium (LD) structure across four SNPs associated to BBD. (Red) strong LD between markers; (white) no LD. The block-like pattern represents the regions of high LD. Pairwise LD among four SNPs listed as squared allelic correlation (r2) and Lewontin’s D’ . (DOCX 162 kb) | study | 100.0 |
(A) DAPC analysis revealed three main genetic clusters where the individuals shown as dots and the groups as inertia ellipses. Eigenvalues of the analyses are displayed inset. (B) Quantile-quantile (QQ) plot of GWA p-values where on x-axis, are expected –log10 P values and on y-axis observed –log10 P values. The plot is showing large deviation from the null distribution where the inflation factor was higher than the threshold of 1, indicating a high genomic inflation in Beech association data and an existence of the population stratification. (PDF 90 kb) | study | 100.0 |
(A) Four highlighted markers with significance level higher than genome-wide threshold (P value >1.585 × 10–5) located on the chromosome (Chr) 5. (B) Alignment of four nucleotide sequences to reference sequence Fagus sylvatica mRNA (Sequence ID: AJ130886.1). The FASTA sequence order corresponds as AX-156994126 (SEQ_1), AX-156989406 (SEQ_2), AX-156988334 (SEQ_3) and AX-157000652 (SEQ_4). Highlighted red nucleotides refer to polymorphism to reference sequence and green nucleotides present diagnostics SNPs, respectively. (DOCX 4155 kb) | study | 100.0 |
Linkage disequilibrium (LD) structure across four SNPs associated to BBD. (Red) strong LD between markers; (white) no LD. The block-like pattern represents the regions of high LD. Pairwise LD among four SNPs listed as squared allelic correlation (r2) and Lewontin’s D’ . (DOCX 162 kb) | study | 97.25 |
肺癌为脑转移最常见的原发肿瘤，约占全部脑转移瘤的30%-40%且为升高趋势，自然中位生存期仅为1个月-2个月。计算机断层扫描（computed tomography, CT）图像一直是放疗中靶区和危及器官勾画的最佳图像资源，但由于对软组织结构区分的能力差，肿瘤边界模糊，即使采用增强CT仍有部分病灶显示不清或不显示，脑转移瘤常多发且伴有水肿，使得通过CT及参考常规的核磁共振成像（magnetic resonance imaging, MRI）胶片勾画靶区时很难界定肿瘤区（gross tumor volume, GTV）边界，往往造成靶区勾画过大、靶区勾画不确定。本研究通过对比影像融合前后勾画的靶区，探讨CT/MRI图像融合技术在肺癌脑转移靶区勾画中的作用。 | other | 99.7 |
扫描后将CT及MRI图像经局域网传输至治疗计划系统（VARIN Eclipse version 10.0），本系统可根据CT图像及MRI图像分别重建三维图像，采用Point Match法，选择基底动脉、晶体、视神经、脑干等作为参考点进行融合，融合最大误差、平均误差均控制在2 mm以内，完成后由一位高年资放疗科医师分别对每例患者增强CT图像及CT/MRI融合后的图像进行靶区勾画。红色为GTVCT，绿色为GTVCT/MRI，利用VARIN治疗计划系统上靶区体积计算功能分别计算出各例患者基于CT/MRI融合图像及增强CT图像所勾画的GTV体积并计算比值，若比值介于0.9-1.1，为两种靶区勾画方法体积接近；比值≤0.9或≥1.1均为两种靶区勾画方法差异明显。最大平均误差用两组GTV边界相差最大值的均数来表示。 | other | 99.8 |
Multiple metastases (A: Enhanced CT image; B: MRI image; C: CT/MRI fusion image). The diagrams (Fig 2 to Fig 4) of A, B, C respectively for the same patients with enhanced CT, brain MRI, CT/MRI fusion images, GTVCT with red line display, GTVCT/MRI green line display. | other | 99.9 |
As a short-day and temperate plant, soybean (Glycine max(L.) Merr.) is sensitive to photo-thermal conditions during flower initiation and development [1–3]. The responses of soybean cultivars to photo-thermal conditions determine the zone of their adaptation and affect yield, plant height, seed quality, etc. [4, 5]. | other | 99.7 |
Flowering time is one of the most important traits associated with seed yield and adaptation of soybean. Soybean flowering time is regulated by both genetic and environmental factors [6, 7]. At least 11 major loci control flowering time and maturity in soybean, including E1– E10 [8–17] and J . Among them, six genes (E1, E2, E3, E4 E9 and J) have been cloned or identified. E1 was reported to be a legume-specific transcription factor which could delay soybean flowering time in long-day conditions . E2 was identified to be an ortholog of the Arabidopsis GIGANTEA gene . E3 and E4 were confirmed to be homologs of PHYA . E9 was recently identified as GmFT2a, an ortholog of Arabidopsis FT . J was the dominant functional allele of GmELF3 . GmFT5a was also identified as a key gene to regulate soybean flowering time . Other orthologs of Arabidopsis flowering genes such as GmCOLs , GmSOC1 , and GmCRY , and many other genes controlling flowering time have also been identified . | study | 99.9 |
Environmental factors, especially photoperiod and temperature, play important roles in flowering time. In previous studies, short day and high temperature accelerated the process from emergence to first flowering of soybean, whereas long day and low temperature delayed flowering time [2, 3, 7]. The interaction between photoperiod and temperature also influences soybean flowering time [2, 3, 7]. However, the genetic mechanism of photo-thermal effects on soybean flowering time is not well documented. | study | 99.94 |
The interaction between gene and environment underlying flowering time has been well elucidated in Arabidopsis thaliana , Boechera stricta and other species. In soybean, the effects of the genes on flowering time and maturity are influenced by environmental conditions . Previous analysis of 39 near-isogenic lines (NILs) with 6 E genes (E1, E2, E3, E4, E5 and E7) indicated that the effects of dominant alleles on flowering were enhanced in the long day and weakened in the short day . The effects of E genes on maturity were also influenced by sowing seasons with different photo-thermal combinations. Each dominant gene had a smaller effect on maturity of soybean planted in summer than in spring . The effects of the QTLs varied with the photoperiodic conditions and latitudinal environments and were population-specific, which enabled the plants to adjust to different climatic conditions [33, 34]. However, the responses of flowering time to photoperiod and temperature has not been systematically analysed. | study | 99.94 |
QTXNetwork is a GPU parallel computing software to reveal genetic and environmental interaction underlying the genetic architecture of complex traits , the algorithm of the software was based on a mixed linear model. The software was used to study the genetic variations of lint yield and its component traits in cotton , and the chromium content and total sugar level in tobacco leaf . | other | 99.7 |
The objectives of this study were to determine the variation of QTL effects under different photo-thermal environments and the interaction between the QTL and environments on soybean flowering time using a diverse set of soybean genotypes from different ecological regions. | study | 100.0 |
The diversity panel used in this study consisted of 91 cultivars originating from different ecological regions in China (75 cultivars) and different maturity groups in the US (16 cultivars). The Chinese cultivars included six sowing season ecotypes, i.e., Northern Spring Sowing type (Nsp) (29 cultivars), Huang-Huai-Hai Spring Sowing type (Hsp) (4 cultivars), Huang-Huai-Hai Summer Sowing type (Hsu) (13 cultivars), Southern Spring Sowing type (Ssp) (13 cultivars), Southern Summer Sowing type (Ssu) (8 cultivars) and Southern Autumn Sowing type (Sau) (8 cultivars) covering a range of latitudes from 20°03’N to 50°15’N. The US cultivars were from different maturity groups (MG 0-VI) (Additional file 1: Table S1). | study | 100.0 |
The pot experiments were conducted outdoor at the Institute of Crop Science, CAAS, Beijing, China (39°54’N, 116°46’E) during 2009 and 2010. In 2009, only 25 cultivars from different ecological regions were used (Additional file 1: Table S1). The pots were arranged in a completely randomized design with three replications in six photo-thermal environments. These cultivars were planted on May 4 (spring) and June 18 (summer) in 2009, and on April 10 (spring) and June 29 (summer) in 2010, so the plants could be exposed to low temperature (LT) by growing in the spring and high temperature (HT) in the summer . Each replicate consisted of five seedlings with uniform growth in each pot. After the cotyledons were fully expanded (VC), the plants were placed in four different photoperiod treatments: short day (SD) (12 h), long day (LD) (16 h), natural day-length of spring sowing in Beijing (SP) and natural day-length of summer sowing in Beijing (SU). Under the SD treatment, seedlings were placed in the natural sunshine for 12 h, followed by 12 h in the darkness from 7 pm to 7 am. A platform truck was used to transfer the plants to the dark room. Under the LD treatment, plants were provided artificial light from 4 am to 6 am and from 6 pm to 8 pm. Incandescent bulbs with photosynthetically active radiation (PAR) at approximately 50 μmols−1m−2 placed above the canopy when the bulbs were the only source of light [37, 38]. The mean natural day-length of planting season (May 4- October 9) in Beijing was 13.82 h, and the longest (June 23) and shortest (October 9) day-length were 15.02 h and 11.45 h, respectively. | study | 100.0 |
The field experiments were also conducted at the Institute of Crop Science, CAAS, Beijing, China in 2014 and 2015. These cultivars were planted on April 30 (spring) (14SP) and June 25 (summer) (14SU) in 2014, and on May 4 (spring) (15SP) and July 1 (summer) (15SU) in 2015. All lines were arranged in a completely randomized design with three replications. | study | 92.1 |
Genomic DNA was isolated from fresh leaves of five plants of each cultivar using the SDS (sodium dodecyl sulfate) method . One hundred and seventy-two SSR makers associated with QTLs controlling phenological traits and other agronomic traits were selected according to previous studies (SoyBase (http://www.soybase.org)). SSR primers were from SoyBase (http://www.soybase.org). The PCR reaction mixture contained 100 ng of genomic DNA, 2 μl of 10 × PCR Buffer (+Mg2+), 2 μl of dNTPs (2 mM), 0.5 μl of SSR primer (10 mM), 0.2 μl of Taq polymerase (10 units/μl) and 13.8 μl of ddH2O in a total volume of 20 μl. The amplification program consisted of 94 °C for 5 min, 35 cycles of 94 °C for 30 s, 49 °C for 30 s, 72 °C for 45 s and 72 °C for 5 min. Then, the PCR products were separated on 6% w/v denaturing polyacrylamide gels, and the fragments were visualized by silver staining. The cultivars were also genotyped with Illumina BARCSoySNP6K iSelectBeadChip (Illumina, San Diego, Calif. USA) containing 5,403 SNPs selected from SoySNP50K . After elimination of SNPs with missing allele >24%, or minor allele frequency <0.05 , a total of 5,107 SNPs remained (Additional file 2: Table S2). SSR and SNP data were used for association mapping, and the SNP data was used for QTXNetwork analysis. | study | 100.0 |
