gene editing

Advances in the Clinical Application of Novel PCSK9 Inhibitors for High-Risk Atherosclerotic Cardiovascular Disease

Dyslipidemia is a major contributor to atherosclerotic cardiovascular disease (ASCVD), the leading cause of morbidity and mortality worldwide. Proprotein convertase subtilisin/kexin type 9 (PCSK9) promotes hepatic low-density lipoprotein receptor (LDLR) degradation, elevating plasma low-density lipoprotein cholesterol (LDL-C) and contributing to plaque initiation, progression, and destabilization via lipid-dependent and -independent pathways (e.g., inflammation, endothelial dysfunction, thrombosis). PCSK9 inhibition is a validated target for intensive lipid management Beyond statins, monoclonal antibodies (evolocumab, alirocumab) and siRNA-based therapy (inclisiran) achieve reductions in LDL-C of >50%. Large outcome trials (FOURIER, ODYSSEY OUTCOMES, ORION series) have demonstrated reduced major adverse cardiovascular events (MACE) and favorable safety in high-risk populations including acute coronary syndrome, familial hypercholesterolemia, and statin intolerance. Newer approaches include oral small molecule inhibitors (e.g., MK-0616, AZD0780), therapeutic vaccines (e.g., AT04A), and gene-editing (e.g., VERVE-101) with improved adherence, durability or potential cure. For patients with very-high-risk or high-risk ASCVD who do not achieve LDL-C targets with traditional therapies, clinical guidelines, including China-specific recommendations, support the use of PCSK9 inhibitors to lower LDL-C and further reduce CV risk.

CRISPR–cas systems in pharmacology: functional pharmacogenomics, drug screening, resistance, and therapeutic translation

Molecular and transcriptional regulation of plant defense responses to aphid infestation

Aphids are one of the important agricultural pests causing substantial yield losses in crops grown across the globe. Aphids are known to cause direct feeding damages and indirect losses due to sooty mold development and plant virus transmission. Plants respond to these attacks by mounting a complex defense response at the infested sites and systemic levels. This multilayered defense response involves a highly coordinated network of phytohormones and other signalling components like Ca2+, mitogen activated protein kinases and reactive oxygen species. Key to these complex responses is a well-regulated gene expression involving several transcription factors. A wide range of transcription factors are structurally and functionally characterized across some model plants and in a few agronomically important crops. These transcription factors play diverse roles such as defense gene expression modulation, regulation of hormone signaling, secondary metabolism, oxidative stress response, cell wall modifications, and phloem-based defense. Understanding the integration of signaling pathways, hormone crosstalk, and transcription factor mediated regulation provides a framework for practical applications, including breeding, genome editing, and elicitor-based strategies. This review highlights how plant defense signaling and transcriptional regulation against aphids can be harnessed to develop sustainable and novel pest management solutions.

Functional Analysis of the Heat-Tolerance Candidate Gene BcNCED3 in Non-Heading Chinese Cabbage (Brassica campestris ssp. chinensis)

It is demonstrated that BcNCED3 positively regulates thermotolerance in non-heading Chinese cabbage and provide a genetic resource for heat-tolerance breeding.

Functional divergence of two soybean cytosolic serine hydroxymethyltransferases in development and defense against soybean cyst nematode

The results indicate that GmSHMT05 sustains overall soybean growth and development in the absence of GmSHMT08, however, GmSHMT08’s gain-of-function in SCN resistance negatively influences pod and root growth, highlighting a potential trade-off between soybean defense and development that may impact yield.

Beyond the Pill – The Future of LipidLowering Therapy: A 2026 Review of Novel Lipid-Lowering Agents

These agents have expanded treatment options for patients with familial dyslipidaemia, severe hypertriglyceridaemia, and persistent residual cardiovascular risk while paving the way for precision medicine.

From Sequential Gland Replacement to Recurrent Gland Coordination: A Comparative Framework for Subventral and Dorsal Oesophageal Gland Effectors Across Plant-Parasitic Nematode Lifestyles

A comparative model explaining when and why subventral and dorsal glands exchange, retain, or alternate their functions across contrasting parasitic lifestyles across contrasting parasitic lifestyles is explained.

LumiCharge: Spherical Harmonic Convolutional Networks for Atomic Charge Prediction in Drug Discovery.

Atomic charge is crucial in drug design for analyzing reactive sites and interactions between ligands and targets. While quantum mechanical methods offer high accuracy, they are generally computationally costly. Conversely, empirical approaches, while computationally efficient, frequently suffer from lack of precision and generalizability. Recent a number of machine learning-based models have been developed for atomic charge predictions, but they struggle with accurately representing molecular structures and capturing the chemical environments affecting atomic charges, thus limiting their generalization and accuracy. To overcome these limitations, we propose LumiCharge, a novel atomic charge prediction framework that incorporates high-order spherical harmonics convolutions and explicitly models multibody interactions. In constructing this model, we employ a strategy that integrates both high- and low-order information, enhancing its geometric spatial perception capability, which is currently underexplored in the field. Benchmark evaluations demonstrate that LumiCharge outperforms state-of-the-art (SOTA) models by 30%-60% across diverse data sets. Additionally, in cross-scale experiments, LumiCharge demonstrates exceptional extrapolation capability and robustness across molecules of varying sizes, effectively overcoming the limitations imposed by molecular sizes. On an external halogen-containing test set, LumiCharge achieves an RMSE of 0.055e, meeting practical application requirements. Finally, a case study of virtual screening for the androgen receptor (AR) target further validates its outstanding accuracy compared to the OPLS3e force field and other deep learning (DL)-based baseline models, highlighting its exceptional generalization capacity and practical utility in real-world scenarios.

