A single-amino acid substitution in the adaptor LAT accelerates TCR proofreading kinetics and alters T-cell selection, maintenance and function.

Mature T cells must discriminate between brief interactions with self-peptides and prolonged binding to agonists. The kinetic proofreading model posits that certain T-cell antigen receptor signaling nodes serve as molecular timers to facilitate such discrimination. However, the physiological significance of this regulatory mechanism and the pathological consequences of disrupting it are unknown. Here we report that accelerating the normally slow phosphorylation of the linker for activation of T cells (LAT) residue Y136 by introducing an adjacent Gly135Asp alteration (LATG135D) disrupts ligand discrimination in vivo. The enhanced self-reactivity of LATG135D T cells triggers excessive thymic negative selection and promotes T-cell anergy. During Listeria infection, LATG135D T cells expand more than wild-type counterparts in response to very weak stimuli but display an imbalance between effector and memory responses. Moreover, despite their enhanced engagement of central and peripheral tolerance mechanisms, mice bearing LATG135D show features associated with autoimmunity and immunopathology. Our data reveal the importance of kinetic proofreading in balancing tolerance and immunity.

Slow phosphorylation of a tyrosine residue in LAT optimizes T cell ligand discrimination.

Self-non-self discrimination is central to T cell-mediated immunity. The kinetic proofreading model can explain T cell antigen receptor (TCR) ligand discrimination; however, the rate-limiting steps have not been identified. Here, we show that tyrosine phosphorylation of the T cell adapter protein LAT at position Y132 is a critical kinetic bottleneck for ligand discrimination. LAT phosphorylation at Y132, mediated by the kinase ZAP-70, leads to the recruitment and activation of phospholipase C-γ1 (PLC-γ1), an important effector molecule for T cell activation. The slow phosphorylation of Y132, relative to other phosphosites on LAT, is governed by a preceding glycine residue (G131) but can be accelerated by substituting this glycine with aspartate or glutamate. Acceleration of Y132 phosphorylation increases the speed and magnitude of PLC-γ1 activation and enhances T cell sensitivity to weaker stimuli, including weak agonists and self-peptides. These observations suggest that the slow phosphorylation of Y132 acts as a proofreading step to facilitate T cell ligand discrimination.

Lck promotes Zap70-dependent LAT phosphorylation by bridging Zap70 to LAT.

T cell-antigen receptor (TCR) signaling requires the sequential activities of the kinases Lck and Zap70. Upon TCR stimulation, Lck phosphorylates the TCR, thus leading to the recruitment, phosphorylation, and activation of Zap70. Lck binds and stabilizes phosho-Zap70 by using its SH2 domain, and Zap70 phosphorylates the critical adaptors LAT and SLP76, which coordinate downstream signaling. It is unclear whether phosphorylation of these adaptors occurs through passive diffusion or active recruitment. We report the discovery of a conserved proline-rich motif in LAT that mediates efficient LAT phosphorylation. Lck associates with this motif via its SH3 domain, and with phospho-Zap70 via its SH2 domain, thereby acting as a molecular bridge that facilitates the colocalization of Zap70 and LAT. Elimination of this proline-rich motif compromises TCR signaling and T cell development. These results demonstrate the remarkable multifunctionality of Lck, wherein each of its domains has evolved to orchestrate a distinct step in TCR signaling.

A liver immune rheostat regulates CD8 T cell immunity in chronic HBV infection.