The population structure was inferred from 63 SSR markers, which were randomly chosen and evenly distributed on 20 chromosomes (Additional file 2: Table S2), using the Bayesian Markov Chain Monte Carlo model via STRUCTURE v.2.3.1 software . The K value (number of subpopulations) was set from 1 to 10 using a burn-in of 50,000, a run length of 100,000, and each K value was obtained with seven independent runs. The ad hoc quantity (ΔK) was estimated through the website (http://taylor0.biology.ucla.edu/structureHarvester) to determine the true K value . The Q matrix was obtained by the CLUMPP software and by integrating the cluster membership coefficient matrices of replicated runs from STRUCTURE. A similar procedure described above was used for population structure analysis based on 5,107 SNP makers. A principal component analysis (PCA) for population structure was conducted by GenAlex 6.5 and the neighbour-joining tree was constructed by POWERMARKER v. 3.25 and MEGA 5. The genetic diversity of the panel was also analysed by POWERMARKER v. 3.25. | study | 100.0 |
The TASSLE v. 3.0 software was used to calculate the linkage disequilibrium (r 2) for all pairwise loci of the SNP markers . The General Linear Model (GLM) and the Q matrix from STRUCTURE software were used to identify the association of 172 SSR and 5,107 SNP markers with flower time . The Bonferroni-corrected thresholds for the p-value were used to determine the significance of association and were 2.90 × 10−4 (0.05/172), and 9.79 × 10−6 (0.05/5107) for SSR and SNP markers, respectively. Functional annotations of SNPs and SSRs were performed using the Phytozome database (https://phytozome.jgi.doe.gov) and SoyBase database (SoyBase (http://soybase.org). | study | 100.0 |
The QTXNetwork software was used to dissect the genetic architecture of the flowering time with 5,107 SNPs. Association mapping was performed using the mixed linear model with environment (E) as a fixed effect, and the loci effects (a, additive effect; aa, epistasis effect) and loci by environment interaction (ae, additive by environment interaction; aae, epistasis by environment interaction) as random effects . The loci with –log10(P-value) > 3.0 in different environments were identified. | study | 100.0 |
A wide range of phenotypic variation was observed in flowering time in the association panel across different photo-thermal conditions (Table 1). All cultivars can flower under the SD or natural-day condition regardless of the sowing season. However, some cultivars in the LD condition failed to flower at the harvest season. The soybean flowering time followed a normal distribution except for flowering time in natural day-length conditions, which was slightly skewed to the early flowering (Table 1, Additional file 3: Figure S1). The duration from emergence (VE) to the beginning bloom (R1) was shorter in the SD than that in the LD condition given the same sowing season. However, the time from emergence (VE) to the beginning bloom (R1) was accelerated in the HT compared with that in the LT under the same day-length.Table 1Descriptive statistics of soybean flowering time in different photo-thermal treatmentsYearEnvironmenta Min.Max.Mean ± SECV(%)SkewnessKurtosis(d)(d)(d)2009b SD + LT22.435.628.4 ± 0.711.850.470.16SD + HT21.531.325.9 ± 0.610.600.17−0.89LD + LT30.0>114.8c >70.9 ± 5.0c 33.87−0.10−0.85LD + HT24.0>80.2c >47.5 ± 3.6c 35.570.62−0.47SP28.5133.652.4 ± 4.945.681.774.59SU25.264.837.9 ± 2.835.11−0.651.942010SD + LT24.035.129.0 ± 0.38.690.10−0.27SD + HT22.131.826.6 ± 0.27.740.18−0.47LD + LT26.7>165.7c >98.0 ± 3.9c 35.67−0.03−0.61LD + HT26.6>103.4c >61.5 ± 2.3c 31.550.41−0.28SP25.3137.553.9 ± 2.949.261.341.37SU23.481.538.8 ± 1.229.181.151.56201414SP19.5132.950.9 ± 3.258.391.120.2514SU18.684.539.2 ± 1.639.510.950.25201515SP19.7124.847.6 ± 2.957.910.99−0.1315SU19.176.036.3 ± 1.436.790.830.08 aSD, 12 h; LD, 16 h; LT, low temperature (spring sowing); HT, high temperature (summer sowing); SP, Spring sowing season with natural day-length in pot experiment; SU, Summer sowing season with natural day-length in pot experiment; 14SP, Spring sowing in 2014 field experiment; 14SU, Summer sowing season in 2014 field experiment; 15SP, Spring sowing season in 2015 field experiment; 15SU, Summer sowing season in 2015 field experiment bA total of 91 cultivars were tested in the experiment in 2010, 2014 and 2015, and a subset of 25 cultivars from different maturity groups were used in the experiments in 2009, the cultivars were listed in the Additional file 1 cSome late cultivars failed to flower before the end of experiment. The flowering time of the latest-flowered cultivar in the same treatment was used as that of the un-flowered cultivars when calculating the means | study | 100.0 |
aSD, 12 h; LD, 16 h; LT, low temperature (spring sowing); HT, high temperature (summer sowing); SP, Spring sowing season with natural day-length in pot experiment; SU, Summer sowing season with natural day-length in pot experiment; 14SP, Spring sowing in 2014 field experiment; 14SU, Summer sowing season in 2014 field experiment; 15SP, Spring sowing season in 2015 field experiment; 15SU, Summer sowing season in 2015 field experiment | other | 99.9 |
Collectively, high temperature and short day had additive effects on accelerating the flowering time. The mean pre-flowering phase was the shortest in the SD + HT condition (25.9 d and 26.6 d in 2009 and 2010, respectively) and the longest in the LD + LT condition (70.9 d and 98.0 d or more in 2009 and 2010, respectively). These results suggest that flowering time can be greatly affected by photo-thermal conditions as described in the previous studies . | study | 100.0 |
The population structure was assessed by STRUCTURE v.2.3.1 software based on SSR and SNP markers and the most likely number of sub-populations were consistent based on the two types of markers. When K = 2, the ad hoc quantity (ΔK) estimation had the highest value (Fig. 1a, Fig. 1b, Additional file 4: Figure S2a and Additional file 4: Figure S2b) . The first sub-population contained 46 cultivars, a majority of which were from the late maturity groups in the Huang-Huai-Hai River Valley, and south China (95.7%). The cultivar ‘Altana’ from the US was also in this group. The second sub-population consisted of 45 cultivars of the early maturity groups (93.3%), which were from northeast China (60%) and the US (33.3%). A cluster analysis and PCA also showed that the genotypes were classified into two groups (Fig. 1c, Fig. 1d, Additional file 4: Figure S2c and Additional file 4: Figure S2d).Fig. 1Population structure of 91 soybean cultivars using 5107 SNP markers. a Estimation of the number of sub-populations. The left figure was a plot of ln (probability of data) vs. K ranging from 1 to 10 and the right figure was a plot of subpopulation number vs. delta K values. b Population structure of 91 soybean cultivars based on SNP markers. The x-axis indicates the cultivars, and the y-axis indicates the Q value from STRUCTURE 2.3.1. The red color represents one sub-group, the green color represents another. c PCA of 91 soybean cultivars with the top two principal components. d Neighbor-joining tree of the 91 soybean cultivars | study | 100.0 |
Population structure of 91 soybean cultivars using 5107 SNP markers. a Estimation of the number of sub-populations. The left figure was a plot of ln (probability of data) vs. K ranging from 1 to 10 and the right figure was a plot of subpopulation number vs. delta K values. b Population structure of 91 soybean cultivars based on SNP markers. The x-axis indicates the cultivars, and the y-axis indicates the Q value from STRUCTURE 2.3.1. The red color represents one sub-group, the green color represents another. c PCA of 91 soybean cultivars with the top two principal components. d Neighbor-joining tree of the 91 soybean cultivars | study | 100.0 |
The averaged numbers of alleles per locus for SNPs and SSRs were 1.648 and 6.657, respectively, and the PIC values for SNPs and SSRs were 0.198 and 0.605, respectively (Table 2). The genetic diversity of SNP (0.250) is less than that of SSR (0.646), which is likely due to the difference of the bi-allele nature of SNP and the multi-allele nature of SSR. However, because the total number of SNPs is 29.7 times as high as that of SSR, indicating that SSR can provide more genetic information than SNP for assessment of genetic relatedness. The Fst between the two sub-populations defined by the STRUCTURE were 0.023 and 0.029 for SSRs and SNPs, respectively, which were similar to that between soybean breeding lines and landraces (0.0267) in a previous study . Low population differentiation indicated a narrow genetic background in modern soybean cultivars.Table 2The genetic diversity of soybean population based on SSR and SNP markersMarkerSNPsSSRsMajor Allele Frquency0.8060.481Alleles per locus1.6486.657Gene Diversity0.2500.646Heterozygosity0.0730.023PIC0.1980.605 Fst 0.0290.023 | study | 100.0 |
