Defining the scope for altering rice leaf anatomy to improve photosynthesis: a modelling approach.

Leaf structure plays an important role in photosynthesis. However, the causal relationship and the quantitative importance of any single structural parameter to the overall photosynthetic performance of a leaf remains open to debate. In this paper, we report on a mechanistic model, eLeaf, which successfully captures rice leaf photosynthetic performance under varying environmental conditions of light and CO2 . We developed a 3D reaction-diffusion model for leaf photosynthesis parameterised using a range of imaging data and biochemical measurements from plants grown under ambient and elevated CO2 and then interrogated the model to quantify the importance of these elements. The model successfully captured leaf-level photosynthetic performance in rice. Photosynthetic metabolism underpinned the majority of the increased carbon assimilation rate observed under elevated CO2 levels, with a range of structural elements making positive and negative contributions. Mesophyll porosity could be varied without any major outcome on photosynthetic performance, providing a theoretical underpinning for experimental data. eLeaf allows quantitative analysis of the influence of morphological and biochemical properties on leaf photosynthesis. The analysis highlights a degree of leaf structural plasticity with respect to photosynthesis of significance in the context of attempts to improve crop photosynthesis.

Potential metabolic mechanisms for inhibited chloroplast nitrogen assimilation under high CO2.

Improving photosynthesis is considered a major and feasible option to dramatically increase crop yield potential. Increased atmospheric CO2 concentration often stimulates both photosynthesis and crop yield, but decreases protein content in the main C3 cereal crops. This decreased protein content in crops constrains the benefits of elevated CO2 on crop yield and affects their nutritional value for humans. To support studies of photosynthetic nitrogen assimilation and its complex interaction with photosynthetic carbon metabolism for crop improvement, we developed a dynamic systems model of plant primary metabolism, which includes the Calvin-Benson cycle, the photorespiration pathway, starch synthesis, glycolysis-gluconeogenesis, the tricarboxylic acid cycle, and chloroplastic nitrogen assimilation. This model successfully captures responses of net photosynthetic CO2 uptake rate (A), respiration rate, and nitrogen assimilation rate to different irradiance and CO2 levels. We then used this model to predict inhibition of nitrogen assimilation under elevated CO2. The potential mechanisms underlying inhibited nitrogen assimilation under elevated CO2 were further explored with this model. Simulations suggest that enhancing the supply of α-ketoglutarate is a potential strategy to maintain high rates of nitrogen assimilation under elevated CO2. This model can be used as a heuristic tool to support research on interactions between photosynthesis, respiration, and nitrogen assimilation. It also provides a basic framework to support the design and engineering of C3 plant primary metabolism for enhanced photosynthetic efficiency and nitrogen assimilation in the coming high-CO2 world.

Overexpression of maize transcription factor mEmBP-1 increases photosynthesis, biomass, and yield in rice.

Identifying new options to improve photosynthetic capacity is a major approach to improve crop yield potential. Here we report that overexpression of the gene encoding the transcription factor mEmBP-1 led to simultaneously increased expression of many genes in photosynthesis, including genes encoding Chl a,b-binding proteins (Lhca and Lhcb), PSII (PsbR3 and PsbW) and PSI reaction center subunits (PsaK and PsaN), chloroplast ATP synthase subunit, electron transport reaction components (Fd1 and PC), and also major genes in the Calvin-Benson-Bassham cycle, including those encoding Rubisco, glyceraldehyde phosphate dehydrogenase, fructose bisphosphate aldolase, transketolase, and phosphoribulokinase. These increased expression of photosynthesis genes resulted in increased leaf chlorophyll pigment, photosynthetic rate, biomass growth, and grain yield both in the greenhouse and in the field. Using EMSA experiments, we showed that mEmBP-1a protein can directly bind to the promoter region of photosynthesis genes, suggesting that the direct binding of mEmBP-1a to the G-box domain of photosynthetic genes up-regulates expression of these genes. Altogether, our results show that mEmBP-1a is a major regulator of photosynthesis, which can be used to increase rice photosynthesis and yield in the field.

Genome-Wide Association Study Unravels LRK1 as a Dark Respiration Regulator in Rice (Oryza sativa L.).