Chronic hepatitis B virus (HBV) infection affects 300 million patients worldwide1,2, in whom virus-specific CD8 T cells by still ill-defined mechanisms lose their function and cannot eliminate HBV-infected hepatocytes3-7. Here we demonstrate that a liver immune rheostat renders virus-specific CD8 T cells refractory to activation and leads to their loss of effector functions. In preclinical models of persistent infection with hepatotropic viruses such as HBV, dysfunctional virus-specific CXCR6+ CD8 T cells accumulated in the liver and, as a characteristic hallmark, showed enhanced transcriptional activity of cAMP-responsive element modulator (CREM) distinct from T cell exhaustion. In patients with chronic hepatitis B, circulating and intrahepatic HBV-specific CXCR6+ CD8 T cells with enhanced CREM expression and transcriptional activity were detected at a frequency of 12-22% of HBV-specific CD8 T cells. Knocking out the inhibitory CREM/ICER isoform in T cells, however, failed to rescue T cell immunity. This indicates that CREM activity was a consequence, rather than the cause, of loss in T cell function, further supported by the observation of enhanced phosphorylation of protein kinase A (PKA) which is upstream of CREM. Indeed, we found that enhanced cAMP-PKA-signalling from increased T cell adenylyl cyclase activity augmented CREM activity and curbed T cell activation and effector function in persistent hepatic infection. Mechanistically, CD8 T cells recognizing their antigen on hepatocytes established close and extensive contact with liver sinusoidal endothelial cells, thereby enhancing adenylyl cyclase-cAMP-PKA signalling in T cells. In these hepatic CD8 T cells, which recognize their antigen on hepatocytes, phosphorylation of key signalling kinases of the T cell receptor signalling pathway was impaired, which rendered them refractory to activation. Thus, close contact with liver sinusoidal endothelial cells curbs the activation and effector function of HBV-specific CD8 T cells that target hepatocytes expressing viral antigens by means of the adenylyl cyclase-cAMP-PKA axis in an immune rheostat-like fashion.

The pathogenic T42A mutation in SHP2 rewires the interaction specificity of its N-terminal regulatory domain.

Mutations in the tyrosine phosphatase Src homology-2 domain-containing protein tyrosine phosphatase-2 (SHP2) are associated with a variety of human diseases. Most mutations in SHP2 increase its basal catalytic activity by disrupting autoinhibitory interactions between its phosphatase domain and N-terminal SH2 (phosphotyrosine recognition) domain. By contrast, some disease-associated mutations located in the ligand-binding pockets of the N- or C-terminal SH2 domains do not increase basal activity and likely exert their pathogenicity through alternative mechanisms. We lack a molecular understanding of how these SH2 mutations impact SHP2 structure, activity, and signaling. Here, we characterize five SHP2 SH2 domain ligand-binding pocket mutants through a combination of high-throughput biochemical screens, biophysical and biochemical measurements, and molecular dynamics simulations. We show that while some of these mutations alter binding affinity to phosphorylation sites, the T42A mutation in the N-SH2 domain is unique in that it also substantially alters ligand-binding specificity, despite being 8 to 10 Å from the specificity-determining region of the SH2 domain. This mutation exerts its effect on sequence specificity by remodeling the phosphotyrosine-binding pocket, altering the mode of engagement of both the phosphotyrosine and surrounding residues on the ligand. The functional consequence of this altered specificity is that the T42A mutant has biased sensitivity toward a subset of activating ligands and enhances downstream signaling. Our study highlights an example of a nuanced mechanism of action for a disease-associated mutation, characterized by a change in protein-protein interaction specificity that alters enzyme activation.

A multi-view graph contrastive learning framework for deciphering spatially resolved transcriptomics data.

Spatially resolved transcriptomics data are being used in a revolutionary way to decipher the spatial pattern of gene expression and the spatial architecture of cell types. Much work has been done to exploit the genomic spatial architectures of cells. Such work is based on the common assumption that gene expression profiles of spatially adjacent spots are more similar than those of more distant spots. However, related work might not consider the nonlocal spatial co-expression dependency, which can better characterize the tissue architectures. Therefore, we propose MuCoST, a Multi-view graph Contrastive learning framework for deciphering complex Spatially resolved Transcriptomic architectures with dual scale structural dependency. To achieve this, we employ spot dependency augmentation by fusing gene expression correlation and spatial location proximity, thereby enabling MuCoST to model both nonlocal spatial co-expression dependency and spatially adjacent dependency. We benchmark MuCoST on four datasets, and we compare it with other state-of-the-art spatial domain identification methods. We demonstrate that MuCoST achieves the highest accuracy on spatial domain identification from various datasets. In particular, MuCoST accurately deciphers subtle biological textures and elaborates the variation of spatially functional patterns.

