Direct cell reprogramming: approaches, mechanisms and progress.

The reprogramming of somatic cells with defined factors, which converts cells from one lineage into cells of another, has greatly reshaped our traditional views on cell identity and cell fate determination. Direct reprogramming (also known as transdifferentiation) refers to cell fate conversion without transitioning through an intermediary pluripotent state. Given that the number of cell types that can be generated by direct reprogramming is rapidly increasing, it has become a promising strategy to produce functional cells for therapeutic purposes. This Review discusses the evolution of direct reprogramming from a transcription factor-based method to a small-molecule-driven approach, the recent progress in enhancing reprogrammed cell maturation, and the challenges associated with in vivo direct reprogramming for translational applications. It also describes our current understanding of the molecular mechanisms underlying direct reprogramming, including the role of transcription factors, epigenetic modifications, non-coding RNAs, and the function of metabolic reprogramming, and highlights novel insights gained from single-cell omics studies.

Weakened APC/C activity at mitotic exit drives cancer vulnerability to KIF18A inhibition.

The efficacy of current antimitotic cancer drugs is limited by toxicity in highly proliferative healthy tissues. A cancer-specific dependency on the microtubule motor protein KIF18A therefore makes it an attractive therapeutic target. Not all cancers require KIF18A, however, and the determinants underlying this distinction remain unclear. Here, we show that KIF18A inhibition drives a modest and widespread increase in spindle assembly checkpoint (SAC) signaling from kinetochores which can result in lethal mitotic delays. Whether cells arrest in mitosis depends on the robustness of the metaphase-to-anaphase transition, and cells predisposed with weak basal anaphase-promoting complex/cyclosome (APC/C) activity and/or persistent SAC signaling through metaphase are uniquely sensitive to KIF18A inhibition. KIF18A-dependent cancer cells exhibit hallmarks of this SAC:APC/C imbalance, including a long metaphase-to-anaphase transition, and slow mitosis overall. Together, our data reveal vulnerabilities in the cell division apparatus of cancer cells that can be exploited for therapeutic benefit.

Epigenetic Regulation of Cardiomyocyte Maturation by Arginine Methyltransferase CARM1.

BACKGROUND: During the neonatal stage, the cardiomyocyte undergoes a constellation of molecular, cytoarchitectural, and functional changes known collectively as cardiomyocyte maturation to increase myocardial contractility and cardiac output. Despite the importance of cardiomyocyte maturation, the molecular mechanisms governing this critical process remain largely unexplored. METHODS: We leveraged an in vivo mosaic knockout system to characterize the role of Carm1, the founding member of protein arginine methyltransferase, in cardiomyocyte maturation. Using a battery of assays, including immunohistochemistry, immuno-electron microscopy imaging, and action potential recording, we assessed the effect of loss of Carm1 function on cardiomyocyte cell growth, myofibril expansion, T-tubule formation, and electrophysiological maturation. Genome-wide transcriptome profiling, H3R17me2a chromatin immunoprecipitation followed by sequencing, and assay for transposase-accessible chromatin with high-throughput sequencing were used to investigate the mechanisms by which CARM1 (coactivator-associated arginine methyltransferase 1) regulates cardiomyocyte maturation. Finally, we interrogated the human syntenic region to the H3R17me2a chromatin immunoprecipitation followed by sequencing peaks for single-nucleotide polymorphisms associated with human heart diseases. RESULTS: We report that mosaic ablation of Carm1 disrupts multiple aspects of cardiomyocyte maturation cell autonomously, leading to reduced cardiomyocyte size and sarcomere thickness, severe loss and disorganization of T tubules, and compromised electrophysiological maturation. Genomics study demonstrates that CARM1 directly activates genes that underlie cardiomyocyte cytoarchitectural and electrophysiological maturation. Moreover, our study reveals significant enrichment of human heart disease-associated single-nucleotide polymorphisms in the human genomic region syntenic to the H3R17me2a chromatin immunoprecipitation followed by sequencing peaks. CONCLUSIONS: This study establishes a critical and multifaceted role for CARM1 in regulating cardiomyocyte maturation and demonstrates that deregulation of CARM1-dependent cardiomyocyte maturation gene expression may contribute to human heart diseases.

