Next-generation plant science: putting big data to work

Regulation of plant epigenetic memory in response to cold and heat stress: towards climate resilient agriculture

Plants have evolved to adapt and grow in hot and cold climatic conditions. Some also adapt to daily and seasonal temperature changes. Epigenetic modifications play an important role in regulating plant tolerance under such conditions. DNA methylation and post-translational modifications of histone proteins influence gene expression during plant developmental stages and under stress conditions, including cold and heat stress. While short-term modifications are common, some modifications may persist and result in stress memory that can be inherited by subsequent generations. Understanding the mechanisms of epigenomes responding to stress and the factors that trigger stress memory is crucial for developing climate-resilient agriculture, but such an integrated view is currently limited. This review focuses on the plant epigenetic stress memory during cold and heat stress. It also discusses the potential of machine learning to modify stress memory through epigenetics to develop climate-resilient crops.

Overview of Biotechnology-Derived Herbicide Tolerance and Insect Resistance Traits in Plant Agriculture

Biotechnology has been central for the acceleration of crop improvement over the last two decades. Since 1994, when the first commercial biotechnology-derived tomato crop was commercialized, the cultivated area for genetically modified crops has reached 185.1 million hactares worldwide. Both the number of crops and the number of traits developed using biotechnology have accounted for this increase. Among the most impactful biotechnology-derived traits are insect resistance and herbicide tolerance, which have greatly contributed to the worldwide increase in agricultural productivity and stabilization of food security. In this chapter, we provide an overview of the history of the biotechnology-derived input traits, the existing genetically engineered commercial crop products carrying insect resistance and herbicide tolerance traits, as well as a perspective on how new technologies could further impact the development of new traits in crops. With the projection of the world population to increase to 9.8 billion by the year 2050 and reduction in available farmland, one of the biggest challenges will be to provide sustainable nourishment to the projected population. Biotechnology will continue to be the key enabler for development of insect resistance and herbicide tolerance traits to overcome that imminent challenge.

System approaches to study root hairs as a single cell plant model: current status and future perspectives

Our current understanding of plant functional genomics derives primarily from measurements of gene, protein and/or metabolite levels averaged over the whole plant or multicellular tissues. These approaches risk diluting the response of specific cells that might respond strongly to the treatment but whose signal is diluted by the larger proportion of non-responding cells. For example, if a gene is expressed at a low level, does this mean that it is indeed lowly expressed or is it highly expressed, but only in a few cells? In order to avoid these issues, we adopted the soybean root hair cell, derived from a single, differentiated root epidermal cell, as a single-cell model for functional genomics. Root hair cells are intrinsically interesting since they are major conduits for root water and nutrient uptake and are also the preferred site of infection by nitrogen-fixing rhizobium bacteria. Although a variety of other approaches have been used to study single plant cells or single cell types, the root hair system is perhaps unique in allowing application of the full repertoire of functional genomic and biochemical approaches. In this mini review, we summarize our published work and place this within the broader context of root biology, with a significant focus on understanding the initial events in the soybean-rhizobium interaction.

Machine learning for Big Data analytics in plants

Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences.