Post-translational Modification Sites Research Articles

Orchestrating ROS regulation: coordinated post-translational modification switches in NADPH oxidases.

Reactive oxygen species (ROS) are among the most important signaling molecules, playing a significant role in plant growth, development, and responses to various environmental stresses. Respiratory burst oxidase homologs (RBOHs) are key enzymes in ROS production. Plants tightly regulate the activation and deactivation of RBOHs through various post-translational modifications (PTMs), including phosphorylation, ubiquitination, S-nitrosylation, and persulfidation. These PTMs fine-tune ROS production, ensuring normal plant growth and development while facilitating rapid responses to abiotic and biotic stresses. This review discusses the effects of different PTMs on RBOH function and their biological relevance. Additionally, we examine the evolutionary conservation of PTM sites and emphasize the complex interplay between multiple PTMs regulating RBOHs.

Phosphorylation of the HMGN1 Nucleosome Binding Domain Decreases Helicity and Interactions with the Acidic Patch.

Intrinsically disordered proteins are abundant in the nucleus and are prime sites for posttranslational modifications that modulate transcriptional regulation. Lacking a defined three-dimensional structure, intrinsically disordered proteins populate an ensemble of several conformational states, which are dynamic and often altered by posttranslational modifications, or by binding to interaction partners. Although there is growing appreciation for the role that intrinsically disordered regions have in regulating protein-protein interactions, we still have a poor understanding of how to determine conformational population shifts, their causes under various conditions, and how to represent and model conformational ensembles. Here, we study the effects of serine phosphorylation in the nucleosome-binding domain of an intrinsically disordered protein - HMGN1 - using NMR spectroscopy, circular dichroism and modelling of protein complexes. We show that phosphorylation induces local conformational changes in the peptide backbone and decreases the helical propensity of the nucleosome binding domain. Modelling studies using AlphaFold3 suggest that phosphorylation disrupts the interface between HMGN1 and the nucleosome acidic patch, but that the models over-predict helicity in comparison to experimental data. These studies help us to build a picture of how posttranslational modifications might shift the conformational populations of disordered regions, alter access to histones, and regulate chromatin compaction.

Sitetack: A deep learning model that improves PTM prediction by using known PTMs.

Post-translational modifications (PTMs) increase the diversity of the proteome and are vital to organismal life and therapeutic strategies. Deep learning has been used to predict PTM locations. Still, limitations in datasets and their analyses compromise success. We evaluated the use of known PTM sites in prediction via sequence-based deep learning algorithms. For each PTM, known locations of that PTM were encoded as a separate amino acid before sequences were encoded via word embedding and passed into a convolutional neural network that predicts the probability of that PTM at a given site. Without labeling known PTMs, our models are on par with others. With labeling, however, we improved significantly upon extant models. Moreover, knowing PTM locations can increase the predictability of a different PTM. Our findings highlight the importance of PTMs for the installation of additional PTMs. We anticipate that including known PTM locations will enhance the performance of other proteomic machine learning algorithms. Sitetack is available as a web tool at https://sitetack.net; the source code, representative datasets, instructions for local use, and select models are available at https://github.com/clair-gutierrez/sitetack. Supplementary data are available at Bioinformatics online.

PTMD 2.0: an updated database of disease-associated post-translational modifications.

Various post-translational modifications (PTMs) participate in nearly all aspects of biological processes by regulating protein functions, and aberrant states of PTMs are frequently associated with human diseases. Here, we present a comprehensive database of PTMs associated with diseases (PTMD 2.0), including 342 624 PTM-disease associations (PDAs) in 15 105 proteins for 93 types of PTMs and 2083 diseases.Based on the distinct PTM states in diseases, we classified all PDAs into six categories: upregulation (U) or downregulation (D) of PTM levels, absence (A) or presence (P) of PTMs, and creation (C) or disruption (N) of PTM sites. We provided detailed annotations for each PDA and carefully annotated disease-associated proteins by integrating the knowledge from 101 additional resources that covered 13 aspects, including disease-associated information, variation and mutation, protein-protein interaction, protein functional annotation, DNA and RNA element, protein structure, chemical-target relationship, mRNA expression, protein expression/proteomics, subcellular localization, biological pathway annotation, functional domain annotation and physicochemical property. With a data volume of ∼8 GB, we anticipate that PTMD 2.0 will serve as a fundamental resource for further analysing the relationships between PTMs and diseases. The online service of PTMD 2.0 is freely available at https://ptmd.biocuckoo.cn/.