Super interactive promoters provide insight into cell type-specific regulatory networks in blood lineage cell types.

Existing studies of chromatin conformation have primarily focused on potential enhancers interacting with gene promoters. By contrast, the interactivity of promoters per se, while equally critical to understanding transcriptional control, has been largely unexplored, particularly in a cell type-specific manner for blood lineage cell types. In this study, we leverage promoter capture Hi-C data across a compendium of blood lineage cell types to identify and characterize cell type-specific super-interactive promoters (SIPs). Notably, promoter-interacting regions (PIRs) of SIPs are more likely to overlap with cell type-specific ATAC-seq peaks and GWAS variants for relevant blood cell traits than PIRs of non-SIPs. Moreover, PIRs of cell-type-specific SIPs show enriched heritability of relevant blood cell trait (s), and are more enriched with GWAS variants associated with blood cell traits compared to PIRs of non-SIPs. Further, SIP genes tend to express at a higher level in the corresponding cell type. Importantly, SIP subnetworks incorporating cell-type-specific SIPs and ATAC-seq peaks help interpret GWAS variants. Examples include GWAS variants associated with platelet count near the megakaryocyte SIP gene EPHB3 and variants associated lymphocyte count near the native CD4 T-Cell SIP gene ETS1. Interestingly, around 25.7% ~ 39.6% blood cell traits GWAS variants residing in SIP PIR regions disrupt transcription factor binding motifs. Importantly, our analysis shows the potential of using promoter-centric analyses of chromatin spatial organization data to identify biologically important genes and their regulatory regions.

HiC-ACT: improved detection of chromatin interactions from Hi-C data via aggregated Cauchy test.

Genome-wide chromatin conformation capture technologies such as Hi-C are commonly employed to study chromatin spatial organization. In particular, to identify statistically significant long-range chromatin interactions from Hi-C data, most existing methods such as Fit-Hi-C/FitHiC2 and HiCCUPS assume that all chromatin interactions are statistically independent. Such an independence assumption is reasonable at low resolution (e.g., 40 kb bin) but is invalid at high resolution (e.g., 5 or 10 kb bins) because spatial dependency of neighboring chromatin interactions is non-negligible at high resolution. Our previous hidden Markov random field-based methods accommodate spatial dependency but are computationally intensive. It is urgent to develop approaches that can model spatial dependence in a computationally efficient and scalable manner. Here, we develop HiC-ACT, an aggregated Cauchy test (ACT)-based approach, to improve the detection of chromatin interactions by post-processing results from methods assuming independence. To benchmark the performance of HiC-ACT, we re-analyzed deeply sequenced Hi-C data from a human lymphoblastoid cell line, GM12878, and mouse embryonic stem cells (mESCs). Our results demonstrate advantages of HiC-ACT in improving sensitivity with controlled type I error. By leveraging information from neighboring chromatin interactions, HiC-ACT enhances the power to detect interactions with lower signal-to-noise ratio and similar (if not stronger) epigenetic signatures that suggest regulatory roles. We further demonstrate that HiC-ACT peaks show higher overlap with known enhancers than Fit-Hi-C/FitHiC2 peaks in both GM12878 and mESCs. HiC-ACT, effectively a summary statistics-based approach, is computationally efficient (∼6 min and ∼2 GB memory to process 25,000 pairwise interactions).

Sub-Band Assisted Z-Scheme for Effective Non-Sacrificial H2O2 Photosynthesis.

Photosynthesis of H2O2 from earth-abundant O2 and H2O molecules offers an eco-friendly route for solar-to-chemical conversion. The persistent challenge is to tune the photo-/thermo- dynamics of a photocatalyst toward efficient electron-hole separation while maintaining an effective driving force for charge transfer. Such a case is achieved here by way of a synergetic strategy of sub-band-assisted Z-Scheme for effective H2O2 photosynthesis via direct O2 reduction and H2O oxidation without a sacrificial agent. The optimized SnS2/g-C3N4 heterojunction shows a high reactivity of 623.0 µmol g-1 h-1 for H2O2 production under visible-light irradiation (λ > 400 nm) in pure water, ≈6 times higher than pristine g-C3N4 (100.5 µmol g-1 h-1). Photodynamic characterizations and theoretical calculations reveal that the enhanced photoactivity is due to a markedly promoted lifetime of trapped active electrons (204.9 ps in the sub-band and >2.0 ns in a shallow band) and highly improved O2 activation, as a result of the formation of a suitable sub-band and catalytic sites along with a low Gibbs-free energy for charge transfer. Moreover, the Z-Scheme heterojunction creates and sustains a large driving force for O2 and H2O conversion to high value-added H2O2.