Intrinsic CD4+ T cell sensitivity and response to a pathogen are set and sustained by avidity for thymic and peripheral complexes of self peptide and MHC.

Interactions of T cell antigen receptors (TCRs) with complexes of self peptide and major histocompatibility complex (MHC) are crucial to T cell development, but their role in peripheral T cell responses remains unclear. Specific and nonspecific stimulation of LLO56 and LLO118 T cells, which transgenically express a TCR specific for the same Listeria monocytogenes epitope, elicited distinct interleukin 2 (IL-2) and phosphorylated kinase Erk responses, the strength of which was set in the thymus and maintained in the periphery in proportion to the avidity of the binding of the TCR to the self peptide-MHC complex. Deprivation of self peptide-MHC substantially compromised the population expansion of LLO56 T cells in response to L. monocytogenes in vivo. Despite their very different self-reactivity, LLO56 T cells and LLO118 T cells bound cognate peptide-MHC with an identical affinity, which challenges associations made between these parameters. Our findings highlight a crucial role for selecting ligands encountered during thymic 'education' in determining the intrinsic functionality of CD4+ T cells.

Author Correction: The Apostasia genome and the evolution of orchids.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

ZAP70, too little, too much can lead to autoimmunity.

Establishing both central and peripheral tolerance requires the appropriate TCR signaling strength to discriminate self- from agonist-peptide bound to self MHC molecules. ZAP70, a cytoplasmic tyrosine kinase, directly interacts with the TCR complex and plays a central and requisite role in TCR signaling in both thymocytes and peripheral T cells. By studying ZAP70 hypomorphic mutations in mice and humans with a spectrum of hypoactive or hyperactive activities, we have gained insights into mechanisms of central and peripheral tolerance. Interestingly, both hypoactive and hyperactive ZAP70 can lead to the development of autoimmune diseases, albeit through distinct mechanisms. Immature thymocytes and mature T cells rely on normal ZAP70 function to complete their development in the thymus and to modulate T cell responses in the periphery. Hypoactive ZAP70 function compromises key developmental checkpoints required to establish central tolerance, allowing thymocytes with potentially self-reactive TCRs a greater chance to escape negative selection. Such 'forbidden clones' may escape into the periphery and may pose a greater risk for autoimmune disease development since they may not engage negative regulatory mechanisms as effectively. Hyperactive ZAP70 enhances thymic negative selection but some thymocytes will, nonetheless, escape negative selection and have greater sensitivity to weak and self-ligands. Such cells must be controlled by mechanisms involved in anergy, expansion of Tregs, and upregulation of inhibitory receptors or signaling molecules. However, such potentially autoreactive cells may still be able to escape control by peripheral negative regulatory constraints. Consistent with findings in Zap70 mutants, the signaling defects in at least one ZAP70 substrate, LAT, can also lead to autoimmune disease. By dissecting the similarities and differences among mouse models of patient disease or mutations in ZAP70 that affect TCR signaling strength, we have gained insights into how perturbed ZAP70 function can lead to autoimmunity. Because of our work and that of others on ZAP70, it is likely that perturbations in other molecules affecting TCR signaling strength will be identified that also overcome tolerance mechanisms and cause autoimmunity. Delineating these molecular pathways could lead to the development of much needed new therapeutic targets in these complex diseases.

A physics-informed neural SDE network for learning cellular dynamics from time-series scRNA-seq data.

