Inter-eye asymmetry of microvascular density in patients on hydroxychloroquine therapy by optical coherence tomography angiography.

AIMS: To explore the inter-eye retinal microvascular density asymmetry of patients on hydroxychloroquine (HCQ) therapy through optical coherence tomography angiography (OCTA). METHODS: 40 subjects were enrolled in this cross-sectional study, including 20 systemic lupus erythematasus patients currently treated with HCQ (40 eyes) and 20 age- and sex-matched normal controls (NCs, 40 eyes). OCTA images were obtained to measure macular and peripapillary mircrovasculatures and microstructures, including vessel density, retinal nerver fiber layer thickness, and peripapillary ganglion cell-inner plexiform layer thickness. The absolute values of the difference between right and left eyes were taken as a measure of inter-eye asymmetry. RESULTS: Macular whole image vessel density (wiVD-M) and perifoveal vessel density (pfVD) of superficial capillary plexus (SCP) were notably reduced in both the right and left eyes of the HCQ treatment group compared with NCs. Specifically, SLE patients treated with HCQ have higher inter-eye asymmetry of wiVD-M of SCP (2.28 ± 1.03 vs 1.27 ± 0.79, p < 0.01) and pfVD of SCP (2.55 ± 1.26 vs 1.78 ± 1.06, p = 0.04) compared with NCs. There were no significant differences in inter-eye asymmetry of structure parameters. Inter-eye asymmetry of wiVD-M of SCP (AUC = 0.80, p < 0.01) and pfVD of SCP (AUC = 0.71, p = 0.02) exhibited greater discrimination power. CONCLUSION: SLE Patients treated with HCQ exhibited a notably higher inter-eye vessel density asymmetry compared to that of NCs. Thus, inter-eye vessel density asymmetry could be used to screen for HCQ retinal toxicity.

Clinical signature and associated immune metabolism of NLRP1 in pan-cancer.

Inflammations have been linked to tumours, suggesting a potential association between NLRP1 and cancer. Nevertheless, a systematic assessment of NLRP1's role across various cancer types currently absent. A comprehensive bioinformatic analysis was conducted to determine whether NLRP1 exhibits prognostic relevance linked to immune metabolism across various cancers. The study leveraged data from the TCGA and GTEx databases to explore the clinical significance, metabolic features, and immunological characteristics of NLRP1, employing various tools such as R, GEPIA, STRING and TISIDB. NLRP1 exhibited differential expression patterns across various cancers, with elevated expression correlating with a more favourable prognosis in lung adenocarcinoma (LUAD) and pancreatic adenocarcinoma (PAAD). Downregulation of NLRP1 reduced tumour metabolic activity in LUAD. Moreover, the mutational signature of NLRP1 was linked to a favourable prognosis. Interestingly, high NLRP1 expression inversely correlated with tumour stemness while positively correlating with tumour immune infiltration in various cancers including LUAD and PAAD. Through extensive big data analysis, we delved into the role of NLRP1 across various tumour types, constructing a comprehensive role map of its involvement in pan-cancer scenarios. Our findings highlight the potential of NLRP1 as a promising therapeutic target specifically in LUAD and PAAD.

The RNA-binding proteins regulate innate antiviral immune signaling by modulating pattern recognition receptors.

Viral infections pose significant threats to human health, leading to a diverse spectrum of infectious diseases. The innate immune system serves as the primary barrier against viruses and bacteria in the early stages of infection. A rapid and forceful antiviral innate immune response is triggered by distinguishing between self-nucleic acids and viral nucleic acids. RNA-binding proteins (RBPs) are a diverse group of proteins which contain specific structural motifs or domains for binding RNA molecules. In the last decade, numerous of studies have outlined that RBPs influence viral replication via diverse mechanisms, directly recognizing viral nucleic acids and modulating the activity of pattern recognition receptors (PRRs). In this review, we summarize the functions of RBPs in regulation of host-virus interplay by controlling the activation of PRRs, such as RIG-I, MDA5, cGAS and TLR3. RBPs are instrumental in facilitating the identification of viral RNA or DNA, as well as viral structural proteins within the cellular cytoplasm and nucleus, functioning as co-receptor elements. On the other hand, RBPs are capable of orchestrating the activation of PRRs and facilitating the transmission of antiviral signals to downstream adaptor proteins by post-translational modifications or aggregation. Gaining a deeper comprehension of the interaction between the host and viruses is crucial for the development of novel therapeutics targeting viral infections.