Linkage disequilibrium was analysed using SNPs with a minor allele frequency more than 5% and missing data less than 24%, the linkage disequilibrium of the population was decayed to r 2 = 0.2 within approximately 300 kb (Fig. 2). The result was consistent with the previous studies in soybean (125 kb -600 kb) .Fig. 2The estimated average linkage disequilibrium decay of soybean genome. The dashed line in red indicates the position where r 2 is 0.2 | study | 100.0 |
A total of 118 SNPs with p < 9.79 × 10−6 and 11 SSRs with p < 2.86 × 10−4 were associated with the phenotypic values when GLM was performed (no loci was detected in 2009). The markers were further clumped based on the linkage disequilibrium blocks defined using the method described previously and resulted in 87 QTLs for flowering time (Table 3). The proportion of genotypic variance explained by QTLs ranged from 13 to 35% among different environments (Table 3, Additional file 5: Figure S3, Additional file 6: Figure S4). The number of detected loci in each environment was different. There were 27, 23, 24, 24, 23, 45, 52 and 36 loci significantly associated with flowering in the LD + LT, LD + HT SP, SU, 14SP, 14SU, 15SP and 15SU, respectively. In addition, a total of 30 loci were detected in both pot experiments and field experiments, suggesting the soybean flowering were controlled by both environment-sensitive loci and environment-insensitive loci.Table 3The loci associated with flowering time and their phenotypic variation explained by the GLM modelMarkerChrPositionLD + LTLD + HTSPSU14SP14SU15SP15SUKnownDistanceReport QTLsa genes(100Kbp)Gm01_53278791Gm01532787910.130.13First flower 16-1Gm01_53675540Gm01536755400.17 N Gm02_10536842Gm02105368420.140.130.13First flower 16–2Gm02_11998056Gm02119980560.19First flower 16–2 Pod maturity 19–1Gm02_22829006Gm02228290060.160.150.16First flower 13–4;First flower 13–2Sat_135Gm02403662150.340.35First flower 13–2Gm03_1077329Gm0310773290.16 N Gm03_5502496Gm0355024960.140.130.15 N Gm03_36634361Gm03366343610.160.14Pod maturity 16–4;Flower number 1–2Gm03_38526701Gm03385267010.13Flower number 1–2Gm04_4497001Gm0444970010.160.160.150.170.130.160.13 GmCOL3a 2.78First flower 22–1Gm04_38840391Gm04388403910.16 N Gm04_42951376Gm04429513760.14Flower number 1–3Gm04_46390533Gm04463905330.14 N Gm05_682648Gm056826480.14 N Gm05_1705841Gm0517058410.13 N Gm05_26685967Gm05266859670.130.13 N Gm05_38636402Gm05386364020.130.130.14 N Gm05_40349605Gm05403496050.160.190.16 N Gm06_2086304Gm0620863040.17 N Gm06_2253042Gm0622530420.180.15 N Satt422Gm0672276380.19Pod maturity 26–1Gm07_3143196Gm0731431960.13First flower 4–2Gm08_11052135Gm08110521350.13Days to maturity 1-g1Gm08_40882335Gm08408823350.170.140.15 N Gm09_2327785Gm0923277850.160.150.15 N Gm09_24238724Gm09242387240.130.140.13First flower 3–4Gm09_39822766Gm09398227660.14Photoperiod insensitivity 5–2Gm09_43508261Gm09435082610.130.170.190.13 N Gm10_2317882Gm1023178820.140.150.140.15Days to flowering 1-g18;Days to maturity 1-g14Gm11_1161553Gm1111615530.17 N Gm11_3950213Gm1139502130.150.140.140.14Pod maturity 24–6Gm11_4519147Gm1145191470.150.17NGm11_5065170Gm1150651700.20.130.140.14Node number 3–3Gm11_6512939Gm1165129390.13 N Gm11_6901726Gm1169017260.15Flower number 1–5;Pod number 1–5Satt197Gm1188794800.30.29First flower 11–1;Pod maturity 17–1Gm11_10847172Gm11108471720.180.140.210.210.230.270.280.23Pod maturity 18–2Gm11_11572077Gm11115720770.170.180.150.180.240.16 N Gm11_16492046Gm11164920460.190.160.150.150.15First flower 11–2;First flower 8–4Gm11_17237725Gm11172377250.170.140.190.170.18Pod maturity 18–1Gm11_21023332Gm11210233320.15First flower 11–2;First flower 8–4Gm11_33034954Gm11330349540.170.190.210.130.250.260.24 N Gm11_33555216Gm11335552160.180.150.150.190.170.190.18 N Gm11_36174968Gm11361749680.160.140.160.160.150.170.18Pod maturity 22–2Gm12_5786241Gm1257862410.180.16Reproductive stage length 7–3;maturity 26–2Gm12_8435100Gm1284351000.14 N Gm12_13354287Gm12133542870.13 N Gm12_14231203Gm12142312030.15 N Satt586Gm13116399800.190.2First flower 11–4;Pod maturity 17–5Gm13_23509779Gm13235097790.140.150.14Photoperiod insensitivity 5–3Gm13_39307253Gm13393072530.15 GmCOL10b 2.25Days to flowering 1-g10Gm14_7302299Gm1473022990.150.140.170.190.15Flower number 1–6Gm14_44697544Gm14446975440.150.15 N Gm14_45457682Gm14454576820.140.14First flower 21–1Gm14_49107190Gm14491071900.140.150.140.270.210.19First flower 21–1Gm15_1265753Gm1512657530.170.150.140.13First flower 12–3;Flower number 1–7Gm15_13098003Gm15130980030.150.150.130.140.170.15First flower 12–3;Flower number 1–7Gm15_25411335Gm15254113350.17 N Gm15_35867161Gm15358671610.160.150.16 N Satt452Gm15389231520.170.20.180.18 N Gm15_45004801Gm15450048010.150.15 N Gm16_5773005Gm1657730050.14 N SSRFTGm16307416000.20.250.270.260.270.23 GmFT2a inter-geneGmFT2aGm16_30766209Gm16307662090.260.180.160.180.160.20.240.28 GmFT2a; 0.20;First flower 9–3 GmFT2b 0.14Gm16_35700223Gm16357002230.160.150.170.170.150.190.230.15First flower 13–8;Photoperiod insensitivity 5–4Gm17_37574384Gm17375743840.13 N Gm17_41063513Gm17410635130.13 N Gm18_4324818Gm1843248180.170.13Pod maturity 22–9Gm18_34401760Gm18344017600.180.170.16Photoperiod insensitivity 2–2Gm18_36929655Gm18369296550.180.20.140.140.16First flower 10–2Satt564Gm18476177950.210.260.210.240.22Flower number 1–9;Pod number 1–8Gm18_57126096Gm18571260960.130.150.140.140.14 N Gm19_5195925Gm1951959250.13 GmCOL2b 2.85 N Gm19_35449676Gm19354496760.14 N Gm19_39723056Gm19397230560.140.140.14First flower 15–2Sat_113Gm19421103320.290.280.290.290.29First flower 4–3;Pod maturity 24–10Satt664Gm19461097000.260.19 GmCOL11b 0.14Flower form 1–4bGm19_46761039Gm19467610390.160.15First flower 13–9;Flower form 1–4;First flower 16–4Satt229Gm19470490740.18First flower 20–2;First flower 13–9;Flower form 1–4Gm19_47514601Gm19475146010.14 E3 inter-geneFlower form 1–4;First flower 16–4;First flower 5–2;First flower 5–3Gm19_49786000Gm19497860000.15First flower 5–3;First flower 8–3;First flower 16–4;Flower form 1–4Satt571Gm2012918090.18Pod maturity 24–5Gm20_3880320Gm2038803200.220.20.170.160.180.18First flower 16–3Gm20_37857633Gm20378576330.140.150.14First flower 16–3;Flower form 1–3Gm20_43146832Gm20431468320.20.220.160.190.160.210.230.19 GmCRY2c 3.3Flower number 1–11Gm20_44260228Gm20442602280.130.140.140.15Flower number 1–11 aQTLs are from http://www.soybase.org; N indicates that there were no reported QTL near the loci related to flowering time; Chr: chromosome; LD + LT: 16 h and spring sowingin 2010; LD + HT: 16 h and summer sowingin 2010. SP, Spring sowing season with natural day-length in 2010 pot experiment; SU, Summer sowing season with natural day-length in 2010 pot experiment; 14SP, Spring sowing in 2014 field experiment; 14SU, Summer sowing season in 2014 field experiment; 15SP, Spring sowing season in 2015 field experiment; 15SU, Summer sowing season in 2015 field experiment | study | 100.0 |
aQTLs are from http://www.soybase.org; N indicates that there were no reported QTL near the loci related to flowering time; Chr: chromosome; LD + LT: 16 h and spring sowingin 2010; LD + HT: 16 h and summer sowingin 2010. SP, Spring sowing season with natural day-length in 2010 pot experiment; SU, Summer sowing season with natural day-length in 2010 pot experiment; 14SP, Spring sowing in 2014 field experiment; 14SU, Summer sowing season in 2014 field experiment; 15SP, Spring sowing season in 2015 field experiment; 15SU, Summer sowing season in 2015 field experiment | other | 99.4 |
A total of 32 markers were significantly associated with flowering time and were specific to photo-thermal (detected in only one environment) (Table 3). A total of 55 markers were associated with flowering time in two and more environments, among these, four markers (Gm11_10847171, Gm16_30766209, Gm16_35700223, Gm20_43146832) were identified in eight environments. The results indicated that these loci were important in controlling soybean flowering under multi-environments. | study | 100.0 |
The most significant loci associated with flowering time varied under different photo-thermal conditions (Table 3, Fig. 3). Among SSR markers, Satt664 on Chr19 was the most significant locus associated with flowering time in the two LD conditions; Sat_135 on Chr02 was the most significant locus in the SP and SU conditions. Whereas, Sat_113 on Chr19 was the most significant locus in the 14SP, 14SU and 15SU conditions, respectively. Satt197 on Chr11 was the most significant locus with flowering time in the 15SP and 15SU conditions, respectively. Among SNPs, Gm11_10847172 was the locus most significantly associated with flowering time in the four conditions (SU, 14SP, 14SU and 15SP). Gm16_30766209 was the locus most significantly associated with flowering time in LD + LT and 15SU conditions, respectively. Gm20_43146832 on Chr20, Gm20_3880320 on Chr20 and Gm11_33034954 on Chr11 were the locus most significantly associated with flowering time in the LD + HT, SP and SU conditions, respectively. The significant SNPs were also detected in more than six environments, indicating that these loci may involve in regulation of flowering time in different photo-thermal conditions.Fig. 3Manhattan plot and linkage disequilibrium block in different environments. Linkage disequilibrium blocks associated with flowering time near Gm11_10847172, Gm11_33034954, Gm16_30766209, Gm20_3880320 and Gm20_43146832. Significance threshold is denoted by the orange line. The up panel was the Manhattan plots of negative log10-transformed P values vs. SNPs. The down panel was haplotype block based on pairwise linkage disequilibriumr2values. LD, 16 h; LT, low temperature (spring sowing); HT, high temperature (summer sowing); SP, Spring sowing season with natural day-length in 2010 pot experiment; SU, Summer sowing season with natural day-length in 2010 pot experiment; 14SP, Spring sowing in 2014 field experiment; 14SU, Summer sowing season in 2014 field experiment; 15SP, Spring sowing season in 2015 field experiment; 15SU, Summer sowing season in 2015 field experiment | study | 100.0 |