The repertoire of G‐protein‐coupled receptor variations in the Japanese population 54KJPN

AbstractG‐protein‐coupled receptors (GPCRs) are the largest superfamily in the human genome and the major targets for the market drugs. Recent massive genomics studies revealed numerous natural variations in the general population. 54KJPN is the most extensive Japanese population genomics study, curating the whole genome sequences from about 54,000 individuals. Here, by analyzing 390 non‐olfactory GPCR genes in the 54KJPN dataset, we annotated 25,443 missense single‐nucleotide variations. Among them, we found 120 major variations that appear with an allele frequency greater than 0.5, including variations that occurred on posttranslational modification sites. Structural alignment of GPCRs using the generic numbering system in the GPCRdb reveals enrichment of alterations in the conserved arginine residue within the DRY motif, which contributes to downstream G‐protein signaling. A comparison with the worldwide 1000 Genomes Project (1KGP) dataset found 23 variations that were present exclusively in the 54KJPN dataset. This study will be the basis for future pharmacogenomics studies for the Japanese population.

Preliminary In silico Analysis of Adenylate Kinase 1 (ADK1) of Echinococcus granulosus as a Candidate for Vaccination against Cystic Echinococcosis

Background: A neglected zoonosis, Cystic Echinococcosis (CE), is most common in developing nations worldwide. Vaccination is, therefore, helpful in preventing the disease. Objective: Predicting the main biochemical properties of E. granulosus Adenylate Kinase 1 (ADK1) and its possible B-cell and T-cell-binding epitopes as a valuable candidate for immunization was the goal of the current study. Methods: Predictions were made to determine biochemical, antigenic, structural, and subcellular characteristics, along with the immunogenic epitopes, using several online servers. Results: The extracellular 22 KDa protein had no allergenicity, while it possessed hydrophilicity (GRAVY: -0.286), stability (instability: 17.48), tolerance to a wide range of temperatures (aliphatic: 93.45), and 17 post-translational modification sites. The secondary structure mostly comprised helices and random coils and the 3D model was generated using Robetta server (confidence: 0.88). Common B-cell epitopes were discovered by three servers and screened for antigenic, allergenic, and solubility traits. Moreover, MHC-associated epitopes for mice and humans were predicted in E. granulosus ADK1 with subsequent screening. Conclusion: This work offers a foundation for further investigation on designing an effective vaccination against CE. Further empirical research study with the examined protein solely or combined with other antigens is needed.

From Enzyme to Vaccine: Preliminary Insights into the Immunogenic Epitopes in Adenylate Kinase 8 (ADK8) of Echinococcus granulosus

Background: Globally, developing livestock countries are affected by Cystic Echinococcosis (CE), a neglected zoonosis. Thus, vaccination would help to prevent this neglected zoonosis. Objective: This study aimed to predict the primary biochemical characteristics of E. granulosus Adenylate Kinase 8 (ADK8) and its potential B- and T-cell-binding epitopes as a candidate for vaccination. Methods: Prediction were conducted on multiple web servers to identify immunogenic epitopes as well as biochemical, antigenic, structural, and subcellular properties. Results: The extracellular 38.78 KDa protein possessed no allergenic fragments, while it showed hydrophilicity (GRAVY: -0.239), wide thermotolerance (aliphatic: 98.02), and 36 post-translational modification sites. The secondary structure mostly included helices and random coils, and the best-generated 3D model by the Robetta server had a confidence score of 0.79. Three servers identified common B-cell epitopes and evaluated them for solubility, allergenicity, and antigenic properties. Furthermore, following the screening, MHC-associated epitopes for humans and mice were predicted in the E. granulosus ADK8. Conclusion: These results provide a basis for future research into the development of a proper vaccine against CE. Additional empirical studies using the protein and/or its epitopes alone or in combination with additional antigens are demanded.

