Lack of affinity signature for germinal center cells that have initiated plasma cell differentiation.

Long-lived plasma cells (PCs) secrete antibodies that can provide sustained immunity against infection. High-affinity cells are proposed to preferentially select into this compartment, potentiating the immune response. We used single-cell RNA-seq to track the germinal center (GC) development of Ighg2A10 B cells, specific for the Plasmodium falciparum circumsporozoite protein (PfCSP). Following immunization with Plasmodium sporozoites, we identified 3 populations of cells in the GC light zone (LZ). One LZ population expressed a gene signature associated with the initiation of PC differentiation and readily formed PCs in vitro. The estimated affinity of these pre-PC B cells was indistinguishable from that of LZ cells that remained in the GC. This remained true when high- or low-avidity recombinant PfCSP proteins were used as immunogens. These findings suggest that the initiation of PC development occurs via an affinity-independent process.

Origin and evolution of the bread wheat D genome.

Bread wheat (Triticum aestivum) is a globally dominant crop and major source of calories and proteins for the human diet. Compared with its wild ancestors, modern bread wheat shows lower genetic diversity, caused by polyploidisation, domestication and breeding bottlenecks1,2. Wild wheat relatives represent genetic reservoirs, and harbour diversity and beneficial alleles that have not been incorporated into bread wheat. Here we establish and analyse extensive genome resources for Tausch's goatgrass (Aegilops tauschii), the donor of the bread wheat D genome. Our analysis of 46 Ae. tauschii genomes enabled us to clone a disease resistance gene and perform haplotype analysis across a complex disease resistance locus, allowing us to discern alleles from paralogous gene copies. We also reveal the complex genetic composition and history of the bread wheat D genome, which involves contributions from genetically and geographically discrete Ae. tauschii subpopulations. Together, our results reveal the complex history of the bread wheat D genome and demonstrate the potential of wild relatives in crop improvement.

Patient privacy in AI-driven omics methods.

Artificial intelligence (AI) in omics analysis raises privacy threats to patients. Here, we briefly discuss risk factors to patient privacy in data sharing, model training, and release, as well as methods to safeguard and evaluate patient privacy in AI-driven omics methods.

A fast and adaptive detection framework for genome-wide chromatin loop mapping from Hi-C data.

Chromatin loop identification plays an important role in molecular biology and 3D genomics research, as it constitutes a fundamental process in transcription and gene regulation. Such precise chromatin structures can be identified across genome-wide interaction matrices via Hi-C data analysis, which is essential for unraveling the intricacies of transcriptional regulation. Given the increasing number of genome-wide contact maps, derived from both in situ Hi-C and single-cell Hi-C experiments, there is a pressing need for efficient and resilient algorithms capable of processing data from diverse experiments rapidly and adaptively. Here, we propose YOLOOP, a novel detection-based framework that is different from the conventional paradigm. YOLOOP stands out for its speed, surpassing the performance of previous state-of-the-art (SOTA) chromatin loop detection methods. It achieves a 30-fold acceleration compared to classification-based methods, up to 20-fold acceleration compared to the SOTA kernel-based framework, and a 5-fold acceleration compared to statistical algorithms. Furthermore, our proposed framework exhibits exceptional generalization capabilities across various cell types, multi-resolution Hi-C maps, and diverse experimental protocols. Compared with the existing paradigms, YOLOOP shows up to a 10% increase in recall and a 15% increase in F1-score, particularly noteworthy in the GM12878 cell line. YOLOOP also offers fast adaptability with straightforward fine-tuning, making it readily applicable to extremely sparse single-cell Hi-C contact maps. It maintains its exceptional speed, completing genome-wide detection at a 10 kb resolution for one single-cell contact map within 1 minute, and for 900-cells-superimposed contact map within 3 minutes, enabling fast analysis on massive amounts of single-cell data.

Single-particle imaging of nanomedicine entering the brain.