Manhattan plot and linkage disequilibrium block in different environments. Linkage disequilibrium blocks associated with flowering time near Gm11_10847172, Gm11_33034954, Gm16_30766209, Gm20_3880320 and Gm20_43146832. Significance threshold is denoted by the orange line. The up panel was the Manhattan plots of negative log10-transformed P values vs. SNPs. The down panel was haplotype block based on pairwise linkage disequilibriumr2values. LD, 16 h; LT, low temperature (spring sowing); HT, high temperature (summer sowing); SP, Spring sowing season with natural day-length in 2010 pot experiment; SU, Summer sowing season with natural day-length in 2010 pot experiment; 14SP, Spring sowing in 2014 field experiment; 14SU, Summer sowing season in 2014 field experiment; 15SP, Spring sowing season in 2015 field experiment; 15SU, Summer sowing season in 2015 field experiment | study | 100.0 |
The alleles of the significant SNPs (Gm11_10847172, Gm11_33034954, Gm16_30766209, Gm20_3880320 and Gm20_43146832) had different effects on flowering time across different photo-thermal conditions (Fig. 4, Additional file 7: Table S3). 53 and 38 cultivars contained T allele and C allele for the SNP Gm11_10847172, respectively. The flowering time of the cultivars with minor allele C were delayed for 38.5 d, 19.8 d, 21.8 d, 10.6 d, 32.4 d, 17.2 d, 33.4 d and 15.9 d compared with that of the cultivars with T allele (major allele) under the LD + LT, LD + HT, SP, SU, 14SP, 14SU, 15SP and 15SU, respectively. Similarly, the cultivars carrying the minor allele G of the SNP Gm20_43146832 were 48.7 d, 32.8 d, 31 d, 14.5 d, 39.4 d, 20.3 d, 37.4 d and 16.8 d later in flowering time than the those carrying the major allele A in the LD + LT, LD + HT, SP, SU, 14SP, 14SU, 15SP and 15SU conditions, respectively. The same patterns of the association of the two alleles with flowering time were also observed at other three significant loci Gm11_33034954, Gm16_30766209 and Gm20_3880320. Generally, LD could extend the difference of the flowering time between the cultivars carrying different alleles, while high temperature (summer sowing) could reduce the difference of the flowering time between the cultivars (Fig. 4, Additional file 7: Table S3).Fig. 4Phenotypic variation between cultivars carrying different alleles of the SNPs significantly associated with flowering time in various environments. The box plot shows the significant difference of days to flowering of the cultivars carrying two alleles of the SNPs. The significant SNPs were Gm11_10847172, Gm11_33034954, Gm16_30766209, Gm20_3880320 and Gm20_43146832. The major allele of significant loci was marked by yellow, and the minor allele was marked by green. Significant differences tested by the student’s t-test are also given (***p < 0.001, **p < 0.01, *p < 0.05). LD, 16 h; LT, low temperature (spring sowing); HT, high temperature (summer sowing); SP, Spring sowing season with natural day-length in 2010 pot experiment; SU, Summer sowing season with natural day-length in 2010 pot experiment; 14SP, Spring sowing in 2014 field experiment; 14SU, Summer sowing season in 2014 field experiment; 15SP, Spring sowing season in 2015 field experiment; 15SU, Summer sowing season in 2015 field experiment | study | 100.0 |
Phenotypic variation between cultivars carrying different alleles of the SNPs significantly associated with flowering time in various environments. The box plot shows the significant difference of days to flowering of the cultivars carrying two alleles of the SNPs. The significant SNPs were Gm11_10847172, Gm11_33034954, Gm16_30766209, Gm20_3880320 and Gm20_43146832. The major allele of significant loci was marked by yellow, and the minor allele was marked by green. Significant differences tested by the student’s t-test are also given (***p < 0.001, **p < 0.01, *p < 0.05). LD, 16 h; LT, low temperature (spring sowing); HT, high temperature (summer sowing); SP, Spring sowing season with natural day-length in 2010 pot experiment; SU, Summer sowing season with natural day-length in 2010 pot experiment; 14SP, Spring sowing in 2014 field experiment; 14SU, Summer sowing season in 2014 field experiment; 15SP, Spring sowing season in 2015 field experiment; 15SU, Summer sowing season in 2015 field experiment | study | 100.0 |
To explore the genotype and environment interaction on soybean flowering time, we used the phenotype in 2010. The heritability of flowering time was 77.78%, and the heritability of additive and epistasis effects were 12.79% and 15.66%, respectively. The heritability of genotype × environment interaction was 49.33%, which was constituted by epistasis × environment interaction (h2 ae = 25.81%) and additive × environment interaction (h2 aae = 23.52%). These results indicated that soybean flowering time was mainly controlled by additive × environment interaction and the epistasis × environment interaction (Table 4).Table 4Estimated heritability of the flowering time in soybeanTotal Heritability (%)h2 a (%)h2 ae (%)h2 aa (%)h2 aae (%)77.7812.7923.5215.6625.81a, additive effect; ae, additive by environment interaction effect; aa, epistasis effect; aae, epistasis by environment interaction effect; h2(%) = heritability(%) | study | 100.0 |
There were 7 loci with significant additive effects and/or additive × environment interaction effects, and 2 pairs of loci with significant epistatic effect and/or epistasis × environment interaction effects on soybean flowering time in six environments (Table 5, Fig. 5, Additional file 8: Figure S5). Gm04_4497001, Gm04_42153936 and Gm15_11855585 had significant additive effect, indicating that the additive loci were stable in different environments, whereas Gm11_36124908, Gm16_30766209, Gm19_44042544 and Gm19_47514601 had both significant additive effects and additive × environment interactions, suggesting that these loci were sensitive to different environments. Among them, Gm11_36124908 was the most significant and had high heritability of additive effect (ha 2 = 6.73%) and additive × environment interaction (hae 2 = 31.96%). In addition, Gm04_4497001 interacted with two other loci (Gm11_36124908, Gm19_47514601) to control phenotypic variation of flowering time, and Gm04_4497001 and Gm19_47514601 had epistasis × environment interaction in the SP condition.Table 5The predicted genetic effects with significant heritability of the flowering time for soybeans in six environmentsLocusEffectPredicted value-Log10PHeritability (%)Candidate GenesGm04_4497001 a −2.145.820.33 Glyma04g06100 Gm04_42153936 a 3.6615.540.97 Glyma04g358100; Glyma04g35720 Gm11_36124908 a −9.6244.816.73 Glyma11g34250 ae1 8.306.467.99 ae2 −6.013.667.99 ae3 −10.149.337.99 ae4 8.366.547.99Gm15_11855585 a −2.658.610.51 Glyma15g15730; Glyma15g15400 Gm16_30766209 a −5.0011.791.81 Glyma16g26660; Glyma16g26690 ae1 5.913.288.61 ae3 −14.2616.218.61 ae4 6.293.658.61Gm19_44042544 a 4.7125.361.61Glyma19g36830 ae1 −4.043.843.07 ae3 8.5014.743.07 ae4 −4.103.953.07Gm19_47514601 a −3.3613.340.82 Glyma19g40980 ae2 −4.404.682.02Gm04_4497001 × Gm11_36124908 aa 3.787.5912.43Gm04_4497001 × Gm19_47514601 aa 1.934.823.23 aae2 3.783.9012.45a, additive effect; aa, epistasis effect; ae1, additive by environment interaction effect in 12 h day length in the spring sowing (SD + LT); ae2, additive by environment interaction effect in natural day treatment in the spring sowing (SP); ae3, additive by environment interaction effect in 16 h day length in the spring sowing (LD + LT); ae4, additive by environment interaction effect in 12 h day length in the summer sowing (SD + HT); aae2, epistasis by environment interaction effect in natural day treatment in the spring sowing (SP); -Log10P = minus log10(P-value); h2(%) = heritability(%) Fig. 5The plot of network of highly significant loci identified for soybean flowering time. The red dots represent the loci with additive effects; the blue dots represent the loci with both additive and environment-specific effects; red lines between two dots represent epistasis (aa); blue lines between two dots represent both epistasis (aa) and environment-specific epistasis (aae) | study | 100.0 |
a, additive effect; aa, epistasis effect; ae1, additive by environment interaction effect in 12 h day length in the spring sowing (SD + LT); ae2, additive by environment interaction effect in natural day treatment in the spring sowing (SP); ae3, additive by environment interaction effect in 16 h day length in the spring sowing (LD + LT); ae4, additive by environment interaction effect in 12 h day length in the summer sowing (SD + HT); aae2, epistasis by environment interaction effect in natural day treatment in the spring sowing (SP); -Log10P = minus log10(P-value); h2(%) = heritability(%) | other | 99.4 |
The plot of network of highly significant loci identified for soybean flowering time. The red dots represent the loci with additive effects; the blue dots represent the loci with both additive and environment-specific effects; red lines between two dots represent epistasis (aa); blue lines between two dots represent both epistasis (aa) and environment-specific epistasis (aae) | study | 99.94 |
