Strigolactones (SLs), the newest group of phytohormones, are involved in a wide range of functions, including the regulation of plant growth and physiology. Besides, emerging evidence suggests that SLs also participate in the promotion of plant environmental stress resilience through mediation of different metabolic genes/pathways. However, thus far little is known about SL-mediated transcriptional changes in rice (Oryza sativa), compared to other model plants. To meet this objective, we analyzed the RNA-seq-based comparative transcriptome data sets of rice SL-deficient dwarf l7 (d17) mutant line and its respective wild-type (WT), obtained from the National Center for Biotechnology Information GenBank. Both, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, in order to identify genes/pathways which function downstream of SLs. With respect to the WT, a large number of functional GO terms, mainly stress-associated terms such as 'response to stress', 'response to stimulus', 'response to chemical', 'response to oxidative stress' and 'reactive oxygen species metabolic process' were significantly suppressed in the d17 mutant plants. In addition, KEGG metabolic pathways such as cvaline, leucine and isoleucine degradation', 'plant hormone signal transduction', 'galactose metabolism', 'fatty acid degradation' and 'phenylalanine metabolism' were also remarkably undermined in the d17 lines relative to the WT. These results imply a possible involvement of rice SLs in the regulation of distinct stress-related metabolic genes/pathways, which may function in environmental stress tolerance of plants. Taken together, the study provides new opportunities to broaden our limited understanding of SL-regulated downstream pathways, especially in rice.

The Granger Causality (GC) statistical test explores the causal relationships between different time series variables. By employing the GC method, the underlying causal links between environmental drivers and global vegetation properties can be untangled, which opens possibilities to forecast the increasing strain on ecosystems by droughts, global warming, and climate change. This study aimed to quantify the spatial distribution of four distinct satellite vegetation products' (VPs) sensitivities to four environmental land variables (ELVs) at the global scale given the GC method. The GC analysis assessed the spatially explicit response of the VPs: (i) the fraction of absorbed photosynthetically active radiation (FAPAR), (ii) the leaf area index (LAI), (iii) solar-induced fluorescence (SIF), and, finally, (iv) the normalized difference vegetation index (NDVI) to the ELVs. These ELVs can be categorized as water availability assessing root zone soil moisture (SM) and accumulated precipitation (P), as well as, energy availability considering the effect of air temperature (T) and solar shortwave (R) radiation. The results indicate SM and P are key drivers, particularly causing changes in the LAI. SM alone accounts for 43%, while P accounts for 41%, of the explicitly caused areas over arid biomes. SM further significantly influences the LAI at northern latitudes, covering 44% of cold and 50% of polar biome areas. These areas exhibit a predominant response to R, which is a possible trigger for snowmelt, showing more than 40% caused by both cold and polar biomes for all VPs. Finally, T's causality is evenly distributed amongst all biomes with fractional covers between similar to 10 and 20%. By using the GC method, the analysis presents a novel way to monitor the planet's ecosystem, based on solely two years as input data, with four VPs acquired by the synergy of Sentinel-3 (S3) and 5P (S5P) satellite data streams. The findings indicated unique, biome-specific responses of vegetation to distinct environmental drivers.