Respiration is a major plant physiological process that generates adenosine triphosphate (ATP) to support the various pathways involved in the plant growth and development. After decades of focused research on basic mechanisms of respiration, the processes and major proteins involved in respiration are well elucidated. However, much less is known about the natural variation of respiration. Here we conducted a survey on the natural variation of leaf dark respiration (Rd) in a global rice minicore diversity panel and applied a genome-wide association study (GWAS) in rice (Oryza sativa L.) to determine candidate loci associated with Rd. This rice minicore diversity panel consists of 206 accessions, which were grown under both growth room (GR) and field conditions. We found that Rd shows high single-nucleotide polymorphism (SNP) heritability under GR and it is significantly affected by genotype-environment interactions. Rd also exhibits strong positive correlation to the leaf thickness and chlorophyll content. GWAS results of Rd collected under GR and field show an overlapped genomic region in the chromosome 3 (Chr.3), which contains a lead SNP (3m29440628). There are 12 candidate genes within this region; among them, three genes show significantly higher expression levels in accessions with high Rd. Particularly, we observed that the LRK1 gene, annotated as leucine rich repeat receptor kinase, was up-regulated four times. We further found that a single significantly associated SNPs at the promoter region of LRK1, was strongly correlated with the mean annual temperature of the regions from where minicore accessions were collected. A rice lrk1 mutant shows only ~37% Rd of that of WT and retarded growth following exposure to 35 °C for 30 days, but only 24% reduction in growth was recorded under normal temperature (25 °C). This study demonstrates a substantial natural variation of Rd in rice and that the LRK1 gene can regulate leaf dark respiratory fluxes, especially under high temperature.

A three-dimensional canopy photosynthesis model in rice with a complete description of the canopy architecture, leaf physiology, and mechanical properties.

In current rice breeding programs, morphological parameters such as plant height, leaf length and width, leaf angle, panicle architecture, and tiller number during the grain filling stage are used as major selection targets. However, so far, there is no robust approach to quantitatively define the optimal combinations of parameters that can lead to increased canopy radiation use efficiency (RUE). Here we report the development of a three-dimensional canopy photosynthesis model (3dCAP), which effectively combines three-dimensional canopy architecture, canopy vertical nitrogen distribution, a ray-tracing algorithm, and a leaf photosynthesis model. Concurrently, we developed an efficient workflow for the parameterization of 3dCAP. 3dCAP predicted daily canopy RUE for different nitrogen treatments of a given rice cultivar under different weather conditions. Using 3dCAP, we explored the influence of three canopy architectural parameters-tiller number, tiller angle and leaf angle-on canopy RUE. Under different weather conditions and different nitrogen treatments, canopy architecture optimized by manipulating these parameters can increase daily net canopy photosynthetic CO2 uptake by 10-52%. Generally, a smaller tiller angle was predicted for most elite rice canopy architectures, especially under scattered light conditions. Results further show that similar canopy RUE can be obtained by multiple different parameter combinations; these combinations share two common features of high light absorption by leaves in the canopy and a high level of coordination between the nitrogen concentration and the light absorbed by each leaf within the canopy. Overall, this new model has potential to be used in rice ideotype design for improved canopy RUE.

Systems model-guided rice yield improvements based on genes controlling source, sink, and flow.

A large number of genes related to source, sink, and flow have been identified after decades of research in plant genetics. Unfortunately, these genes have not been effectively utilized in modern crop breeding. This perspective paper aims to examine the reasons behind such a phenomenon and propose a strategy to resolve this situation. Specifically, we first systematically survey the currently cloned genes related to source, sink, and flow; then we discuss three factors hindering effective application of these identified genes, which include the lack of effective methods to identify limiting or critical steps in a signaling network, the misplacement of emphasis on properties, at the leaf, instead of the whole canopy level, and the non-linear complex interaction between source, sink, and flow. Finally, we propose the development of systems models of source, sink and flow, together with a detailed simulation of interactions between them and their surrounding environments, to guide effective use of the identified elements in modern rice breeding. These systems models will contribute directly to the definition of crop ideotype and also identification of critical features and parameters that limit the yield potential in current cultivars.

Source-sink interaction: a century old concept under the light of modern molecular systems biology.

Many approaches to engineer source strength have been proposed to enhance crop yield potential. However, a well-co-ordinated source-sink relationship is required finally to realize the promised increase in crop yield potential in the farmer's field. Source-sink interaction has been intensively studied for decades, and a vast amount of knowledge about the interaction in different crops and under different environments has been accumulated. In this review, we first introduce the basic concepts of source, sink and their interactions, then summarize current understanding of how source and sink can be manipulated through both environmental control and genetic manipulations. We show that the source-sink interaction underlies the diverse responses of crops to the same perturbations and argue that development of a molecular systems model of source-sink interaction is required towards a rational manipulation of the source-sink relationship for increased yield. We finally discuss both bottom-up and top-down routes to develop such a model and emphasize that a community effort is needed for development of this model.

Analysis of RNA translation with a deep learning architecture provides new insight into translation control.

