Human antibody C10 neutralizes by diminishing Zika but enhancing dengue virus dynamics.

The human monoclonal antibody (HmAb) C10 potently cross-neutralizes Zika virus (ZIKV) and dengue virus. Analysis of antibody fragment (Fab) C10 interactions with ZIKV and dengue virus serotype 2 (DENV2) particles by cryoelectron microscopy (cryo-EM) and amide hydrogen/deuterium exchange mass spectrometry (HDXMS) shows that Fab C10 binding decreases overall ZIKV particle dynamics, whereas with DENV2, the same Fab causes increased dynamics. Testing of different Fab C10:DENV2 E protein molar ratios revealed that, at higher Fab ratios, especially at saturated concentrations, the Fab enhanced viral dynamics (detected by HDXMS), and observation under cryo-EM showed increased numbers of distorted particles. Our results suggest that Fab C10 stabilizes ZIKV but that with DENV2 particles, high Fab C10 occupancy promotes E protein dimer conformational changes leading to overall increased particle dynamics and distortion of the viral surface. This is the first instance of a broadly neutralizing antibody eliciting virus-specific increases in whole virus particle dynamics.

A Human Bi-specific Antibody against Zika Virus with High Therapeutic Potential.

Zika virus (ZIKV), a mosquito-borne flavivirus, causes devastating congenital birth defects. We isolated a human monoclonal antibody (mAb), ZKA190, that potently cross-neutralizes multi-lineage ZIKV strains. ZKA190 is highly effective in vivo in preventing morbidity and mortality of ZIKV-infected mice. NMR and cryo-electron microscopy show its binding to an exposed epitope on DIII of the E protein. ZKA190 Fab binds all 180 E protein copies, altering the virus quaternary arrangement and surface curvature. However, ZIKV escape mutants emerged in vitro and in vivo in the presence of ZKA190, as well as of other neutralizing mAbs. To counter this problem, we developed a bispecific antibody (FIT-1) comprising ZKA190 and a second mAb specific for DII of E protein. In addition to retaining high in vitro and in vivo potencies, FIT-1 robustly prevented viral escape, warranting its development as a ZIKV immunotherapy.

Giant pyroelectricity in nanomembranes.

Pyroelectricity describes the generation of electricity by temporal temperature change in polar materials1-3. When free-standing pyroelectric materials approach the 2D crystalline limit, how pyroelectricity behaves remained largely unknown. Here, using three model pyroelectric materials whose bonding characters along the out-of-plane direction vary from van der Waals (In2Se3), quasi-van der Waals (CsBiNb2O7) to ionic/covalent (ZnO), we experimentally show the dimensionality effect on pyroelectricity and the relation between lattice dynamics and pyroelectricity. We find that, for all three materials, when the thickness of free-standing sheets becomes small, their pyroelectric coefficients increase rapidly. We show that the material with chemical bonds along the out-of-plane direction exhibits the greatest dimensionality effect. Experimental observations evidence the possible influence of changed phonon dynamics in crystals with reduced thickness on their pyroelectricity. Our findings should stimulate fundamental study on pyroelectricity in ultra-thin materials and inspire technological development for potential pyroelectric applications in thermal imaging and energy harvesting.

Recognition of cyanobacteria promoters via Siamese network-based contrastive learning under novel non-promoter generation.

It is a vital step to recognize cyanobacteria promoters on a genome-wide scale. Computational methods are promising to assist in difficult biological identification. When building recognition models, these methods rely on non-promoter generation to cope with the lack of real non-promoters. Nevertheless, the factitious significant difference between promoters and non-promoters causes over-optimistic prediction. Moreover, designed for E. coli or B. subtilis, existing methods cannot uncover novel, distinct motifs among cyanobacterial promoters. To address these issues, this work first proposes a novel non-promoter generation strategy called phantom sampling, which can eliminate the factitious difference between promoters and generated non-promoters. Furthermore, it elaborates a novel promoter prediction model based on the Siamese network (SiamProm), which can amplify the hidden difference between promoters and non-promoters through a joint characterization of global associations, upstream and downstream contexts, and neighboring associations w.r.t. k-mer tokens. The comparison with state-of-the-art methods demonstrates the superiority of our phantom sampling and SiamProm. Both comprehensive ablation studies and feature space illustrations also validate the effectiveness of the Siamese network and its components. More importantly, SiamProm, upon our phantom sampling, finds a novel cyanobacterial promoter motif ('GCGATCGC'), which is palindrome-patterned, content-conserved, but position-shifted.