Nanomedicine has emerged as a revolutionary strategy of drug delivery. However, fundamentals of the nano-neuro interaction are elusive. In particular, whether nanocarriers can cross the blood-brain barrier (BBB) and release the drug cargo inside the brain, a basic process depicted in numerous books and reviews, remains controversial. Here, we develop an optical method, based on stimulated Raman scattering, for imaging nanocarriers in tissues. Our method achieves a suite of capabilities-single-particle sensitivity, chemical specificity, and particle counting capability. With this method, we visualize individual intact nanocarriers crossing the BBB of mouse brains and quantify the absolute number by particle counting. The fate of nanocarriers after crossing the BBB shows remarkable heterogeneity across multiple scales. With a mouse model of aging, we find that blood-brain transport of nanocarriers decreases with age substantially. This technology would facilitate development of effective therapeutics for brain diseases and clinical translation of nanocarrier-based treatment in general.

Rapid single-particle chemical imaging of nanoplastics by SRS microscopy.

Plastics are now omnipresent in our daily lives. The existence of microplastics (1 µm to 5 mm in length) and possibly even nanoplastics (<1 µm) has recently raised health concerns. In particular, nanoplastics are believed to be more toxic since their smaller size renders them much more amenable, compared to microplastics, to enter the human body. However, detecting nanoplastics imposes tremendous analytical challenges on both the nano-level sensitivity and the plastic-identifying specificity, leading to a knowledge gap in this mysterious nanoworld surrounding us. To address these challenges, we developed a hyperspectral stimulated Raman scattering (SRS) imaging platform with an automated plastic identification algorithm that allows micro-nano plastic analysis at the single-particle level with high chemical specificity and throughput. We first validated the sensitivity enhancement of the narrow band of SRS to enable high-speed single nanoplastic detection below 100 nm. We then devised a data-driven spectral matching algorithm to address spectral identification challenges imposed by sensitive narrow-band hyperspectral imaging and achieve robust determination of common plastic polymers. With the established technique, we studied the micro-nano plastics from bottled water as a model system. We successfully detected and identified nanoplastics from major plastic types. Micro-nano plastics concentrations were estimated to be about 2.4 ± 1.3 × 105 particles per liter of bottled water, about 90% of which are nanoplastics. This is orders of magnitude more than the microplastic abundance reported previously in bottled water. High-throughput single-particle counting revealed extraordinary particle heterogeneity and nonorthogonality between plastic composition and morphologies; the resulting multidimensional profiling sheds light on the science of nanoplastics.

Connection between protein-tyrosine kinase inhibition and coping with oxidative stress in Bacillus subtilis.

In bacteria, attenuation of protein-tyrosine phosphorylation occurs during oxidative stress. The main described mechanism behind this effect is the H2O2-triggered conversion of bacterial phospho-tyrosines to protein-bound 3,4-dihydroxyphenylalanine. This disrupts the bacterial tyrosine phosphorylation-based signaling network, which alters the bacterial polysaccharide biosynthesis. Herein, we report an alternative mechanism, in which oxidative stress leads to a direct inhibition of bacterial protein-tyrosine kinases (BY-kinases). We show that DefA, a minor peptide deformylase, inhibits the activity of BY-kinase PtkA when Bacillus subtilis is exposed to oxidative stress. High levels of PtkA activity are known to destabilize B. subtilis pellicle formation, which leads to higher sensitivity to oxidative stress. Interaction with DefA inhibits both PtkA autophosphorylation and phosphorylation of its substrate Ugd, which is involved in exopolysaccharide formation. Inactivation of defA drastically reduces the capacity of B. subtilis to cope with oxidative stress, but it does not affect the major oxidative stress regulons PerR, OhrR, and Spx, indicating that PtkA inhibition is the main pathway for DefA involvement in this stress response. Structural analysis identified DefA residues Asn95, Tyr150, and Glu152 as essential for interaction with PtkA. Inhibition of PtkA depends also on the presence of a C-terminal α-helix of DefA, which resembles PtkA-interacting motifs from known PtkA activators, TkmA, SalA, and MinD. Loss of either the key interacting residues or the inhibitory helix of DefA abolishes inhibition of PtkA in vitro and impairs postoxidative stress recovery in vivo, confirming the involvement of these structural features in the proposed mechanism.

AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data.

Cancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties and represent a potentially effective therapeutic target toward long-term remission by means of differentiation induction. By leveraging an artificial intelligence approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human-induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the Library of Integrated Network-based Cellular Signatures (LINCS) database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of five out of six tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron and A-443654 offer a potential avenue for targeted therapies against breast CSCs.

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.

Microwave Encounters Ionic Liquid: Synergistic Mechanism, Synthesis and Emerging Applications.

Progress in microwave (MW) energy application technology has stimulated remarkable advances in manufacturing and high-quality applications of ionic liquids (ILs) that are generally used as novel media in chemical engineering. This Review focuses on an emerging technology via the combination of MW energy and the usage of ILs, termed microwave-assisted ionic liquid (MAIL) technology. In comparison to conventional routes that rely on heat transfer through media, the contactless and unique MW heating exploits the electromagnetic wave-ions interactions to deliver energy to IL molecules, accelerating the process of material synthesis, catalytic reactions, and so on. In addition to the inherent advantages of ILs, including outstanding solubility, and well-tuned thermophysical properties, MAIL technology has exhibited great potential in process intensification to meet the requirement of efficient, economic chemical production. Here we start with an introduction to principles of MW heating, highlighting fundamental mechanisms of MW induced process intensification based on ILs. Next, the synergies of MW energy and ILs employed in materials synthesis, as well as their merits, are documented. The emerging applications of MAIL technologies are summarized in the next sections, involving tumor therapy, organic catalysis, separations, and bioconversions. Finally, the current challenges and future opportunities of this emerging technology are discussed.

Nkx2.3 transcription factor is a key regulator of mucous cell identity in salivary glands.

Saliva is vital to oral health, fulfilling multiple functions in the oral cavity. Three pairs of major salivary glands and hundreds of minor salivary glands contribute to saliva production. The secretory acinar cells within these glands include two distinct populations. Serous acinar cells secrete a watery saliva containing enzymes, while mucous acinar cells secrete a more viscous fluid containing highly glycosylated mucins. Despite their shared developmental origins, the parotid gland (PG) is comprised of only serous acinar cells, while the sublingual gland (SLG) contains predominantly mucous acinar cells. The instructive signals that govern the identity of serous versus mucous acinar cell phenotypes are not yet known. The homeobox transcription factor Nkx2.3 is uniquely expressed in the SLG. Disruption of the Nkx2.3 gene was reported to delay the maturation of SLG mucous acinar cells. To examine whether Nkx2.3 plays a role in directing the mucous cell phenotype, we analyzed SLG from Nkx2.3-/- mice using RNAseq, immunostaining and proteomic analysis of saliva. Our results indicate that Nkx2.3, most likely in concert with other transcription factors uniquely expressed in the SLG, is a key regulator of the molecular program that specifies the identity of mucous acinar cells.

The SCR-17 and SCR-18 glycans in human complement Factor H enhance its regulatory function.

Human complement factor H (CFH) plays a central role in regulating activated C3b to protect host cells. CFH contain 20 short complement regulator (SCR) domains and eight N-glycosylation sites. The N-terminal SCR domains mediate C3b degradation while the C-terminal CFH domains bind to host cell surfaces to protect these. Our earlier study of Pichia-generated CFH fragments indicated a self-association site at SCR-17/18 that comprises a dimerization site for human factor H. Two N-linked glycans are located on SCR-17 and SCR-18. Here, when we expressed SCR-17/18 without glycans in an E. coli system, analytical ultracentrifugation showed that no dimers were now formed. To investigate this novel finding, full-length CFH and its C-terminal fragments were purified from human plasma and Pichia pastoris respectively, and their glycans were enzymatically removed using PNGase F. Using size-exclusion chromatography, mass spectrometry, and analytical ultracentrifugation, SCR-17/18 from Pichia showed notably less dimer formation without its glycans, confirming that the glycans are necessary for the formation of SCR-17/18 dimers. By surface plasmon resonance, affinity analyses interaction showed decreased binding of deglycosylated full-length CFH to immobilised C3b, showing that CFH glycosylation enhances the key CFH regulation of C3b. We conclude that our study revealed a significant new aspect of CFH regulation based on its glycosylation and its resulting dimerisation.

Opportunities and challenges for ChatGPT and large language models in biomedicine and health.