We also found that the direction of additive × environment interaction effect on soybean flowering time is dependent on photoperiod, whereas the magnitude of additive × environment interaction effect is dependent on temperature (Table 5, Additional file 8: Figure S5). For instance, the additive by environment interaction of Gm19_44042544 had a negative effect in the SD condition but positive in the LD condition, showing that the locus could enhance flowering time in the SD condition but delay flowering time in the LD condition. In contrast, the additive by environment interaction of Gm16_30766209 and Gm11_36124908 were positive in the SD condition but negative in the LD condition, suggesting that these loci could delay flowering time in the SD condition and accelerate flowering time in the LD condition. In response to photoperiod, the locus Gm19_44042544 showed opposite effect on flowering time compared with Gm16_30766209 and Gm11_36124908. On the other hand, for Gm16_30766209 and Gm11_36124908, the magnitude of delaying effect on flowering time was larger in the HT condition than in the LT condition, and the effect of Gm19_44042544 on the delay of flowering was also larger in the HT condition than that of the LT condition. These results indicate that high temperature could enhance both the positive or negative effects on flowering time in the SD conditions. | study | 100.0 |
In the present study, a large variation of days to flowering was observed among different environments and 49.33% of total phenotypic variation was contributed by environmental and genetic interaction, indicating that photo-thermal conditions played an essential role in determining soybean flowering time in addition to the genetic effects. The photo-thermal treatments in the current study provided a good opportunity for dissecting for dissecting the effects of photoperiod and temperature on soybean flowering time. | study | 100.0 |
The environmental effect on the genetic variation of soybean flowering time had not been well documented . In our previous study, 71 of 91 cultivars originated from different latitudes in China were selected to analyse the effects of photoperiod and temperature and the interaction between photoperiod and temperature on flowering time . The results enhanced the understanding of the photo-thermal effects on flowering time at the phenotypic level. However, the effects of loci related to flowering time across photo-thermal conditions were not reported. | study | 100.0 |
In this study, the effects of flowering-time-related loci in different photo-thermal conditions have been evaluated. Some loci were detected in only one environment, others were in multiple environments. The number of loci and their associated effects varied across different photo-thermal conditions. Interestingly, none of the loci was associated with the flowering time in the SD treatment. In the previous Arabidopsis studies, there were few QTLs linked to flowering time of the plant grown in Sweden than Italian conditions. It was speculated that the Sweden condition may represent saturated vernalization conditions, which could reduce the variation in flowering time among genotypes and result in reducing or removing the expression of some genes . Similarly, soybean is a typical short day crop, we speculate that short day may also normalize soybean flowering time and remove contribution of some genes. The phenotypic variance of cultivars from different maturity groups became small in SD condition. Short days could reduce the effect of the dominant alleles of each dominant E genes on delaying flowering and maturity time in soybean . | study | 100.0 |
Further analysis of the QTL detected by QTXNetwork confirmed the genetic variation underlying soybean flowering time across different environments. The expression of flowering time genes was influenced by environmental conditions, which is consistent with the results on Arabidopsis thaliana . Jia et al. (2014) identified gene × environment interaction of cotton yield traits via the software QTXNetwork and classified genetic loci into three types: constituted loci (having no interaction with the environment), environment-specific loci (detected only in one environment), and environment-sensitive loci (the effect of the loci being dependent upon the environment) . Our study identified the same types of loci with both additive and epistatic effects, and their interactions with the environment that controlled soybean flowering time. Our result is inconsistent with previous finding that soybean flowering time is mainly controlled by the additive effect . This inconsistency may result from different genetic backgrounds of materials used in different studies. Previous evidence showed that epistasis played an important role in controlling flowering time, and epistasis explained a portion of the ‘missing heritability’ in plants . In Arabidopsis, phytochrome A (PhyA) interacts with CO protein in the photoperiod pathway, and CO interacts with gibberellins to regulate the expression of FT in the GA pathway . Gm04_4497001 (CO) identified in the present study may be a core locus of epistasis interacting with other loci for controlling soybean flowering time. In our previous studies on soybean photo-thermal responses, we proposed that photoperiod determines whether soybean plant is reproductive or vegetative, whereas temperature controls its developmental rate, and the magnitude of temperature effects depends upon the developmental status of the plants (reproductive or vegetative) [53, 54]. Through the analysis of the interaction between genotypes and environments in the current study, we found that whether the additive × environment interaction effect on soybean flowering time was positive or negative was dependent on photoperiod, whereas the magnitude of additive × environment interaction effect was on temperature, which is consistent with the model of photo-thermal interactions on flowering time in soybean [53, 54]. | study | 99.94 |
In this study, SSR markers were mainly selected based on the previous linkage analysis related to important agronomic traits, particularly phenological traits. Nine of the 11 significant SSR markers found in this study were previously reported to be linked to flowering time and maturity. Several SNPs identified in the present study were located in or adjacent to the previously reported QTLs (Table 2). Two clusters of significant markers in Gm11 (10 Mb-17 Mb) and Gm11 (33 Mb-36 Mb) were significantly associated with flowering time. Gm11 (10 Mb-17 Mb) contained two flowering time related QTLs [55, 56] and two maturity QTLs , this region was also reported to be linked to flowering time in an association population . The cluster of significant markers on Gm19 (46 Mb-48 Mb) was consistently identified to be closely linked to soybean flowering time through linkage mapping and related to maturity and plant height through association mapping (Table 3). The cluster of significant markers on Gm20 (43 Mb-44 Mb) identified the same genomic region of flower number QTLs. The markers in those regions could potentially be used by soybean breeders to improve soybean adaptability. Additionally, 35 novel loci associated with soybean flowering time were identified. | study | 100.0 |
Identification of genes involved in soybean flowering time may give us a better understanding of the genetic mechanism underlying the environmental regulation on soybean flowering time (Table 3, Fig. 6, Additional file 9: Table S4, Additional file 10: Table S5). The loci Gm04_4497001, Gm16_30766209 and Gm19_47514601 were identified to be associated with flowering time using both TASSEL and QTXNetwork software. Of the four important flowering genes Glyma04g06240 (GmCOL3a), Glyma16g26660 (GmFT2a), Glyma16g26690 (GmFT2b) and Glyma19g41210 (E3 or GmPhyA3) which were within 300 kb of the significant SNPs, Glyma04g06240 (GmCOL3a) is located at 277.4 kb downstream of the peak SNP Gm04_4497001. CONSTANS (CO) is the key transcriptional activator of the gene that encodes the “florigen” protein FLOWERING LOCUS T (FT) in Arabidopsis . Glyma16g26660 and Glyma16g26690 were close to the significant SNP Gm16_30766209, with physical distances of 19.9 kb and 14.3 kb, respectively. Glyma16g26660 and Glyma16g26690 are the key flowering time genes GmFT2a and GmFT2b, and GmFT2a is identified as the key flowering integrator in soybean . Gm19_47514601 is located between exon 2 and exon 3 of Glyma19g41210 (E3 or GmPhyA3), which encodes the phytochrome A (PHYA) protein , a far-red receptor involved in stabilizing the flowering activator CONSTANS (CO) protein during the late afternoon . The peak SNP, Gm20_3880320, detected in the SP condition was located 61.6 kb upstream of the gene Glyma20g03988, a homolog of PFT1 (phytochrome and flowering time regulatory protein 1) in Arabidopsis, which was an activator of flowering in a photoperiod pathway . In the LD + HT condition, the peak SNP, Gm20_43146832, is 169.2 kb upstream of the gene Glyma20g35020, a homologous gene encoding COP1-interacting protein, which is a regulator of light-regulated genes and a potential direct downstream target of COP1 for mediating light control of gene expression . Gm11_33034954 was the peak SNP in SU conditions, and 215.2 kb upstream of the flower gene Glyma11g31940, which was predicted to encode auxin response factor 8. The peak SNP, Gm11_10847172, detected in the SU, 14SP, 14SU and 15SP four conditions was located 294.25 Kb upstream of the gene Glyma11g15504, a homolog of CONSTANS protein, which has not been reported in soybean. These results indicate that our methods of association mapping and genetic effect analysis across different photo-thermal conditions were efficient in detecting the major and significant genomic regions (QTL) and genes regulating soybean flowering time. The markers associated with these loci can be utilized as markers for marker-assisted breeding for improving soybean adaptation.Fig. 6The positions of flowering time-related loci and their corresponding candidate genes. The positions of candidate genes were marked in black, the loci were shown in red, and the known flowering genes were underlined. The position of the first locus on each chromosome was set as zero, and the left number showed the relative in the genome, 1 = 100 kb | study | 100.0 |
