Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature

Differentially localized protein identification for breast cancer based on deep learning in immunohistochemical images

The mislocalization of proteins leads to breast cancer, one of the world's most prevalent cancers, which can be identified from immunohistochemical images. Here, based on the deep learning framework, location prediction models were constructed using the features of breast immunohistochemical images. Ultimately, six differentially localized proteins that with stable differentially predictive localization, maximum localization differences, and whose predicted results are not affected by removing a single image are obtained (CCNT1, NSUN5, PRPF4, RECQL4, UTP6, ZNF500). Further verification reveals that these proteins are not differentially expressed, but are closely associated with breast cancer and have great classification performance. Potential mechanism analysis shows that their co-expressed or co-located proteins and RNAs may affect their localization, leading to changes in interactions and functions that further causes breast cancer. They have the potential to help shed light on the molecular mechanisms of breast cancer and provide assistance for its early diagnosis and treatment.

Controlled Environment Ecosystem: A Cutting-Edge Technology in Speed Breeding

The controlled environment ecosystem is a meticulously designed plant growing chamber utilized for cultivating biofortified crops and microgreens, addressing hidden hunger and malnutrition prevalent in the growing population. The integration of speed breeding within such controlled environments effectively eradicates morphological disruptions encountered in traditional breeding methods such as inbreeding depression, male sterility, self-incompatibility, embryo abortion, and other unsuccessful attempts. In contrast to the unpredictable climate conditions that often prolong breeding cycles to 10-15 years in traditional breeding and 4-5 years in transgenic breeding within open ecosystems, speed breeding techniques expedite the achievement of breeding objectives and F1-F6 generations within 2-3 years under controlled growing conditions. In comparison, traditional breeding may take 5-10 years for plant population line creation, 3-5 years for field trials, and 1-2 years for variety release. The effectiveness of speed breeding in trait improvement and population line development varies across different crops, requiring approximately 4 generations in rice and groundnut, 5 generations in soybean, pea, and oat, 6 generations in sorghum, Amaranthus sp., and subterranean clover, 6-7 generations in bread wheat, durum wheat, and chickpea, 7 generations in broad bean, 8 generations in lentil, and 10 generations in Arabidopsis thaliana annually within controlled environment ecosystems. Artificial intelligence leverages neural networks and algorithm models to screen phenotypic traits and assess their role in diverse crop species. Moreover, in controlled environment systems, mechanistic models combined with machine learning effectively regulate stable nutrient use efficiency, water use efficiency, photosynthetic assimilation product, metabolic use efficiency, climatic factors, greenhouse gas emissions, carbon sequestration, and carbon footprints. However, any negligence, even minor, in maintaining optimal photoperiodism, temperature, humidity, and controlling pests or diseases can lead to the deterioration of crop trials and speed breeding techniques within the controlled environment system. Further comparative studies are imperative to comprehend and justify the efficacy of climate management techniques in controlled environment ecosystems compared to natural environments, with or without soil.

Photosynthesis in newly developed leaves of heat-tolerant wheat acclimates to long-term nocturnal warming

We examined photosynthetic traits of pre-existing and newly developed flag leaves of four wheat genotypes grown in controlled-environment experiments. In newly developed leaves, acclimation of the maximum rate of net CO2 assimilation (An) to warm nights (i.e. increased An) was associated with increased capacity of Rubisco carboxylation and photosynthetic electron transport, with Rubisco activation state probably contributing to increased Rubisco activity. Metabolite profiling linked acclimation of An to greater accumulation of monosaccharides and saturated fatty acids in leaves; these changes suggest roles for osmotic adjustment of leaf turgor pressure and maintenance of cell membrane integrity. By contrast, where An decreased under warm nights, the decline was related to lower stomatal conductance and rates of photosynthetic electron transport. Decreases in An occurred despite higher basal PSII thermal stability in all genotypes exposed to warm nights: Tcrit of 45-46.5 °C in non-acclimated versus 43.8-45 °C in acclimated leaves. Pre-existing leaves showed no change in An-temperature response curves, except for an elite heat-tolerant genotype. These findings illustrate the impact of night-time warming on the ability of wheat plants to photosynthesize during the day, thereby contributing to explain the impact of global warming on crop productivity.

Effects of exogenous zinc (ZnSO4·7H2O) on photosynthetic characteristics and grain quality of hybrid rice

Rice is an important food crop and zinc (Zn) is a beneficial microelement. However, there are few reports on the effect of zinc on yield and physiological characteristics of rice. In this study, exogenous zinc (ZnSO4·7H2O) was applied on plant to explore the effects of zinc on rice yield, quality and photosynthetic capacity. The results showed that appropriate concentration of zinc could increase the net photosynthetic rate (Pn) of rice leaves, and Zn2 (2 mg/L ZnSO4•7H2O) treatment was the most significant. However, the Zn treatment had no positive effect on rice yield except under the concentration of Zn2. Meanwhile, the result showed that Zn treatment could increase chalkiness degree (CD) and chalky grain rate (CGR), decreased amylose content (AC), increased protein content and changed protein composition of rice. The above indexes were most significant in Zn2 treatment. In addition, the Zn2 treatment significantly increased rapid viscosity analyzer (RVA) of rice. In conclusion, the results of this study suggested that Zn treatment could enhance the photosynthetic capacity of rice leaves, and improve the processing quality, taste quality and nutritional quality of rice. However, it will have a negative impact on the appearance quality of rice and cannot be used to increase rice production. This study will provide a basis for the application of zinc in rice production.