ChatGPT has drawn considerable attention from both the general public and domain experts with its remarkable text generation capabilities. This has subsequently led to the emergence of diverse applications in the field of biomedicine and health. In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health. Specifically, we explore the areas of biomedical information retrieval, question answering, medical text summarization, information extraction and medical education and investigate whether LLMs possess the transformative power to revolutionize these tasks or whether the distinct complexities of biomedical domain presents unique challenges. Following an extensive literature survey, we find that significant advances have been made in the field of text generation tasks, surpassing the previous state-of-the-art methods. For other applications, the advances have been modest. Overall, LLMs have not yet revolutionized biomedicine, but recent rapid progress indicates that such methods hold great potential to provide valuable means for accelerating discovery and improving health. We also find that the use of LLMs, like ChatGPT, in the fields of biomedicine and health entails various risks and challenges, including fabricated information in its generated responses, as well as legal and privacy concerns associated with sensitive patient data. We believe this survey can provide a comprehensive and timely overview to biomedical researchers and healthcare practitioners on the opportunities and challenges associated with using ChatGPT and other LLMs for transforming biomedicine and health.

PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction.

MOTIVATION: Effective drug delivery systems are paramount in enhancing pharmaceutical outcomes, particularly through the use of cell-penetrating peptides (CPPs). These peptides are gaining prominence due to their ability to penetrate eukaryotic cells efficiently without inflicting significant damage to the cellular membrane, thereby ensuring optimal drug delivery. However, the identification and characterization of CPPs remain a challenge due to the laborious and time-consuming nature of conventional methods, despite advances in proteomics. Current computational models, however, are predominantly tailored for balanced datasets, an approach that falls short in real-world applications characterized by a scarcity of known positive CPP instances. RESULTS: To navigate this shortfall, we introduce PractiCPP, a novel deep-learning framework tailored for CPP prediction in highly imbalanced data scenarios. Uniquely designed with the integration of hard negative sampling and a sophisticated feature extraction and prediction module, PractiCPP facilitates an intricate understanding and learning from imbalanced data. Our extensive computational validations highlight PractiCPP's exceptional ability to outperform existing state-of-the-art methods, demonstrating remarkable accuracy, even in datasets with an extreme positive-to-negative ratio of 1:1000. Furthermore, through methodical embedding visualizations, we have established that models trained on balanced datasets are not conducive to practical, large-scale CPP identification, as they do not accurately reflect real-world complexities. In summary, PractiCPP potentially offers new perspectives in CPP prediction methodologies. Its design and validation, informed by real-world dataset constraints, suggest its utility as a valuable tool in supporting the acceleration of drug delivery advancements. AVAILABILITY AND IMPLEMENTATION: The source code of PractiCPP is available on Figshare at https://doi.org/10.6084/m9.figshare.25053878.v1.

DrugMGR: a deep bioactive molecule binding method to identify compounds targeting proteins.

MOTIVATION: Understanding the intermolecular interactions of ligand-target pairs is key to guiding the optimization of drug research on cancers, which can greatly mitigate overburden workloads for wet labs. Several improved computational methods have been introduced and exhibit promising performance for these identification tasks, but some pitfalls restrict their practical applications: (i) first, existing methods do not sufficiently consider how multigranular molecule representations influence interaction patterns between proteins and compounds; and (ii) second, existing methods seldom explicitly model the binding sites when an interaction occurs to enable better prediction and interpretation, which may lead to unexpected obstacles to biological researchers. RESULTS: To address these issues, we here present DrugMGR, a deep multigranular drug representation model capable of predicting binding affinities and regions for each ligand-target pair. We conduct consistent experiments on three benchmark datasets using existing methods and introduce a new specific dataset to better validate the prediction of binding sites. For practical application, target-specific compound identification tasks are also carried out to validate the capability of real-world compound screen. Moreover, the visualization of some practical interaction scenarios provides interpretable insights from the results of the predictions. The proposed DrugMGR achieves excellent overall performance in these datasets, exhibiting its advantages and merits against state-of-the-art methods. Thus, the downstream task of DrugMGR can be fine-tuned for identifying the potential compounds that target proteins for clinical treatment. AVAILABILITY AND IMPLEMENTATION: https://github.com/lixiaokun2020/DrugMGR.