Mechanistic insights into the regulation of cell wall hydrolysis by FtsEX and EnvC at the bacterial division site.

The peptidoglycan (PG) cell wall produced by the bacterial division machinery is initially shared between the daughters and must be split to promote cell separation and complete division. In gram-negative bacteria, enzymes that cleave PG called amidases play major roles in the separation process. To prevent spurious cell wall cleavage that can lead to cell lysis, amidases like AmiB are autoinhibited by a regulatory helix. Autoinhibition is relieved at the division site by the activator EnvC, which is in turn regulated by the ATP-binding cassette (ABC) transporter-like complex called FtsEX. EnvC is also known to be autoinhibited by a regulatory helix (RH), but how its activity is modulated by FtsEX and the mechanism by which it activates the amidases have remained unclear. Here, we investigated this regulation by determining the structure of Pseudomonas aeruginosa FtsEX alone with or without bound ATP, in complex with EnvC, and in a FtsEX-EnvC-AmiB supercomplex. In combination with biochemical studies, the structures reveal that ATP binding is likely to activate FtsEX-EnvC and promote its association with AmiB. Furthermore, the AmiB activation mechanism is shown to involve a RH rearrangement. In the activated state of the complex, the inhibitory helix of EnvC is released, freeing it to associate with the RH of AmiB, which liberates its active site for PG cleavage. These regulatory helices are found in many EnvC proteins and amidases throughout gram-negative bacteria, suggesting that the activation mechanism is broadly conserved and a potential target for lysis-inducing antibiotics that misregulate the complex.

The maize PLASTID TERMINAL OXIDASE (PTOX) locus controls the carotenoid content of kernels.

Carotenoids perform a broad range of important functions in humans; therefore, carotenoid biofortification of maize (Zea mays L.), one of the most highly produced cereal crops worldwide, would have a global impact on human health. PLASTID TERMINAL OXIDASE (PTOX) genes play an important role in carotenoid metabolism; however, the possible function of PTOX in carotenoid biosynthesis in maize has not yet been explored. In this study, we characterized the maize PTOX locus by forward- and reverse-genetic analyses. While most higher plant species possess a single copy of the PTOX gene, maize carries two tandemly duplicated copies. Characterization of mutants revealed that disruption of either copy resulted in a carotenoid-deficient phenotype. We identified mutations in the PTOX genes as being causal of the classic maize mutant, albescent1. Remarkably, overexpression of ZmPTOX1 significantly improved the content of carotenoids, especially ß-carotene (provitamin A), which was increased by ~threefold, in maize kernels. Overall, our study shows that maize PTOX locus plays an important role in carotenoid biosynthesis in maize kernels and suggests that fine-tuning the expression of this gene could improve the nutritional value of cereal grains.

Cracking the pattern of tumor evolution based on single-cell copy number alterations.

Copy number alterations (CNAs) are a key characteristic of tumor development and progression. The accumulation of various CNAs during tumor development plays a critical role in driving tumor evolution. Heterogeneous clones driven by distinct CNAs have different selective advantages, leading to differential patterns of tumor evolution that are essential for developing effective cancer therapies. Recent advances in single-cell sequencing technology have enabled genome-wide copy number profiling of tumor cell populations at single-cell resolution. This has made it possible to explore the evolutionary patterns of CNAs and accurately discover the mechanisms of intra-tumor heterogeneity. Here, we propose a two-step statistical approach that distinguishes neutral, linear, branching and punctuated evolutionary patterns for a tumor cell population based on single-cell copy number profiles. We assessed our approach using a variety of simulated and real single-cell genomic and transcriptomic datasets, demonstrating its high accuracy and robustness in predicting tumor evolutionary patterns. We applied our approach to single-cell DNA sequencing data from 20 breast cancer patients and observed that punctuated evolution is the dominant evolutionary pattern in breast cancer. Similar conclusions were drawn when applying the approach to single-cell RNA sequencing data obtained from 132 various cancer patients. Moreover, we found that differential immune cell infiltration is associated with specific evolutionary patterns. The source code of our study is available at https://github.com/FangWang-SYSU/PTEM.

A social theory-enhanced graph representation learning framework for multitask prediction of drug-drug interactions.