How rice adapts to high temperatures

High-temperature stress affects crop yields worldwide. Identifying thermotolerant crop varieties and understanding the basis for this thermotolerance would have important implications for agriculture, especially in the face of climate change. Rice (Oryza sativa) varieties have evolved protective strategies to acclimate to high temperature, with different thermotolerance levels. In this review, we examine the morphological and molecular effects of heat on rice in different growth stages and plant organs, including roots, stems, leaves and flowers. We also explore the molecular and morphological differences among thermotolerant rice lines. In addition, some strategies are proposed to screen new rice varieties for thermotolerance, which will contribute to the improvement of rice for agricultural production in the future.

Meta-QTL and haplo-pheno analysis reveal superior haplotype combinations associated with low grain chalkiness under high temperature in rice

Chalk, an undesirable grain quality trait in rice, is primarily formed due to high temperatures during the grain-filling process. Owing to the disordered starch granule structure, air spaces and low amylose content, chalky grains are easily breakable during milling thereby lowering head rice recovery and its market price. Availability of multiple QTLs associated with grain chalkiness and associated attributes, provided us an opportunity to perform a meta-analysis and identify candidate genes and their alleles contributing to enhanced grain quality. From the 403 previously reported QTLs, 64 Meta-QTLs encompassing 5262 non-redundant genes were identified. MQTL analysis reduced the genetic and physical intervals and nearly 73% meta-QTLs were narrower than 5cM and 2Mb, revealing the hotspot genomic regions. By investigating expression profiles of 5262 genes in previously published datasets, 49 candidate genes were shortlisted on the basis of their differential regulation in at least two of the datasets. We identified non-synonymous allelic variations and haplotypes in 39 candidate genes across the 3K rice genome panel. Further, we phenotyped a subset panel of 60 rice accessions by exposing them to high temperature stress under natural field conditions over two Rabi cropping seasons. Haplo-pheno analysis uncovered haplotype combinations of two starch synthesis genes, GBSSI and SSIIa, significantly contributing towards the formation of grain chalk in rice. We, therefore, report not only markers and pre-breeding material, but also propose superior haplotype combinations which can be introduced using either marker-assisted breeding or CRISPR-Cas based prime editing to generate elite rice varieties with low grain chalkiness and high HRY traits.

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.

High-throughput screening paradigms in ecotoxicity testing: Emerging prospects and ongoing challenges

The rapidly increasing number of new production chemicals coupled with stringent implementation of global chemical management programs necessities a paradigm shift towards boarder uses of low-cost and high-throughput ecotoxicity testing strategies as well as deeper understanding of cellular and sub-cellular mechanisms of ecotoxicity that can be used in effective risk assessment. The latter will require automated acquisition of biological data, new capabilities for big data analysis as well as computational simulations capable of translating new data into in vivo relevance. However, very few efforts have been so far devoted into the development of automated bioanalytical systems in ecotoxicology. This is in stark contrast to standardized and high-throughput chemical screening and prioritization routines found in modern drug discovery pipelines. As a result, the high-throughput and high-content data acquisition in ecotoxicology is still in its infancy with limited examples focused on cell-free and cell-based assays. In this work we outline recent developments and emerging prospects of high-throughput bioanalytical approaches in ecotoxicology that reach beyond in vitro biotests. We discuss future importance of automated quantitative data acquisition for cell-free, cell-based as well as developments in phytotoxicity and in vivo biotests utilizing small aquatic model organisms. We also discuss recent innovations such as organs-on-a-chip technologies and existing challenges for emerging high-throughput ecotoxicity testing strategies. Lastly, we provide seminal examples of the small number of successful high-throughput implementations that have been employed in prioritization of chemicals and accelerated environmental risk assessment.

Rice Chalky Grain 5 regulates natural variation for grain quality under heat stress

Heat stress occurring during rice (Oryza sativa) grain development reduces grain quality, which often manifests as increased grain chalkiness. Although the impact of heat stress on grain yield is well-studied, the genetic basis of rice grain quality under heat stress is less explored as quantifying grain quality is less tractable than grain yield. To address this, we used an image-based colorimetric assay (Red, R; and Green, G) for genome-wide association analysis to identify genetic loci underlying the phenotypic variation in rice grains exposed to heat stress. We found the R to G pixel ratio (RG) derived from mature grain images to be effective in distinguishing chalky grains from translucent grains derived from control (28/24°C) and heat stressed (36/32°C) plants. Our analysis yielded a novel gene, rice Chalky Grain 5 (OsCG5) that regulates natural variation for grain chalkiness under heat stress. OsCG5 encodes a grain-specific, expressed protein of unknown function. Accessions with lower transcript abundance of OsCG5 exhibit higher chalkiness, which correlates with higher RG values under stress. These findings are supported by increased chalkiness of OsCG5 knock-out (KO) mutants relative to wildtype (WT) under heat stress. Grains from plants overexpressing OsCG5 are less chalky than KOs but comparable to WT under heat stress. Compared to WT and OE, KO mutants exhibit greater heat sensitivity for grain size and weight relative to controls. Collectively, these results show that the natural variation at OsCG5 may contribute towards rice grain quality under heat stress.