SnapHiC: a computational pipeline to identify chromatin loops from single-cell Hi-C data.

Single-cell Hi-C (scHi-C) analysis has been increasingly used to map chromatin architecture in diverse tissue contexts, but computational tools to define chromatin loops at high resolution from scHi-C data are still lacking. Here, we describe Single-Nucleus Analysis Pipeline for Hi-C (SnapHiC), a method that can identify chromatin loops at high resolution and accuracy from scHi-C data. Using scHi-C data from 742 mouse embryonic stem cells, we benchmark SnapHiC against a number of computational tools developed for mapping chromatin loops and interactions from bulk Hi-C. We further demonstrate its use by analyzing single-nucleus methyl-3C-seq data from 2,869 human prefrontal cortical cells, which uncovers cell type-specific chromatin loops and predicts putative target genes for noncoding sequence variants associated with neuropsychiatric disorders. Our results indicate that SnapHiC could facilitate the analysis of cell type-specific chromatin architecture and gene regulatory programs in complex tissues.

Alternative splicing-related long noncoding RNA ANRIL facilitates hepatocellular carcinoma by targeting the miR-199a-5p/SRSF1 axis and impacting Anillin.

Hepatocellular carcinoma (HCC) is characterized by aberrant alternative splicing (AS), which plays an important part in the pathological process of this disease. However, available reports about genes and mechanisms involved in AS process are limited. Our previous research has identified ANRIL as a long noncoding RNA related to the AS process of HCC. Here, we investigated the exact effect and the mechanism of ANRIL on HCC progress. The ANRIL expression profile was validated using the real-time quantitative polymerase chain reaction assay. The western blot analysis and IHC assay were conducted on candidate targets, including SRSF1 and Anillin. The clinicopathological features of 97 patients were collected and analyzed. Loss-of and gain-of-function experiments were conducted. The dual-luciferase reporter assay was applied to verify the interaction between ANRIL, miR-199a-5p, and SRSF1. Anomalous upregulation of ANRIL in HCC was observed, correlating with worse clinicopathological features of HCC. HCC cell proliferation, mobility, tumorigenesis, and metastasis were impaired by depleting ANRIL. We found that ANRIL acts as a sponger of miRNA-199a-5p, resulting in an elevated level of its target protein SRSF1. The phenotypes induced by ANRIL/miR-199a-5p/SRSF1 alteration are associated with Anillin, a validated HCC promoter. ANRIL is an AS-related lncRNA promoting HCC progress by modulating the miR-199a-5p/SRSF1 axis. The downstream effector of this axis in the development of HCC is Anillin.

A systematic evaluation of Hi-C data enhancement methods for enhancing PLAC-seq and HiChIP data.

The three-dimensional organization of chromatin plays a critical role in gene regulation. Recently developed technologies, such as HiChIP and proximity ligation-assisted ChIP-Seq (PLAC-seq) (hereafter referred to as HP for brevity), can measure chromosome spatial organization by interrogating chromatin interactions mediated by a protein of interest. While offering cost-efficiency over genome-wide unbiased high-throughput chromosome conformation capture (Hi-C) data, HP data remain sparse at kilobase (Kb) resolution with the current sequencing depth in the order of 108 reads per sample. Deep learning models, including HiCPlus, HiCNN, HiCNN2, DeepHiC and Variationally Encoded Hi-C Loss Enhancer (VEHiCLE), have been developed to enhance the sequencing depth of Hi-C data, but their performance on HP data has not been benchmarked. Here, we performed a comprehensive evaluation of HP data sequencing depth enhancement using models developed for Hi-C data. Specifically, we analyzed various HP data, including Smc1a HiChIP data of the human lymphoblastoid cell line GM12878, H3K4me3 PLAC-seq data of four human neural cell types as well as of mouse embryonic stem cells (mESC), and mESC CCCTC-binding factor (CTCF) PLAC-seq data. Our evaluations lead to the following three findings: (i) most models developed for Hi-C data achieve reasonable performance when applied to HP data (e.g. with Pearson correlation ranging 0.76-0.95 for pairs of loci within 300 Kb), and the enhanced datasets lead to improved statistical power for detecting long-range chromatin interactions, (ii) models trained on HP data outperform those trained on Hi-C data and (iii) most models are transferable across cell types. Our results provide a general guideline for HP data enhancement using existing methods designed for Hi-C data.