MOTIVATION: Learning cellular dynamics through reconstruction of the underlying cellular potential energy landscape (aka Waddington landscape) from time-series single-cell RNA sequencing (scRNA-seq) data is a current challenge. Prevailing data-driven computational methods can be hampered by the lack of physical principles to guide learning from complex data, resulting in reduced prediction accuracy and interpretability when applied to infer cell population dynamics. RESULTS: Here, we propose PI-SDE, a physics-informed neural stochastic differential equation (SDE) framework that combines the Hamilton-Jacobi (HJ) equation and neural SDE to learn cellular dynamics. Grounded in potential energy theory of biological systems, PI-SDE integrates the principle of least action by enforcing the HJ equation when reconstructing cellular potential energy function. This approach not only facilitates accurate predictions, but also improves interpretability, especially in the reconstructed potential energy landscape. Through benchmarking on two real scRNA-seq datasets, we demonstrate the importance of incorporating the HJ regularization term in dynamic inference, especially in predicting gene expression at held-out time points. Meanwhile, the learned potential energy landscape provides biologically interpretable insights into the process of cell differentiation. Our framework enhances model performance, while maintaining robustness and stability. AVAILABILITY: PI-SDE software is available at https://github.com/QiJiang-QJ/PI-SDE.

Graspot: a graph attention network for spatial transcriptomics data integration with optimal transport.

SUMMARY: Spatial transcriptomics (ST) technologies enable the measurement of mRNA expression while simultaneously capturing spot locations. By integrating ST data, the 3D structure of a tissue can be reconstructed, yielding a comprehensive understanding of the tissue's intricacies. Nevertheless, a computational challenge persists: how to remove batch effects while preserving genuine biological structure variations across ST data. To address this, we introduce Graspot, a graph attention network designed for spatial transcriptomics data integration with unbalanced optimal transport. Graspot adeptly harnesses both gene expression and spatial information to align common structures across multiple ST datasets. It embeds multiple ST datasets into a unified latent space, facilitating the partial alignment of spots from different slices. Demonstrating superior performance compared to existing methods on four real ST datasets, Graspot excels in ST data integration, including tasks that require partial alignment. In particular, Graspot efficiently integrates multiple ST slices and guides coordinate alignment. In addition, Graspot accurately aligns the spatio-temporal transcriptomics data to reconstruct human heart developmental processes. AVAILABILITY AND IMPLEMENTATION: Graspot software is available at https://github.com/zhan009/Graspot.

Divergent electroencephalogram resting-state functional network alterations in subgroups of autism spectrum disorder: a symptom-based clustering analysis.

Autism spectrum disorder (ASD) is characterized by etiological and phenotypic heterogeneity. Despite efforts to categorize ASD into subtypes, research on specific functional connectivity changes within ASD subgroups based on clinical presentations is limited. This study proposed a symptom-based clustering approach to identify subgroups of ASD based on multiple clinical rating scales and investigate their distinct Electroencephalogram (EEG) functional connectivity patterns. Eyes-opened resting-state EEG data were collected from 72 children with ASD and 63 typically developing (TD) children. A data-driven clustering approach based on Social Responsiveness Scales-Second Edition and Vinland-3 scores was used to identify subgroups. EEG functional connectivity and topological characteristics in four frequency bands were assessed. Two subgroups were identified: mild ASD (mASD, n = 37) and severe ASD (sASD, n = 35). Compared to TD, mASD showed increased functional connectivity in the beta band, while sASD exhibited decreased connectivity in the alpha band. Significant between-group differences in global and regional topological abnormalities were found in both alpha and beta bands. The proposed symptom-based clustering approach revealed the divergent functional connectivity patterns in the ASD subgroups that was not observed in typical ASD studies. Our study thus provides a new perspective to address the heterogeneity in ASD research.

Rethinking therapeutic strategies of dual-target drugs: An update on pharmacological small-molecule compounds in cancer.

Oncogenes and tumor suppressors are well-known to orchestrate several signaling cascades, regulate extracellular and intracellular stimuli, and ultimately control the fate of cancer cells. Accumulating evidence has recently revealed that perturbation of these key modulators by mutations or abnormal protein expressions are closely associated with drug resistance in cancer therapy; however, the inherent drug resistance or compensatory mechanism remains to be clarified for targeted drug discovery. Thus, dual-target drug development has been widely reported to be a promising therapeutic strategy for improving drug efficiency or overcoming resistance mechanisms. In this review, we provide an overview of the therapeutic strategies of dual-target drugs, especially focusing on pharmacological small-molecule compounds in cancer, including small molecules targeting mutation resistance, compensatory mechanisms, synthetic lethality, synergistic effects, and other new emerging strategies. Together, these therapeutic strategies of dual-target drugs would shed light on discovering more novel candidate small-molecule drugs for the future cancer treatment.

