RESUMO
Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include data acquisition and processing approaches that use unmanned aerial vehicles (UAVs) and surface geophysical techniques. In particular, we applied these approaches to a soybean farm in Arkansas to characterize the soil-plant coupled spatial and temporal heterogeneity, as well as to identify key environmental factors that influence plant growth and yield. UAV-based multitemporal acquisition of high-resolution RGB (red-green-blue) imagery and direct measurements were used to monitor plant height and photosynthetic activity. We present an algorithm that efficiently exploits the high-resolution UAV images to estimate plant spatial abundance and plant vigor throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas, providing easily interpretable information that can be used to guide near-real-time farming decisions. Additionally, high-resolution multitemporal surface geophysical measurements of apparent soil electrical conductivity were used to estimate the spatial heterogeneity of soil texture. By integrating the multiscale multitype soil and plant datasets, we identified the spatiotemporal co-variance between soil properties and plant development and yield. Our novel approach for early season monitoring of plant spatial abundance identified areas of low productivity controlled by soil clay content, while temporal analysis of geophysical data showed the impact of soil moisture and irrigation practice (controlled by topography) on plant dynamics. Our study demonstrates the effective coupling of UAV data products with geophysical data to extract critical information for farm management.
RESUMO
AIMS: Causal transcripts at genomic loci associated with type 2 diabetes (T2D) are mostly unknown. The chr8p23.1 variant rs4841132, associated with an insulin-resistant diabetes risk phenotype, lies in the second exon of a long non-coding RNA (lncRNA) gene, LOC157273, located 175 kilobases from PPP1R3B, which encodes a key protein regulating insulin-mediated hepatic glycogen storage in humans. We hypothesized that LOC157273 regulates expression of PPP1R3B in human hepatocytes. METHODS: We tested our hypothesis using Stellaris fluorescent in situ hybridization to assess subcellular localization of LOC157273; small interfering RNA (siRNA) knockdown of LOC157273, followed by RT-PCR to quantify LOC157273 and PPP1R3B expression; RNA-seq to quantify the whole-transcriptome gene expression response to LOC157273 knockdown; and an insulin-stimulated assay to measure hepatocyte glycogen deposition before and after knockdown. RESULTS: We found that siRNA knockdown decreased LOC157273 transcript levels by approximately 80%, increased PPP1R3B mRNA levels by 1.7-fold, and increased glycogen deposition by >50% in primary human hepatocytes. An A/G heterozygous carrier (vs. three G/G carriers) had reduced LOC157273 abundance due to reduced transcription of the A allele and increased PPP1R3B expression and glycogen deposition. CONCLUSION: We show that the lncRNA LOC157273 is a negative regulator of PPP1R3B expression and glycogen deposition in human hepatocytes and a causal transcript at an insulin-resistant T2D risk locus.