Current machine learning-based methods have achieved inspiring predictions in the scenarios of mono-type and multi-type drug-drug interactions (DDIs), but they all ignore enhancive and depressive pharmacological changes triggered by DDIs. In addition, these pharmacological changes are asymmetric since the roles of two drugs in an interaction are different. More importantly, these pharmacological changes imply significant topological patterns among DDIs. To address the above issues, we first leverage Balance theory and Status theory in social networks to reveal the topological patterns among directed pharmacological DDIs, which are modeled as a signed and directed network. Then, we design a novel graph representation learning model named SGRL-DDI (social theory-enhanced graph representation learning for DDI) to realize the multitask prediction of DDIs. SGRL-DDI model can capture the task-joint information by integrating relation graph convolutional networks with Balance and Status patterns. Moreover, we utilize task-specific deep neural networks to perform two tasks, including the prediction of enhancive/depressive DDIs and the prediction of directed DDIs. Based on DDI entries collected from DrugBank, the superiority of our model is demonstrated by the comparison with other state-of-the-art methods. Furthermore, the ablation study verifies that Balance and Status patterns help characterize directed pharmacological DDIs, and that the joint of two tasks provides better DDI representations than individual tasks. Last, we demonstrate the practical effectiveness of our model by a version-dependent test, where 88.47 and 81.38% DDI out of newly added entries provided by the latest release of DrugBank are validated in two predicting tasks respectively.

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction.

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, natural language processing based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.

DMGL-MDA: A dual-modal graph learning method for microbe-drug association prediction.

The interaction between human microbes and drugs can significantly impact human physiological functions. It is crucial to identify potential microbe-drug associations (MDAs) before drug administration. However, conventional biological experiments to predict MDAs are plagued by drawbacks such as time-consuming, high costs, and potential risks. On the contrary, computational approaches can speed up the screening of MDAs at a low cost. Most computational models usually use a drug similarity matrix as the initial feature representation of drugs and stack the graph neural network layers to extract the features of network nodes. However, different calculation methods result in distinct similarity matrices, and message passing in graph neural networks (GNNs) induces phenomena of over-smoothing and over-squashing, thereby impacting the performance of the model. To address these issues, we proposed a novel graph representation learning model, dual-modal graph learning for microbe-drug association prediction (DMGL-MDA). It comprises a dual-modal embedding module, a bipartite graph network embedding module, and a predictor module. To assess the performance of DMGL-MDA, we compared it against state-of-the-art methods using two benchmark datasets. Through cross-validation, we illustrated the superiority of DMGL-MDA. Furthermore, we conducted ablation experiments and case studies to validate the effective performance of the model.

Low-intensity ultrasound ameliorates brain organoid integration and rescues microcephaly deficits.

Human brain organoids represent a remarkable platform for modeling neurological disorders and a promising brain repair approach. However, the effects of physical stimulation on their development and integration remain unclear. Here, we report that low-intensity ultrasound significantly increases neural progenitor cell proliferation and neuronal maturation in cortical organoids. Histological assays and single-cell gene expression analyses reveal that low-intensity ultrasound improves the neural development in cortical organoids. Following organoid grafts transplantation into the injured somatosensory cortices of adult mice, longitudinal electrophysiological recordings and histological assays reveal that ultrasound-treated organoid grafts undergo advanced maturation. They also exhibit enhanced pain-related gamma-band activity and more disseminated projections into the host brain than the untreated groups. Finally, low-intensity ultrasound ameliorates neuropathological deficits in a microcephaly brain organoid model. Hence, low-intensity ultrasound stimulation advances the development and integration of brain organoids, providing a strategy for treating neurodevelopmental disorders and repairing cortical damage.

Protection of the receptor binding domain (RBD) dimer against SARS-CoV-2 and its variants.

IMPORTANCE: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants achieved immune escape and became less virulent and easily transmissible through rapid mutation in the spike protein, thus the efficacy of vaccines on the market or in development continues to be challenged. Updating the vaccine, exploring compromise vaccination strategies, and evaluating the efficacy of candidate vaccines for the emerging variants in a timely manner are important to combat complex and volatile SARS-CoV-2. This study reports that vaccines prepared from the dimeric receptor-binding domain (RBD) recombinant protein, which can be quickly produced using a mature and stable process platform, had both good immunogenicity and protection in vivo and could completely protect rodents from lethal challenge by SARS-CoV-2 and its variants, including the emerging Omicron XBB.1.16, highlighting the value of dimeric recombinant vaccines in the post-COVID-19 era.