Primed and ready: nanopore metabarcoding can now recover highly accurate consensus barcodes that are generally indel-free.

BACKGROUND: DNA metabarcoding applies high-throughput sequencing approaches to generate numerous DNA barcodes from mixed sample pools for mass species identification and community characterisation. To date, however, most metabarcoding studies employ second-generation sequencing platforms like Illumina, which are limited by short read lengths and longer turnaround times. While third-generation platforms such as the MinION (Oxford Nanopore Technologies) can sequence longer reads and even in real-time, application of these platforms for metabarcoding has remained limited possibly due to the relatively high read error rates as well as the paucity of specialised software for processing such reads. RESULTS: We show that this is no longer the case by performing nanopore-based, cytochrome c oxidase subunit I (COI) metabarcoding on 34 zooplankton bulk samples, and benchmarking the results against conventional Illumina MiSeq sequencing. Nanopore R10.3 sequencing chemistry and super accurate (SUP) basecalling model reduced raw read error rates to ~ 4%, and consensus calling with amplicon_sorter (without further error correction) generated metabarcodes that were ≤ 1% erroneous. Although Illumina recovered a higher number of molecular operational taxonomic units (MOTUs) than nanopore sequencing (589 vs. 471), we found no significant differences in the zooplankton communities inferred between the sequencing platforms. Importantly, 406 of 444 (91.4%) shared MOTUs between Illumina and nanopore were also found to be free of indel errors, and 85% of the zooplankton richness could be recovered after just 12-15 h of sequencing. CONCLUSION: Our results demonstrate that nanopore sequencing can generate metabarcodes with Illumina-like accuracy, and we are the first study to show that nanopore metabarcodes are almost always indel-free. We also show that nanopore metabarcoding is viable for characterising species-rich communities rapidly, and that the same ecological conclusions can be obtained regardless of the sequencing platform used. Collectively, our study inspires confidence in nanopore sequencing and paves the way for greater utilisation of nanopore technology in various metabarcoding applications.

Multimodal investigation of dynamic brain network alterations in autism spectrum disorder: Linking connectivity dynamics to symptoms and developmental trajectories.

BACKGROUND: Autism spectrum disorder (ASD) has been associated with disrupted brain connectivity, yet a comprehensive understanding of the dynamic neural underpinnings remains lacking. This study employed concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques to investigate dynamic functional connectivity (dFC) patterns and neurovascular characteristics in children with ASD. We also explored associations between neurovascular characteristics and the developmental trajectory of adaptive behavior in individuals with ASD. METHODS: Resting-state EEG and fNIRS data were simultaneously recorded from 58 ASD and 63 TD children. We implemented a k-means clustering approach to extract the dFC states for each modality. In addition, a multimodal covariance network (MCN) was constructed from the EEG and fNIRS dFC features to capture the neurovascular characteristics linked to ASD. RESULTS: EEG analyses revealed atypical properties of dFC states in the beta and gamma bands in children with ASD compared to TD children. For fNIRS, the ASD group exhibited atypical properties of dFC states such as duration and transitions relative to the TD group. The MCN analysis revealed significantly suppressed functional covariance between right superior temporal and left Broca's areas, alongside enhanced right dorsolateral prefrontal-left Broca covariance in ASD. Notably, we found that early neurovascular characteristics can predict the developmental progress of adaptive functioning in ASD. CONCLUSION: The multimodal investigation revealed distinct dFC patterns and neurovascular characteristics associated with ASD, elucidating potential neural mechanisms underlying core symptoms and their developmental trajectories. Our study highlights that integrating complementary neuroimaging modalities may aid in unraveling the complex neurobiology of ASD.