Accurate annotation of coding regions in RNAs is essential for understanding gene translation. We developed a deep neural network to directly predict and analyze translation initiation and termination sites from RNA sequences. Trained with human transcripts, our model learned hidden rules of translation control and achieved a near perfect prediction of canonical translation sites across entire human transcriptome. Surprisingly, this model revealed a new role of codon usage in regulating translation termination, which was experimentally validated. We also identified thousands of new open reading frames in mRNAs or lncRNAs, some of which were confirmed experimentally. The model trained with human mRNAs achieved high prediction accuracy of canonical translation sites in all eukaryotes and good prediction in polycistronic transcripts from prokaryotes or RNA viruses, suggesting a high degree of conservation in translation control. Collectively, we present a general and efficient deep learning model for RNA translation, generating new insights into the complexity of translation regulation.

Inferring Characteristics of the Tumor Immune Microenvironment of Patients with HNSCC from Single-Cell Transcriptomics of Peripheral Blood.

In this study, we explore the possibility of inferring characteristics of the tumor immune microenvironment from the blood. Specifically, we investigate two datasets of patients with head and neck squamous cell carcinoma with matched single-cell RNA sequencing (scRNA-seq) from peripheral blood mononuclear cells (PBMCs) and tumor tissues. Our analysis shows that the immune cell fractions and gene expression profiles of various immune cells within the tumor microenvironment can be inferred from the matched PBMC scRNA-seq data. We find that the established exhausted T-cell signature can be predicted from the blood and serve as a valuable prognostic blood biomarker of immunotherapy response. Additionally, our study reveals that the inferred ratio between tumor memory B- and regulatory T-cell fractions is predictive of immunotherapy response and is superior to the well-established cytolytic and exhausted T-cell signatures. These results highlight the promising potential of PBMC scRNA-seq in cancer immunotherapy and warrant, and will hopefully facilitate, further investigations on a larger scale. The code for predicting tumor immune microenvironment from PBMC scRNA-seq, TIMEP, is provided, offering other researchers the opportunity to investigate its prospective applications in various other indications. SIGNIFICANCE: Our work offers a new and promising paradigm in liquid biopsies to unlock the power of blood single-cell transcriptomics in cancer immunotherapy.

LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features.

Despite the revolutionary impact of immune checkpoint blockade (ICB) in cancer treatment, accurately predicting patient responses remains challenging. Here, we analyzed a large dataset of 2,881 ICB-treated and 841 non-ICB-treated patients across 18 solid tumor types, encompassing a wide range of clinical, pathologic and genomic features. We developed a clinical score called LORIS (logistic regression-based immunotherapy-response score) using a six-feature logistic regression model. LORIS outperforms previous signatures in predicting ICB response and identifying responsive patients even with low tumor mutational burden or programmed cell death 1 ligand 1 expression. LORIS consistently predicts patient objective response and short-term and long-term survival across most cancer types. Moreover, LORIS showcases a near-monotonic relationship with ICB response probability and patient survival, enabling precise patient stratification. As an accurate, interpretable method using a few readily measurable features, LORIS may help improve clinical decision-making in precision medicine to maximize patient benefit. LORIS is available as an online tool at https://loris.ccr.cancer.gov/ .

Human aneuploid cells depend on the RAF/MEK/ERK pathway for overcoming increased DNA damage.

Aneuploidy is a hallmark of human cancer, yet the molecular mechanisms to cope with aneuploidy-induced cellular stresses remain largely unknown. Here, we induce chromosome mis-segregation in non-transformed RPE1-hTERT cells and derive multiple stable clones with various degrees of aneuploidy. We perform a systematic genomic, transcriptomic and proteomic profiling of 6 isogenic clones, using whole-exome DNA, mRNA and miRNA sequencing, as well as proteomics. Concomitantly, we functionally interrogate their cellular vulnerabilities, using genome-wide CRISPR/Cas9 and large-scale drug screens. Aneuploid clones activate the DNA damage response and are more resistant to further DNA damage induction. Aneuploid cells also exhibit elevated RAF/MEK/ERK pathway activity and are more sensitive to clinically-relevant drugs targeting this pathway, and in particular to CRAF inhibition. Importantly, CRAF and MEK inhibition sensitize aneuploid cells to DNA damage-inducing chemotherapies and to PARP inhibitors. We validate these results in human cancer cell lines. Moreover, resistance of cancer patients to olaparib is associated with high levels of RAF/MEK/ERK signaling, specifically in highly-aneuploid tumors. Overall, our study provides a comprehensive resource for genetically-matched karyotypically-stable cells of various aneuploidy states, and reveals a therapeutically-relevant cellular dependency of aneuploid cells.