Spatial Transcriptomic Study Reveals Heterogeneous Metabolic Adaptation and a Role of Pericentral PPARα/CAR/Ces2a Axis During Fasting in Mouse Liver.

Spatial heterogeneity and plasticity of the mammalian liver are critical for systemic metabolic homeostasis in response to fluctuating nutritional conditions. Here, a spatially resolved transcriptomic landscape of mouse livers across fed, fasted and refed states using spatial transcriptomics is generated. This approach elucidated dynamic temporal-spatial gene cascades and how liver zonation-both expression levels and patterns-adapts to shifts in nutritional status. Importantly, the pericentral nuclear receptor Nr1i3 (CAR) as a pivotal regulator of triglyceride metabolism is pinpointed. It is showed that the activation of CAR in the pericentral region is transcriptionally governed by Pparα. During fasting, CAR activation enhances lipolysis by upregulating carboxylesterase 2a, playing a crucial role in maintaining triglyceride homeostasis. These findings lay the foundation for future mechanistic studies of liver metabolic heterogeneity and plasticity in response to nutritional status changes, offering insights into the zonated pathology that emerge during liver disease progression linked to nutritional imbalances.

TopMarker: Computational screening biomarkers of hepatocellular carcinoma from transcriptome and interactome based on differential network topological parameters.

Identifying diagnostic biomarkers for cancer is crucial in the field of personalized medicine. The available transcriptome and interactome provide unprecedented opportunities and challenges for biomarker screening. From a systematic perspective, network-based medicine methods provide alternative approaches to organizing the available high-throughput omics data for deciphering molecular interactions and their associations with phenotypic states. In this work, we propose a bioinformatics strategy named TopMarker for discovering diagnostic biomarkers by comparing the network topology differences in control and disease samples. Specifically, we build up gene-gene interaction networks in the two states of control and disease respectively. The network rewiring status across the two networks results in differential network topologies reflecting dynamics and changes in normal samples when compared with those in disease. Thus, we identify the potential biomarker genes with differential network topological parameters between the control and disease gene networks. For a proof-of-concept study, we introduce the computational pipeline of biomarker discovery in hepatocellular carcinoma (HCC). We prove the effectiveness of the proposed TopMarker method using these candidate biomarkers in classifying HCC samples and validate its signature capability across numerous independent datasets. We also compare the discriminant power of biomarker genes identified by TopMarker with those identified by other baseline methods. The higher classification performances and functional implications indicate the advantages of our proposed method for discovering biomarkers from differential network topology.

Assessment of prognostic factors in patients with primary ocular adnexal lymphoma when considering competing risk elements.

BACKGROUND: Accurate prognostic factors for primary ocular adnexal lymphoma (POAL) are scarce. Survival models and prognostic factors derived without considering competing risk factors suffer from major statistical errors. This study aimed to accurately assess prognostic factors in POAL patients using competing risk models, and compare this to the traditional COX proportional hazards model. METHODS: This retrospective study utilised data from the Surveillance, Epidemiology, and End Results (SEER) program 2010-2015 and included patients with B-cell POAL. The cumulative incidence function and Gray's test for cause-specific survival were calculated as univariate analysis. The competing risk models were a Fine-Gray subdistribution hazard model and a cause-specific model, and a traditional COX model was employed as a multivariate analysis. RESULTS: This study enrolled 846 eligible patients with POAL: 60 patients (7.09%) died from POAL and 123 patients (14.54%) died from other causes. Multivariate competing risk models indicated that age, laterality, histology subtype, the 7th edition of American Joint Committee on Cancer stage T, and radiotherapy were independent predictors for cause-specific survival of patients with POAL. There was high consistency between the two competing risk models. The COX model made several misestimations on the statistical significance and hazard ratios of prognostic factors. CONCLUSIONS: This study established competing risk models as a method to assess POAL prognostic factors, which was more accurate than traditional methods that do not consider competing risk elements.

Assessment of SWI/SNF chromatin remodeling complex related genes as potential biomarkers and therapeutic targets in pan-cancer.