Comprehensive bioinformatics assessments of the ROP34 of Toxoplasma gondii to approach vaccine candidates

IntroductionRhoptries proteins (ROPs) are crucial throughout different stages of the Toxoplasma gondii (T. gondii) lifecycle, playing key roles in both the invasion of host cells and their subsequent survival. ROP34 is particularly noteworthy as it significantly influences host gene expression and aids in the transition from the tachyzoite to the bradyzoite form.Materials and methodsThis research utilized various bioinformatics tools to assess physico-chemical properties, allergenic and antigenic characteristics, sites for post-translational modifications (PTMs) and protein's secondary and three-dimensional structures of the ROP34 protein. Furthermore, the study identified potential B-cell, MHC-binding, and cytotoxic T-lymphocyte (CTL) epitopes within the ROP34 sequence.ResultsThe ROP34 peptide comprised 553 amino acid residues, with a calculated average molecular weight (MW) of 61.60149 kDa, an aliphatic index of 73.98, and a GRAVY score of − 0.554. The antigenicity of the multi-epitope peptide was estimated to be 0.526563 and 0.6025 by the ANTIGENpro and VaxiJen servers, respectively, suggesting ROP34 as an immunogenic protein with no allergenic potential. Secondary structure analysis revealed a composition of 52.80% random coil, 36.17% alpha helix, and 11.03% extended strand. The Ramachandran plot for the refined model depicted that 97.46% of the residues were situated in the favored region.ConclusionThis in silico research serves as a foundation for designing effective immunization tactics to target toxoplasmosis. The present article lays the groundwork for future studies and offers perspectives for the advancement of an appropriate toxoplasmosis vaccine.Graphical

SLAM: Structure-aware lysine β-hydroxybutyrylation prediction with protein language model

Post-translational modifications (PTMs) diversify protein functions by adding/removing chemical groups to certain amino acid. As a newly-reported PTM, lysine β-hydroxybutyrylation (Kbhb) presents a new avenue to functional proteomics. Therefore, accurate and efficient prediction of Kbhb sites is imperative. However, the current experimental methods for identifying PTM sites are often expensive and time-consuming. Up to now, there is no computational method proposed for Kbhb sites detection. To this end, we present the first deep learning-based method, termed SLAM, to in silico identify lysine β-hydroxybutyrylation sites. The performance of SLAM is evaluated on both 5-fold cross-validation and independent test, achieving 0.890, 0.899, 0.907 and 0.923 in terms of AUROC values, on the general and species-specific independent test sets, respectively. As one example, we predicted the potential Kbhb sites in human S-adenosyl-L-homocysteine hydrolase, which is in agreement with experimentally-verified Kbhb sites. In summary, our method could enable accurate and efficient characterization of novel Kbhb sites that are crucial for the function and stability of proteins and could be applied in the structure-guided identification of other important PTM sites. The SLAM online service and source code is available at https://ai4bio.online/SLAM and https://github.com/Gabriel-QIN/SLAM, respectively.

1D and 2D Feature Extraction Based on AAC and DC Protein Descriptors for Classification of Acetylation in Lysine Proteins using Convolutional Neural Network