Directed graph attention networks for predicting asymmetric drug-drug interactions.

It is tough to detect unexpected drug-drug interactions (DDIs) in poly-drug treatments because of high costs and clinical limitations. Computational approaches, such as deep learning-based approaches, are promising to screen potential DDIs among numerous drug pairs. Nevertheless, existing approaches neglect the asymmetric roles of two drugs in interaction. Such an asymmetry is crucial to poly-drug treatments since it determines drug priority in co-prescription. This paper designs a directed graph attention network (DGAT-DDI) to predict asymmetric DDIs. First, its encoder learns the embeddings of the source role, the target role and the self-roles of a drug. The source role embedding represents how a drug influences other drugs in DDIs. In contrast, the target role embedding represents how it is influenced by others. The self-role embedding encodes its chemical structure in a role-specific manner. Besides, two role-specific items, aggressiveness and impressionability, capture how the number of interaction partners of a drug affects its interaction tendency. Furthermore, the predictor of DGAT-DDI discriminates direction-specific interactions by the combination between two proximities and the above two role-specific items. The proximities measure the similarity between source/target embeddings and self-role embeddings. In the designated experiments, the comparison with state-of-the-art deep learning models demonstrates the superiority of DGAT-DDI across a direction-specific predicting task and a direction-blinded predicting task. An ablation study reveals how well each component of DGAT-DDI contributes to its ability. Moreover, a case study of finding novel DDIs confirms its practical ability, where 7 out of the top 10 candidates are validated in DrugBank.

Drug-drug interaction prediction with learnable size-adaptive molecular substructures.

Drug-drug interactions (DDIs) are interactions with adverse effects on the body, manifested when two or more incompatible drugs are taken together. They can be caused by the chemical compositions of the drugs involved. We introduce gated message passing neural network (GMPNN), a message passing neural network which learns chemical substructures with different sizes and shapes from the molecular graph representations of drugs for DDI prediction between a pair of drugs. In GMPNN, edges are considered as gates which control the flow of message passing, and therefore delimiting the substructures in a learnable way. The final DDI prediction between a drug pair is based on the interactions between pairs of their (learned) substructures, each pair weighted by a relevance score to the final DDI prediction output. Our proposed method GMPNN-CS (i.e. GMPNN + prediction module) is evaluated on two real-world datasets, with competitive results on one, and improved performance on the other compared with previous methods. Source code is freely available at https://github.com/kanz76/GMPNN-CS.

CProMG: controllable protein-oriented molecule generation with desired binding affinity and drug-like properties.

MOTIVATION: Deep learning-based molecule generation becomes a new paradigm of de novo molecule design since it enables fast and directional exploration in the vast chemical space. However, it is still an open issue to generate molecules, which bind to specific proteins with high-binding affinities while owning desired drug-like physicochemical properties. RESULTS: To address these issues, we elaborate a novel framework for controllable protein-oriented molecule generation, named CProMG, which contains a 3D protein embedding module, a dual-view protein encoder, a molecule embedding module, and a novel drug-like molecule decoder. Based on fusing the hierarchical views of proteins, it enhances the representation of protein binding pockets significantly by associating amino acid residues with their comprising atoms. Through jointly embedding molecule sequences, their drug-like properties, and binding affinities w.r.t. proteins, it autoregressively generates novel molecules having specific properties in a controllable manner by measuring the proximity of molecule tokens to protein residues and atoms. The comparison with state-of-the-art deep generative methods demonstrates the superiority of our CProMG. Furthermore, the progressive control of properties demonstrates the effectiveness of CProMG when controlling binding affinity and drug-like properties. After that, the ablation studies reveal how its crucial components contribute to the model respectively, including hierarchical protein views, Laplacian position encoding as well as property control. Last, a case study w.r.t. protein illustrates the novelty of CProMG and the ability to capture crucial interactions between protein pockets and molecules. It's anticipated that this work can boost de novo molecule design. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this article are freely available at https://github.com/lijianing0902/CProMG.

CMMS-GCL: cross-modality metabolic stability prediction with graph contrastive learning.