The positions of flowering time-related loci and their corresponding candidate genes. The positions of candidate genes were marked in black, the loci were shown in red, and the known flowering genes were underlined. The position of the first locus on each chromosome was set as zero, and the left number showed the relative in the genome, 1 = 100 kb | study | 99.8 |
The photo-thermal treatments in the current study were designed to simulate the natural conditions in three main soybean production regions in China, so the results could facilitate soybean breeding in those regions. The treatment of long day-length and spring-sowing in the current study is similar to the growth conditions in the northeast spring-sowing region, whereas the short day-length with spring-sowing and summer-sowing treatments resemble with the growth conditions in the south spring-sowing and south summer-sowing regions. The natural day-length with different sowing seasons in Beijing simulates the growth conditions of spring and summer-sowing soybeans in the Huang-Huai-Hai River Valley. The peak locus on Gm19 (Satt664) under the LD + LT treatment is a useful marker for marker-assisted selection of adaptation in the northeast China, whereas the loci Sat_135, Gm11_10847172, Gm11_33034954, and Gm20_3880320, could be utilized for selection in the Huang-Huai-Hai River Valley. The markers, Gm16_30766209 and Gm11_36124908, detected in both the LD and SD conditions could be utilized for selection in both northeast and south China. | study | 100.0 |
In this study, a total of 87 markers (11 SSRs and 76 SNPs) associated with flowering time of soybean were identified via GWAS. The number and effect of loci associated with flowering time of soybean depended on the photo-thermal conditions. The loci with large effects were found to be located on Gm 11, Gm 16 and Gm 20, consistent with previous reports. The variation of soybean flowering time among the cultivars mainly resulted from gene × environment interactions, particularly epistasis × environment interaction and additive × environment interaction. Gm04_4497001 (close to GmCOL3a), Gm16_307609 (close to GmFT2a and GmFT2b), and Gm19_47514601 (close to E3 or GmPhyA3) are important for controlling flowering time. Among them, Gm04_4497001 may be the major locus with epistatic interaction with other loci for controlling flowering time. The direction and magnitude of the interaction between loci and environments were dependent on photo-thermal conditions, indicating that photoperiod determines the developmental status of plant (vegetative or vegetative), but temperature controls the developmental rate of plant. In summary, the results provide insights into the genetic basis of soybean flowering time and markers could be used for marker-assisted breeding to improve soybean adaptation. | study | 99.94 |
Additional file 1: Table S1.The origin, ecotypes and maturity groups of the soybean cultivars in this study. (DOCX 27 kb) Additional file 2: Table S2.Polymorphic SSR and SNP markers used for this study. (XLSX 230 kb) Additional file 3: Figure S1.The histogram of soybean flowering time in each environment. (a) The histogram of soybean flowering time in 2009. (b) The histogram of soybean flowering time in 2010. (c) The histogram of soybean flowering time in 2014 and 2015, respectively. (DOCX 5435 kb) Additional file 4: Figure S2.Population structure of 91 soybean cultivars using 63 SSR markers. (a) Estimation of the number of sub-populations. The left figure was a plot of ln (probability of data) vs. K ranging from 1 to 12 and the right figure was a plot of subpopulation number vs. delta K values. (b) Population structure of 91 soybean cultivars based on 63 SSR markers. The x-axis indicates the cultivars, and the y-axis indicates the Q value from STRUCTURE 2.3.1. The red color represents one sub-group, the green color represents another. (c) PCA of 91 soybean cultivars with the top two principal components. (d) Neighbor-joining tree of the 91 soybean cultivars. (DOCX 498 kb) Additional file 5: Figure S3.Genome-wide association scan for flowering time in different environments using SNPs. (a) The Quantile-Quantile Plot; (b) Manhattan plot for days to flowering. P-values (negative log-transformed) are shown in the plot relative to their position on each of the 20 chromosomes. The horizontal pink line indicates the genome-wide significant threshold (9.79 × 10−6). (DOCX 469 kb) Additional file 6: Figure S4.Manhattan plot for days to flowering in the association panel in different environments using SSRs. (a) Quantile-Quantile Plot (b) Manhattan plot for days to flowering. P-values (negative log-transformed) are shown in the plot relative to their genetic positions, the horizontal pink line indicates the genome-wide significant threshold (2.86 × 10−4). (DOCX 384 kb) Additional file 7: Table S3.The mean flowering time of the accession carrying different alleles. (DOCX 21 kb) Additional file 8: Figure S5.The plot of the interactions between significant loci with the flowering time and environment detected by the QTXNetwork. Red columns represent general QTX effects for all six environments. The green lines denote the n-th environment-specific effect. 1, SD + LT condition; 2, SP condition; 3, LD + LT condition; 4, SD + HT condition; 4–42, Gm04_4497001; 4–154, Gm04_42153936; 11–190, Gm11_36124908; 15–116, Gm15_11855585; 16–152, Gm16_30766209; 19–208, Gm19_44042544; 19–243, Gm19_47514601. (DOCX 407 kb) Additional file 9: Table S4.The significant loci associated with flowering time and related candidate genes. (DOCX 27 kb) Additional file 10: Table S5.The position of the loci and the corresponding candidate genes. (XLSX 18 kb) | study | 100.0 |
The histogram of soybean flowering time in each environment. (a) The histogram of soybean flowering time in 2009. (b) The histogram of soybean flowering time in 2010. (c) The histogram of soybean flowering time in 2014 and 2015, respectively. (DOCX 5435 kb) | other | 99.9 |
Population structure of 91 soybean cultivars using 63 SSR markers. (a) Estimation of the number of sub-populations. The left figure was a plot of ln (probability of data) vs. K ranging from 1 to 12 and the right figure was a plot of subpopulation number vs. delta K values. (b) Population structure of 91 soybean cultivars based on 63 SSR markers. The x-axis indicates the cultivars, and the y-axis indicates the Q value from STRUCTURE 2.3.1. The red color represents one sub-group, the green color represents another. (c) PCA of 91 soybean cultivars with the top two principal components. (d) Neighbor-joining tree of the 91 soybean cultivars. (DOCX 498 kb) | study | 100.0 |
Genome-wide association scan for flowering time in different environments using SNPs. (a) The Quantile-Quantile Plot; (b) Manhattan plot for days to flowering. P-values (negative log-transformed) are shown in the plot relative to their position on each of the 20 chromosomes. The horizontal pink line indicates the genome-wide significant threshold (9.79 × 10−6). (DOCX 469 kb) | study | 99.94 |
Manhattan plot for days to flowering in the association panel in different environments using SSRs. (a) Quantile-Quantile Plot (b) Manhattan plot for days to flowering. P-values (negative log-transformed) are shown in the plot relative to their genetic positions, the horizontal pink line indicates the genome-wide significant threshold (2.86 × 10−4). (DOCX 384 kb) | study | 99.94 |
The plot of the interactions between significant loci with the flowering time and environment detected by the QTXNetwork. Red columns represent general QTX effects for all six environments. The green lines denote the n-th environment-specific effect. 1, SD + LT condition; 2, SP condition; 3, LD + LT condition; 4, SD + HT condition; 4–42, Gm04_4497001; 4–154, Gm04_42153936; 11–190, Gm11_36124908; 15–116, Gm15_11855585; 16–152, Gm16_30766209; 19–208, Gm19_44042544; 19–243, Gm19_47514601. (DOCX 407 kb) | study | 99.94 |
Immunomodulatory agents used as complementary or alternative medicines have become popular for treating different immune disorders. Especially, co-administration of immunomodulatory agents and anti-tumor drugs is used to reduce the harmful side effect of chemotherapy . Numerous natural substances extracted from plants or animals were found to be beneficial to ameliorate disease symptoms by stimulating both innate and adaptive immunity. Among these, the bioactive polysaccharides isolated from natural source have recently been studied as a new immunopotentiator source for their profound effect on the immune system with relative nontoxicity and no significant side effects . They exert a variety of immune regulatory functions including the activation of immune-related cells, promotion of cytokine or chemokine secretion as well as activation of complement system . Until now, various polysaccharides have been applied as immunomodulators in clinic, such as lentinan (LNT) , krestin (PSK) , Astragalus polysaccharides (APS) and Panax ginseng polysaccharides (GPS) . | review | 99.9 |
