QKI shuttles internal m7G-modified transcripts into stress granules and modulates mRNA metabolism.

N7-methylguanosine (m7G) modification, routinely occurring at mRNA 5' cap or within tRNAs/rRNAs, also exists internally in messenger RNAs (mRNAs). Although m7G-cap is essential for pre-mRNA processing and protein synthesis, the exact role of mRNA internal m7G modification remains elusive. Here, we report that mRNA internal m7G is selectively recognized by Quaking proteins (QKIs). By transcriptome-wide profiling/mapping of internal m7G methylome and QKI-binding sites, we identified more than 1,000 high-confidence m7G-modified and QKI-bound mRNA targets with a conserved "GANGAN (N = A/C/U/G)" motif. Strikingly, QKI7 interacts (via C terminus) with the stress granule (SG) core protein G3BP1 and shuttles internal m7G-modified transcripts into SGs to regulate mRNA stability and translation under stress conditions. Specifically, QKI7 attenuates the translation efficiency of essential genes in Hippo signaling pathways to sensitize cancer cells to chemotherapy. Collectively, we characterized QKIs as mRNA internal m7G-binding proteins that modulate target mRNA metabolism and cellular drug resistance.

Selenium-GPX4 axis protects follicular helper T cells from ferroptosis.

Follicular helper T (TFH) cells are a specialized subset of CD4+ T cells that essentially support germinal center responses where high-affinity and long-lived humoral immunity is generated. The regulation of TFH cell survival remains unclear. Here we report that TFH cells show intensified lipid peroxidation and altered mitochondrial morphology, resembling the features of ferroptosis, a form of programmed cell death that is driven by iron-dependent accumulation of lipid peroxidation. Glutathione peroxidase 4 (GPX4) is the major lipid peroxidation scavenger and is necessary for TFH cell survival. The deletion of GPX4 in T cells selectively abrogated TFH cells and germinal center responses in immunized mice. Selenium supplementation enhanced GPX4 expression in T cells, increased TFH cell numbers and promoted antibody responses in immunized mice and young adults after influenza vaccination. Our findings reveal the central role of the selenium-GPX4-ferroptosis axis in regulating TFH homeostasis, which can be targeted to enhance TFH cell function in infection and following vaccination.

R-2-hydroxyglutarate attenuates aerobic glycolysis in leukemia by targeting the FTO/m6A/PFKP/LDHB axis.

R-2-hydroxyglutarate (R-2HG), a metabolite produced by mutant isocitrate dehydrogenases (IDHs), was recently reported to exhibit anti-tumor activity. However, its effect on cancer metabolism remains largely elusive. Here we show that R-2HG effectively attenuates aerobic glycolysis, a hallmark of cancer metabolism, in (R-2HG-sensitive) leukemia cells. Mechanistically, R-2HG abrogates fat-mass- and obesity-associated protein (FTO)/N6-methyladenosine (m6A)/YTH N6-methyladenosine RNA binding protein 2 (YTHDF2)-mediated post-transcriptional upregulation of phosphofructokinase platelet (PFKP) and lactate dehydrogenase B (LDHB) (two critical glycolytic genes) expression and thereby suppresses aerobic glycolysis. Knockdown of FTO, PFKP, or LDHB recapitulates R-2HG-induced glycolytic inhibition in (R-2HG-sensitive) leukemia cells, but not in normal CD34+ hematopoietic stem/progenitor cells, and inhibits leukemogenesis in vivo; conversely, their overexpression reverses R-2HG-induced effects. R-2HG also suppresses glycolysis and downregulates FTO/PFKP/LDHB expression in human primary IDH-wild-type acute myeloid leukemia (AML) cells, demonstrating the clinical relevance. Collectively, our study reveals previously unrecognized effects of R-2HG and RNA modification on aerobic glycolysis in leukemia, highlighting the therapeutic potential of targeting cancer epitranscriptomics and metabolism.

Close relatives of MERS-CoV in bats use ACE2 as their functional receptors.

