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A goal of biology is to predict how mutations combine to alter phenotypes, fitness and disease. It is often assumed that mutations combine additively or with interactions that can be predicted. Here, we show using simulations that, even for the simple example of the lambda phage transcription factor CI repressing a gene, this assumption is incorrect and that perfect measurements of the effects of mutations on a trait and mechanistic understanding can be insufficient to predict what happens when two mutations are combined. This apparent paradox arises because mutations can have different biophysical effects to cause the same change in a phenotype and the outcome in a double mutant depends upon what these hidden biophysical changes actually are. Pleiotropy and non-monotonic functions further confound prediction of how mutations interact. Accurate prediction of phenotypes and disease will sometimes not be possible unless these biophysical ambiguities can be resolved using additional measurements. In quantitative genetics, it is widely assumed that mutations combine additively or epistasis can be predicted with statistical or mechanistic models. Here, the authors use the phage lambda repressor model to show how biophysical ambiguity and non-monotonic functions confound phenotypic prediction.

Manipulating grain size is an effective strategy for increasing cereal yields. Here we identify a pathway composed of five subunits of the heterotrimeric G proteins that regulate grain length in rice. The Gβ protein is essential for plant survival and growth. Gα provides a foundation for grain size expansion. Three Gγ proteins, DEP1, GGC2 and GS3, antagonistically regulate grain size. DEP1 and GGC2, individually or in combination, increase grain length when in complex with Gβ. GS3, having no effect on grain size by itself, reduces grain length by competitively interacting with Gβ. By combining different G-protein variants, we can decrease grain length by up to 35% or increase it by up to 19%, which leads to over 40% decreasing to 28% increasing of grain weight. The wide existence of such a conserved system among angiosperms suggests a possible general predictable approach to manipulating grain/organ sizes. Grain size is a major determinant of cereal yield. Here the authors characterize five subunits of the rice heterotrimeric G proteins and find that manipulating the three Gγ proteins can achieve designed grain size, which provides a predictable approach to improving grain yield and quality.

Accurate information on urban tree species composition is critical for urban green space ecosystem management. However, achieving large-scale, high-precision species identification in complex metropolitan environments remains challenging. This study assessed the potential of medium-resolution multi-temporal optical imagery combined with airborne LiDAR for tree species classification in large heterogeneous urban areas (> 5000 km²). The results indicate that precise large-scale identification of urban tree species distribution is feasible by integrating multi-seasonal Sentinel-2 imagery with airborne LiDAR data based on a Random Forest hierarchical classification model. The overall classification accuracies for deciduous broadleaf species and evergreen broadleaf species were 63.32% and 76.77%, respectively. Multi-temporal spectra were the primary explanatory variables, with spring bands significantly affecting the classification of deciduous broadleaf species. For evergreen broadleaf species, each season has its own dominant spectral information. Classifications combining data from three seasons outperformed single- or two-season combinations. The incorporation of LiDAR-derived metrics improved the classification results for most species, with accuracy increases of up to 18.75% point for deciduous broadleaf species. Overall, the results demonstrate the effectiveness of combining medium-resolution multi-temporal optical imagery with LiDAR data for urban tree species classification, laying a foundation for quantifying ecosystem services provided by urban trees through remote sensing.

Plant responses to abiotic stresses are coordinated by arrays of growth and developmental processes. Indole-3-acetic acid (IAA) and abscisic acid (ABA) play critical roles in developmental programmes and environmental responses, respectively, through complex signalling and metabolism networks. However, crosstalk between the two phytohormones in the stress responses remains largely unknown. Here, it is reported that a GH3 family gene, OsGH3-2, encoding an enzyme catalysing IAA conjugation to amino acids, is involved in the modulation of ABA level and stress tolerance. Expression of OsGH3-2 was induced by drought but was suppressed by cold. Overexpression of OsGH3-2 in rice caused significant morphological aberrations related to IAA deficiency, such as dwarfism, smaller leaves, and fewer crown roots and root hairs. The overexpressing line showed significantly reduced carotene, ABA, and free IAA levels, greater stomata aperture, and faster water loss, and was hypersensitive to drought stress. However, the overexpressing line showed increased cold tolerance, which was due to the combined effects of reduced free IAA content, alleviated oxidative damage, and decreased membrane penetrability. Furthermore, expression levels of some ABA synthesis- and stress-related genes were significantly changed in the overexpression line. It was conclude that OsGH3-2 modulates both endogenous free IAA and ABA homeostasis and differentially affects drought and cold tolerance in rice.

