Investigations of brain-wide functional and structural networks of dopaminergic and CamKIIα-positive neurons in VTA with DREADD-fMRI and neurotropic virus tracing technologies.

BACKGROUND: The ventral tegmental area (VTA) contains heterogeneous cell populations. The dopaminergic neurons in VTA play a central role in reward and cognition, while CamKIIα-positive neurons, composed mainly of glutamatergic and some dopaminergic neurons, participate in the reward learning and locomotor activity behaviors. The differences in brain-wide functional and structural networks between these two neuronal subtypes were comparatively elucidated. METHODS: In this study, we applied a method combining Designer Receptors Exclusively Activated by Designer Drugs (DREADD) and fMRI to assess the cell type-specific modulation of whole-brain neural networks. rAAV encoding the cre-dependent hM3D was injected into the right VTA of DAT-cre or CamKIIα-cre transgenic rats. The global brain activities elicited by DREADD stimulation were then detected using BOLD-fMRI. Furthermore, the cre-dependent antegrade transsynaptic viral tracer H129ΔTK-TT was applied to label the outputs of VTA neurons. RESULTS: We found that DREADD stimulation of dopaminergic neurons induced significant BOLD signal changes in the VTA and several VTA-related regions including mPFC, Cg and Septum. More regions responded to selective activation of VTA CamKIIα-positive neurons, resulting in increased BOLD signals in VTA, Insula, mPFC, MC_R (Right), Cg, Septum, Hipp, TH_R, PtA_R, and ViC_R. Along with DREADD-BOLD analysis, further neuronal tracing identified multiple cortical (MC, mPFC) and subcortical (Hipp, TH) brain regions that are structurally and functionally connected by VTA dopaminergic and CamKIIα-positive neurons. CONCLUSIONS: Our study dissects brain-wide structural and functional networks of two neuronal subtypes in VTA and advances our understanding of VTA functions.

Popularizing Recombinant Baculovirus-derived OneBac System for Laboratory Production of all Recombinant Adeno-associated Virus Vector Serotypes.

BACKGROUND: Recombinant adeno-associated virus (rAAV) has been widely used as an efficient transgenic vector in biomedical research, as well as gene therapy. Serotype-associated transduction efficiency, tissue- or cell-type tropism and immunological profile are major considerations in the various applications of rAAVs. There are increasing needs for different serotypes of rAAV, either naturally isolated or artificially engineered. However, affordable and scalable production of a desired serotype of rAAV remains very difficult, especially for researchers lacking relevant experience. OBJECTIVE: On the basis of our previously established single recombinant baculovirus expression vector (BEV)-derived OneBac system, we have optimized the process and expanded the rAAV production range to the full range of serotypes rAAV1-13. METHODS: Firstly, the AAV Cap gene was optimized to translate by ribosome leaky scanning and the gene of interest (GOI) was cloned into the pFD/Cap-(ITR-GOI)-Rep2 shuttle plasmid. Following the classical Bac-to-Bac method, sufficient BEV stock containing all rAAV packaging elements can be quickly obtained. Finally, we can repeatedly scale up the production of rAAVs in one week by using a single BEV to infect suspension-cultured Sf9 cells. The rAAV1-13 shows relatively high yields ranging from 5×104 to 4×105 VG/cell. More than 1×1015 VG purified rAAVs can be easily obtained from 5 L suspension-cultured Sf9 cells. RESULTS: As expected, rAAV serotypes 1-13 show different potencies for in vitro transduction and cell-type tropisms. CONCLUSION: In summary, the single BEV-derived OneBac system should prove popular for laboratory scaling-up production of any serotype of rAAV.

A strategy for finding the optimal scale of plant core collection based on Monte Carlo simulation.

Core collection is an ideal resource for genome-wide association studies (GWAS). A subcore collection is a subset of a core collection. A strategy was proposed for finding the optimal sampling percentage on plant subcore collection based on Monte Carlo simulation. A cotton germplasm group of 168 accessions with 20 quantitative traits was used to construct subcore collections. Mixed linear model approach was used to eliminate environment effect and GE (genotype × environment) effect. Least distance stepwise sampling (LDSS) method combining 6 commonly used genetic distances and unweighted pair-group average (UPGMA) cluster method was adopted to construct subcore collections. Homogeneous population assessing method was adopted to assess the validity of 7 evaluating parameters of subcore collection. Monte Carlo simulation was conducted on the sampling percentage, the number of traits, and the evaluating parameters. A new method for "distilling free-form natural laws from experimental data" was adopted to find the best formula to determine the optimal sampling percentages. The results showed that coincidence rate of range (CR) was the most valid evaluating parameter and was suitable to serve as a threshold to find the optimal sampling percentage. The principal component analysis showed that subcore collections constructed by the optimal sampling percentages calculated by present strategy were well representative.

Establishing an efficient way to utilize the drought resistance germplasm population in wheat.

Drought resistance breeding provides a hopeful way to improve yield and quality of wheat in arid and semiarid regions. Constructing core collection is an efficient way to evaluate and utilize drought-resistant germplasm resources in wheat. In the present research, 1,683 wheat varieties were divided into five germplasm groups (high resistant, HR; resistant, R; moderate resistant, MR; susceptible, S; and high susceptible, HS). The least distance stepwise sampling (LDSS) method was adopted to select core accessions. Six commonly used genetic distances (Euclidean distance, Euclid; Standardized Euclidean distance, Seuclid; Mahalanobis distance, Mahal; Manhattan distance, Manhat; Cosine distance, Cosine; and Correlation distance, Correlation) were used to assess genetic distances among accessions. Unweighted pair-group average (UPGMA) method was used to perform hierarchical cluster analysis. Coincidence rate of range (CR) and variable rate of coefficient of variation (VR) were adopted to evaluate the representativeness of the core collection. A method for selecting the ideal constructing strategy was suggested in the present research. A wheat core collection for the drought resistance breeding programs was constructed by the strategy selected in the present research. The principal component analysis showed that the genetic diversity was well preserved in that core collection.