Many polysaccharides derived from marine organisms have novel structural characteristics and possess excellent biological functionalities. Holothurian glycosaminoglycan (HG), which is the dominated component of sea cucumber, exhibited multiple bioactivities, such as anticoagulation , immune regulation , anti-cancer and antiviral activities . The sea cucumber Apostichopus (Stichopus) japonicus is the most popular source of fucosylated chondroitin sulfates (FCS). The different environment and extraction process may influence the fine structural characterization of glycosaminoglycans from the sea cucumbers Apostichopus (Stichopus) japonicas . In our previous study, we isolated a novel glycosaminoglycan from Apostichopus japonicus (AHG) (Mw 98.07 kDa), the structure of which contained a chondroitin sulfate-like backbone together with large quantity of one fucopyranosyl residue attaching to the 3-O position of β-d-glucuronic (GlcA) and 4-O and/or 6-O positions of N-acetyl-β-d-galactosamine (GalNAc). The molar ratio of GlcUA, GalNAc, Fucose (Fuc) and sulfate of AHG was 0.97:1.00:1.13:3.85 . In addition, it was revealed that AHG has not only immunoregulation capability on both innate and adaptive immune in vitro, but also protective effects toward the hematopoietic function of bone marrow and immune organs in myelosuppressed mice . However, the protective effects of AHG on immunological effector cells and organs against immunosuppression and oxidative damage are poorly understood. | study | 99.94 |
In present study, we investigated the effect of AHG on anti-tumor activity of nature killer (NK) cells and cytotoxic T lymphocytes (CTLs) in vitro. Furthermore, the immunomodulatory effects of AHG on spleen lymphocyte proliferation, cytokines secretion, intracellular free Ca2+ concentration (second messengers to activate lymphocytes), delayed-type hypersensitivity (DTH) reaction, as well as antioxidant activity in cyclophosphamide (CY)-induced immunosuppressed mice were also systematically elucidated in vivo. | study | 100.0 |
The absorbance at 570 nm (A570) measured by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay can reflect the splenocytes viability. The A570 values of AHG groups did not decrease compared with those of normal control (NC) group (Figure 1). It was suggested that AHG at 0.5–50 μg/mL had no toxic effect on mouse splenocytes. On the contrary, AHG at 1–10 μg/mL significantly increased the amount of splenocytes, which indicated that treatment of AHG at low concentration could promote splenocytes proliferation responses. | study | 100.0 |
To evaluate the effect of AHG on tumor cell elimination mediated by NK cells, the cytotoxicity of splenocytes against NK cells-sensitive YAC-1 lymphoma cells was investigated. As shown in Figure 2, compared with the NC group, the treatment with AHG (0.5–50 μg/mL) enhanced NK cells cytotoxicity significantly (p < 0.01). The maximum effective concentration was 5 μg/mL. The cytotoxicity of NK cells in AHG groups with the concentration ranged from 1 to 25 μg/mL were higher than that in LNT (40 μg/mL) group (p < 0.05). These results indicated that AHG strengthened the activity of NK cell against tumor cells. | study | 100.0 |
The phenotypic profile of a representative population of bone marrow dendritic cell (DCs) was determined using flow cytometer. After DCs were cultured in the presence of GM-CSF and IL-4 for seven days, they differentiated into mature DCs that expressed high levels of CD80 antigen, from 2.17 to 83.81%. | study | 100.0 |
B16 melanoma cells were used as the target cells to evaluate the specific cytotoxicity of activated-CTLs. The results shown in Figure 3 indicated that the cytotoxicity of CTLs in AHG groups ranged from 0.5 to 25 μg/mL, significantly higher than that in NC group. AHG at 5 μg/mL possessed the highest cytotoxicity (p < 0.01). Thus, AHG could enhance the cytotoxic of CTL stimulated by DCs on melanoma cells in different degree. | study | 100.0 |
The effects of AHG on mitogen-stimulated murine spleen lymphocyte proliferation were presented in Figure 4. Apparently, the proliferative responses of lymphocytes induced by both Concanavalin A (Con A) and Lipopolysaccharide (LPS) were reduced remarkably in CY-treated mice (p < 0.01) compared with the NC group. Combining with Con A or LPS, AHG could enhance the proliferation of splenocytes in a dose-dependent manner. The 5 and 10 mg/kg AHG combined with Con A significantly promoted and strengthened the proliferation of the splenocytes compared with that of MC group (p < 0.01). Similar result was also found in the stimulation of AHG combined with LPS on lymphocyte proliferation, while, in synergistic stimulation with LPS, AHG provided obvious increase of the splenocyte proliferation at 1, 5 and 10 mg/kg compared to the MC group (p < 0.05). Especially at the dose of 10 mg/kg, the A570 values were nearly equal to that of normal mice. These results indicated that co-mitogenic activities of AHG, with either Con A or LPS, were dose-dependent. | study | 100.0 |
The results in Figure 5 showed that the cytokines including interleukin-2 (IL-2), interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α) and interleukin-4 (IL-4) secreted by splenocytes in CY-treated mice were lower than those in normal animal (p < 0.01). However, there was a dose-dependent increase of cytokines production after AHG administration in CY-treated mice. More interestingly, the 10 mg/kg AHG provided obvious promotion and strengthening of cytokines (IL-2, IFN-γ, TNF-α and IL-4) secretion (p < 0.01), and led to slight increases versus values seen in the normal control mice. | study | 100.0 |
As shown in Figure 6, the intracellular free Ca2+ concentration was lowest in the model control (MC) group. The treatment of AHG resulted in dose-dependent increase in the amount of Ca2+ when compared with MC group. Particularly, the level of Ca2+ in AHG group at 10 mg/kg nearly reached the level in NC group. These results showed that AHG was capable of reversing the decrease of Ca2+ concentration in CY-treated mice to the normal level. | study | 100.0 |
The effect of the AHG treatments on the DTH reaction to sheep red blood cells (SRBC) in mice was displayed in Figure 7. The footpad thickness of mice in MC group was markedly lower than that in NC group (p < 0.05). AHG treatment exhibited an enhancement on footpad edema volume in CY-treated mice. Significant change was observed at the highest dose of AHG (10 mg/kg, p < 0.01). Thus, it was suggested that AHG strengthened the T-cell-mediated immune response in mice. | study | 100.0 |
CY could induce free radical production, followed by cytotoxicity and oxidative stress. As shown in Table 1, Table 2 and Table 3, CY significantly reduced the T-AOC, SOD, CAT and GSH-Px activities in hearts, livers and kidneys. In comparison with MC group, AHG administration could enhance the T-AOC, SOD, CAT and GSH-Px in different tissues dose-dependently. Particularly, the level of T-AOC, SOD, CAT and GSH-Px were almost recovered to normal level when treated with AHG at the concentration of 10 mg/kg (p < 0.01). Meanwhile, Table 1, Table 2 and Table 3 showed the significant increases in MDA levels in the hearts, livers and kidneys in mice of CY-treated group (p < 0.01), while AHG treatment at 1, 5, and 10 mg/kg could cause a dose-dependent decrease in the accumulation of tissue MDA. In addition, when the dose of AHG reached to 10 mg/kg, the contents of MDA were recovered to the normal levels. The results suggested that AHG can enhance anti-oxidative activities in liver, kidney and heart. | study | 100.0 |
NK cells and CTLs are two major populations of cytotoxic lymphocytes, and play important roles in the control of tumor growth and metastasis . In this work, IL-2 (also termed T-cell growth factor) was used to enhance the generation and cytotoxic activity of NK and CTL cells in vitro . NK cells can efficiently kill cells without previous sensitization. Obviously, treatment with AHG dose-dependently accelerated the cytotoxicity of NK cells to Yac-1 lymphoma cells in this study. DCs are the most potent professional antigen presenting cells that can induce antigen-specific CTL immune responses and regular adaptive immune response, as well as participate in differentiation of T cells subset and inherent immunity response . Tumor lysate-pulsed DCs presented tumor antigens to T cells by both major histocompatibility complex (MHC) class I- and class II-pathways, and then provide the potential to induce efficient antitumor immune responses . Flow cytometry analysis showed that these DCs, which expressed high levels of CD80 antigen, had typical mature phenotypic markers. Functionally, these cells gained the capacity to stimulate allogeneic T cells. The results indicated that primed T cells in vitro with B16 melanoma cells lysate-pulsed DCs were able to induce specific CTL against B16 tumor cells. Especially, after stimulated with various concentration of AHG, the cytotoxicity of specific-CTL were much stronger than that in NC group. Taken together, it was demonstrated that AHG can significantly promote NK cells and specific-CTL antineoplastic activity. | study | 99.94 |
We further evaluated the immunomodulatory effects of AHG in a CY-induced immunosuppression murine model. CY, an alkylating agent, is an important chemotherapeutic drug in malignant tumor treatment. However, CY intake can injure DNA of normal cells and cause myelosuppression, immunosuppression and oxidative stress on various tissues, which sometimes are life-threatening . In the present study, we used CY as an immunosuppressive agent to establish a model of weakened immunity. As expected, CY resulted in immunodeficiency, as evidence by significantly reducing splenocyte proliferation, cytokine secretion, intracellular free Ca2+ concentration, and inhibiting SRBC-induced DTH reaction as well as damaging antioxidant system. These remarkable differences in various immune parameters between the CY-treated group and the NC group indicated that the immunosuppression model was applicable for in vivo experiments. | study | 100.0 |
Splenocyte proliferation is a crucial event reflecting both cellular and humoral immune response because of its considerable sensitivity . Lymphocytes stimulated by Con A in vitro may be used to evaluate T lymphocyte activity associated with cellular immunity, while those stimulated by LPS may be used to evaluate B lymphocyte activity, which participate in the humoral immunity . In this study, CY-induced suppression of Con A-induced T-lymphocyte proliferation and LPS-induced B-lymphocyte proliferation were recovered by AHG. Proliferation assay results suggested that AHG can significantly increase the activation potential of T and B cell proliferation and enhance the immune response in immunosuppression mice. | study | 100.0 |