A variety of adverse conditions including drought stress severely affect rice production. Root system plays a critical role in drought avoidance, which is one of the major mechanisms of drought resistance. In this study, we adopted genome-wide association study (GWAS) to dissect the genetic basis controlling various root traits by using a natural population consisting of 529 representative rice accessions. A total of 413 suggestive associations, containing 143 significant associations, were identified for 21 root traits, such as maximum root length, root volume, and root dry weight under normal and drought stress conditions at the maturation stage. More than 80 percent of the suggestive loci were located in the region of reported QTLs for root traits, while about 20 percent of suggestive loci were novel loci detected in this study. Besides, 11 reported root-related genes, including DRO1, WOX11, and OsPID, were found to co-locate with the association loci. We further proved that the association results can facilitate the efficient identification of causal genes for root traits by the two case studies of Nal1 and OsJAZ1. These loci and their candidate causal genes provide an important basis for the genetic improvement of root traits and drought resistance.

An important challenge in genetics, evolution and biotechnology is to understand and predict how mutations combine to alter phenotypes, including molecular activities, fitness and disease. In diploids, mutations in a gene can combine on the same chromosome or on different chromosomes as a “heteroallelic combination”. However, a direct comparison of the extent, sign, and stability of the genetic interactions between variants within and between alleles is lacking. Here we use thermodynamic models of protein folding and ligand-binding to show that interactions between mutations within and between alleles are expected in even very simple biophysical systems. Protein folding alone generates within-allele interactions and a single molecular interaction is sufficient to cause between-allele interactions and dominance. These interactions change differently, quantitatively and qualitatively as a system becomes more complex. Altering the concentration of a ligand can, for example, switch alleles from dominant to recessive. Our results show that intra-molecular epistasis and dominance should be widely expected in even the simplest biological systems but also reinforce the view that they are plastic system properties and so a formidable challenge to predict. Accurate prediction of both intra-molecular epistasis and dominance will require either detailed mechanistic understanding and experimental parameterization or brute-force measurement and learning. The authors examine how mutations combine to alter phenotypes in biophysical models of proteins and conclude that non-additive interactions (epistasis and dominance) are frequent, context-dependent and so challenging to predict in even the simplest of biological systems.

Hybrid rice has greatly contributed to the global increase of rice productivity. A major component that facilitated the development of hybrids was a mutant showing photoperiod-sensitive male sterility (PSMS) with its fertility regulated by day length. Transcriptome studies have shown that large portions of the eukaryotic genomic sequences are transcribed to long noncoding RNAs (lncRNAs). However, the potential roles for only a few lncRNAs have been brought to light at present. Thus, great efforts have to be invested to understand the biological functions of lncRNAs. Here we show that a lncRNA of 1,236 bases in length, referred to as long-day–specific male-fertility–associated RNA (LDMAR), regulates PSMS in rice. We found that sufficient amount of the LDMAR transcript is required for normal pollen development of plants grown under long-day conditions. A spontaneous mutation causing a single nucleotide polymorphism (SNP) between the wild-type and mutant altered the secondary structure of LDMAR. This change brought about increased methylation in the putative promoter region of LDMAR, which reduced the transcription of LDMAR specifically under long-day conditions, resulting in premature programmed cell death (PCD) in developing anthers, thus causing PSMS. Thus, a lncRNA could directly exert a major effect on a trait like a structure gene, and a SNP could alter the function of a lncRNA similar to amino acid substitution in structural genes. Molecular elucidating of PSMS has important implications for understanding molecular mechanisms of photoperiod regulation of many biological processes and also for developing male sterile germplasms for hybrid crop breeding.