Post-Translational Modification (PTM) denotes a biochemical alteration observed in an amino acid, playing crucial roles in protein activity, functionality, and the regulation of protein structure. The recognition of associated PTMs serves as a fundamental basis for understanding biological processes, therapeutic interventions for diseases, and the development of pharmaceutical agents. Using computational approaches (in silico) offers an efficient and cost-effective means to identify PTM sites swiftly. The exploration of protein classification commences with extracting protein sequence features that are subsequently transformed into numerical features for utilization in classification algorithms. Feature extraction methodologies involve using protein descriptors like Amino Acid Composition (AAC) and Dipeptide Composition (DC). Yet, these approaches exhibit a limitation by neglecting crucial amino acid sequence details. Moreover, both descriptor techniques generate a limited number of 1-dimensional (1D) features, which may not be ideal for processing through the Convolutional Neural Network (CNN) classification method. This investigation presents a novel approach to enhance feature diversity through protein sequence segmentation techniques, employing adjacent and overlapping segment strategies. Furthermore, the study illustrates the organization of features into 1D and 2D formats to facilitate processing through 1D CNN and 2D CNN classification methodologies. The findings of this research endeavour highlight the potential for enhancing the accuracy of acetylation classification in lysine proteins through the multiplication of protein sequence segments in a 2D configuration. The highest accuracy achieved for AAC and DC-based feature extraction methods is 77.39% and 76.75%, respectively.

Multi-step HPLC fractionation enabled in-depth and unbiased characterization of histone PTMs

Histone post-translational modifications (PTMs) are critical epigenetic regulatory factors. Histone PTMs are highly dynamic and complicated, encompassing over 30 structurally diverse modifications across nearly 180 amino acid residues, which generated extensive information regarding histone marks. In proteomics-based characterization of histone PTMs, chemical derivatization and antibody-based affinity enrichment were frequently utilized to improve the identification depth. However, chemical derivatization suffered from the occurrence of side reactions, and antibody-based affinity enrichment focused on specific PTM types of interest. In this research, we developed a multi-step fractionation strategy for comprehensively unbiased detection of histone PTM sites. By combining protein-level fractionation with peptide-level alkaline and acid phase fractionation, we developed the Multidimensional Fractionation based Histone Mark Identification Technology (MudFIT) and increased PTM identification to a total of 264 histone PTM sites. To the best of our knowledge, this strategy achieved the most comprehensive characterization of histone PTM sites in a single proteomics study. Using the same starting amount of sample, MudFIT identified more Kac sites and Kac peptides than those in antibody-based acetylated peptide enrichment. Moreover, in addition to well-studied histone marks, we discovered 36 potential new histone PTM sites including H2BK116bu, H4R45me2, H1K63pr, and uncovered unknown histone PTM types like aminoadipic on lysine and nitrosylation on tyrosine. Our data provided a method and resource for in-depth characterization of histone PTM sites, facilitating further biological understanding of histone marks.

In silico analysis of the effects of non-synonymous SNPs associated with human GSK3B gene on its structure and function

Single Nucleotide Polymorphisms (SNPs) are abundantly identified by next generation sequencing (NGS) technology. Glycogen synthase kinase-3 beta (GSK3B), a widely expressed protein kinase, plays pivotal roles in cellular pathways. However, study on SNPs associated with GSK3B and their functional consequences is lacking. In this study, we analysed non-synonymous SNPs of GSK3B gene and their implications using computational tools. From NCBI dbSNP, 103,087 SNPs of GSK3B were initially gathered, later narrowed down to 255 unique nsSNPs. Around one-third of the nsSNPs resulted in charge and polarity change of the amino acids of the protein. 41 nsSNPs were found to significantly alter the stability of GSK3B protein (ΔΔG ≤ -1 or ≥ 1 kcal/mol) and few of them also affected the disorderness at the mutation site. Evolutionary conservation of the nsSNPs in the protein revealed 25 nsSNP may be deleterious to GSK3B protein function. Finally, 4 critical nsSNPs (Y161C, R167G, P225L and Y234D) were identified that can significantly alter both the stability and function of GSK3B. Furthermore, this study predicted 60 post-translational modification sites in GSK3B among which 26 sites contained nsSNPs. Interestingly, 7 upstream ORFs (uORFs) with high ribosomal occupancy were also detected in GSK3B mRNA that can reduce the expression of GSK3B protein. Altogether, this study has employed various in silico methods to characterize GSK3B nsSNPs, but they have limitations. These tools often overlook the cellular context, interacting partners, PTMs and the dynamic nature of proteins, which can affect protein behaviour and function. Despite these limitations, in silico tools are valuable for initial screening and prioritizing SNPs. The prioritized SNPs obtained in this study (Y161C, R167G, P225L and Y234D) should be experimentally validated using techniques like genome editing, biochemical assays, interactome analysis in cell lines and animal models to confirm their biological relevance.Clinical trial registration: Not Applicable.