HELM-GPT: de novo macrocyclic peptide design using generative pre-trained transformer.

MOTIVATION: Macrocyclic peptides hold great promise as therapeutics targeting intracellular proteins. This stems from their remarkable ability to bind flat protein surfaces with high affinity and specificity while potentially traversing the cell membrane. Research has already explored their use in developing inhibitors for intracellular proteins, such as KRAS, a well-known driver in various cancers. However, computational approaches for de novo macrocyclic peptide design remain largely unexplored. RESULTS: Here, we introduce HELM-GPT, a novel method that combines the strength of the hierarchical editing language for macromolecules (HELM) representation and generative pre-trained transformer (GPT) for de novo macrocyclic peptide design. Through reinforcement learning (RL), our experiments demonstrate that HELM-GPT has the ability to generate valid macrocyclic peptides and optimize their properties. Furthermore, we introduce a contrastive preference loss during the RL process, further enhanced the optimization performance. Finally, to co-optimize peptide permeability and KRAS binding affinity, we propose a step-by-step optimization strategy, demonstrating its effectiveness in generating molecules fulfilling both criteria. In conclusion, the HELM-GPT method can be used to identify novel macrocyclic peptides to target intracellular proteins. AVAILABILITY AND IMPLEMENTATION: The code and data of HELM-GPT are freely available on GitHub (https://github.com/charlesxu90/helm-gpt).

Ultraflat single-crystal hexagonal boron nitride for wafer-scale integration of a 2D-compatible high-κ metal gate.

Hexagonal boron nitride (hBN) has emerged as a promising protection layer for dielectric integration in the next-generation large-scale integrated electronics. Although numerous efforts have been devoted to growing single-crystal hBN film, wafer-scale ultraflat hBN has still not been achieved. Here, we report the epitaxial growth of 4 in. ultraflat single-crystal hBN on Cu0.8Ni0.2(111)/sapphire wafers. The strong coupling between hBN and Cu0.8Ni0.2(111) suppresses the formation of wrinkles and ensures the seamless stitching of parallelly aligned hBN domains, resulting in an ultraflat single-crystal hBN film on a wafer scale. Using the ultraflat hBN as a protective layer, we integrate the wafer-scale ultrathin high-κ dielectrics onto two-dimensional (2D) materials with a damage-free interface. The obtained hBN/HfO2 composite dielectric exhibits an ultralow current leakage (2.36 × 10-6 A cm-2) and an ultrathin equivalent oxide thickness of 0.52 nm, which meets the targets of the International Roadmap for Devices and Systems. Our findings pave the way to the synthesis of ultraflat 2D materials and integration of future 2D electronics.

Transcription factor PbNAC71 regulates xylem and vessel development to control plant height.

Dwarfism is an important agronomic trait in fruit breeding programs. However, the germplasm resources required to generate dwarf pear (Pyrus spp.) varieties are limited. Moreover, the mechanisms underlying dwarfism remain unclear. In this study, "Yunnan" quince (Cydonia oblonga Mill.) had a dwarfing effect on "Zaosu" pear. Additionally, the dwarfism-related NAC transcription factor gene PbNAC71 was isolated from pear trees comprising "Zaosu" (scion) grafted onto "Yunnan" quince (rootstock). Transgenic Nicotiana benthamiana and pear OHF-333 (Pyrus communis) plants overexpressing PbNAC71 exhibited dwarfism, with a substantially smaller xylem and vessel area relative to the wild-type controls. Yeast one-hybrid, dual-luciferase, chromatin immunoprecipitation-qPCR, and electrophoretic mobility shift assays indicated that PbNAC71 downregulates PbWalls are thin 1 expression by binding to NAC-binding elements in its promoter. Yeast two-hybrid assays showed that PbNAC71 interacts with the E3 ubiquitin ligase PbRING finger protein 217 (PbRNF217). Furthermore, PbRNF217 promotes the ubiquitin-mediated degradation of PbNAC71 by the 26S proteasome, thereby regulating plant height as well as xylem and vessel development. Our findings reveal a mechanism underlying pear dwarfism and expand our understanding of the molecular basis of dwarfism in woody plants.