Accurate estimation of crop area is essential to adjusting the regional crop planting structure and the rational planning of water resources. However, it is quite challenging to map crops accurately by high-resolution remote sensing images because of the ecological gradient and ecological convergence between crops and non-crops. The purpose of this study is to explore the combining application of high-resolution multi-temporal Sentinel-1 (S1) radar backscatter and Sentinel-2 (S2) optical reflectance images for maize mapping in highly complex and heterogeneous landscapes in the middle reaches of Heihe River, northwest China. We proposed a new two-step method of vegetation extraction and followed by maize extraction, that is, extract the vegetation-covered areas first to reduce the inter-class variance by using a Random Forest (RF) classifier based on S2 data, and then extract the maize distribution in the vegetation area by using another RF classifier based on S1 and/or S2 data. The results demonstrate that the vegetation extraction classifier successfully identified vegetation-covered regions with an overall accuracy above 96% in the study area, and the accuracy of the maize extraction classifier constructed by the combined multi-temporal S1 and S2 images is significantly improved compared with that S1 (alone) or S2 (alone), with an overall accuracy of 87.63%, F1_Score of 0.86, and Kappa coefficient of 0.75. In addition, with the introduction of multi-temporal S1 and/or S2 images in crop growing season, the constructed RF model is more beneficial to maize mapping.

Blast caused by fungalMagnaporthe oryzaeis a devastating disease of rice (Oryza sativa) worldwide, and this fungus also infects barley (Hordeum vulgare). At least 11 rice WRKY transcription factors have been reported to regulate rice response toM. oryzaeeither positively or negatively. However, the relationships of these WRKYs in the rice defense signaling pathway againstM. oryzaeare unknown. Previous studies have revealed that rice WRKY13 (as a transcriptional repressor) and WRKY45-2 enhance resistance toM. oryzae.Here, we show that rice WRKY42, functioning as a transcriptional repressor, suppresses resistance toM. oryzae. WRKY42-RNA interference (RNAi) andWRKY42-overexpressing (oe) plants showed increased resistance and susceptibility toM. oryzae, accompanied by increased or reduced jasmonic acid (JA) content, respectively, compared with wild-type plants. JA pretreatment enhanced the resistance ofWRKY42-oe plants toM. oryzae. WRKY13 directly suppressedWRKY42. WRKY45-2, functioning as a transcriptional activator, directly activatedWRKY13. In addition, WRKY13 directly suppressedWRKY45-2by feedback regulation. TheWRKY13-RNAiWRKY45-2-oe andWRKY13-oeWRKY42-oe double transgenic lines showed increased susceptibility toM. oryzaecompared withWRKY45-2-oe and WRKY13-oe plants, respectively. These results suggest that the three WRKYs form a sequential transcriptional regulatory cascade. WRKY42 may negatively regulate rice response toM. oryzaeby suppressing JA signaling-related genes, andWRKY45-2transcriptionally activates WRKY13, whose encoding protein in turn transcriptionally suppressesWRKY42to regulate rice resistance toM. oryzae.

Background Simultaneous analysis of multiple functional-related phytohormones and their metabolites will improve our understanding of interactions among different hormones in the same biologic process. Results A method was developed for simultaneous quantification of multiple phytohormones, abscisic acid, indole-3-acetic acid (IAA), jasmonic acid (JA), and salicylic acid, hormone conjugates, IAA-aspartic acid, JA-isoleucine, and methyl JA, and phytoalexins, momilactone A, naringenin, and sakuranetin. This method combines a convenient procedure for preparing filtrated crude extracted samples and a sensitive quantification assay using ultra fast liquid chromatography-electrospray ionization tandem mass spectrometry (UFLC-ESI-MS). With this method, we determined the dynamic profiles of defense-related phytohormones, hormone metabolites, and phytoalexins in the interaction of rice with Xanthomonas oryzae pv. oryzae ( Xoo ), which causes bacterial blight, one of the most devastating diseases of rice worldwide. Conclusion This UFLC-ESI-MS method is convenient, sensitive, reliable, and inexpensive for quantification of multiple phytohormones and metabolites compared to current methods. The results obtained by application of this method in studying rice-bacterial interaction provide a basis for understanding the molecular mechanisms of rice defense responses.