Genome-wide association study and network analysis of in vitro transformation in Populus trichocarpa support key roles of diverse phytohormone pathways and cross talk.

Wide variation in amenability to transformation and regeneration (TR) among many plant species and genotypes presents a challenge to the use of genetic engineering in research and breeding. To help understand the causes of this variation, we performed association mapping and network analysis using a population of 1204 wild trees of Populus trichocarpa (black cottonwood). To enable precise and high-throughput phenotyping of callus and shoot TR, we developed a computer vision system that cross-referenced complementary red, green, and blue (RGB) and fluorescent-hyperspectral images.

GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa.

Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping.

GWAS identifies candidate genes controlling adventitious rooting in .

Adventitious rooting (AR) is critical to the propagation, breeding, and genetic engineering of trees. The capacity for plants to undergo this process is highly heritable and of a polygenic nature; however, the basis of its genetic variation is largely uncharacterized. To identify genetic regulators of AR, we performed a genome-wide association study (GWAS) using 1148 genotypes of .

Robust High-Throughput Phenotyping with Deep Segmentation Enabled by a Web-Based Annotator.

The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale with low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts.

Clusters of polymorphic transmembrane genes control resistance to schistosomes in snail vectors.

Schistosomiasis is a debilitating parasitic disease infecting hundreds of millions of people. Schistosomes use aquatic snails as intermediate hosts. A promising avenue for disease control involves leveraging innate host mechanisms to reduce snail vectorial capacity.

Neither heat pulse, nor multigenerational exposure to a modest increase in water temperature, alters the susceptibility of Guadeloupean to infection.

There are increasing concerns regarding the role global climate change will have on many vector-borne diseases. Both mathematical models and laboratory experiments suggest that schistosomiasis risk may change as a result of the effects of increasing temperatures on the planorbid snails that host schistosomes. Heat pulse/heat shock of the BS90 strain of was shown to increase the rate of infection by , but the result was not replicable in a follow up experiment by a different lab.

Akkermansia muciniphila mediates negative effects of IFNγ on glucose metabolism.

Cross-talk between the gut microbiota and the host immune system regulates host metabolism, and its dysregulation can cause metabolic disease. Here, we show that the gut microbe Akkermansia muciniphila can mediate negative effects of IFNγ on glucose tolerance. In IFNγ-deficient mice, A.