Middle East respiratory syndrome coronavirus (MERS-CoV) and several bat coronaviruses use dipeptidyl peptidase-4 (DPP4) as an entry receptor1-4. However, the receptor for NeoCoV-the closest known MERS-CoV relative found in bats-remains unclear5. Here, using a pseudotype virus entry assay, we found that NeoCoV and its close relative, PDF-2180, can efficiently bind to and use specific bat angiotensin-converting enzyme 2 (ACE2) orthologues and, less favourably, human ACE2 as entry receptors through their receptor-binding domains (RBDs) on the spike (S) proteins. Cryo-electron microscopy analysis revealed an RBD-ACE2 binding interface involving protein-glycan interactions, distinct from those of other known ACE2-using coronaviruses. We identified residues 337-342 of human ACE2 as a molecular determinant restricting NeoCoV entry, whereas a NeoCoV S pseudotyped virus containing a T510F RBD mutation efficiently entered cells expressing human ACE2. Although polyclonal SARS-CoV-2 antibodies or MERS-CoV RBD-specific nanobodies did not cross-neutralize NeoCoV or PDF-2180, an ACE2-specific antibody and two broadly neutralizing betacoronavirus antibodies efficiently inhibited these two pseudotyped viruses. We describe MERS-CoV-related viruses that use ACE2 as an entry receptor, underscoring a promiscuity of receptor use and a potential zoonotic threat.

DNA N6-methyldeoxyadenosine in mammals and human disease.

N6-methyladenine (6mA) is the most prevalent DNA modification in prokaryotes. However, its presence and significance in eukaryotes remain elusive. Recently, with methodology advances in detection and sequencing of 6mA in eukaryotes, 6mA is back in the spotlight. Although multiple studies have reported that 6mA is an important epigenetic mark in eukaryotes and plays a regulatory role in DNA transcription, transposon activation, stress response, and other bioprocesses, there are some discrepancies in the current literature. We review the recent advances in 6mA research in eukaryotes, especially in mammals. In particular, we describe the abundance/distribution of 6mA, its potential role in regulating gene expression, identified regulators, and pathological roles in human diseases, especially in cancer. The limitations faced by the field and future perspectives in 6mA research are also discussed.

ML-PLIC: a web platform for characterizing protein-ligand interactions and developing machine learning-based scoring functions.

Cracking the entangling code of protein-ligand interaction (PLI) is of great importance to structure-based drug design and discovery. Different physical and biochemical representations can be used to describe PLI such as energy terms and interaction fingerprints, which can be analyzed by machine learning (ML) algorithms to create ML-based scoring functions (MLSFs). Here, we propose the ML-based PLI capturer (ML-PLIC), a web platform that automatically characterizes PLI and generates MLSFs to identify the potential binders of a specific protein target through virtual screening (VS). ML-PLIC comprises five modules, including Docking for ligand docking, Descriptors for PLI generation, Modeling for MLSF training, Screening for VS and Pipeline for the integration of the aforementioned functions. We validated the MLSFs constructed by ML-PLIC in three benchmark datasets (Directory of Useful Decoys-Enhanced, Active as Decoys and TocoDecoy), demonstrating accuracy outperforming traditional docking tools and competitive performance to the deep learning-based SF, and provided a case study of the Serine/threonine-protein kinase WEE1 in which MLSFs were developed by using the ML-based VS pipeline in ML-PLIC. Underpinning the latest version of ML-PLIC is a powerful platform that incorporates physical and biological knowledge about PLI, leveraging PLI characterization and MLSF generation into the design of structure-based VS pipeline. The ML-PLIC web platform is now freely available at http://cadd.zju.edu.cn/plic/.

Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors.

Machine learning including modern deep learning models has been extensively used in drug design and screening. However, reliable prediction of molecular properties is still challenging when exploring out-of-domain regimes, even for deep neural networks. Therefore, it is important to understand the uncertainty of model predictions, especially when the predictions are used to guide further experiments. In this study, we explored the utility and effectiveness of evidential uncertainty in compound screening. The evidential Graphormer model was proposed for uncertainty-guided discovery of KDM1A/LSD1 inhibitors. The benchmarking results illustrated that (i) Graphormer exhibited comparative predictive power to state-of-the-art models, and (ii) evidential regression enabled well-ranked uncertainty estimates and calibrated predictions. Subsequently, we leveraged time-splitting on the curated KDM1A/LSD1 dataset to simulate out-of-distribution predictions. The retrospective virtual screening showed that the evidential uncertainties helped reduce false positives among the top-acquired compounds and thus enabled higher experimental validation rates. The trained model was then used to virtually screen an independent in-house compound set. The top 50 compounds ranked by two different ranking strategies were experimentally validated, respectively. In general, our study highlighted the importance to understand the uncertainty in prediction, which can be recognized as an interpretable dimension to model predictions.

Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning?

