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The dramatic increase in greenhouse gas (GHG) emissions by humans over the past century and a half has created an urgency for monitoring, reporting, and verifying GHG emissions as a first step toward mitigating the effects of climate change. Fifteen percent of global GHG emissions come from agriculture, and companies in the food and beverage industry are starting to set climate goals. We examined the GHG emissions reporting practices and climate goals of the top 100 global food and beverage companies (as ranked by Food Engineering) and determined whether their goals are aligned with the science of keeping climate warming well below a 2°C increase. Using publicly disclosed data in CDP Climate reports and company sustainability reports, we found that about two thirds of the top 100 global food and beverage companies disclose at least part of their total company emissions and set some sort of climate goal that includes scope 1 and 2 emissions. However, only about half have measured, disclosed, and set goals for scope 3 emissions, which often encompass about 88% of a company's emissions across the entire value chain on average. We also determined that companies, despite setting scope 1, 2, and 3 emission goals, may be missing the mark on whether their goals are significantly reducing global emissions. Our results present the current disclosure and emission goals of the top 100 global food and beverage companies and highlight an urgent need to begin and continue to set truly ambitious, science-aligned climate goals. 

Transcription factors (TFs) play a central role in regulating molecular level responses of plants to external stresses such as water limiting conditions, but identification of such TFs in the genome remains a challenge. Here, we describe a network-based supervised machine learning framework that accurately predicts and ranks all TFs in the genome according to their potential association with drought tolerance. We show that top ranked regulators fall mainly into two ‘age’ groups; genes that appeared first in land plants and genes that emerged later in the Oryza clade. TFs predicted to be high in the ranking belong to specific gene families, have relatively simple intron/exon and protein structures, and functionally converge to regulate primary and secondary metabolism pathways. Repeated trials of nested cross-validation tests showed that models trained only on regulatory network patterns, inferred from large transcriptome datasets, outperform models trained on heterogenous genomic features in the prediction of known drought response regulators. A new R/Shiny based web application, called the DroughtApp, provides a primer for generation of new testable hypotheses related to regulation of drought stress response. Furthermore, to test the system we experimentally validated predictions on the functional role of the rice transcription factor OsbHLH148, using RNA sequencing of knockout mutants in response to drought stress and protein-DNA interaction assays. Our study exemplifies the integration of domain knowledge for prioritization of regulatory genes in biological pathways of well-studied agricultural traits. 

Due to its tropical origin and adaptation, rice is significantly impacted by cold stress, and consequently sustains large losses in growth and productivity. Currently, rice is the second most consumed cereal in the world and production losses caused by extreme temperature events in the context of "major climatic changes" can have major impacts on the world economy. We report here an analysis of rice genotypes in response to low-temperature stress, studied through physiological gas-exchange parameters, biochemical changes in photosynthetic pigments and antioxidants, and at the level of gene and protein expression, towards an understanding and identification of multiple low-temperature tolerance mechanisms. The first effects of cold stress were observed on photosynthesis among all genotypes. However, the tropical japonica genotypes Secano do Brazil and Cypress had a greater reduction in gas exchange parameters like photosynthesis and water use efficiency in comparison to the temperate japonica Nipponbare and M202 genotypes. The analysis of biochemical profiles showed that despite the impacts of low temperature on tolerant plants, they quickly adjusted to maintain their cellular homeostasis by an accumulation of antioxidants and osmolytes like phenolic compounds and proline. The cold tolerant and sensitive genotypes showed a clear difference in gene expression at the transcript level for OsGH3-2, OsSRO1a, OsZFP245, and OsTPP1, as well as for expression at the protein level for LRR-RLKs, bHLH, GLYI, and LTP1 proteins. This study exemplifies the cold tolerant features of the temperate japonica Nipponbare and M202 genotypes, as observed through the analysis of physiological and biochemical responses and the associated changes in gene and protein expression patterns. The genes and proteins showing differential expression response are notable candidates towards understanding the biological pathways affected in rice and for engineering cold tolerance, to generate cultivars capable of maintaining growth, development, and reproduction under cold stress. We also propose that the mechanisms of action of the genes analyzed are associated with the tolerance response. 

Predicting gene functions from genome sequence alone has been difficult, and the functions of a large fraction of plant genes remain unknown. However, leveraging the vast amount of currently available gene expression data has the potential to facilitate our understanding of plant gene functions, especially in determining complex traits. Gene coexpression networks—created by integrating multiple expression datasets—connect genes with similar patterns of expression across multiple conditions. Dense gene communities in such networks, commonly referred to as modules, often indicate that the member genes are functionally related. As such, these modules serve as tools for generating new testable hypotheses, including the prediction of gene function and importance. Recently, we have seen a paradigm shift from the traditional “global” to more defined, context-specific coexpression networks. Such coexpression networks imply genetic correlations in specific biological contexts such as during development or in response to a stress. In this short review, we highlight a few recent studies that attempt to fill the large gaps in our knowledge about cellular functions of plant genes using context-specific coexpression networks.