Recent research has uncovered a surprisingly high occurrence of aberrant expression and mutations in the genes that encode subunits of the SWI/SNF chromatin-remodeling complexes (SCRC). Nevertheless, the carcinogenic effects of aberrant expression and mutations in SWI/SNF genes have only been acknowledged in recent times, resulting in a comparatively limited understanding of these modifications. In this study, we comprehensively analyzed the expression difference, somatic mutation, potential biological pathways, stromal or immune cell infiltration, and drug sensitivity of SCRC-related genes (SCRGs) in pan-cancer. Furthermore, the evolutionary trend, prognostic signature, and immunotherapy response of SCRGs in kidney renal clear cell carcinoma (KIRC) were also evaluated. The expression of SCRGs was changed in 13 out of 14 tumor types, strongly linked to prognosis, and mutated in 30.9% of tumor patients. SCRGs were also closely associated with immune-related pathways and tumor metastasis pathways. The expression of SCRGs was positively associated with the immune score or stromal score but negatively correlated with Tumor purity. Three potential drugs (FK866, Ispinesib mesylate, and WZ3105) were identified to target the SCRGs. In KIRC, scRNA-seq analysis showed that the enrichment of SCRC and the communication frequency with immune cells were significantly declined during tumor cell progression. A prognostic signature was constructed in KIRC and was effective in predicting the prognosis for KIRC. Aberrant expression of eleven prognostic genes identified from the KIRC prognostic signature and the cytotoxicity of FK866 and Ispinesib mesylate to KIRC were verified by qRT-PCR and CCK-8 assay, respectively. Our study identified SCRGs as potential biomarker and therapeutic targets, providing new insights into SCRC for tumor-targeted therapy.

A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma.

Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological classifier to accurately predict post-surgery localized (stage I-III) papillary renal cell carcinoma (pRCC) recurrence. The multi-classifier system demonstrates significantly higher predictive accuracy for recurrence-free survival (RFS) compared to the three single classifiers alone in the training set and in both validation sets (C-index 0.831-0.858 vs. 0.642-0.777, p < 0.05). The RFS in our multi-classifier-defined high-risk stage I/II and grade 1/2 groups is significantly worse than in the low-risk stage III and grade 3/4 groups (p < 0.05). Our multi-classifier system is a practical and reliable predictor for recurrence of localized pRCC after surgery that can be used with the current staging system to more accurately predict disease course and inform strategies for individualized adjuvant therapy.

DeepSAP: A Novel Brain Image-Based Deep Learning Model for Predicting Stroke-Associated Pneumonia From Spontaneous Intracerebral Hemorrhage.

RATIONALE AND OBJECTIVE: Stroke-associated pneumonia (SAP) often appears as a complication following intracerebral hemorrhage (ICH), leading to poor prognosis and increased mortality rates. Previous studies have typically developed prediction models based on clinical data alone, without considering that ICH patients often undergo CT scans immediately upon admission. As a result, these models are subjective and lack real-time applicability, with low accuracy that does not meet clinical needs. Therefore, there is an urgent need for a quick and reliable model to timely predict SAP. METHODS: In this retrospective study, we developed an image-based model (DeepSAP) using brain CT scans from 244 ICH patients to classify the presence and severity of SAP. First, DeepSAP employs MRI-template-based image registration technology to eliminate structural differences between samples, achieving statistical quantification and spatial standardization of cerebral hemorrhage. Subsequently, the processed images and filtered clinical data were simultaneously input into a deep-learning neural network for training and analysis. The model was tested on a test set to evaluate diagnostic performance, including accuracy, specificity, and sensitivity. RESULTS: Brain CT scans from 244 ICH patients (mean age, 60.24; 66 female) were divided into a training set (n = 170) and a test set (n = 74). The cohort included 143 SAP patients, accounting for 58.6% of the total, with 66 cases classified as moderate or above, representing 27% of the total. Experimental results showed an AUC of 0.93, an accuracy of 0.84, a sensitivity of 0.79, and a precision of 0.95 for classifying the presence of SAP. In comparison, the model relying solely on clinical data showed an AUC of only 0.76, while the radiomics method had an AUC of 0.74. Additionally, DeepSAP achieved an optimal AUC of 0.84 for the SAP grading task. CONCLUSION: DeepSAP's accuracy in predicting SAP stems from its spatial normalization and statistical quantification of the ICH region. DeepSAP is expected to be an effective tool for predicting and grading SAP in clinic.

Limitations of Classically Simulable Measurements for Quantum State Discrimination.

In the realm of fault-tolerant quantum computing, stabilizer operations play a pivotal role, characterized by their remarkable efficiency in classical simulation. This efficiency sets them apart from nonstabilizer operations within the quantum computational theory. In this Letter, we investigate the limitations of classically simulable measurements in distinguishing quantum states. We demonstrate that any pure magic state and its orthogonal complement of odd prime dimensions cannot be unambiguously distinguished by stabilizer operations, regardless of how many copies of the states are supplied. We also reveal intrinsic similarities and distinctions between the quantum resource theories of magic states and entanglement in quantum state discrimination. The results emphasize the inherent limitations of classically simulable measurements and contribute to a deeper understanding of the quantum-classical boundary.