The activated splenic lymphocytes play an important role in the innate and adaptive immuneresponses by producing cytokines . Cytokines are important in cell–cell communication in the immune system and play key roles in improving the body’s defense mechanism. The T helper (Th) cells are divided into Th1 and Th2 according to the function and their difference in secretion of cytokines. Th1 cells secrete IL-2, IL-12, IFN-γ, and TNF-α, participating in cell-mediated immune responses, while Th2 cells secrete IL-4, IL-5, IL-6, and IL-10, which promote humoral or allergic responses. IL-2, originally described as T-cell growth factor, stimulates the proliferation and differentiation of T cells and increases the IFN-γ secretion of NK cells . IFN-γ is a pro-inflammatory cytokine endowed with potential immunomodulatory effects on Th1 cells differentiation and macrophage activation to acquire microbicidal and antiviral effector functions . IL-4 is a critical participant in allergic inflammation. It induces the differentiation of Th cells into Th2 cells and the growth of B cells . TNF-α, discovered by its antitumor activity, acts as a host defence factor in immunologic and inflammatory responses . The present results showed that cytokines (IL-2, TNF-α, IL-4 and IFN-γ) expression in the AHG-treated group was much higher than that in CY-treated group and high-dose AHG-treat (10 mg/kg) could secrete more cytokines (IL-2, TNF-α, IL-4 and IFN-γ) compared with normal control, suggesting that AHG can reversed the splenocytes function reduced by CY. In addition, AHG can active Th1 and Th2 cell at the same time. | study | 99.94 |
Calcium ions, known as the most widely used intracellular secondary messengers, play an essential role in lymphocyte function, which participate in proliferation, differentiation and gene transcription of lymphocyte . The elevation in the concentration of Ca2+ in the cytosol triggers the activation and proliferation of lymphocytes, especially promotes the transcription factors translocating from cytoplasm to the nucleus and binds to the promoter. The final consequence is initiating the transcription of specific cytokine genes . It has been mentioned that PSG-1, the polysaccharide obtained from Ganoderma atrum, may activate spleen lymphocytes via Ca2+/CaN/NFAT/IL-2 signaling pathway, similar to the polysaccharide fraction of Panax ginseng . In our study, the concentrations of Ca2+ were noticeably increased after AHG administration. Particularly, Ca2+ concentration in the AHG group at 100 mg/kg reached the level of that in NC group. These results showed that AHG was capable of recovering the decrease of the concentration of Ca2+ in CY-treated mice to the normal level, which may be the root of the enhancement of splenocyte cytokines secretion. | study | 100.0 |
Delayed-type hypersensitivity (DTH), as the fourth type of hypersensitivity reaction, is an important type of cell-mediated pathologic response, and plays a pivotal role in evaluating T cell-mediated immune responses . In this research, SRBC were used to induce footpad DTH reaction. We found that AHG potentiated the SRBC-induced DTH reaction in footpads of CY-treated mice and counteracted the inhibitory effect of CY on the DTH reaction. The foot volume was increased after AHG treatment, suggesting the cell-mediated immune in CY-treated mice was enhanced. | study | 100.0 |
Accumulating evidence strongly suggests that many polysaccharides have immunomodulatory activity usually accompanying with antioxidant activity, such as polysaccharides from Dietary litchi pulp , Cordyceps militaris , and Polygoni Multiflori Radix . The CY-induced overproduction of oxidant compounds associates with the inflammatory response, and can lead to reduced function of virtually all immune cells . Aberrant production or regulation of reactive oxygen species (ROS) cause tissue damage and loss of function in a number of tissues and organs . The lipid peroxidation decreases membrane fluidity, which adversely affects immune responses. Therefore, the relevance of antioxidants is particularly critical for the functionality of immune system . The antioxidant enzymes such as SOD, CAT and GSH-Px in tissues can convert active oxygen molecules into non-toxic compounds to protect against oxidative stress and tissue damage. T-AOC reflects or represents the capacity of the non-enzymatic antioxidant defense system. MDA, involved in forming lipid radicals and oxygen uptake, is a marker for endogenous lipid peroxidation . In our present study, CY treatment resulted in the suppression of T-AOC, SOD, CAT and GSH-Px in heart, liver and kidney as well as an increase in the MDA level. However, the treatment of AHG (1, 5 and 10 mg/kg) significantly increased the levels of T-AOC, SOD, CAT and GSH-Px as well as decreased the MDA levels in tested tissues. These findings showed that AHG can be effective in scavenging various types of oxygen free radicals and their products, indicating that AHG was able to protect against oxidative stress induced by CY in vivo. Further investigation is necessary to verify the precise repair mechanism of AHG and the interaction with other medicines. | study | 99.94 |
AHG was prepared as the protocols previously described . RMPI 1640 was purchased from Hyclone (Thermo Fisher, Shanghai, China). Fetal bovine serum was purchased from Gibco (Thermo Fisher, Shanghai, China). Con A, LPS, penicillin and streptomycin were purchased from Sigma-Aldrich (St. Louis, MO, USA). CY and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) was obtained from Solabio (Beijing, China). Anti-mouse FITC-labeled CD 80 monoclonal antibodies was purchased from eBioscience (San Diego, CA, USA). Mouse IL-2, IL-4, TNF-α, IFN-γ Enzyme-Linked Immunosorbent Assay (ELISA) kit was obtained from Dakewe (Beijing, China). Fluo-3/AM fluorescent probe and BCA protein assay kit was purchased from Beyotime (Shanghai, China). Dimethyl sulfoxide (DMSO) was purchased from Sangong Biotech (Shanghai, China). GSH-PX, SOD, CAT, T-AOC and MDA kits were purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). Injectable LNT, used as positive control, was purchased from Nanjing Easeheal Pharmaceutical Co., Ltd. (Nanjing, China). | other | 99.7 |
Male Kunming mice weighing 25–30 g were purchased from Qingdao Institute for Drug Control (Qingdao, China, SCXK2009007), and maintained under controlled conditions (temperature: 25 ± 2 °C, humidity: 50 ± 5%, 12 h dark-light cycle). The animals were acclimated for 7 days with free access to standard diets and sterile water. All of the animal experiments adhered to strict compliance according to Animal Ethics Committee of School of Medicine and Pharmacy, Ocean University of China for the use and care of animals. | other | 98.75 |
The extirpated spleen was removed in germ-free condition, and gently grinded in aseptic phosphate-buffered saline (PBS) through a stainless steel meshes. The splenocyte suspensions were resuspended in erythrocyte lysis buffer for 5 min to remove the red blood cells. After centrifuged at 300 g for 5 min, cell numbers and viability (over 95%) were assessed microscopically using trypan blue dye exclusion technique. | study | 99.94 |
B16 melanoma cells and Yac-1 lymphoma cells were obtained from institute of cell biology, Chinese academy sciences (Shanghai, China). Cells were cultured in RPMI 1640 medium supplemented with penicillin/streptomycin (100 IU/mL and 100 μg/mL, respectively) and 10% heat-inactivated FBS in an atmosphere of 5% CO2 and 90% relative humidity at 37 °C. | study | 51.3 |
The splenocytes (5 × 106/mL) were stimulated with serial concentrations of AHG (0.5–50 μg/mL) for 24 h at 37 °C. The cells treated with the medium alone were used as normal control. The cytotoxic effect of AHG on splenocyte cells was measured by MTT assay. The experiment was repeated three times. | study | 100.0 |
The splenocytes were activated for 24 h in the presence of IL-2. For cytotoxicity assays, cells (effector cells, 5 × 106/mL) were further co-cultured with YAC-1 (target cells, 5 × 105/mL) in the 96-well plates at 37 °C in 5% CO2. Each well was added various concentrations of AHG, or LNT (40 μg/mL, positive control) and medium alone, respectively. After 20 h, the activity of NK cell was determined by MTT assay and calculated by the following formula: NK cell activity (%) = [ODT − (ODS − ODE)]/ODT × 100%, where ODT is optical density value of target cells control, ODS is optical density value of test samples and ODE is optical density value of control effector cells. | study | 100.0 |
Primary bone marrow-derived DCs were flushed from the femurs and tibiae of mice in sterile conditions, and incubated in RPMI 1640 medium at 37 °C in 5% CO2. On Day 3, non-adherent cells were discarded. The cells were further cultured for 4 days in fresh medium containing GM-CSF (10 ng/mL) and IL-4 (10 ng/mL). On Day 7, DCs were incubated in the presence of freeze-thawed B16 tumor lysates. B16 melanoma cells were lysed by rapid freezing (liquid nitrogen) and thawing at 37 °C in physiological saline three times. After 24 h, LPS (1 μg/mL) was added into the culture for DC-maturation for another three days. The mature B16 melanoma cells lysate-pulsed DCs were harvested, which was determined using- flow cytometer (Beckman Coulter, Brea, CA, USA) after incubating with CD80-FITC. | study | 100.0 |
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