The Duality of Raman Scattering.

ConspectusFirst predicted more than 100 years ago, Raman scattering is a cornerstone of photonics, spectroscopy, and imaging. The conventional framework of understanding Raman scattering was built on Raman cross section σRaman. Carrying a dimension of area, σRaman characterizes the interaction strength between light and molecules during inelastic scattering. The numerical values of σRaman turn out to be many orders of magnitude smaller in comparison to the linear absorption cross sections σAbsorption of similar molecular systems. Such an enormous gap has been the reason for researchers to believe the extremely feeble Raman scattering ever since its discovery. However, this prevailing picture is conceptually problematic or at least incomplete due to the fact that Raman scattering and linear absorption belong to different orders of light-matter interaction.In this Account, we will summarize an alternate way to think about Raman scattering, which we term stimulated response formulation. To capture the third-order interaction nature of Raman scattering, we introduced stimulated Raman cross section, σSRS, defined as the intrinsic molecular property in response to the external photon fluxes. Foremost, experimental measurement of σSRS turns out to be not weak at all or even larger when fairly compared with electronic counterparts of the same order. The analytical expression for σSRS derived from quantum electrodynamics also supports the measurement and proves that σSRS is intrinsically strong. Hence, σRaman and σSRS can be extremely small and large, respectively, for the same molecule at the same time. Our subsequent theoretical studies show that stimulated response formulation can unify spontaneous emission, stimulated emission, spontaneous Raman, and stimulated Raman via eq 10, in a coherent and symmetric way. In particular, an Einstein-coefficient-like equation, eq 12a, was derived, showing that σRaman can be explicitly expressed as σSRS multiplied by an effective photon flux arising from zero-point fluctuation of the vacuum. The feeble vacuum fluctuation hence explains how σSRS can be intrinsically strong while, at the same time, σRaman ends up being many orders of magnitude smaller when both compared to the electronic counterparts. These two sides of the same coin prompted us to propose "the duality of Raman scattering" (Table 1). Finally, this formulation naturally leads to a quantitative treatment of stimulated Raman scattering (SRS) microscopy, providing an intuitive, molecule-centric explanation as to how SRS microscopy can outperform regular Raman microscopy. Hence, as unveiled by the new formulation, a duality of Raman scattering has emerged, with implications for both fundamental science and practical technology.

Lumpy skin disease virus ORF127 protein suppresses type I interferon responses by inhibiting K63-linked ubiquitination of tank binding kinase 1.

Lumpy skin disease (LSD) is a severe animal infectious disease caused by lumpy skin disease virus (LSDV), inducing extensive nodules on the cattle mucosa or the scarfskin. LSDV genome encodes multiple proteins to evade host innate immune response. However, the underlying molecular mechanisms are poorly understood. In this study, we found that LSDV could suppress the expression of IFN-ß and interferon-stimulated genes (ISGs) in MDBK cells during the early stage of infection. Subsequently, an unbiased screen was performed to screen the LSDV genes with inhibitory effects on the type I interferon (IFN-I) production. ORF127 protein was identified as one of the strongest inhibitory effectors on the expression of IFN-ß and ISGs, meanwhile, the 1-43 aa of N-terminal of ORF127 played a vital role in suppressing the expression of IFN-ß. Overexpression of ORF127 could significantly promote LSDV replication through inhibiting the production of IFN-ß and ISGs in MDBK cells. Mechanism study showed that ORF127 specifically interacted with TBK1 and decreased the K63-linked polyubiquitination of TBK1 which suppressed the phosphorylation of TBK1 and ultimately decreased the production of IFN-ß. In addition, truncation mutation analysis indicated that the 1-43 aa of N-terminal of ORF127 protein was the key structural domain for its interaction with TBK1. In short, these results validated that ORF127 played a negative role in regulating IFN-ß expression through cGAS-STING signaling pathway. Taken together, this study clarified the molecular mechanism of ORF127 gene antagonizing IFN-I-mediated antiviral, which will helpfully provide new strategies for the treatment and prevention of LSD.