Increased RNA and protein degradation is required for counteracting transcriptional burden and proteotoxic stress in human aneuploid cells.

Aneuploidy results in a stoichiometric imbalance of protein complexes that jeopardizes cellular fitness. Aneuploid cells thus need to compensate for the imbalanced DNA levels by regulating their RNA and protein levels, but the underlying molecular mechanisms remain unknown. Here, we dissected multiple diploid vs. aneuploid cell models. We found that aneuploid cells cope with transcriptional burden by increasing several RNA degradation pathways, and are consequently more sensitive to the perturbation of RNA degradation. At the protein level, aneuploid cells mitigate proteotoxic stress by reducing protein translation and increasing protein degradation, rendering them more sensitive to proteasome inhibition. These findings were recapitulated across hundreds of human cancer cell lines and primary tumors, and aneuploidy levels were significantly associated with the response of multiple myeloma patients to proteasome inhibitors. Aneuploid cells are therefore preferentially dependent on several key nodes along the gene expression process, creating clinically-actionable vulnerabilities in aneuploid cells.

Microenvironment shapes small-cell lung cancer neuroendocrine states and presents therapeutic opportunities.

Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intratumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the cell-extrinsic drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the TME exhibit substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAFs) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting exceptionally poor prognosis. Our work provides a comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLC's adaptable nature, opening possibilities for reprogramming the TME-tumor communications that shape SCLC tumor states.

Optimizing cancer immunotherapy response prediction by tumor aneuploidy score and fraction of copy number alterations.

Identifying patients that are likely to respond to cancer immunotherapy is an important, yet highly challenging clinical need. Using 3139 patients across 17 different cancer types, we comprehensively studied the ability of two common copy-number alteration (CNA) scores-the tumor aneuploidy score (AS) and the fraction of genome single nucleotide polymorphism encompassed by copy-number alterations (FGA)-to predict survival following immunotherapy in both pan-cancer and individual cancer types. First, we show that choice of cutoff during CNA calling significantly influences the predictive power of AS and FGA for patient survival following immunotherapy. Remarkably, by using proper cutoff during CNA calling, AS and FGA can predict pan-cancer survival following immunotherapy for both high-TMB and low-TMB patients. However, at the individual cancer level, our data suggest that the use of AS and FGA for predicting immunotherapy response is currently limited to only a few cancer types. Therefore, larger sample sizes are needed to evaluate the clinical utility of these measures for patient stratification in other cancer types. Finally, we propose a simple, non-parameterized, elbow-point-based method to help determine the cutoff used for calling CNAs.

Predicting tumor immune microenvironment and checkpoint therapy response of head & neck cancer patients from blood immune single-cell transcriptomics.

The immune state of tumor microenvironment is crucial for determining immunotherapy response but is not readily accessible. Here we investigate if we can infer the tumor immune state from the blood and further predict immunotherapy response. First, we analyze a dataset of head and neck squamous cell carcinoma (HNSCC) patients with matched scRNA-Seq of peripheral blood mononuclear cells (PBMCs) and tumor tissues. We find that the tumor immune cell fractions of different immune cell types and many of the genes they express can be inferred from the matched PBMC scRNA-Seq. Second, analyzing another HNSCC dataset with PBMC scRNA-Seq and immunotherapy response, we find that the inferred ratio between tumor memory B and regulatory T cell fractions is predictive of immunotherapy response and is superior to the well-established cytolytic and exhausted T-cell signatures. Overall, these results showcase the potential of scRNA-Seq liquid biopsies in cancer immunotherapy, calling for their larger-scale testing. Significance: This head and neck cancer study demonstrates the potential of using blood single-cell transcriptomics to (1) infer the tumor immune status and (2) predict immunotherapy response from the tumor immune status inferred from blood. These results showcase the potential of single-cell transcriptomics liquid biopsies for further advancing personalized cancer immunotherapy.

Pan-Cancer Analysis of Patient Tumor Single-Cell Transcriptomes Identifies Promising Selective and Safe Chimeric Antigen Receptor Targets in Head and Neck Cancer.

Chimeric antigen receptor (CAR) T cell therapies have yielded transformative clinical successes for patients with blood tumors, but their full potential remains to be unleashed against solid tumors. One challenge is finding selective targets, which we define intuitively to be cell surface proteins that are expressed widely by cancer cells but minimally by healthy cells in the tumor microenvironment and other normal tissues. Analyzing patient tumor single-cell transcriptomics data, we first defined and quantified selectivity and safety scores of existing CAR targets for indications in which they are in clinical trials or approved. We then sought new candidate cell surface CAR targets that have better selectivity and safety scores than those currently being tested. Remarkably, in almost all cancer types, we could not find such better targets, testifying to the near optimality of the current target space. However, in human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSC), for which there is currently a dearth of existing CAR targets, we identified a total of twenty candidate novel CAR targets, five of which have both superior selectivity and safety scores. These newly identified cell surface targets lay a basis for future investigations that may lead to better CAR treatments in HNSC.