A comprehensive comparison on cell-type composition inference for spatial transcriptomics data.

Spatial transcriptomics (ST) technologies allow researchers to examine transcriptional profiles along with maintained positional information. Such spatially resolved transcriptional characterization of intact tissue samples provides an integrated view of gene expression in its natural spatial and functional context. However, high-throughput sequencing-based ST technologies cannot yet reach single cell resolution. Thus, similar to bulk RNA-seq data, gene expression data at ST spot-level reflect transcriptional profiles of multiple cells and entail the inference of cell-type composition within each ST spot for valid and powerful subsequent analyses. Realizing the critical importance of cell-type decomposition, multiple groups have developed ST deconvolution methods. The aim of this work is to review state-of-the-art methods for ST deconvolution, comparing their strengths and weaknesses. In particular, we construct ST spots from single-cell level ST data to assess the performance of 10 methods, with either ideal reference or non-ideal reference. Furthermore, we examine the performance of these methods on spot- and bead-level ST data by comparing estimated cell-type proportions to carefully matched single-cell ST data. In comparing the performance on various tissues and technological platforms, we concluded that RCTD and stereoscope achieve more robust and accurate inferences.

eSCAN: scan regulatory regions for aggregate association testing using whole-genome sequencing data.

Multiple statistical methods for aggregate association testing have been developed for whole-genome sequencing (WGS) data. Many aggregate variants in a given genomic window and ignore existing knowledge to define test regions, resulting in many identified regions not clearly linked to genes, and thus, limiting biological understanding. Functional information from new technologies (such as Hi-C and its derivatives), which can help link enhancers to their effector genes, can be leveraged to predefine variant sets for aggregate testing in WGS data. Here, we propose the eSCAN (scan the enhancers) method for genome-wide assessment of enhancer regions in sequencing studies, combining the advantages of dynamic window selection in SCANG (SCAN the Genome), a previously developed method, with the advantages of incorporating putative regulatory regions from annotation. eSCAN, by searching in putative enhancers, increases statistical power and aids mechanistic interpretation, as demonstrated by extensive simulation studies. We also apply eSCAN for blood cell traits using NHLBI Trans-Omics for Precision Medicine WGS data. Results from real data analysis show that eSCAN is able to capture more significant signals, and these signals are of shorter length (indicating higher resolution fine-mapping capability) and drive association of larger regions detected by other methods.

Copper Catalyzed [3+2] Annulation Reaction of Exocyclic Sulfonyl Enamides for the Synthesis of N,O-Spiroketal and Spiroketal.

A copper-catalyzed [3+2] annulation reaction of exocyclic enamines/enol ethers with 1,4-benzoquinone esters has been developed, providing facile access to N,O-spiroketals and spiroketals under mild conditions with broad substrate scope (26 examples, 71-94 % yields). Gram scale synthesis and chemical transformations demonstrated that this method is potentially useful in the synthesis of natural products and drugs containing a N,O- spiroketal moiety. The chiral N,O-spiroketal could be obtained with 98 % ee after recrystallization, when a chiral SaBOX ligand was employed.

Three-Dimensional Multifrequency MR Elastography for Microvascular Invasion and Prognosis Assessment in Hepatocellular Carcinoma.