An extensive in silico analysis of missense mutations of the human AIMP2 gene

HLD17 (Hypomyelinating Leukodystrophy 17) is an inherited white matter disorder characterized by insufficient myelin production due to biallelic loss of function mutations in the aminoacyl-tRNA synthetase complex-interacting multifunctional protein 2 (AIMP2) gene. In silico analysis of SNVs (single nucleotide variants) in the AIMP2 gene is an efficient and cost-effective method for analyzing and predicting the impact of mutations on protein function and disease pathophysiology.The study used dbSNP and Ensembl databases to obtain data on 343 nonsynonymous single nucleotide variants (nsSNVs) in the human AIMP2 gene. Six prediction algorithm tools were used to assess the effects of these nsSNVs on AIMP2's functions and structures. Results showed that 18 nsSNVs were located within functional domains, while 10 nsSNVs led to decreased protein stability. The structural and functional properties of the AIMP2 protein were investigated using databases such as Predict Protein, Mutpred2, and HOPE. ConSurf analysis provided information about conserved nsSNVs. GeneMANIA and STRING software tools were used to predict interactions between gene-gene and protein-protein, respectively. Phyre2 and I-TASSER web servers were used to predict the 3D structures of wild-type and mutant proteins.In addition, having the challenge of probable post-translational modification sites in the AIMP2, we made predictions using various bioinformatics tools. Consecuently, three minor mutations (L138Q, V161E, and I188N) and five major mutations (C23S, D121G, I122S, P128S, and W268S) were found to affect the AIMP2 protein's structure or function. Anyway, These mutations need to be further studied and confirmed through experimental investigation and GWAS studies.

Bacterial Production of CDKL5 Catalytic Domain: Insights in Aggregation, Internal Translation and Phosphorylation Patterns.

Cyclin-dependent kinase-like 5 (CDKL5) is a serine/threonine protein kinase involved in human brain development and functioning. Mutations in CDKL5, especially in its catalytic domain, cause a severe developmental condition named CDKL5 deficiency disorder. Nevertheless, molecular studies investigating the structural consequences of such mutations are still missing. The CDKL5 catalytic domain harbors different sites of post-translational modification, such as phosphorylations, but their role in catalytic activity, protein folding, and stability has not been entirely investigated. With this work, we describe the expression pattern of the CDKL5 catalytic domain in Escherichia coli demonstrating that it predominantly aggregates. However, the use of solubility tags, the lowering of the expression temperature, the manual codon optimization to overcome an internal translational start, and the incubation of the protein with K+ and MgATP allow the collection of a soluble catalytically active kinase. Interestingly, the resulting protein exhibits hypophosphorylation compared to its eukaryotic counterpart, proving that bacteria are a useful tool to achieve almost unmodified CDKL5. Posing questions about the CDKL5 autoactivation mechanism and the determinants for its stability, this research provides a valuable platform for comparative biophysical studies between bacterial and eukaryotic-expressed proteins, contributing to our understanding of neurodevelopmental disorders associated with CDKL5 dysfunction.

A Comparative Analysis of the Immunoglobulin Repertoire in Leukemia Cells and B Cells in Chinese Acute Myeloid Leukemia by High-Throughput Sequencing.