Binding affinity prediction largely determines the discovery efficiency of lead compounds in drug discovery. Recently, machine learning (ML)-based approaches have attracted much attention in hopes of enhancing the predictive performance of traditional physics-based approaches. In this study, we evaluated the impact of structural dynamic information on the binding affinity prediction by comparing the models trained on different dimensional descriptors, using three targets (i.e. JAK1, TAF1-BD2 and DDR1) and their corresponding ligands as the examples. Here, 2D descriptors are traditional ECFP4 fingerprints, 3D descriptors are the energy terms of the Smina and NNscore scoring functions and 4D descriptors contain the structural dynamic information derived from the trajectories based on molecular dynamics (MD) simulations. We systematically investigate the MD-refined binding affinity prediction performance of three classical ML algorithms (i.e. RF, SVR and XGB) as well as two common virtual screening methods, namely Glide docking and MM/PBSA. The outcomes of the ML models built using various dimensional descriptors and their combinations reveal that the MD refinement with the optimized protocol can improve the predictive performance on the TAF1-BD2 target with considerable structural flexibility, but not for the less flexible JAK1 and DDR1 targets, when taking docking poses as the initial structure instead of the crystal structures. The results highlight the importance of the initial structures to the final performance of the model through conformational analysis on the three targets with different flexibility.

Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency.

Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field.

Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening.

Molecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths. However, to meet the efficiency demands of SBVS, these algorithms and functions are often simplified, prioritizing speed over accuracy.The emergence of deep learning (DL) has exerted a profound impact on diverse fields, ranging from natural language processing to computer vision and drug discovery. DeepMind's AlphaFold2 has impressively exhibited its ability to accurately predict protein structures solely from amino acid sequences, highlighting the remarkable potential of DL in conformation prediction. This groundbreaking advancement circumvents the traditional search-scoring frameworks in LD, enhancing both accuracy and processing speed and thereby catalyzing a broader adoption of DL algorithms in binding pose prediction. Nevertheless, a consensus on certain aspects remains elusive.In this Account, we delineate the current status of employing DL to augment LD within the VS paradigm, highlighting our contributions to this domain. Furthermore, we discuss the challenges and future prospects, drawing insights from our scholarly investigations. Initially, we present an overview of VS and LD, followed by an introduction to DL paradigms, which deviate significantly from traditional search-scoring frameworks. Subsequently, we delve into the challenges associated with the development of DL-based LD (DLLD), encompassing evaluation metrics, application scenarios, and physical plausibility of the predicted conformations. In the evaluation of LD algorithms, it is essential to recognize the multifaceted nature of the metrics. While the accuracy of binding pose prediction, often measured by the success rate, is a pivotal aspect, the scoring/screening power and computational speed of these algorithms are equally important given the pivotal role of LD tools in VS. Regarding application scenarios, early methods focused on blind docking, where the binding site is unknown. However, recent studies suggest a shift toward identifying binding sites rather than solely predicting binding poses within these models. In contrast, LD with a known pocket in VS has been shown to be more practical. Physical plausibility poses another significant challenge. Although DLLD models often achieve higher success rates compared to traditional methods, they may generate poses with implausible local structures, such as incorrect bond angles or lengths, which are disadvantageous for postprocessing tasks like visualization. Finally, we discuss the future perspectives for DLLD, emphasizing the need to improve generalization ability, strike a balance between speed and accuracy, account for protein conformation flexibility, and enhance physical plausibility. Additionally, we delve into the comparison between generative and regression algorithms in this context, exploring their respective strengths and potential.

Mechanistic Insights into Tanshinone IIA in the Amelioration of Post-Thyroidectomy Hypoparathyroidism.