BACKGROUND: Pretreatment identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is important when selecting treatment strategies. PURPOSE: To improve models for predicting MVI and recurrence-free survival (RFS) by developing nomograms containing three-dimensional (3D) MR elastography (MRE). STUDY TYPE: Prospective. POPULATION: 188 patients with HCC, divided into a training cohort (n = 150) and a validation cohort (n = 38). In the training cohort, 106/150 patients completed a 2-year follow-up. FIELD STRENGTH/SEQUENCE: 1.5T 3D multifrequency MRE with a single-shot spin-echo echo planar imaging sequence, and 3.0T multiparametric MRI (mp-MRI), consisting of diffusion-weighted echo planar imaging, T2-weighted fast spin echo, in-phase out-of-phase T1-weighted fast spoiled gradient-recalled dual-echo and dynamic contrast-enhanced gradient echo sequences. ASSESSMENT: Multivariable analysis was used to identify the independent predictors for MVI and RFS. Nomograms were constructed for visualization. Models for predicting MVI and RFS were built using mp-MRI parameters and a combination of mp-MRI and 3D MRE predictors. STATISTICAL TESTS: Student's t-test, Mann-Whitney U test, chi-squared or Fisher's exact tests, multivariable analysis, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan-Meier analysis and log rank tests. P < 0.05 was considered significant. RESULTS: Tumor c and liver c were independent predictors of MVI and RFS, respectively. Adding tumor c significantly improved the diagnostic performance of mp-MRI (AUC increased from 0.70 to 0.87) for MVI detection. Of the 106 patients in the training cohort who completed the 2-year follow up, 34 experienced recurrence. RFS was shorter for patients with MVI-positive histology than MVI-negative histology (27.1 months vs. >40 months). The MVI predicted by the 3D MRE model yielded similar results (26.9 months vs. >40 months). The MVI and RFS nomograms of the histologic-MVI and model-predicted MVI-positive showed good predictive performance. DATA CONCLUSION: Biomechanical properties of 3D MRE were biomarkers for MVI and RFS. MVI and RFS nomograms were established. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

DNAJ protein gene expansion mechanism in Panicoideae and PgDNAJ functional identification in pearl millet.

KEY MESSAGE: Analyze the evolutionary pattern of DNAJ protein genes in the Panicoideae, including pearl millet, to identify and characterize the biological function of PgDNAJ genes in pearl millet. Global warming has become a major factor threatening food security and human development. It is urgent to analyze the heat-tolerant mechanism of plants and cultivate crops that are adapted to high temperature conditions. The Panicoideae are the second largest subfamily of the Poaceae, widely distributed in warm temperate and tropical regions. Many of these species have been reported to have strong adaptability to high temperature stress, such as pearl millet, foxtail millet and sorghum. The evolutionary differences in DNAJ protein genes among 12 Panicoideae species and 10 other species were identified and analyzed. Among them, 79% of Panicoideae DNAJ protein genes were associated with retrotransposon insertion. Analysis of the DNAJ protein pan-gene family in six pearl millet accessions revealed that the non-core genes contained significantly more TEs than the core genes. By identifying and analyzing the distribution and types of TEs near the DNAJ protein genes, it was found that the insertion of Copia and Gypsy retrotransposons provided the source of expansion for the DNAJ protein genes in the Panicoideae. Based on the analysis of the evolutionary pattern of DNAJ protein genes in Panicoideae, the PgDNAJ was obtained from pearl millet through identification. PgDNAJ reduces the accumulation of reactive oxygen species caused by high temperature by activating ascorbate peroxidase (APX), thereby improving the heat resistance of plants. In summary, these data provide new ideas for mining potential heat-tolerant genes in Panicoideae, and help to improve the heat tolerance of other crops.

Design, synthesis, and cell-based in vitro assay of deoxyinosine-mixed SATE-dCDN prodrugs that activate all common STING variants.