It is common knowledge that immunoglobulin (Ig) is produced by B lymphocytes and mainly functions as an antibody. However, it has been shown recently that myeloblasts from acute myeloid leukemia (AML) could also express Ig and that AML-Ig played a role in leukemogenesis and AML progression. The difference between Ig from myeloblasts and B cells has not been explored. Studying the characteristics of the Ig repertoire in myeloblasts and B cells will be helpful to understand the function and significance of AML-Ig. We performed 5' RACE-related PCR coupled with PacBio sequencing to analyze the Ig repertoire in myeloblasts and B cells from Chinese AML patients. Myeloblasts expressed all five classes of IgH, especially Igγ, with a high expression frequency. Compared with B-Ig in the same patient, AML-Ig showed different biased V(D)J usages and mutation patterns. In addition, the CDR3 length distribution of AML-Ig was significantly different from those of B-Ig. More importantly, mutations of AML-IgH, especially Igμ, Igα, and Igδ, were different from that of B-IgH in each AML patient, and the mutations frequently occurred at the sites of post-translational modification. AML-Ig has distinct characteristics of variable regions and mutations, which may have implications for disease monitoring and personalized therapy.

Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model

Post-translational modifications (PTMs) are pivotal in modulating protein functions and influencing cellular processes like signaling, localization, and degradation. The complexity of these biological interactions necessitates efficient predictive methodologies. In this work, we introduce PTMGPT2, an interpretable protein language model that utilizes prompt-based fine-tuning to improve its accuracy in precisely predicting PTMs. Drawing inspiration from recent advancements in GPT-based architectures, PTMGPT2 adopts unsupervised learning to identify PTMs. It utilizes a custom prompt to guide the model through the subtle linguistic patterns encoded in amino acid sequences, generating tokens indicative of PTM sites. To provide interpretability, we visualize attention profiles from the model’s final decoder layer to elucidate sequence motifs essential for molecular recognition and analyze the effects of mutations at or near PTM sites to offer deeper insights into protein functionality. Comparative assessments reveal that PTMGPT2 outperforms existing methods across 19 PTM types, underscoring its potential in identifying disease associations and drug targets.

VUStruct: a compute pipeline for high throughput and personalized structural biology.

Effective diagnosis and treatment of rare genetic disorders requires the interpretation of a patient's genetic variants of unknown significance (VUSs). Today, clinical decision-making is primarily guided by gene-phenotype association databases and DNA-based scoring methods. Our web-accessible variant analysis pipeline, VUStruct, supplements these established approaches by deeply analyzing the downstream molecular impact of variation in context of 3D protein structure. VUStruct's growing impact is fueled by the co-proliferation of protein 3D structural models, gene sequencing, compute power, and artificial intelligence. Contextualizing VUSs in protein 3D structural models also illuminates longitudinal genomics studies and biochemical bench research focused on VUS, and we created VUStruct for clinicians and researchers alike. We now introduce VUStruct to the broad scientific community as a mature, web-facing, extensible, High Performance Computing (HPC) software pipeline. VUStruct maps missense variants onto automatically selected protein structures and launches a broad range of analyses. These include energy-based assessments of protein folding and stability, pathogenicity prediction through spatial clustering analysis, and machine learning (ML) predictors of binding surface disruptions and nearby post-translational modification sites. The pipeline also considers the entire input set of VUS and identifies genes potentially involved in digenic disease. VUStruct's utility in clinical rare disease genome interpretation has been demonstrated through its analysis of over 175 Undiagnosed Disease Network (UDN) Patient cases. VUStruct-leveraged hypotheses have often informed clinicians in their consideration of additional patient testing, and we report here details from two cases where VUStruct was key to their solution. We also note successes with academic research collaborators, for whom VUStruct has informed research directions in both computational genomics and wet lab studies.

In Silico Analysis of the Missense Variants of Uncertain Significance of CTNNB1 Gene Reported in GnomAD Database.