Ferroptosis: Latest evidence and perspectives on plant-derived natural active compounds mitigating doxorubicin-induced cardiotoxicity.

Doxorubicin (DOX) is a chemotherapy drug widely used in clinical settings, acting as a first-line treatment for various malignant tumors. However, its use is greatly limited by the cardiotoxicity it induces, including doxorubicin-induced cardiomyopathy (DIC). The mechanisms behind DIC are not fully understood, but its potential biological mechanisms are thought to include oxidative stress, inflammation, energy metabolism disorders, mitochondrial damage, autophagy, apoptosis, and ferroptosis. Recent studies have shown that cardiac injury induced by DOX is closely related to ferroptosis. Due to their high efficacy, availability, and low side effects, natural medicine treatments hold strong clinical potential. Currently, natural medicines have been shown to mitigate DOX-induced ferroptosis and ease DIC through various functions such as antioxidation, iron ion homeostasis correction, lipid metabolism regulation, and mitochondrial function improvement. Therefore, this review summarizes the mechanisms of ferroptosis in DIC and the regulation by natural plant products, with the expectation of providing a reference for future research and development of inhibitors targeting ferroptosis in DIC. This review explores the mechanisms of ferroptosis in doxorubicin-induced cardiomyopathy (DIC) and summarizes how natural plant products can alleviate DIC by inhibiting ferroptosis through reducing oxidative stress, correcting iron ion homeostasis, regulating lipid metabolism, and improving mitochondrial function.

E3 ubiquitin ligase RNF128 attenuates colitis and colorectal tumorigenesis by triggering the degradation of IL-6 receptors.

INTRODUCTION: Intestinal immune dysregulation is strongly linked to the occurrence and formation of tumors. RING finger protein 128 (RNF128) has been identified to play distinct immunoregulatory functions in innate and adaptive systems. However, the physiological roles of RNF128 in intestinal inflammatory conditions such as colitis and colorectal cancer (CRC) remain controversial. OBJECTIVES: To elucidate the function and mechanism of RNF128 in colitis and CRC. METHODS: Animal models of dextran sodium sulfate (DSS)-induced colitis and azoxymethane (AOM)/DSS-induced CRC were established in WT and Rnf128-deficient mice and evaluated by histopathology. Co-immunoprecipitation and ubiquitination analyses were employed to investigate the role of RNF128 in IL-6-STAT3 signaling. RESULTS: RNF128 was significantly downregulated in clinical CRC tissues compared with paired peritumoral tissues. Rnf128-deficient mice were hypersusceptible to both colitis induced by DSS and CRC induced by AOM/DSS or APC mutation. Loss of RNF128 promoted the proliferation of CRC cells and STAT3 activation during the early transformative stage of carcinogenesis in vivo and in vitro when stimulated by IL-6. Mechanistically, RNF128 interacted with the IL-6 receptor α subunit (IL-6Rα) and membrane glycoprotein gp130 and mediated their lysosomal degradation in ligase activity-dependent manner. Through a series of point mutations in the IL-6 receptor, we identified that RNF128 promoted K48-linked polyubiquitination of IL-6Rα at K398/K401 and gp130 at K718/K816/K866. Additionally, blocking STAT3 activation effectively eradicated the inflammatory damage of Rnf128-deficient mice during the transformative stage of carcinogenesis. CONCLUSION: RNF128 attenuates colitis and colorectal tumorigenesis by inhibiting IL-6-STAT3 signaling, which sheds novel insights into the modulation of IL-6 receptors and the inflammation-to-cancer transition.

A novel approach with tofacitinib for the management of keratoderma blennorrhagicum in reactive arthritis: a case report.

Reactive arthritis(ReA), a form of arthritis occurring post-infection, manifests with antecedent infection symptoms, arthritis, and extra-articular manifestations, categorizing it as spondyloarthritis. "Keratoderma blennorrhagicum" (characterized by pustular hyperkeratosis on palms and soles, resembling pustular psoriasis) represents the most typical skin manifestation of ReA, occurring in acute or chronic phases. Severe lesions necessitate systemic disease modifying anti-rheumatic drugs (DMARDs) or biologic therapies. This article reports a case of ReA with sacroiliitis and widespread pustular eruptions following a urinary tract infection. Treatment with sulfasalazine and thalidomide significantly improved sacroiliitis, but the skin rash remained persistent and recurring. Subsequent use of adalimumab and secukinumab resulted in worsening skin rash, prompting a switch to tofacitinib, leading to a remarkable improvement in pustular eruptions after 20 days of treatment. This case demonstrates successful application of tofacitinib in treating severe keratoderma blennorrhagicum refractory to conventional DMARDs and biologics, offering insights into JAK inhibition for challenging rheumatic diseases with skin involvement.

MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning.

Assessing changes in protein-protein binding affinity due to mutations helps understanding a wide range of crucial biological processes within cells. Despite significant efforts to create accurate computational models, predicting how mutations affect affinity remains challenging due to the complexity of the biological mechanisms involved. In the present work, a geometric deep learning framework called MuToN is introduced for quantifying protein binding affinity change upon residue mutations. The method, designed with geometric attention networks, is mechanism-aware. It captures changes in the protein binding interfaces of mutated complexes and assesses the allosteric effects of amino acids. Experimental results highlight MuToN's superiority compared to existing methods. Additionally, MuToN's flexibility and effectiveness are illustrated by its precise predictions of binding affinity changes between SARS-CoV-2 variants and the ACE2 complex.

Improving urban emergency medical service systems through brownfield transformation in Huangshi, China.

A comprehensive emergency medical service (EMS) system significantly enhances a city's capacity to prevent and mitigate disasters. Using Huangshi as a case study, this research evaluated the service radium coverage rate of the current EMS system by examining its transport capacity, population density, and prevalence rate, finding it to be only 61.49% with an inefficient spatial layout. To address this, we proposed transforming urban brownfields into EMS parks. By selecting the most suitable brownfields based on capacity and service radius, we increased the coverage rate to 90.21%. We introduced a new "consultation-referral" model, where existing EMS facilities serve as pre-diagnosis and triage centers, and the urban brownfield EMS parks function as isolation and centralized treatment centers. GIS network analysis confirmed the feasibility, showing all transit times to be under 30 min. The methodology outlined in this study-comprising "demand assessment, supply optimization, and feasibility verification"-not only strengthens the city's EMS system but also facilitates the renewal of urban brownfields. This approach can serve as a valuable reference for enhancing EMS systems in other cities.

Microglial forkhead box O3a deficiency attenuates LPS-induced neuro-inflammation and depressive-like behaviour through regulating the expression of peroxisome proliferator-activated receptor-γ.

BACKGROUND AND PURPOSE: Depression is closely linked with microglial activation and neuro-inflammation. Peroxisome proliferator-activated receptor-γ (PPAR-γ) plays an important role in M2 activation of microglia. Forkhead box (FOX) O3a has been implicated in the regulation of mood-relevant behaviour. However, little is known about the inflammatory mechanisms of in the microglia of the brain. Here, we have investigated the role of microglial FOXO3a/PPAR-γ in the development of depression. EXPERIMENTAL APPROACH: The effect of FOXO3a on microglia inflammation was analysed in vitro and in lipopolysaccharide (LPS)-induced depression-like behaviours in vivo. ChIP-seq and Dual-luciferase reporter assays were used to confirm the interaction between FOXO3a and PPAR-γ. Behavioural changes were measured, while inflammatory cytokines, microglial phenotype and morphological properties were determined by ELISA, qRT-PCR, western blotting and immunostaining. KEY RESULTS: Overexpression of FOXO3a significantly attenuated expression of PPAR-γ and enhanced the microglial polarization towards the M1 phenotype, while knockdown of FOXO3a had the opposite effect. FOXO3a binds to the promoters of PPAR-γ and decreases its transcription activity. Importantly, deacetylation and activation of FOXO3a regulate LPS-induced neuro-inflammation by inhibiting the expression of PPAR-γ in microglia cells, supporting the antidepressant potential of histone deacetylase inhibitors. Microglial FOXO3a deficiency in mice alleviated LPS-induced neuro-inflammation and depression-like behaviours but failed to reduce anxiety behaviour, whereas pharmacological inhibition of PPAR-γ by GW9662 restored LPS-induced microglial activation and depressive-like behaviours in microglial FOXO3a-deficient mice. CONCLUSION AND IMPLICATIONS: FOXO3a/PPAR-γ axis plays an important role in microglial activation and depression, identifying a new therapeutic avenue for the treatment of major depression.

LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data.