MOTIVATION: Metabolic stability plays a crucial role in the early stages of drug discovery and development. Accurately modeling and predicting molecular metabolic stability has great potential for the efficient screening of drug candidates as well as the optimization of lead compounds. Considering wet-lab experiment is time-consuming, laborious, and expensive, in silico prediction of metabolic stability is an alternative choice. However, few computational methods have been developed to address this task. In addition, it remains a significant challenge to explain key functional groups determining metabolic stability. RESULTS: To address these issues, we develop a novel cross-modality graph contrastive learning model named CMMS-GCL for predicting the metabolic stability of drug candidates. In our framework, we design deep learning methods to extract features for molecules from two modality data, i.e. SMILES sequence and molecule graph. In particular, for the sequence data, we design a multihead attention BiGRU-based encoder to preserve the context of symbols to learn sequence representations of molecules. For the graph data, we propose a graph contrastive learning-based encoder to learn structure representations by effectively capturing the consistencies between local and global structures. We further exploit fully connected neural networks to combine the sequence and structure representations for model training. Extensive experimental results on two datasets demonstrate that our CMMS-GCL consistently outperforms seven state-of-the-art methods. Furthermore, a collection of case studies on sequence data and statistical analyses of the graph structure module strengthens the validation of the interpretability of crucial functional groups recognized by CMMS-GCL. Overall, CMMS-GCL can serve as an effective and interpretable tool for predicting metabolic stability, identifying critical functional groups, and thus facilitating the drug discovery process and lead compound optimization. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this article are freely available at https://github.com/dubingxue/CMMS-GCL.

Single-cell profiling reveals kidney CD163+ dendritic cell participation in human lupus nephritis.

OBJECTIVES: The current work aimed to provide a comprehensive single-cell landscape of lupus nephritis (LN) kidneys, including immune and non-immune cells, identify disease-associated cell populations and unravel their participation within the kidney microenvironment. METHODS: Single-cell RNA and T cell receptor sequencing were performed on renal biopsy tissues from 40 patients with LN and 6 healthy donors as controls. Matched peripheral blood samples from seven LN patients were also sequenced. Multiplex immunohistochemical analysis was performed on an independent cohort of 60 patients and validated using flow cytometric characterisation of human kidney tissues and in vitro assays. RESULTS: We uncovered a notable enrichment of CD163+ dendritic cells (DC3s) in LN kidneys, which exhibited a positive correlation with the severity of LN. In contrast to their counterparts in blood, DC3s in LN kidney displayed activated and highly proinflammatory phenotype. DC3s showed strong interactions with CD4+ T cells, contributing to intrarenal T cell clonal expansion, activation of CD4+ effector T cell and polarisation towards Th1/Th17. Injured proximal tubular epithelial cells (iPTECs) may orchestrate DC3 activation, adhesion and recruitment within the LN kidneys. In cultures, blood DC3s treated with iPTECs acquired distinct capabilities to polarise Th1/Th17 cells. Remarkably, the enumeration of kidney DC3s might be a potential biomarker for induction treatment response in LN patients. CONCLUSION: The intricate interplay involving DC3s, T cells and tubular epithelial cells within kidneys may substantially contribute to LN pathogenesis. The enumeration of renal DC3 holds potential as a valuable stratification feature for guiding LN patient treatment decisions in clinical practice.

TBC1D5 reverses the capability of HIF-2α in tumor progression and lipid metabolism in clear cell renal cell carcinoma by regulating the autophagy.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is known for abnormal lipid metabolism and widespread activation of HIF-2α. Recently, the importance of autophagy in ccRCC has been focused, and it has potential connections with HIF-2α and lipid metabolism. However, the specific regulatory mechanism between HIF-2α, autophagy, and lipid metabolism in ccRCC is still unclear. METHODS: In this study, Bioinformatics Analysis and Sequencing of the whole transcriptome were used to screen our target. The expression of TBC1D5 in renal clear cell carcinoma was confirmed by database analysis, immunohistochemistry, PCR and Western blot. The effects of TBC1D5 on tumor cell growth, migration, invasion and lipid metabolism were examined by CCK8, Transwell and oil red staining, and the mechanism of TBC1D5 on autophagy was investigated by Western blot, fluorescence microscopy and electron microscopy. Chloroquine and rapamycin were used to verified the key role of autophagy in effects of TBC1D5 on tumor cell. The regulatory mechanism of TBC1D5 in renal clear cell carcinoma (RCC) was investigated by shhif-2α, shTBC1D5, mimic, inhibitor, ChIP and Luciferase experiments. The animal model of ccRCC was used to evaluate the biological function of TBC1D5 in vivo. RESULTS: In this study, TBC1D5 was found to be an important bridge between autophagy and HIF-2α. Specifically, TBC1D5 is significantly underexpressed in ccRCC, serving as a tumor suppressor which inhibits tumor progression and lipid accumulation, and is negatively regulated by HIF-2α. Further research has found that TBC1D5 regulates the autophagy pathway to reverse the biological function of HIF-2α in ccRCC. Mechanism studies have shown that HIF-2α regulates TBC1D5 through hsa-miR-7-5p in ccRCC, thereby affecting tumor progression and lipid metabolism through autophagy. CONCLUSIONS: Our research reveals a completely new pathway, HIF-2α/hsa-miR-7-5p/TBC1D5 pathway affects ccRCC progression and lipid metabolism by regulating autophagy.