Developing therapeutic strategies to modulate the activity of all prevalent variants (wild-type, HAQ, R232H, AQ, and R293Q) of the stimulator of interferon genes (STING) is still of great interest to treating immune-related diseases. Herein, we synthesized six novel deoxyinosine-mixed deoxyribose cyclic dinucleotide prodrugs (SATE-dCDN) including a combination of hypoxanthine and other bases (A, U, C, T, and G) for a cell-based in vitro assay. The HPLC assay indicated that deoxyinosine-mixed SATE (S-acylthioalkyl ester)-dCDN prodrugs retained high serum stability. The IRF3-responsive luciferase assay in THP1-Lucia cells showed that the activity of the prodrugs with purine bases (SATE-3',3'-c-di-dIMP, SATE-3',3'-c-di-dIdAMP, and SATE-3',3'-c-di-dIdGMP) was higher than that of the prodrugs with pyrimidine bases (SATE-3',3'-c-di-dIdUMP, SATE-3',3'-c-di-dIdTMP, and SATE-3',3'-c-di-dIdCMP), among which prodrug 14a (SATE-3',3'-c-di-dIdAMP) with hypoxanthine and adenine bases exhibited the highest activity with an EC50 value of 0.046 µM. The IRF3 responsive dual-luciferase reporter assay in HEK293T cells transfected with plasmids expressing different STING variants further showed that prodrug 14a could activate all five most common hSTING variants, including the refractory hSTINGR232H and hSTINGQ variants. Furthermore, prodrug 14a also induced the production of the highest levels of mRNA of IFN-ß, CXCL10, IL-6 and TNF-α through STING-dependent IRF and NF-κB signaling pathways in THP-1 cells. These results suggested that the combination of deoxyinosine with a SATE-dCDN prodrug could modulate the broad-spectrum activity of all common STING variants.

One-shot distributed algorithms for addressing heterogeneity in competing risks data across clinical sites.

OBJECTIVE: To characterize the interplay between multiple medical conditions across sites and account for the heterogeneity in patient population characteristics across sites within a distributed research network, we develop a one-shot algorithm that can efficiently utilize summary-level data from various institutions. By applying our proposed algorithm to a large pediatric cohort across four national Children's hospitals, we replicated a recently published prospective cohort, the RISK study, and quantified the impact of the risk factors associated with the penetrating or stricturing behaviors of pediatric Crohn's disease (PCD). METHODS: In this study, we introduce the ODACoRH algorithm, a one-shot distributed algorithm designed for the competing risks model with heterogeneity. Our approach considers the variability in baseline hazard functions of multiple endpoints of interest across different sites. To accomplish this, we build a surrogate likelihood function by combining patient-level data from the local site with aggregated data from other external sites. We validated our method through extensive simulation studies and replication of the RISK study to investigate the impact of risk factors on the PCD for adolescents and children from four children's hospitals within the PEDSnet, A National Pediatric Learning Health System. To evaluate our ODACoRH algorithm, we compared results from the ODACoRH algorithms with those from meta-analysis as well as those derived from the pooled data. RESULTS: The ODACoRH algorithm had the smallest relative bias to the gold standard method (-0.2%), outperforming the meta-analysis method (-11.4%). In the PCD association study, the estimated subdistribution hazard ratios obtained through the ODACoRH algorithms are identical on par with the results derived from pooled data, which demonstrates the high reliability of our federated learning algorithms. From a clinical standpoint, the identified risk factors for PCD align well with the RISK study published in the Lancet in 2017 and other published studies, supporting the validity of our findings. CONCLUSION: With the ODACoRH algorithm, we demonstrate the capability of effectively integrating data from multiple sites in a decentralized data setting while accounting for between-site heterogeneity. Importantly, our study reveals several crucial clinical risk factors for PCD that merit further investigations.

Pixelwise calibration method for a telecentric structured light system.

This paper introduces a pixelwise calibration method designed for a structured light system utilizing a camera attached with a telecentric lens. In the calibration process, a white flat surface and a flat surface with circle dots serve as the calibration targets. After deriving the properties of the pinhole projector through a conventional camera calibration method using circle dots and determining the camera's attributes via 3D feature points estimation through iterative optimizations, the white surface calibration target was positioned at various poses and reconstructed with initial camera and projector calibration data. Each 3D reconstruction was fitted with a virtual ideal plane that was further used to create the pixelwise phase-to-coordinate mapping. To optimize the calibration accuracy, various angled poses of the calibration target are employed to refine the initial results. Experimental findings show that the proposed approach offers high calibration accuracy for a structured light system using a telecentric lens.

Combined Genome-Wide Association Study and Transcriptome Analysis Reveal Candidate Genes for Resistance to Rust (Puccinia graminis) in Dactylis glomerata.

