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.

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.

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.

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.

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.

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/ .

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.

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.

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.

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.

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.

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.

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.

Scheduling of nitrogen fertilizer topdressing during panicle differentiation to improve grain yield of rice with a long growth duration.

Topdressing at panicle differentiation (PF) according to soil fertility and regularity of rice nutrient absorption is an important agronomic practice used in cultivation of rice cultivars with a long growth duration. We studied the impacts of timing of nitrogen fertilizer application during PF on photosynthesis and yield-related agronomic traits in 'Y-Liang-You 900' and 'Y-Liang-You 6', which are representative rice cultivars with a long growth duration. Data for two years showed that timing of topdressing application during PF affected panicles per unit area, percentage grain set, spikelets per panicle, and leaf photosynthetic traits during the grain-filling period. Topdressing at the initial stage of flag-leaf extension resulted in higher grain yield (typically by 10.55-19.95%) than in plants without topdressing. Grain yield was significantly correlated with flag leaf photosynthetic rate and leaf SPAD value (r = 0.5640 and r = 0.5589, respectively; p < 0.01) at an advanced grain-filling stage (30 days after heading). Surprisingly, grain yield was not correlated with carbohydrate remobilization from the stem and sheath. For rice cultivars with a long growth duration, nitrogen-fertilizer topdressing must be applied at the initial stage of flag-leaf extension to delay leaf senescence during the grain-filling stage and realize the enhanced yield potential.

Morphological and physiological factors contributing to early vigor in the elite rice cultivar 9,311.

Huanghuazhan (HHZ) and 9,311 are two elite rice cultivars in China. They have achieved high yield through quite different mechanisms. One of the major features that gives high yield capacity to 9,311 is its strong early vigor, i.e., faster establishment of its seedling as well as its better growth in its early stages. To understand the mechanistic basis of early vigor in 9,311, as compared to HHZ the cultivar, we have examined, under controlled environmental conditions, different morphological and physiological traits that may contribute to its early vigor. Our results show that the fresh weight of the seeds, at germination, not only determined the seedling biomass at 10 days after germination (DAG), but was also responsible for ~ 80% of variations in plant biomass between the two cultivars even up to 30 DAG. Furthermore, the 9,311 cultivar had a larger root system, which led to its higher nitrogen uptake capacity. Other noteworthy observations about 9,311 being a better cultivar than HHZ are: (i) Ten out of 15 genes involved in nitrogen metabolism were much more highly expressed in its roots; (ii) it had a higher water uptake rate, promoting better root-to-shoot nitrogen transfer; and (iii) consistent with the above, it had higher leaf photosynthetic rate and stomatal conductance. All of the above identified features explain, to a large extent, why the 9,311, as compared to HHZ, exhibits much more vigorous early growth.

Architectural and Physiological Features to Gain High Yield in an Elite Rice Line YLY1.

Identification of traits strongly associated with high yield can help future gene engineering towards improvements of productivity. Here we systematically determine the major architectural and physiological features associated with high yield in two elite historical hybrid rice cultivars, i.e., YLY1 and LYP9. Data from a six-year experiment show that high yield of YLY1 are related to a number of architectural and physiological parameters. Compared to LYP9, YLY1 had 5.5% and 47.3% higher canopy photosynthesis under high and low photosynthetic photon flux densities, respectively, during the grain filling stage, an average 1.5% higher proportion of biomass allocation to above-ground tissues, a 4.5%-10.5% higher photosynthate reserve in leaf sheath before grain filling, and a more efficient photosynthate translocation during grain filling and finally an average 25.2% higher number of productive tillers. These features differ dramatically from features associated with high yield in YLY900 and Yongyou12#, two other high-yielding rice cultivars in China. These identified features and their combinations can support designing new strategies in the future high-yield rice breeding.

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.