Unraveling the Efficacy of AR Antagonists Bearing N-(4-(Benzyloxy)phenyl)piperidine-1-sulfonamide Scaffold in Prostate Cancer Therapy by Targeting LBP Mutations.

Point mutations in the androgen receptor (AR) are significant drivers of resistance in prostate cancer (PCa), posing a great challenge to the development of effective treatment strategies. Building on our previous discovery of the suboptimal AR antagonist T1-12, we developed LT16, which contains an N-(4-(benzyloxy)phenyl)piperidine-1-sulfonamide scaffold through structural optimization and comprehensive screening against T878A-mutated AR. LT16 outperformed existing antiandrogens by fully antagonizing clinical AR mutations and effectively suppressing castration- and enzalutamide-resistant LNCaP cells proliferation in vitro. Mechanically, LT16 was found to disrupt AR nuclear translocation, hinder AR homodimerization, and suppress transcription of AR-regulated genes by competitive binding to the ligand binding pocket. Further in vivo experiments demonstrated that LT16 significantly reduced both regular- and enzalutamide-resistant LNCaP tumor volume and serum prostate-specific antigen levels in mice. These findings position LT16 as a promising and innovative therapeutic for advanced PCa, particularly in cases where resistance to current therapies is a concern.

PepBAN: A Deep Learning Framework with Bilinear Attention and Adversarial Learning for Peptide-Protein Interaction Prediction

Accurate prediction of the peptide-protein interaction (PepPI) is crucial for developing peptide-based therapeutics and vaccines. However, this computational task has traditionally faced significant challenges, such as the scarcity of structure data along with the corresponding label of the binding affinity for bound complexes. To address these challenges, we introduce PepBAN, a deep learning framework for modeling PepPI predictions. PepBAN incorporates two technical advancements: (1) adopting the protein language model ESM-2 to characterize proteins and ESM-2 or a graph-based foundation model for peptides without structure data and (2) leveraging the conditional domain adversarial learning to enhance generalization across a broad range of protein targets, especially when there are limited binding data. At the core of PepBAN is a bilinear attention network (BAN) that effectively learns the pattern of pairwise local interactions, enables the identification of key residues participating in the peptide-protein interactions, and offers an intuitive approach to interpret the underlying mechanisms of PepPIs via analyzing attention weights. Our numerical experiments demonstrated that PepBAN outperformed the previous state-of-the-art models across several well-established benchmark studies. Furthermore, we evaluated PepBAN's applicability in predicting cyclic peptide-protein interactions, a task that poses significant challenges due to the presence of noncanonical amino acids. These nonstandard residues require specialized handling, which most existing sequence-based PepPI prediction models did not adequately address, and we adopt an atom-resolved molecular graph approach to process cyclic peptides. Despite this complexity, PepBAN demonstrated a clear advantage by achieving a superior prediction performance and offering a distinct edge in tackling the emerging chemical space of cyclic peptides, which has great potential for novel therapeutic development. In summary, PepBAN serves as a valuable tool for advancing peptide-based drug and therapeutic development.

SynGFN: learning across chemical space with generative flow-based molecular discovery

ChargeNet: E(3) Equivariant Graph Attention Network for Atomic Charge Prediction

Atomic charge is a fundamental quantum chemical property essential for advancing drug design and discovery. Although quantum mechanics (QM) methods offer the highest level of accuracy, their computational demands scale quadratically with the number of atoms, limiting their practicality for large-scale applications. In light of this, empirical and semiempirical methods have been introduced to improve computational efficiency, albeit often at the expense of accuracy. The advent of artificial intelligence has witnessed a growing application of machine learning (ML) techniques to accelerate atomic charge predictions. However, existing ML models often suffer from low accuracy and limited generalization capabilities. To address these challenges, we introduce an advanced equivariant graph attention neural network specifically engineered to model long-range atomic electrostatic interactions with high precision. This model introduces a sophisticated global graph attention mechanism, enabling it to capture charge contributions across multiple scales. By utilizing a combination of structural symmetry-preserving transformations and multiscale attention, our approach not only preserves the inherent symmetries of molecular structures but also substantially improves the model's accuracy, generalization, and robustness in complex scenarios. Our empirical analyses demonstrate that, compared to leading baseline models, the proposed model improves charge prediction accuracy by over 40% on average across various charge-calculation schemes. Remarkably, the model achieves superior performance on the external RESP (restrained electrostatic potential) test data sets, with a 54.6% improvement over the baseline. Additionally, we evaluated our charge model under the setting of virtual screening, where it outperforms both the OPLS3 charges and baseline deep learning models across all evaluation metrics, highlighting its extensive potential for scientific discovery.