Rust disease is a common plant disease that can cause wilting, slow growth of plant leaves, and even affect the growth and development of plants. Orchardgrass (Dactylis glomerata L.) is native to temperate regions of Europe, which has been introduced as a superior forage grass in temperate regions worldwide. Orchardgrass has rich genetic diversity and is widely distributed in the world, which may contain rust resistance genes not found in other crops. Therefore, we collected a total of 333 orchardgrass accessions from different regions around the world. Through a genome-wide association study (GWAS) analysis conducted in four different environments, 91 genes that overlap or are adjacent to significant single nucleotide polymorphisms (SNPs) were identified as potential rust disease resistance genes. Combining transcriptome data from susceptible (PI292589) and resistant (PI251814) accessions, the GWAS candidate gene DG5C04160.1 encoding glutathione S-transferase (GST) was found to be important for orchardgrass rust (Puccinia graminis) resistance. Interestingly, by comparing the number of GST gene family members in seven species, it was found that orchardgrass has the most GST gene family members, containing 119 GST genes. Among them, 23 GST genes showed significant differential expression after inoculation with the rust pathogen in resistant and susceptible accessions; 82% of the genes still showed significantly increased expression 14 days after inoculation in resistant accessions, while the expression level significantly decreased in susceptible accessions. These results indicate that GST genes play an important role in orchardgrass resistance to rust (P. graminis) stress by encoding GST to reduce its oxidative stress response.

The evolution and expansion of RWP-RK gene family improve the heat adaptability of elephant grass (Pennisetum purpureum Schum.).

BACKGROUND: Along with global warming, resulting in crop production, exacerbating the global food crisis. Therefore, it is urgent to study the mechanism of plant heat resistance. However, crop resistance genes were lost due to long-term artificial domestication. By analyzing the potential heat tolerance genes and molecular mechanisms in other wild materials, more genetic resources can be provided for improving the heat tolerance of crops. Elephant grass (Pennisetum purpureum Schum.) has strong adaptability to heat stress and contains abundant heat-resistant gene resources. RESULTS: Through sequence structure analysis, a total of 36 RWP-RK members were identified in elephant grass. Functional analysis revealed their close association with heat stress. Four randomly selected RKDs (RKD1.1, RKD4.3, RKD6.6, and RKD8.1) were analyzed for expression, and the results showed upregulation under high temperature conditions, suggesting their active role in response to heat stress. The members of RWP-RK gene family (36 genes) in elephant grass were 2.4 times higher than that of related tropical crops, rice (15 genes) and sorghum (15 genes). The 36 RWPs of elephant grass contain 15 NLPs and 21 RKDs, and 73% of RWPs are related to WGD. Among them, combined with the DAP-seq results, it was found that RWP-RK gene family expansion could improve the heat adaptability of elephant grass by enhancing nitrogen use efficiency and peroxidase gene expression. CONCLUSIONS: RWP-RK gene family expansion in elephant grass is closely related to thermal adaptation evolution and speciation. The RKD subgroup showed a higher responsiveness than the NLP subgroup when exposed to high temperature stress. The promoter region of the RKD subgroup contains a significant number of MeJA and ABA responsive elements, which may contribute to their positive response to heat stress. These results provided a scientific basis for analyzing the heat adaptation mechanism of elephant grass and improving the heat tolerance of other crops.

Radiotherapy-Triggered Proteolysis Targeting Chimera Prodrug Activation in Tumors.

Proteolysis targeting chimera (PROTAC) is an emerging protein degradation strategy, which shows excellent advantages in targeting those so-called "undruggable" proteins. However, the potential systemic toxicity of PROTACs caused by undesired off-tissue protein degradation may limit the application of PROTACs in clinical practice. Here we reported a radiotherapy-triggered PROTAC prodrug (RT-PROTAC) activation strategy to precisely and spatiotemporally control protein degradation through X-ray radiation. We demonstrated this concept by incorporating an X-ray inducible phenyl azide-cage to a bromodomain (BRD)-targeting PROTAC to form the first RT-PROTAC. The RT-PROTAC prodrug exhibits little activity but can be activated by X-ray radiation in vitro and in vivo. Activated RT-PROTAC degrades BRD4 and BRD2 with a comparable effect to the PROTAC degrader and shows a synergistic antitumor potency with radiotherapy in the MCF-7 xenograft model. Our work provides an alternative strategy to spatiotemporally control protein degradation in vivo and points to an avenue for reducing the undesired systemic toxicity of PROTACs.