Reconstructing the topology of gene regulatory network from gene expression data has been extensively studied. With the abundance functional transcriptomic data available, it is now feasible to systematically decipher regulatory interaction dynamics in a logic form such as a Boolean network (BN) framework, which qualitatively indicates how multiple regulators aggregated to affect a common target gene. However, inferring both the network topology and gene interaction dynamics simultaneously is still a challenging problem since gene expression data are typically noisy and data discretization is prone to information loss. We propose a new method for BN inference from time-series transcriptional profiles, called LogicGep. LogicGep formulates the identification of Boolean functions as a symbolic regression problem that learns the Boolean function expression and solve it efficiently through multi-objective optimization using an improved gene expression programming algorithm. To avoid overly emphasizing dynamic characteristics at the expense of topology structure ones, as traditional methods often do, a set of promising Boolean formulas for each target gene is evolved firstly, and a feed-forward neural network trained with continuous expression data is subsequently employed to pick out the final solution. We validated the efficacy of LogicGep using multiple datasets including both synthetic and real-world experimental data. The results elucidate that LogicGep adeptly infers accurate BN models, outperforming other representative BN inference algorithms in both network topology reconstruction and the identification of Boolean functions. Moreover, the execution of LogicGep is hundreds of times faster than other methods, especially in the case of large network inference.

AIM2 enhances Candida albicans infection through promoting macrophage apoptosis via AKT signaling.

Candida albicans is among the most prevalent invasive fungal pathogens for immunocompromised individuals and novel therapeutic approaches that involve immune response modulation are imperative. Absent in melanoma 2 (AIM2), a pattern recognition receptor for DNA sensing, is well recognized for its involvement in inflammasome formation and its crucial role in safeguarding the host against various pathogenic infections. However, the role of AIM2 in host defense against C. albicans infection remains uncertain. This study reveals that the gene expression of AIM2 is induced in human and mouse innate immune cells or tissues after C. albicans infection. Furthermore, compared to their wild-type (WT) counterparts, Aim2-/- mice surprisingly exhibit resistance to C. albicans infection, along with reduced inflammation in the kidneys post-infection. The resistance of Aim2-/- mice to C. albicans infection is not reliant on inflammasome or type I interferon production. Instead, Aim2-/- mice display lower levels of apoptosis in kidney tissues following infection than WT mice. The deficiency of AIM2 in macrophages, but not in dendritic cells, results in a phenocopy of the resistance observed in Aim2-/- mice against C. albican infection. The treatment of Clodronate Liposome, a reagent that depletes macrophages, also shows the critical role of macrophages in host defense against C. albican infection in Aim2-/- mice. Furthermore, the reduction in apoptosis is observed in Aim2-/- mouse macrophages following infection or treatment of DNA from C. albicans in comparison with controls. Additionally, higher levels of AKT activation are observed in Aim2-/- mice, and treatment with an AKT inhibitor reverses the host resistance to C. albicans infection. The findings collectively demonstrate that AIM2 exerts a negative regulatory effect on AKT activation and enhances macrophage apoptosis, ultimately compromising host defense against C. albicans infection. This suggests that AIM2 and AKT may represent promising therapeutic targets for the management of fungal infections.

IRF7 Exacerbates Candida albicans Infection by Compromising CD209-Mediated Phagocytosis and Autophagy-Mediated Killing in Macrophages.

IFN regulatory factor 7 (IRF7) exerts anti-infective effects by promoting the production of IFNs in various bacterial and viral infections, but its role in highly morbid and fatal Candida albicans infections is unknown. We unexpectedly found that Irf7 gene expression levels were significantly upregulated in tissues or cells after C. albicans infection in humans and mice and that IRF7 actually exacerbates C. albicans infection in mice independent of its classical function in inducing IFNs production. Compared to controls, Irf7-/- mice showed stronger phagocytosis of fungus, upregulation of C-type lectin receptor CD209 expression, and enhanced P53-AMPK-mTOR-mediated autophagic signaling in macrophages after C. albicans infection. The administration of the CD209-neutralizing Ab significantly hindered the phagocytosis of Irf7-/- mouse macrophages, whereas the inhibition of p53 or autophagy impaired the killing function of these macrophages. Thus, IRF7 exacerbates C. albicans infection by compromising the phagocytosis and killing capacity of macrophages via regulating CD209 expression and p53-AMPK-mTOR-mediated autophagy, respectively. This finding reveals a novel function of IRF7 independent of its canonical IFNs production and its unexpected role in enhancing fungal infections, thus providing more specific and effective targets for antifungal therapy.