SOX9 maintains reserve stem cells and preserves radioresistance in mouse small intestine.

BACKGROUND & AIMS: Reserve intestinal stem cells (rISCs) are quiescent/slowly cycling under homeostatic conditions, allowing for their identification with label-retention assays. rISCs mediate epithelial regeneration after tissue damage by converting to actively proliferating stem cells (aISCs) that self renew and demonstrate multipotency, which are defining properties of stem cells. Little is known about the genetic mechanisms that regulate the production and maintenance of rISCs. High expression levels of the transcription factor Sox9 (Sox9(high)) are associated with rISCs. This study investigates the role of SOX9 in regulating the rISC state. METHODS: We used fluorescence-activated cell sorting to isolate cells defined as aISCs (Lgr5(high)) and rISCs (Sox9(high)) from Lgr5(EGFP) and Sox9(EGFP) reporter mice. Expression of additional markers associated with active and reserve ISCs were assessed in Lgr5(high) and Sox9(high) populations by single-cell gene expression analyses. We used label-retention assays to identify whether Sox9(high) cells were label-retatining cells (LRCs). Lineage-tracing experiments were performed in Sox9-CreERT2 mice to measure the stem cell capacities and radioresistance of Sox9-expressing cells. Conditional SOX9 knockout mice and inducible-conditional SOX9 knockout mice were used to determine whether SOX9 was required to maintain LRCs and rISC function. RESULTS: Lgr5(high) and a subset of crypt-based Sox9(high) cells co-express markers of aISC and rISC (Lgr5, Bmi1, Lrig1, and Hopx). LRCs express high levels of Sox9 and are lost in SOX9-knockout mice. SOX9 is required for epithelial regeneration after high-dose irradiation. Crypts from SOX9-knockout mice have increased sensitivity to radiation, compared with control mice, which could not be attributed to impaired cell-cycle arrest or DNA repair. CONCLUSIONS: SOX9 limits proliferation in LRCs and imparts radiation resistance to rISCs in mice.

[Studies on extraction process of the main saponin constituents from the stem bark of Kalopanax septemlobus in Guangxi].

Using orthogonal experiment design, the total saponin constituents were obtained by refluxing extraction with alcohol and separated by macroporous adsorption resin and n-Butyl alcohol from the stem bark of Kalopanax septemlobus. According to the purity analysis and the yield, the extraction process was optimized. The results showed that the main saponin constituents were gained with a yield of 1.32% by using macroporous adsorption resin but 1.05% by using n-Butyl alcohol. The former was more efficient than the latter on both yield and color. The optimal process with isolation by macroporous adsorption resin is cheap, simple and practical.

A high-throughput platform for stem cell niche co-cultures and downstream gene expression analysis.

Stem cells reside in 'niches', where support cells provide critical signalling for tissue renewal. Culture methods mimic niche conditions and support the growth of stem cells in vitro. However, current functional assays preclude statistically meaningful studies of clonal stem cells, stem cell-niche interactions, and genetic analysis of single cells and their organoid progeny. Here, we describe a 'microraft array' (MRA) that facilitates high-throughput clonogenic culture and computational identification of single intestinal stem cells (ISCs) and niche cells. We use MRAs to demonstrate that Paneth cells, a known ISC niche component, enhance organoid formation in a contact-dependent manner. MRAs facilitate retrieval of early enteroids for quantitative PCR to correlate functional properties, such as enteroid morphology, with differences in gene expression. MRAs have broad applicability to assaying stem cell-niche interactions and organoid development, and serve as a high-throughput culture platform to interrogate gene expression at early stages of stem cell fate choices.

[Prediction of methane emission of paddy field based on the support vector regression model].

The methane emission data of paddy fields was obtained by using the static chamber and gas chromatography, and six parameters including atmospheric temperature, soil temperature at 5 cm depth, pH of soil, Eh of soil, soil moisture and ground biomass were selected as the primary influencing factors of methane emission. The support vector regression (epsilon-SVR) model was built on the optimization of structural risk minimization, and the parameters of the epsilon-SVR model were optimized using Leave-one-out Cross Validation (LOOCV). The prediction accuracy of model was evaluated by k-fold cross validation with the mean relative error (MRE) and the root mean square error (RMSE). In addition, the accuracy of the epsilon-SVR model was analyzed by comparison with the Back Propagation-Artificial Neural Network (BP-ANN) model. The results indicated that the predicted value of the epsilon-SVR model with the parameters C and epsilon optimized by LOOCV was in good agreement with the measured value, and the average MRE of test samples was 44% and the average RMSE was 16.21 mg x (m2 x h)(-1) in the process of 11-fold cross validation. Compared with the BP-ANN model, the correlation coefficient was 0.863, and all the indicators were better. It demonstrated that the 8-SVR model could be applied to the prediction of methane emission of paddy fields.