Common evolutionary trajectory of short life-cycle in Brassicaceae ruderal weeds.

Weed species are detrimental to crop yield. An understanding of how weeds originate and adapt to field environments is needed for successful crop management and reduction of herbicide use. Although early flowering is one of the weed trait syndromes that enable ruderal weeds to overcome frequent disturbances, the underlying genetic basis is poorly understood. Here, we establish Cardamine occulta as a model to study weed ruderality. By genome assembly and QTL mapping, we identify impairment of the vernalization response regulator gene FLC and a subsequent dominant mutation in the blue-light receptor gene CRY2 as genetic drivers for the establishment of short life cycle in ruderal weeds. Population genomics study further suggests that the mutations in these two genes enable individuals to overcome human disturbances through early deposition of seeds into the soil seed bank and quickly dominate local populations, thereby facilitating their spread in East China. Notably, functionally equivalent dominant mutations in CRY2 are shared by another weed species, Rorippa palustris, suggesting a common evolutionary trajectory of early flowering in ruderal weeds in Brassicaceae.

Diurnal and Seasonal Variations of Photosynthetic Energy Conversion Efficiency of Field Grown Wheat.

Improving canopy photosynthetic light use efficiency and energy conversion efficiency (ε c ) is a major option to increase crop yield potential. However, so far, the diurnal and seasonal variations of canopy light use efficiency (LUE) and ε c are largely unknown due to the lack of an efficient method to estimate ε c in a high temporal resolution. Here we quantified the dynamic changes of crop canopy LUE and ε c during a day and a growing season with the canopy gas exchange method. A response curve of whole-plant carbon dioxide (CO2) flux to incident photosynthetically active radiation (PAR) was further used to calculate ε c and LUE at a high temporal resolution. Results show that the LUE of two wheat cultivars with different canopy architectures at five stages varies between 0.01 to about 0.05 mol CO2 mol-1 photon, with the LUE being higher under medium PAR. Throughout the growing season, the ε c varies from 0.5 to 3.7% (11-80% of the maximal ε c for C3 plants) with incident PAR identified as a major factor controlling variation of ε c . The estimated average ε c from tillering to grain filling stages was about 2.17%, i.e., 47.2% of the theoretical maximal. The estimated season-averaged radiation use efficiency (RUE) was 1.5-1.7 g MJ-1, which was similar to the estimated RUE based on biomass harvesting. The large variations of LUE and ε c imply a great opportunity to improve canopy photosynthesis for greater wheat biomass and yield potential.

3dCAP-Wheat: An Open-Source Comprehensive Computational Framework Precisely Quantifies Wheat Foliar, Nonfoliar, and Canopy Photosynthesis.

Canopy photosynthesis is the sum of photosynthesis of all above-ground photosynthetic tissues. Quantitative roles of nonfoliar tissues in canopy photosynthesis remain elusive due to methodology limitations. Here, we develop the first complete canopy photosynthesis model incorporating all above-ground photosynthetic tissues and validate this model on wheat with state-of-the-art gas exchange measurement facilities. The new model precisely predicts wheat canopy gas exchange rates at different growth stages, weather conditions, and canopy architectural perturbations. Using the model, we systematically study (1) the contribution of both foliar and nonfoliar tissues to wheat canopy photosynthesis and (2) the responses of wheat canopy photosynthesis to plant physiological and architectural changes. We found that (1) at tillering, heading, and milking stages, nonfoliar tissues can contribute ~4, ~32, and ~50% of daily gross canopy photosynthesis (A cgross; ~2, ~15, and ~-13% of daily net canopy photosynthesis, A cnet) and absorb ~6, ~42, and ~60% of total light, respectively; (2) under favorable condition, increasing spike photosynthetic activity, rather than enlarging spike size or awn size, can enhance canopy photosynthesis; (3) covariation in tissue respiratory rate and photosynthetic rate may be a major factor responsible for less than expected increase in daily A cnet; and (4) in general, erect leaves, lower spike position, shorter plant height, and proper plant densities can benefit daily A cnet. Overall, the model, together with the facilities for quantifying plant architecture and tissue gas exchange, provides an integrated platform to study canopy photosynthesis and support rational design of photosynthetically efficient wheat crops.