BACKGROUND: Thyroidectomy causes impaired blood supply to the parathyroid glands, which leads to hypoparathyroidism. Tanshinone IIA (Tan IIA) is helpful in blood activation and cardiovascular protection. Therefore, the efficacy of Tan IIA in improving hypoparathyroidism was explored in this study. METHODS: New Zealand white rabbits were utilized to establish a unilateral parathyroid gland ischemia injury model. The model was created by selectively ligating the main blood supply vessel of one parathyroid gland, and the rabbits were then divided into three groups receiving 1, 5, and 10 mg/kg of Tan IIA. Serum calcium and parathyroid hormone (PTH) levels were measured using specialized assay kits. Immunohistochemistry was used to assess the microvessel density (MVD) in parathyroid glands. Western blotting (WB) was used to analyze protein expression related to the PI3K/AKT signaling pathway and the pathway-associated HIF-1α and VEGF. Moreover, MMP-2 and MMP-9 involved in angiogenesis were detected by WB. RESULTS: Tan IIA treatment effectively restored serum calcium and PTH levels in a dose-dependent manner. Notably, MVD in the parathyroid glands increased significantly, especially at higher doses. The Tan IIA treatment also elevated the p-PI3K/PI3K and p-AKT/AKT ratios, indicating that the PI3K/AKT pathway was reactivated. Moreover, Tan IIA significantly restored the decreased expression levels of VEGF and HIF-1α caused by parathyroid surgery. Additionally, Tan IIA increased MMP-2 and MMP-9 levels. CONCLUSION: Tan IIA activates the PI3K/AKT pathway, promotes angiogenesis by modulating VEGF, HIF-1α, MMP-2, and MMP-9, thereby further enhancing MVD within the parathyroid glands. This study demonstrates that Tan IIA improved post-thyroidectomy hypoparathyroidism.

Histone H3 trimethylation at lysine 36 guides m6A RNA modification co-transcriptionally.

DNA and histone modifications have notable effects on gene expression1. Being the most prevalent internal modification in mRNA, the N6-methyladenosine (m6A) mRNA modification is as an important post-transcriptional mechanism of gene regulation2-4 and has crucial roles in various normal and pathological processes5-12. However, it is unclear how m6A is specifically and dynamically deposited in the transcriptome. Here we report that histone H3 trimethylation at Lys36 (H3K36me3), a marker for transcription elongation, guides m6A deposition globally. We show that m6A modifications are enriched in the vicinity of H3K36me3 peaks, and are reduced globally when cellular H3K36me3 is depleted. Mechanistically, H3K36me3 is recognized and bound directly by METTL14, a crucial component of the m6A methyltransferase complex (MTC), which in turn facilitates the binding of the m6A MTC to adjacent RNA polymerase II, thereby delivering the m6A MTC to actively transcribed nascent RNAs to deposit m6A co-transcriptionally. In mouse embryonic stem cells, phenocopying METTL14 knockdown, H3K36me3 depletion also markedly reduces m6A abundance transcriptome-wide and in pluripotency transcripts, resulting in increased cell stemness. Collectively, our studies reveal the important roles of H3K36me3 and METTL14 in determining specific and dynamic deposition of m6A in mRNA, and uncover another layer of gene expression regulation that involves crosstalk between histone modification and RNA methylation.

PROTAC-DB 2.0: an updated database of PROTACs.

Proteolysis targeting chimeras (PROTACs), which harness the ubiquitin-proteasome system to selectively induce targeted protein degradation, represent an emerging therapeutic technology with the potential to modulate traditional undruggable targets. Over the past few years, this technology has moved from academia to industry and more than 10 PROTACs have been advanced into clinical trials. However, designing potent PROTACs with desirable drug-like properties still remains a great challenge. Here, we report an updated online database, PROTAC-DB 2.0, which is a repository of structural and experimental data about PROTACs. In this 2nd release, we expanded the number of PROTACs to 3270, which corresponds to a 96% expansion over the first version. Meanwhile, the numbers of warheads (small molecules targeting the proteins of interest), linkers, and E3 ligands (small molecules recruiting E3 ligases) have increased to over 360, 1500 and 80, respectively. In addition, given the importance and the limited number of the crystal target-PROTAC-E3 ternary complex structures, we provide the predicted ternary complex structures for PROTACs with good degradation capability using our PROTAC-Model method. To further facilitate the analysis of PROTAC data, a new filtering strategy based on the E3 ligases is also added. PROTAC-DB 2.0 is available online at http://cadd.zju.edu.cn/protacdb/.

Synchronously Polishing the Lead-Rich Surface and Passivating Surface Defects of CsPb(Br/I)3 Quantum Dots for High-Performance Pure-Red PeLEDs.

Mixed-halide CsPb(Br/I)3 perovskite quantum dots (QDs) are regarded as one of the most promising candidates for pure-red perovskite light-emitting diodes (PeLEDs) due to their precise spectral tuning property. However, the lead-rich surface of these QDs usually results in halide ion migration and nonradiative recombination loss, which remains a great challenge for high-performance PeLEDs. To solve the above issues, we employ a chelating agent of 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid hydrate (DOTA) to polish the lead-rich surface of the QDs and meanwhile introduce a new ligand of 2,3-dimercaptosuccinic acid (DMSA) to passivate surface defects of the QDs. This synchronous post-treatment strategy results in high-quality CsPb(Br/I)3 QDs with suppressed halide ion migration and an improved photoluminescence quantum yield, which enables us to fabricate spectrally stable pure-red PeLEDs with a peak external quantum efficiency of 23.2%, representing one of the best performance pure-red PeLEDs based on mixed-halide CsPb(Br/I)3 QDs reported to date.