CTNNB1 pathogenic variants are related to the improper functioning of the WNT/β-catenin pathway, promoting the development of different types of cancer of somatic origin. Bioinformatics analyses of genetic variation are a great tool to understand the possible consequences of these variants on protein structure and function and their probable implication in pathologies. The objective of this study is to describe the impact of the missense variants of uncertain significance (VUS) of the CTNNB1 gene on structure and function of the β-catenin protein. The CTNNB1 variants were obtained from the GnomAD v2.1.1 database; subsequently, a bioinformatic analysis was performed using the VarSome, UCSC Genome Browser, UniProt, the Kinase Library database, and DynaMut2 platforms to evaluate clinical significance, gene conservation, consensus sites for post-translational modifications, and the dynamics and stability of proteins. The GnomAD v2.1.1 database included 826 variants of the CTNNB1 gene, of which 385 were in exons and exon/intron boundaries. Among these variants, 214 were identified as missense, of which 146 were classified as VUS. Notably, 12 variants were in proximity to consensus sites for post-translational modifications (PTMs). The in silico analysis showed a slight tendency towards probably pathogenic for c.59C>T (p.Ala20Val) and c.983T>C (p.Met328Thr) missense VUS. These findings provide possible functional implications of these variants in some types of cancer.

Deciphering the Genetic Variation: A Comparative Analysis of Parental and Attenuated Strains of the QXL87 Vaccine for Infectious Bronchitis.

The QXL87 live attenuated vaccine strain for infectious bronchitis represents the first approved QX type (GI-19 lineage) vaccine in China. This strain was derived from the parental strain CK/CH/JS/2010/12 through continuous passage in SPF chicken embryos. To elucidate the molecular mechanism behind its attenuation, whole-genome sequencing was conducted on both the parental and attenuated strains. Analysis revealed 145 nucleotide mutations in the attenuated strain, leading to 48 amino acid mutations in various proteins, including Nsp2 (26), Nsp3 (14), Nsp4 (1), S (4), 3a (1), E (1), and N (1). Additionally, a frameshift mutation caused by a single base insertion in the ORFX resulted in a six-amino-acid extension. Subsequent comparison of post-translational modification sites, protein structure, and protein-protein binding sites between the parental and attenuated strains identified three potential virulence genes: Nsp2, Nsp3, and S. The amino acid mutations in these proteins not only altered their conformation but also affected the distribution of post-translational modification sites and protein-protein interaction sites. Furthermore, three potential functional mutation sites-P106S, A352T, and L472F, all located in the Nsp2 protein-were identified through PROVEAN, PolyPhen, and I-Mutant. Overall, our findings suggest that Nsp2, Nsp3, and S proteins may play a role in modulating IBV pathogenicity, with a particular focus on the significance of the Nsp2 protein. This study contributes to our understanding of the molecular mechanisms underlying IBV attenuation and holds promise for the development of safer live attenuated IBV vaccines using reverse genetic approaches.

Chaperone- and PTM-mediated activation of IRF1 tames radiation-induced cell death and the inflammatory response.

The key role of structural cells in immune modulation has been revealed with the advent of single-cell multiomics, but the underlying mechanism remains poorly understood. Here, we revealed that the transcriptional activation of interferon regulatory factor 1 (IRF1) in response to ionizing radiation, cytotoxic chemicals and SARS-CoV-2 viral infection determines the fate of structural cells and regulates communication between structural and immune cells. Radiation-induced leakage of mtDNA initiates the nuclear translocation of IRF1, enabling it to regulate the transcription of inflammation- and cell death-related genes. Novel posttranslational modification (PTM) sites in the nuclear localization sequence (NLS) of IRF1 were identified. Functional analysis revealed that mutation of the acetylation site and the phosphorylation sites in the NLS blocked the transcriptional activation of IRF1 and reduced cell death in response to ionizing radiation. Mechanistically, reciprocal regulation between the single-stranded DNA sensors SSBP1 and IRF1, which restrains radiation-induced and STING/p300-mediated PTMs of IRF1, was revealed. In addition, genetic deletion or pharmacological inhibition of IRF1 tempered radiation-induced inflammatory cell death, and radiation mitigators also suppressed SARS-CoV-2 NSP-10-mediated activation of IRF1. Thus, we revealed a novel cytoplasm-oriented mechanism of IRF1 activation in structural cells that promotes inflammation and highlighted the potential effectiveness of IRF1 inhibitors against immune disorders.