Tri- and tetra-nucleotide microsatellite DNA markers for assessing genetic diversity, population structure, and demographics in the Holmgren milk-vetch (Astragalus holmgreniorum)

Development of SSR markers for Astragalus lehmannianus, a vulnerable species from northwestern China

PREMISE

Astragalus lehmannianus (Fabaceae) is a vulnerable species found in the cold deserts of northwestern China. We aimed to characterize polymorphic microsatellite loci for A. lehmannianus to support future studies of population genetic dynamics and conservation management of the species.

METHODS AND RESULTS

We used next-generation sequencing to detect polymorphic microsatellites. Twenty-five potential microsatellite loci were identified, 12 of which were polymorphic and present in the three study populations of A. lehmannianus. Levels of observed and expected heterozygosities were 0.000-1.000 and 0.000-0.827, respectively. Furthermore, two and five of the 12 developed primers were successfully amplified in two congeneric species, A. arpilobus and A. oxyglottis, respectively.

CONCLUSIONS

These newly developed microsatellite markers can be used to determine population diversity and to develop conservation strategies in A. lehmannianus.

Systems Modeling at Multiple Levels of Regulation: Linking Systems and Genetic Networks to Spatially Explicit Plant Populations

Selection and adaptation of individuals to their underlying environments are highly dynamical processes, encompassing interactions between the individual and its seasonally changing environment, synergistic or antagonistic interactions between individuals and interactions amongst the regulatory genes within the individual. Plants are useful organisms to study within systems modeling because their sedentary nature simplifies interactions between individuals and the environment, and many important plant processes such as germination or flowering are dependent on annual cycles which can be disrupted by climate behavior. Sedentism makes plants relevant candidates for spatially explicit modeling that is tied in with dynamical environments. We propose that in order to fully understand the complexities behind plant adaptation, a system that couples aspects from systems biology with population and landscape genetics is required. A suitable system could be represented by spatially explicit individual-based models where the virtual individuals are located within time-variable heterogeneous environments and contain mutable regulatory gene networks. These networks could directly interact with the environment, and should provide a useful approach to studying plant adaptation.