High-resolution sequencing of nine elite upland cotton cultivars uncovers genic variations and breeding improvement targets.

Structural variations (SVs) are critical factors affecting genome evolution and important traits. However, identification results and functional analyses of SVs in upland cotton are rare. Here, based on the genetic relationships, breeding history and cumulative planting area of upland cotton in China, nine predominant cultivars from the past 60 years (1950s-2010s) were selected for long read sequencing to uncover genic variations and breeding improvement targets for this crop. Based on the ZM24 reference genome, 0.88-1.47 × 104 SVs per cultivar were identified, and an SV set was constructed. SVs affected the expression of a large number of genes during fiber elongation, and a transposable element insertion resulted in the glandless phenotype in upland cotton. Six widespread inversions were identified based on nine draft genomes and high-throughput chromosome conformation capture data. Multiple haplotype blocks that were always associated with aggregated SVs were demonstrated to play a pivotal role in the agronomic traits of upland cotton and drove its adaptation to the northern planting region. Exotic introgression was the source of these haplotype blocks and increased the genetic diversity of upland cotton. Our results enrich the genome resources of upland cotton, and the identified SVs will promote genetic and breeding research in cotton.

PARP inhibition leads to synthetic lethality with key splicing-factor mutations in myelodysplastic syndromes.

BACKGROUND: Splicing factors are frequently mutated in patients with myelodysplastic syndromes and acute myeloid leukaemia. Recent studies have revealed convergent molecular defects caused by splicing factor mutations, among which R-loop dysregulation and resultant genome instability are suggested as contributing factors to disease progression. On the other hand, understanding how mutant cells survive upon aberrant R-loop formation and genome instability is essential for developing novel therapeutics. METHODS: The immunoprecipitation was performed to identify R-loops in association with PARP1/poly-ADP-ribosylation. The western blot, immunofluorescence, and flow cytometry assays were used to test the cell viability, cell cycle arrest, apoptosis, and ATM activation in mutant cells following the treatment of the PARP inhibitor. The Srsf2(P95H) knock-in murine hematopoietic cells and MLL-AF9 transformed leukaemia model were generated to investigate the potential of the PARP inhibitor as a therapy for haematological malignancies. RESULTS: The disease-causing mutations in SRSF2 activate PARP and elevate the overall poly-ADP-ribosylation levels of proteins in response to R-loop dysregulation. In accordance, mutant cells are more vulnerable to the PARP inhibitors in comparison to the wild-type counterpart. Notably, the synthetic lethality was further validated in the Srsf2(P95H) knock-in murine hematopoietic cell and MLL-AF9 leukaemia model. CONCLUSIONS: Our findings suggest that mutant cells antagonise the genome threat caused by R-loop disruption by PARP activation, thus making PARP targeting a promising therapeutic strategy for myeloid cancers with mutations in SRSF2.

The Host Protein CAD Regulates the Replication of FMDV through the Function of Pyrimidines' De Novo Synthesis.