BiTSC 2: Bayesian inference of tumor clonal tree by joint analysis of single-cell SNV and CNA data.

The rapid development of single-cell DNA sequencing (scDNA-seq) technology has greatly enhanced the resolution of tumor cell profiling, providing an unprecedented perspective in characterizing intra-tumoral heterogeneity and understanding tumor progression and metastasis. However, prominent algorithms for constructing tumor phylogeny based on scDNA-seq data usually only take single nucleotide variations (SNVs) as markers, failing to consider the effect caused by copy number alterations (CNAs). Here, we propose BiTSC$^2$, Bayesian inference of Tumor clonal Tree by joint analysis of Single-Cell SNV and CNA data. BiTSC$^2$ takes raw reads from scDNA-seq as input, accounts for the overlapping of CNA and SNV, models allelic dropout rate, sequencing errors and missing rate, as well as assigns single cells into subclones. By applying Markov Chain Monte Carlo sampling, BiTSC$^2$ can simultaneously estimate the subclonal scCNA and scSNV genotype matrices, subclonal assignments and tumor subclonal evolutionary tree. In comparison with existing methods on synthetic and real tumor data, BiTSC$^2$ shows high accuracy in genotype recovery, subclonal assignment and tree reconstruction. BiTSC$^2$ also performs robustly in dealing with scDNA-seq data with low sequencing depth and variant missing rate. BiTSC$^2$ software is available at https://github.com/ucasdp/BiTSC2.

Innovative biomarkers TCN2 and LY6E can significantly inhibit respiratory syncytial virus infection.

BACKGROUND: Respiratory syncytial virus (RSV) is a prominent etiological agent of lower respiratory tract infections in children, responsible for approximately 80% of cases of pediatric bronchiolitis and 50% of cases of infant pneumonia. Despite notable progress in the diagnosis and management of pediatric RSV infection, the current biomarkers for early-stage detection remain insufficient to meet clinical needs. Therefore, the development of more effective biomarkers for early-stage pediatric respiratory syncytial virus infection (EPR) is imperative. METHODS: The datasets used in this study were derived from the Gene Expression Omnibus (GEO) database. We used GSE188427 dataset as the training set to screen for biomarkers. Biomarkers of EPR were screened by Weighted Gene Co-expression Network Analysis (WGCNA), three machine-learning algorithms (LASSO regression, Random Forest, XGBoost), and other comprehensive bioinformatics analysis techniques. To evaluate the diagnostic value of these biomarkers, multiple external and internal datasets were employed as validation sets. Next, an examination was performed to investigate the relationship between the screened biomarkers and the infiltration of immune cells. Furthermore, an investigation was carried out to identify potential small molecule compounds that interact with selected diagnostic markers. Finally, we confirmed that the expression levels of the selected biomarkers exhibited a significant increase following RSV infection, and they were further identified as having antiviral properties. RESULTS: The study found that lymphocyte antigen 6E (LY6E) and Transcobalamin-2 (TCN2) are two biomarkers with diagnostic significance in EPR. Analysis of immune cell infiltration showed that they were associated with activation of multiple immune cells. Furthermore, our analysis demonstrated that small molecules, 3'-azido-3'-deoxythymine, methotrexate, and theophylline, have the potential to bind to TCN2 and exhibit antiviral properties. These compounds may serve as promising therapeutic agents for the management of pediatric RSV infections. Additionally, our data revealed an upregulation of LY6E and TCN2 expression in PBMCs from patients with RSV infection. ROC analysis indicated that LY6E and TCN2 possessed diagnostic value for RSV infection. Finally, we confirmed that LY6E and TCN2 expression increased after RSV infection and further inhibited RSV infection in A549 and BEAS-2B cell lines. Importantly, based on TCN2, our findings revealed the antiviral properties of a potentially efficacious compound, vitamin B12. CONCLUSION: LY6E and TCN2 are potential peripheral blood diagnostic biomarkers for pediatric RSV infection. LY6E and TCN2 inhibit RSV infection, indicating that LY6E and TCN2 are potential therapeutic target for RSV infection.

