[Exploration of transcriptome SSR markers and its application in genetic diversity assessment of Asarum sieboldii].

To explore the genetic diversity of Asarum sieboldii this study developed SSR markers based on transcriptome sequencing results and five populations of A.sieboldii from different regions were used as samples for genetic diversity assessment using software such as GenALEx 6.5, NTSYS 2.1, and Structure 2.3.4. The results showed that 16 SSR markers with high polymorphism and good repeatability were selected from the A.sieboldii transcriptome. Primers designed based on the flanking sequences of these markers successfully amplified 56 polymorphic fragments from 150 individual samples of the five A.sieboldii populations. On average, each primer amplified 3.5 polymorphic fragments, ranging from 2 to 8. The mean values of expected heterozygosity(H_e), Shannon's diversity index(I), Nei's gene diversity index(H), and the polymorphic information content(PIC) were 0.172, 0.281, 0.429, and 0.382, respectively. The mean population differentiation coefficient(F_(ST)) was 0.588, consistent with the analysis of molecular variance(AMOVA) results, which indicated greater genetic variation among A.sieboldii populations(69%) than that within populations(31%). The percentage of polymorphic loci(PPL) ranged from highest to lowest as SNJ>LN>SY>SZ>TB. Principal coordinate analysis(PCoA) and UPGMA clustering analysis further revealed genetic clustering of A.sieboldii individuals based on their geographical distribution, consistent with the results of the structure clustering analysis. In summary, the SSR markers developed from the transcriptome effectively assessed the genetic differentiation and population structure of natural A.sieboldii populations, revealing a relatively low genetic diversity in A.sieboldii, with genetic variation primarily observed at the population level and a correlation between population differentiation and geographic distance.

Evaluating the predictive factors of resting energy expenditure and validating predictive equations for Chinese obese children.

BACKGROUND: To study the predictive factors of resting energy expenditure (REE) and evaluate the accuracy of predicted equations with indirect calorimeter (IC) in Chinese school-age children, particularly for the obese population. METHODS: Recruited children were from the department of child healthcare in Nanjing children's hospital during July 2014-September 2015. Anthropometric parameters and body composition were measured by bioelectrical impedance. Measured REE was assessed by IC. Predicted REE was estimated using ten published equations. RESULTS: 248 children aged 7-13 years were recruited, including 148 obese [body mass index standard deviation score (BMISDS) = 2.48 ± 0.91] and 100 non-obese (BMISDS = - 0.96 ± 1.08). The unit mass of REE (REE/kg) in obese group (29.06 ± 5.74) was lower than that in non-obese group (37.51 ± 6.56). The stepwise regression showed that age, BMISDS and fat-free mass (FFM) had a major impact on REE/kg as the regression equation: Y = 54.41 - 1.36 × X1 - 2.25 × X2 - 0.16 × X3 (Y REE/kg, X1 age, X2 BMISDS, X3 FFM; R = 0.633, R2 = 0.401, P < 0.01). The accuracy of predicted REE in obese subjects was 62.16% by the new predictive equations. CONCLUSIONS: The REE/kg in obese children was lower and closely correlated with age, BMISDS and FFM. It is necessary to validate the new predictive equation in a larger sample to estimate energy requirements, particularly for children with obesity.