Foot-and-mouth disease virus (FMDV) is a single-stranded picornavirus that causes economically devastating disease in even-hooved animals. There has been little research on the function of host cells during FMDV infection. We aimed to shed light on key host factors associated with FMDV replication during acute infection. We found that HDAC1 overexpression in host cells induced upregulation of FMDV RNA and protein levels. Activation of the AKT-mammalian target of rapamycin (mTOR) signaling pathway using bpV(HOpic) or SC79 also promoted FMDV replication. Furthermore, short hairpin RNA (shRNA)-induced suppression of carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase (CAD), a transcription factor downstream of the AKT-mTOR signaling pathway, resulted in downregulation of FMDV RNA and protein levels. Coimmunoprecipitation assays showed that the ACTase domain of CAD could interact with the FMDV 2C protein, suggesting that the ACTase domain of CAD may be critical in FMDV replication. CAD proteins participate in de novo pyrimidine synthesis. Inhibition of FMDV replication by deletion of the ACTase domain of CAD in host cells could be reversed by supplementation with uracil. These results revealed that the contribution of the CAD ACTase domain to FMDV replication is dependent on de novo pyrimidine synthesis. Our research shows that HDAC1 promotes FMDV replication by regulating de novo pyrimidine synthesis from CAD via the AKT-mTOR signaling pathway. IMPORTANCE Foot-and-mouth disease virus is an animal virus of the Picornaviridae family that seriously harms the development of animal husbandry and foreign trade of related products, and there is still a lack of effective means to control its harm. Replication complexes would generate during FMDV replication to ensure efficient replication cycles. 2C is a common viral protein in the replication complex of Picornaviridae virus, which is thought to be an essential component of membrane rearrangement and viral replication complex formation. The host protein CAD is a key protein in the pyrimidines de novo synthesis. In our research, the interaction of CAD and FMDV 2C was demonstrated in FMDV-infected BHK-21 cells, and it colocalized with 2C in the replication complex. The inhibition of the expression of FMDV 3D protein through interference with CAD and supplementation with exogenous pyrimidines reversed this inhibition, suggesting that FMDV might recruit CAD through the 2C protein to ensure pyrimidine supply during replication. In addition, we also found that FMDV infection decreased the expression of the host protein HDAC1 and ultimately inhibited CAD activity through the AKT-mTOR signaling pathway. These results revealed a unique means of counteracting the virus in BHK-21 cells lacking the interferon (IFN) signaling pathway. In conclusion, our study provides some potential targets for the development of drugs against FMDV.

Is RNA editing truly absent in the complex thalloid liverworts (Marchantiopsida)? Evidence of extensive RNA editing from Cyathodium cavernarum.

RNA editing is a crucial modification in plants' organellar transcripts that converts cytidine to uridine (C-to-U; and sometimes uridine to cytidine) in RNA molecules. This post-transcriptional process is controlled by the PLS-class protein with a DYW domain, which belongs to the pentatricopeptide repeat (PPR) protein family. RNA editing is widespread in land plants; however, complex thalloid liverworts (Marchantiopsida) are the only group reported to lack both RNA editing and DYW-PPR protein. The liverwort Cyathodium cavernarum (Marchantiopsida, Cyathodiaceae), typically found in cave habitats, was newly found to have 129 C-to-U RNA editing sites in its chloroplast and 172 sites in its mitochondria. The Cyathodium genus, specifically C. cavernarum, has a large number of PPR editing factor genes, including 251 DYW-type PPR proteins. These DYW-type PPR proteins may be responsible for C-to-U RNA editing in C. cavernarum. Cyathodium cavernarum possesses both PPR DYW proteins and RNA editing. Our analysis suggests that the remarkable RNA editing capability of C. cavernarum may have been acquired alongside the emergence of DYW-type PPR editing factors. These findings provide insight into the evolutionary pattern of RNA editing in land plants.

Investigating the role of killer cell immunoglobulin-like receptors and human leukocyte antigen genetic variants in hepatitis C virus infection.

The genetic diversity of killer cell immunoglobulin-like receptors (KIRs) and human leukocyte antigen (HLA) genes influences the host's immune response to viral pathogens. This study aims to explore the impact of five single nucleotide polymorphisms (SNPs) in KIR3DL2 and HLA-A genes on hepatitis C virus (HCV) infection. A total of 2251 individuals were included in the case-control study. SNPs including KIR3DL2 rs11672983, rs3745902, rs1654644, and HLA-A rs3869062, rs12202296 were genotyped. By controlling various confounding factors using a modified logistic regression model, as well as incorporating stratified analysis, joint effects analysis, and multidimensional bioinformatics analysis, we analyzed the relationship between SNPs and HCV infection. The logistic regression analysis showed a correlation between KIR3DL2 rs11672983 AA, KIR3DL2 rs3745902 TT, and increased HCV susceptibility (p < 0.01). Stratified analysis indicated that KIR3DL2 rs1654644 and HLA-A rs3869062 also heightened HCV susceptibility in certain subgroups. A linear trend of rising HCV infection rates was observed when combining KIR3DL2 rs11672983 AA and KIR3DL2 rs3745902 TT (ptrend = 0.007). Bioinformatics analysis suggested these SNPs' regulatory potential and their role in altering messenger RNA secondary structure, implying their functional relevance in HCV susceptibility. Our findings indicate that KIR3DL2 rs11672983 AA and KIR3DL2 rs3745902 TT are significantly associated with increased susceptibility to HCV infection.