Melatonin as an immunomodulator in CD19-targeting CAR-T cell therapy: managing cytokine release syndrome.

BACKGROUND: Chimeric antigen receptor CAR-T cell therapies have ushered in a new era of treatment for specific blood cancers, offering unparalleled efficacy in cases of treatment resistance or relapse. However, the emergence of cytokine release syndrome (CRS) as a side effect poses a challenge to the widespread application of CAR-T cell therapies. Melatonin, a natural hormone produced by the pineal gland known for its antioxidant and anti-inflammatory properties, has been explored for its potential immunomodulatory effects. Despite this, its specific role in mitigating CAR-T cell-induced CRS remains poorly understood. METHODS: In this study, our aim was to investigate the potential of melatonin as an immunomodulatory agent in the context of CD19-targeting CAR-T cell therapy and its impact on associated side effects. Using a mouse model, we evaluated the effects of melatonin on CAR-T cell-induced CRS and overall survival. Additionally, we assessed whether melatonin administration had any detrimental effects on the antitumor efficacy and persistence of CD19 CAR-T cells. RESULTS: Our findings demonstrate that melatonin effectively mitigated the severity of CAR-T cell-induced CRS in the mouse model, leading to improved overall survival outcomes. Remarkably, melatonin administration did not compromise the antitumor effectiveness or persistence of CD19 CAR-T cells, indicating its compatibility with therapeutic goals. These results suggest melatonin's potential as an immunomodulatory compound to alleviate CRS without compromising the therapeutic benefits of CAR-T cell therapy. CONCLUSION: The study's outcomes shed light on melatonin's promise as a valuable addition to the existing treatment protocols for CAR-T cell therapies. By attenuating CAR-T cell-induced CRS while preserving the therapeutic impact of CAR-T cells, melatonin offers a potential strategy for optimizing and refining the safety and efficacy profile of CAR-T cell therapy. This research contributes to the evolving understanding of how to harness immunomodulatory agents to enhance the clinical application of innovative cancer treatments.

A point mutation in the gene encoding magnesium chelatase I subunit influences strawberry leaf color and metabolism.

Magnesium chelatase (MgCh) catalyzes the insertion of magnesium into protoporphyrin IX, a vital step in chlorophyll (Chl) biogenesis. The enzyme consists of 3 subunits, MgCh I subunit (CHLI), MgCh D subunit (CHLD), and MgCh H subunit (CHLH). The CHLI subunit is an ATPase that mediates catalysis. Previous studies on CHLI have mainly focused on model plant species, and its functions in other species have not been well described, especially with regard to leaf coloration and metabolism. In this study, we identified and characterized a CHLI mutant in strawberry species Fragaria pentaphylla. The mutant, noted as p240, exhibits yellow-green leaves and a low Chl level. RNA-Seq identified a mutation in the 186th amino acid of the CHLI subunit, a base conserved in most photosynthetic organisms. Transient transformation of wild-type CHLI into p240 leaves complemented the mutant phenotype. Further mutants generated from RNA-interference (RNAi) and CRISPR/Cas9 gene editing recapitulated the mutant phenotype. Notably, heterozygous chli mutants accumulated more Chl under low light conditions compared with high light conditions. Metabolite analysis of null mutants under high light conditions revealed substantial changes in both nitrogen and carbon metabolism. Further analysis indicated that mutation in Glu186 of CHLI does not affect its subcellular localization nor the interaction between CHLI and CHLD. However, intramolecular interactions were impaired, leading to reduced ATPase and MgCh activity. These findings demonstrate that Glu186 plays a key role in enzyme function, affecting leaf coloration via the formation of the hexameric ring itself, and that manipulation of CHLI may be a means to improve strawberry plant fitness and photosynthetic efficiency under low light conditions.