Genomic Analysis and Virulence Assessment of Hypervirulent Klebsiella pneumoniae K16-ST660 in Severe Cervical Necrotizing Fasciitis.

OBJECTIVE: To investigate the source of infection in a patient with recurrent severe neck infections caused by Klebsiella pneumoniae and to analyze the virulence of isolates obtained from different sites of the patient. METHODS: We collected preoperative neck abscess puncture fluid, intraoperative neck drainage fluid, sputum, intestinal fecal specimens, and blood samples from a patient who visited Wuxi Second People's Hospital twice between 2017 and 2018. We conducted isolation, identification, drug sensitivity tests, and string tests on the isolates. Capsule serotyping and virulence gene analysis were performed using PCR. The genetic relationship of different isolates was assessed by Multilocus Sequence Typing and virulence was evaluated using the Galleria mellonella infection model. Additionally, whole-genome sequencing was used to analyze the chromosomal and plasmid genes of one isolate. RESULTS: Klebsiella pneumoniae was detected in the sputum and fecal specimens from both hospitalizations, as well as the preoperative ultrasound-guided puncture fluid and intraoperative drainage fluid from the first hospitalization, resulting in six isolates. These isolates were all K16 serotype, positive in the string test, and identified as ST660 by Multilocus Sequence Typing, indicating they belonged to the same clone. Virulence gene analysis showed that wcaG, iucB, iroNB, rmpA, rmpA2, Aer, kfuBC, ureA, fimH, mrkD, uge, and peg344 were positive, while allS, cf29a, and Wzy_K1 were negative. In the Galleria mellonella virulence assay, the lethality of different isolates was dose-dependent. The K16 group showed significantly higher larval mortality compared to other control groups (including K1, K2, K5, K20, and K57 groups). Genome sequencing revealed that plasmid p17388 carried numerous virulence genes and insertion sequences, particularly ISKPN74, and showed high homology with other Klebsiella plasmids. CONCLUSION: This study is the first to report severe cervical necrotizing fasciitis caused by the K16-ST660 Klebsiella pneumoniae Isolate. The high virulence of these isolates was confirmed by the Galleria mellonella virulence assay and the detection of numerous virulence genes. In-depth analysis of plasmid p17388 suggests that ISKPN74 may enhance stable integration of the plasmid into the bacterial chromosome through recombinases and transposases, thereby reducing the likelihood of plasmid loss and increasing bacterial virulence. Additionally, IS5 family insertion sequences may carry extra promoters or enhancers that, when inserted upstream of mucoviscosity-associated genes such as rmpA, may increase the transcription levels of downstream genes. This ISKPN74-mediated integration or insertion reveals a complex genetic mechanism that may contribute to the severity of infections caused by ST660 isolates. Our findings offer new insights into the virulence and structure of ST660-K16 Klebsiella pneumoniae, suggesting that further investigation into the specific mechanisms by which these insertion sequences enhance virulence could aid in developing novel infection management strategies.

A voltage-gated sodium channel is essential for the positive selection of CD4(+) T cells.

The sustained entry of Ca(2+) into CD4(+)CD8(+) double-positive thymocytes is required for positive selection. Here we identified a voltage-gated Na(+) channel (VGSC) that was essential for positive selection of CD4(+) T cells. Pharmacological inhibition of VGSC activity inhibited the sustained Ca(2+) influx induced by positively selecting ligands and the in vitro positive selection of CD4(+) but not CD8(+) T cells. In vivo short hairpin RNA (shRNA)-mediated knockdown of the gene encoding a regulatory ß-subunit of a VGSC specifically inhibited the positive selection of CD4(+) T cells. Ectopic expression of VGSC in peripheral AND CD4(+) T cells bestowed the ability to respond to a positively selecting ligand, which directly demonstrated that VGSC expression was responsible for the enhanced sensitivity. Thus, active VGSCs in thymocytes provide a mechanism by which a weak positive selection signal can induce the sustained Ca(2+) signals required for CD4(+) T cell development.